W3009: Integrated Systems Research and Development in Automation and Sensors for Sustainability of Specialty Crops

(Multistate Research Project)

Status: Inactive/Terminating

Project Menu

SAES-422 Reports

10/04/2019

Arizona

Raja, R., Slaughter, D.C., Fennimore, S.A., Nguyen, T.T., Vuong, V.L., Sinha, N., Tourte, L., Smith, R.F. & Siemens, M.C. 2019. Crop signaling: A novel crop recognition technique for robotic weed control. Biosystems Eng. (submitted)

Govindaraj. D.K., Zhu, L., Siemens, M.C., Nolte, K.D., Brassill, N., Rios, D.V., Galvez, R., Fonseca, J.M. & Ravishankar, S. 2018. Modified Coring Tool Designs Reduce Iceberg Lettuce Cross-Contamination. J. Food Protection. 82(3): 454-462. DOI: 10.4315/0362-028X.JFP-18-317.

Lefcourt, A.M., Siemens, M.C. & Rivadeneira, P. 2018. Optical parameters for using visible-wavelength reflectance or fluorescence imaging to detect bird excrements in produce fields. Appl. Sci., 9(4), [715]; DOI:10.3390/app9040715.

Everard, C.D., Kim, M.S., Siemens, M.C., Cho, H., Lefcourt, A.M. & O’Donnell, C. 2018. A multispectral imaging system using solar illumination to distinguish fecal matter on leafy greens and soils. Biosystems Eng. 171: 258-264.

California

Vougioukas, S.G. (2019). Agricultural Robotics. Annual Review of Control, Robotics, and Autonomous Systems. 2:365-392. https://doi.org/10.1146/annurev-control-053018-023617

Charlton, D., Edward Taylor, j. E., Vougioukas, S.G. (2019). Can Wages Rise Quickly Enough to Keep Workers in the Fields? Choices, 2nd Quarter 34(2). http://www.choicesmagazine.org/choices-magazine/submitted-articles/can-wages-rise-quickly-enough-to-keep-workers-in-the-fields

Charlton, D., Edward Taylor, J.E., Vougioukas, S.G., Rutledge, Z. (2019). Innovations for a Shrinking Agricultural Workforce. Choices, 2nd Quarter 34(2). http://www.choicesmagazine.org/choices-magazine/submitted-articles/estimating-value-damages-and-remedies-when-farm-data-are-misappropriated/innovations-for-a-shrinking-agricultural-workforce

Peng, C., Vougioukas, S.G. (2019). Scheduling performance of harvest-aiding crop-transport robots under varying earliness in access to transport-request predictions. Accepted. ASABE Annual International Meeting. Boston, Massachusetts.

Kizer, E., S. K. Upadhyaya, C. Ko-Madden, F. Rojo, K. Drechsler, and J. Meyers.2018. Good to the last drop-Getting the most out of precision irrigation. Progressive Crop Consultant. May/June: 20,22,24-26.

Bazzi, C.L., K. Schenatto, S. K. Upadhyaya, F. Rojo, E. Kizer, and C. Ko-madden. 2019. Optimal placement of proximal sensors for precision irrigation in tree crops. J. Precision Ag. 20(4):663-674.

Dhillon, R., F. Rojo., S. K. Upadhyaya, J. Roach, R. Coates, and M. Delwiche. 2019. Prediction of plant water status in almond and walnut trees using a continous leaf monitoring system. Precision Ag. 20(4):723-745.

Bazzi, C. L., K. Schenatto, S. Upadhyaya and F. Rojo. 2018. Optimal placement of proximal sensors for precision irrigation for in tree crops. Proceedings of the 14th International Conference on Precision Agriculture, Montreal, Canada. 8pp.

Drechsler, K., I. Kisekka, and S. Upadhyaya. 2018. A comprehensive stress index for evaluating water stress in almond trees. Proceedings of the 14th International Conference on Precision Agriculture, Montreal, Canada. 9pp.

Ko-Madden, C. T. 2018 Optimal placement of minimal number of proximal sensors for precision irrigation management. Unpublished MS thesis, Biological and Agricultural Engineering Department, University of California Davis, 145pp.

Kizer, E. E. 2018. A precision irrigation scheme to manage plant water status using leaf monitors in almonds. Unpublished MS thesis, Biological and Agricultural Engineering Department, University of California Davis. 116pp.

Florida

Chen, Y., W. S. Lee, H. Gan, N. Peres, C. Fraisse, Y. Zhang, and Y. He. 2019. Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote Sensing, 11: 1584. Doi:10.3390/rs11131584.

Lin, P., W. S. Lee, Y. M. Chen, N. Peres, and C. Fraisse. 2019. A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field. Precision Agriculture. Published online: 07 June 2019. https://doi.org/10.1007/s11119-019-09673-7.

Iowa

Articles

Kshetri, S., B. L. Steward, J.J. Jiken, L. Tang, and M. Tekeste. 2019. Investigating effects of interaction of single tine and rotating tine mechanism with soil on weeding performance using simulated weeds. Transactions of the ASABE. doi: 10.13031/trans.13301

Schramm, M. W., H. M. Hanna, M. J. Darr, and S. J. Hoff, and B. L. Steward. 2019. Sub-second wind velocity changes one meter above the ground. Applied Engineering in Agriculture. doi: 10.13031/aea.12264.

Zhang, W., J. Gai, L. Tang, Y. Ding, Q. Liao. 2019. Double-DQN-based path smoothing and tracking control method for in-field robotic vehicle navigation. Computers and Electronics in Agriculture. DOI: 10.1016/j.compag.2019.104985.

Tu, X., J. Gai, L. Tang. 2019. Robust navigation control of a 4WD/4WS agricultural robotic vehicle. Computers and Electronics in Agriculture 164. DOI: 10.1016/j.compag.2019.104892

Gai, J., L. Tang, B. L. Steward. 2019. Automated crop plant detection based on the fusion of color and depth images for robotic weed control. Journal of Field Robotics 2019: 1-18. DOI: 10.1002/rob.21897.

Xiang, L., Y. Bao, L. Tang, D. Ortiz, M. G. Salas-Fernandez. 2019. Automated morphological traits extraction for sorghum plants via 3D point cloud data analysis. Computers and Electronics in Agriculture 162: 951-961. DOI: 10.1016/j.compag.2019.05.043.

Bao, Y., L. Tang, S. Srinivasan, P. S. Schnable. 2018. Plant architectural traits characterization for maize using time-of-flight 3D imaging. Biosystems Engineering 178: 86-101. DOI: 10.1016/j.biosystemseng.2018.11.005.

Book Chapter

Steward, B. L., J. Gai, and L. Tang. 2019. The use of agricultural robots in weed management and control. In Robotics and Automation for Improving Agriculture. ed. J. Billingsley. Burleigh Dodds Science Publishing: Cambridge, UK.

Conference Paper

Pennsylvania

Shi, X., Choi, D., Heinemann, P., Lynch, J., and Hanlon, M. 2019. RootRobot: A field-based platform for maize root system architecture phenotyping. ASABE Paper No. 1900806. American Society of Agricultural and Biological Engineers. 6 pp.

He, L., Zeng, L., and Choi, D. 2019. Investigation of sensor-based irrigation systems for apple orchards. NABEC Paper No. 19-013. American Society of Agricultural and Biological Engineers. ASABE: St. Joseph, MI.

Zahid, A., He, L. and Zeng, L. 2019. Development of a Robotic End Effector for Apple Tree Pruning. ASABE Paper No. 1900964. American Society of Agricultural and Biological Engineers. ASABE: St. Joseph, MI.

He, L., Zhang, X., Ye, Y., Karkee, M., and Zhang, Q. 2019. Effect of shaking location and duration on mechanical harvesting of fresh market apples. Applied Engineering in Agriculture, 35(2), 175-183.

Feng, J., Zeng, L., and He, L. 2019. Apple fruit recognition algorithm based on multi-spectral dynamic image analysis. Sensors, 19(4), p. 949.

Lee, C., Choi, D., Pecchia, J., He, L., & Heinemann, P. 2019. Development of A Mushroom Harvesting Assistance System using Computer Vision. 2019 ASABE Annual International Meeting, Paper No. 190050, page 1-5, July 7 – July 10, 2019.

Jarvinen, T., Choi, D., Heinemann, P., Schupp, J., & Baugher, T. A. 2019. Tree trunk position estimation for accurate fruit counts in apple yield mapping2019 ASABE Annual International Meeting, Paper No. 1900918, page 1-7, July 7 – July 10, 2019.

Shi, Xiaomeng., Choi, D., Heinemann, P., Lynch, J., & Hanlon, M. 2019. RootRobot: A Field-based Platform for Maize Root System Architecture Phenotyping. 2019 ASABE Annual International Meeting, Paper No.1900806, page 1-7, July 7 – July 10, 2019.

Jarvinen, T., Choi, D., Heinemann, P., & Baugher, T. A. 2018. Multiple object tracking-by-detection for apple fruit counting on a tree canopy. 2018 ASABE Annual International Meeting, Paper No. 1801193, page 1-8, July 29 – Aug 1, 2018.

Choi, D., & Jarvinen, T. 2018. "A video processing strategy using camera movement estimation for apple yield forecasting." Proceedings of the 9th International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering, page 1-5, Jeju, South Korea, May 28-30, 2018.

Wang, C., Lee, W. S., Zou, X., Choi, D., Gan, H., & Diamond, J. 2018. Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images. Precision Agriculture. ISBN/ISSN #/Case #/DOI #: https://doi.org/10.1007/s11119-018-9574-5. Online publication.

Washington

Bhusal, S., K. Khanal, S. Goel, M. Karkee, and M. Taylor. 2019. Bird deterrence in a vineyard using an unmanned aerial system (UAS). Transactions of the ASABE; 62(2): 561-569 (doi: 10.13031/trans.12923).

Chakraborty, M., L.R. Khot, S. Sankaran, and P. Jacoby. 2019. Evaluation of mobile 3D light detection and ranging based canopy mapping system for tree fruit crops. Computers and Electronics in Agriculture,158: 284-293 (doi: 10.1016/j.compag.2019.02.012).

Chakraborty, M., L.R. Khot, and R.T. Peters. 2019. Assessing suitability of modified center pivot irrigation systems in corn production using low altitude aerial imaging techniques. Information Processing in Agriculture, In Press (doi: 10.1016/j.inpa.2019.06.001).

Chandel, A., L.R. Khot, Y. Osroosh, and R.T. Peters. 2018. Thermal-RGB imager derived in-field apple surface temperature estimates for sunburn management. Agricultural and Forest Meteorology, 253-254: 132-140 (doi: 10.1016/j.agrformet.2018.02.013).

He, L., X. Zhang, Y. Ye, M. Karkee, and Q. Zhang. 2019. Effect of shaking location and duration on mechanical harvesting of fresh market apples. Applied Engineering in Agriculture; 35(2): 175-183 (doi: 10.13031/aea.12974).

Hohimer, C.J., H. Wang, S. Bhusal, J. Miller, C. Mo, and M. Karkee. 2019. Design and field evaluation of a robot apple harvesting system with 3D printed soft-robotic end-effector. Transactions of the ASABE; 62(2): 405-414 (doi: 10.13031/trans.12986).

Khanal, K., S. Bhusal, M. Karkee, P. Scharf, and Q. Zhang. 2019. Design of improved and semi-automated red raspberry cane bundling and tying machine based on the field evaluation results. Transactions of the ASABE. 62(3): 821-829 (doi: 10.13031/trans.12973).

Osroosh, Y., L.R. Khot, and R.T. Peters. 2019. Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager. Computers and Electronics in Agriculture, 157: 509–517 (doi: 10.1016/j.compag.2019.01.023).

Pena Quinones, A.J., M. Keller, M.R. Salazar-Gutierrez, L.R. Khot, and G. Hoogenboom. 2019. Comparison between grapevine tissue temperature and air temperature. Scientia Horticulturae, 247: 407–420 (doi: 10.1016/j.scienta.2018.12.032).

Ranjan, R., A. Chandel, L.R. Khot, H. Bahlol, J. Zhou, R. Boydston, and P. Miklas. 2019. Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Information Processing in Agriculture, In Press (doi: 10.1016/j.inpa.2019.01.005)

Ranjan, R., G. Shi, R. Sinha, L. R. Khot, G. Hoheisel, and M. Grieshop. 2019. Automated solid set canopy delivery system for large scale spray applications in perennial tree–fruit crops. Transactions of the ASABE, 62(3): 585-592 (doi: 10.13031/trans.13258).

Sharda, A., M. Karkee, G. Hoheisel, and Q. Zhang. 2019. Design and evaluation of solid set canopy delivery system for spray application in high-density apple orchards. Applied Engineering in Agriculture 35(5): 751-757 (doi: 10.13031/aea.12512).

Sinha, R., L.R. Khot, A. Rathnayake, Z. Gao, and N. Rayapati. 2019. Visible−near infrared spectroradiometry-based detection of grapevine leafroll-associated virus in a red−fruited wine grape cultivar. Computers and Electronics in Agriculture, 162: 165-173 (doi: 10.1016/j.compag.2019.04.008).

Sinha, R., L.R. Khot, GA. Hoheisel, M. Grieshop, and H.Y. Bahlol. 2019. Feasibility of a solid set canopy delivery system for efficient agrochemical delivery in vertical shoot positioning trained vineyards. Biosystems Engineering, 179: 59-70 (doi: 10.1016/j.biosystemseng.2018.12.011).

Karkee, M., J. Gordón, B. Sallto and M. Whiting, Optimizing fruit production efficiencies via mechanization. 2019. In Achieving sustainable cultivation of temperate zone tree fruits and berries, Volume 1 - Physiology, genetics and cultivation (Editor: Dr Greg Lang); Burleigh Dodds Science Publishing.

Zhang, Q., M. Karkee, and A. Tabb, 2019. The Use of Agricultural Robots in Orchard Management. In Robotics and Automation for a More Sustainable Agriculture (Editor: John Billingsley); rXiv preprint arXiv:1907.13114.

Zhang, Q. 2019. Basics of Hydraulic Systems (2^nd Edition). CRC Press, (324 pp).

Zhang, X., C. Mo, M.D. Whiting, and Q. Zhang. 2019. Plant-based compositions for the protection of plants from cold damage. US Patent (filed, Docket No. 12770096TA).

West Virginia

L.J. Nixon, A. Tabb, W. M. Morrison, K. Rice, E. G. Brockerhoff, T.C. Leskey, S. Goldson, M. Rostas, Volatile release, mobility, and mortality of diapausing Halyomorpha halys during simulated shipping movements and temperature changes," J Pest Sci (2019). Doi 10.1007/s10340-019-01084-x

A. Dias, A. Tabb and H. Medeiros, “Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network," in IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3003-3010, Oct. 2018. Doi 10.1109/LRA.2018.2849498

Stumph, M. Hernandez Virto, H. Medeiros, A. Tabb, S. Wolford, K. Rice, T. Leskey, “Quantifcation of Dispersal Patterns of Invasive Insects with Unmanned Aerial Vehicles," in 2019 IEEE International Conference on Robotics and Automation (ICRA), 2019. doi: 10.1109/ICRA.2019.8794116 and arXiv:1903.00815 [cs.RO].

P.A. Dias, Z. Shen, A. Tabb and H. Medeiros, “FreeLabel: a publicly available annotation tool based on freehand traces," in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). doi: 10.1109/WACV.2019.00010

Datasets and code releases

Tabb, K. E. Duncan, C. N. Topp, “Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets," [Data set]. Zenodo. 2019. 10.5281/zenodo.3333709, 10.5281/zenodo.3344906

Tabb, “Code from: Using cameras for precise measurement of two-dimensional plant features," Ag Data Commons, 2019. https://data.nal.usda.gov/dataset/code-using-cameras-precise-measurement-two-dimensional-plant-features

A. Dias, A. Tabb, H. Medeiros, “Multi-species fruit flower detection using a refined semantic segmentation network," Ag Data Commons, 2018. 10.15482/USDA.ADC/1423466

11/05/2020

Arizona
Raja, R., Slaughter, D.C., Fennimore, S.A., Nguyen, T.T., Vuong, V.L., Sinha, N., Tourte, L., Smith, R.F. & Siemens, M.C. 2019. Crop signaling: A novel crop recognition technique for robotic weed control. Biosystems Eng. 187: 278-291.

California
Rojo, Francisco, Rajveer Dhillon, Shrinivasa Upadhyaya, and Bryan Jenkins. 2020. Development of a dynamic model to estimate canopy par interception. Biosystems Eng. 198:120-136
Rojo, Francisco, Rajveer Dhillon, and Shrinivasa Upadhyaya. 2020. Comparing ground-based par interception data with UAV images and sun position. Submitted for publication is Applied Engineering in Agriculture.
Khorsandi, F., P.D. Ayers, E.J. Fong. 2019. Evaluation of Crush Protection Devices for agricultural All-Terrain Vehicles. Biosystems Engineering. Volume 185, September 2019, Pages 161-173
Peng, C., Vougioukas, S.G. (2020). Deterministic predictive dynamic scheduling for crop-transport co-robots acting as harvesting aids. Computers and Electronics in Agriculture, 178, 105702. https://doi.org/10.1016/j.compag.2020.105702
Fei, Z., Shepard, J., Vougioukas, S.G. (2020). Instrumented Picking Bag for Measuring Fruit Weight During Manual Harvesting. Transactions of the American Society of Agricultural and Biological Engineering. IN PRESS.
Thayer, T., Vougioukas, S.G., Goldberg, K., Carpin, S. (2020). Multi-Robot Routing Algorithms for Robots Operating in Vineyards. IEEE Transactions on Automation Science and Engineering, 17(3): 1184-1194. https://doi.org/10.1109/TASE.2020.2980854
Seyyedhasani, H., Peng, C., Jang, W., Vougioukas, S.G. (2020). Collaboration of Human Pickers and Crop-transporting Robots during Harvesting - Part I: Model and Simulator Development. Computers and Electronics in Agriculture. (172): p.105324. https://doi.org/10.1016/j.compag.2020.105324
Seyyedhasani, H., Peng, C., Jang, W., Vougioukas, S.G. (2020). Collaboration of Human Pickers and Crop-transporting Robots during Harvesting - Part II: Simulator Evaluation and Robot-Scheduling Case-study. Computers and Electronics in Agriculture. (172): p.105323. https://doi.org/10.1016/j.compag.2020.105323
Agricultural All-Terrain Vehicle Safety. Committee on Agricultural Safety and Health Research and Extension. 2020. Agricultural All-Terrain Vehicle Safety. USDA-NIFA. Washington, DC.
Ayers, P.D., F.K. Khorsandi, M.J. Poland, C.T. Hilliard. 2019. Foldable rollover protective structures: Universal lift-assist design. Biosystems Engineering. Volume 185, September 2019, Pages 116-125
Khorsandi, et al. In Press. A manuscript titled “Agricultural All-Terrain Vehicle Safety: Hazard Control Methods Using the Haddon Matrix” will be published in Journal of Aeromedicine.
Donis-Gonzalez, I.R., Valero, C., Momin, M.A., Kaur, A., Slaughter, D.C., 2020. Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes. AGRONOMY-BASEL Vol. 10: DOI: 10.3390/agronomy10010148.
Donis-Gonzalez, I.R., Sidelli, G., Bergman, S.M., Slaughter, D.C., Crisosto, C.H. 2020. Color Vision System to Assess English Walnut (Juglans Regia) Kernel Pellicle Color. Postharvest Biology and Technology. Volume 167, September 2020, 111199.doi.org/10.1016/j.postharvbio.2020.111199
Olenskyj, A.G., I.R. Donis-González, G.M. Bornhorst. 2020. Nondestructive characterization of structural changes during in vitro gastric digestion of apples using 3D time-series micro-computed tomography. Journal of Food Engineering. Volume 267, February 2020, 109692
Raja, R., Slaughter, D.C., Fennimore, S.A., Nguyen, T.T., Vuong, V.L., Sinha, N., Tourte, L., Smith, R.F., Siemens, M.C., 2019. Crop signalling: A novel crop recognition technique for robotic weed control. Biosystems Engineering. Vol 187: 278-291. DOI: 10.1016/j.biosystemseng.2019.09.011.
Raja, R., Nguyen, T.T., Slaughter, D.C., Fennimore, S.A., 2020. Real-time robotic weed knife control system for tomato and lettuce based on geometric appearance of plant labels. Biosystems Engineering. Vol. 194: 152-164.
Kennedy, H. Fennimore, S.A., Slaughter, D.C., Nguyen, T.T., Vuong, V.L., Raja, R., Smith R.F., Crop Signal Markers Facilitate Crop Detection and Weed Removal from Lettuce and Tomato by an Intelligent Cultivator. Weed Technology. DOI: https://doi.org/10.1017/wet.2019.120

Georgia
Iqbal, J., Xu, R., Halloran, H., and Li, C. 2020. Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing. Electronics, 9 (9), 1550.
Ni, X, Li, C, Jiang, H, and Takeda, F. 2020. Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield. Horticulture Research, 7 (1), 1-14.
Iqbal J., Xu, R., Sun, S., and Li, C. 2020. Simulation of an autonomous mobile robot for LiDAR-based in-field phenotyping and navigation. Robotics, 9 (2), 46.
Jiang, Y. and Li, C. 2020. Convolutional neural networks for image-based high throughput plant phenotyping: A review. Plant Phenomics, 2020 (4152816).
Jiang, Y., Snider, J. L., Li, C., Rains, G. C., and Paterson, A. H. 2020. Ground based hyperspectral imaging to characterize canopy-level photosynthetic activities. Remote Sensing, 12 (2), 315.
Zhang, M., Jiang, Y., Li, C., and Yang, F. 2020. Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging. Biosystems Engineering, 192, 159-175.

IOWA
Kshetri, S., B. L. Steward, J.J. Jiken, L. Tang, and M. Tekeste. 2019. Investigating effects of interaction of single tine and rotating tine mechanism with soil on weeding performance using simulated weeds. Transactions of the ASABE 62(5): 1283-1291.
Gai, J., L. Tang, and B.L. Steward. 2020. Automated crop plant detection based on the fusion of color and depth images for robotic weed control. Journal of Field Robotics. 37(1): 35-52.
Schramm, M. W., H. M. Hanna, M. J. Darr, and S. J. Hoff, and B. L. Steward. 2019. Sub-second wind velocity changes one meter above the ground. Applied Engineering in Agriculture 35(5): 697-704.
Mantilla-Perez, M. B., Y. Bao, L.Tang, P. S. Schnable, M. G. Salas-Fernandez. 2020. Towards "smart canopy" sorghum: discovery of the genetic control of leaf angle across layers. Plant Physiology, DOI: https://doi.org/10.1104/pp.20.00632

Steward, B. L., H. M. Hanna, P. M. Dixon, R. K. Mompremier. 2019. Measuring and modeling the movement of spray droplets into off-target areas. ASABE Paper No. 1901496. St. Joseph, Mich.: ASABE. DOI: doi.org/10.13031/aim.201901496
Gai, J., T. Tuel, L. Xiang, L. Tang. 2020. PhenoBot 3.0 - an Autonomous Robot for Field-based Maize/Sorghum Plant Phenotyping, Phenome 2020, Tucson, AZ, February 24-27.
Gai, J., T. Tuel, L. Xiang, L. Tang. 2020. Developing the Control System of an Autonomous Robot for Field-based Maize/Sorghum Plant Phenotyping, 2020 ASABE Annual International Meeting, Omaha, Nebraska, July 12–15, 2020.
Xiang, L., J. Gai, L. Tang. 2020. Developing a high-throughput stereo vision system for plant phenotyping. 2020 Phenome, Tucson, AZ, Feb. 24-27, 2020.
Xiang, L., L. Tang, J. Gai, & L. Wang. 2020. PhenoStereo: a high-throughput stereo vision system for field-based plant phenotyping-with an application in sorghum stem diameter estimation. 2020 ASABE Annual International Virtual Meeting. July 13-15, 2020. Paper No. 2001190

Michigan
Rady, A.M., Guyer, D.E., Donis-Gonzalez, I.R., Kirk, W., Watson, N.J. 2020. A comparison of different optical instruments and machine learning techniques to identify sprouting activity in potatoes during storage. Journal of Food Measurement and Characterization. doi.org/10.1007/s11694-020-00590-2.

Pennsylvania
Refereed Journals
Caliskan-Aydogan, O., H. Yi, J.R. Schupp, D. Choi, P. H. Heinemann, V. M. Puri. 2020. Changes in thermal properties of 'Gala' apple during the growing season. Transactions of the ASABE, 63(2), 305-315.
Fu, H., Karkee, M., He, L., Duan, J., Li, J., & Zhang, Q. (2020). Bruise Patterns of Fresh Market Apples Caused by Fruit-to-Fruit Impact. Agronomy, 10(1), 13.
Kon, T. M., J. R. Schupp, M. A. Schupp, and H.E. Winzeler. 2020. Screening thermal shock as an apple blossom thinning strategy. II. Pollen tube growth and spur leaf injury in response to thermal shock temperature and duration. HortScience, 55(5), 632-636.
Kon, T. M., M. A. Schupp, H.E. Winzeler and J. R. Schupp. 2020. Screening thermal shock as an apple blossom thinning strategy. I. Stigmatic receptivity, pollen tube growth, and leaf injury in response to thermal shock temperature and timing. HortScience, 55(5), 625-631.
Zahid, A., He, L., Zeng, L., Choi, D., Schupp, J., & Heinemann, P. (2020). Development of a Robotic End-Effector for Apple Tree Pruning. Transactions of the ASABE, 63, 847-856.
Zahid, A., Mahmud, M., He, L., Choi, D., Schupp, J., & Heinemann, P. Development of an Integrated 3R End-effector with a Cartesian Manipulator for Pruning Apple Trees. Computers and Electronics in Agriculture, 179.
Zeng, L., Feng, J., & He, L. (2020). Semantic segmentation of sparse 3D point cloud based on geometrical features for trellis-structured apple orchard. Biosystems Engineering, 196, 46-55.
Zhang, X., He, L., Karkee, M., Whitting, M., & Zhang, Q. (2020). Field Evaluation of Targeted Shake-and-Catch Harvesting Technologies for Fresh Market Apple. Transactions of the ASABE. [In press].
Zhang, X., He, L., Zhang, J., Whiting, M. D., Karkee, M., & Zhang, Q. (2020). Determination of key canopy parameters for mass mechanical apple harvesting using supervised machine learning and principal component analysis (PCA). Biosystems Engineering, 193, 247-263.

Non-refereed publications
Choi, D., J. Schupp, T. Baugher, and L. He. Evaluation of effective canopy depths of apple trees for optimal machine sensing performance – Year2/2. PA Fruit News 100(1):39-41.
He, L. (2020). Drip Irrigation and Sensor-Based Precision Irrigation. In the Penn State Tree Fruit Production Guide. (2020-2021), (pp. 426-430).
He, L., & Weber, D. (2020). Updates on Soil Moisture-Based Irrigation for Orchards. Pennsylvania Fruit News.
He, L., D. Choi, J. Schupp and T. Baugher. 2020. A sensor-based irrigation test system for apple orchards (Final report). PA Fruit News 100(1):24-26.
He, L., J. Schupp, D. Choi and D. Weber. 2020. Branch and fruit accessibility for mechanical operations with various tree canopies (Year 1 report). PA Fruit News 100(1):22-24.
Huang, M., He, L., Jiang, X., Choi, D., & Pecchia, J. (2020). Hand-picking Dynamic Analysis for Robotic Agaricus Mushroom Harvesting. Paper No. 2000415. 2020 ASABE Annual International Meeting.
Jarvinen, T., Choi, D., Heinemann, P., & Baugher, T. A. (2019). Tree trunk position estimation for accurate fruit counts in apple yield mapping. 2019 ASABE Annual International Meeting, Paper No. 1900918, July 7 – July 10, 2019. (pp. 1-7).
Jiang, X., He, L., & Tong, J. (2020). Investigation of Soil Wetting Pattern in Drip Irrigation using LoraWAN Technology. Paper No. 2000419. 2020 ASABE Annual International Meeting.
Lee, C.-H., Choi, D., Pecchia, J., He, L., & Heinemann, P. (2019). Development of A Mushroom Harvesting Assistance System using Computer Vision. 2019 ASABE Annual International Meeting, Paper No. 190050, July 7 – July 10, 2019. (pp. 1-5).
Mahmud, M. S., & He, L. (2020). Measuring Tree Canopy Density Using A Lidar-Guided System for Precision Spraying. Paper No. 2000554. 2020 ASABE Annual International Meeting.
Mirbod, O., Choi, D., Heinemann, P., & Marini, R. (2020). Towards image-based measurement of accurate apple size and yield using stereo vision cameras. 2020 ASABE Annual International Meeting, Paper No. 2001115, July 12- 15, 2020. (pp. 1-6).
Schupp, J., H. E. Winzeler and M. Schupp. 2020. Blossom Thinning Pennsylvania Apples Using the Pollen Tube Growth Model. PA Fruit News 100 (1):46-47.
Schupp, J., H. E. Winzeler and M. Schupp. 2020. Development of a High Density, Highly Mechanized, Pedestrian Peach System. PA Fruit News 100 (1): 43-44.
Schupp, J., L. He, H. E. Winzeler, M. Schupp and M. Clowney. 2020. Improving orchard performance with terrain analysis using drone technology and Geographical Information Systems. PA Fruit News 100 (1):45-46.
Shi, X., Choi, D., Heinemann, P., Lynch, J. P., & Hanlon, M. (2019). RootRobot: A Field-based Platform for Maize Root System Architecture Phenotyping. 2019 ASABE Annual International Meeting, Paper No.1900806, July 7 – July 10, 2019. (pp. 1-7).
Zahid, A., He, L., Choi, D., Schupp, J., & Heinemann, P. (2020). Collision free Path Planning of a Robotic Manipulator for Pruning Apple Trees. Paper No. 2000439. 2020 ASABE Annual International Meeting.
Zhang, H., He, L., Di Gioia, F., Choi, D., & Heinemann, P. (2020). Internet of Things (IoT)-based Precision Irrigation with LoRaWAN Technology Applied to High Tunnel Vegetable Production. Paper No. 2000762. 2020 ASABE Annual International Meeting.

Patent
Lyons, D.J. & Heinemann, P.H. 2019. US patent No. 10,448,578 B2: Selective Automated Blossom Thinning. October 22, 2019.

Extension Presentation
Schupp, J. 2020. Blossom thinning Golden Delicious using lime sulfur and the pollen tube growth model. Mid-Atlantic Fruit and Vegetable Conference, Hershey, PA. January 28, 2020.
Schupp, J., 2020. Research on fruit thinning. Ohio Produce Network, Columbus, OH. 23 Jan 2020.
Schupp, J., 2020. Research on orchard systems/ pruning. Ohio Produce Network, Columbus, OH. 23 Jan 2020
Schupp, J., and D. Weber. 2020. Demonstrations of the REDpulse pneumatic defoliator for increasing red coloration of apples. Biglerville, PA. 19 and 20 August 2020.
Schupp, J., He, L., H. E. Winzeler, M. Schupp and M. Clowney. 2020. Improving orchard performance with terrain analysis using drone technology and Geographical Information Systems (GIS). Mid-Atlantic Fruit and Vegetable Conference, Hershey, PA. January 28-30, 2020 (poster).
Schupp, J., M. Schupp and H. E. Winzeler. 2020. High density mechanized pedestrian peach system. Mid-Atlantic Fruit and Vegetable Conference, Hershey, PA. January 28-30, 2020 (poster).

Award
Crassweller, R., K. Peter, G. Krawczyk, J. Schupp, T. Ford, M. Brittingham, J. Johnson, L. LaBorde, J. Harper, K., Kephart, R. Pifer, K. Kelley, L. He, P. Heinemann, D. Biddinger, M. Lopez-Uribe, R. Marini, T. Baugher, D. Weber, L. Kime, E. Crow, E. Weaver, B. Lehman. 2020. 2020-21 Penn State Tree Fruit Production Guide. The Outstanding Book Publication from the American Society for Horticultural Science.

Washington
Journal Articles
Bahlol, H., A. Chandel, G.-A. Hoheisel and L. R. Khot. 2020. Developing understanding on orchard sprayer air-assists and volume output patterns using smart spray analytical system. Crop Protection, 127: 104977. https://doi.org/10.1016/j.cropro.2019.104977
Bahlol, H. Y., A. Chandel, G.-A. Hoheisel, and L. R. Khot. 2020. Smart spray analytical system for orchard sprayer calibration: a-proof-of-concept and preliminary results. Transactions of the ASABE, 62(6): 29-35. https://doi.org/10.13031/trans.13196 .
Davidson, J., S. Bhusal, C. Mo, M. Karkee, and Q. Zhang. 2020. Robotic Manipulation for Specialty Crop Harvesting: A Review of Manipulator and End-Effector Technologies. Global Journal of Agriculture and Allied Sciences, 2(1), 25-41. https://doi.org/10.35251/gjaas.2020.004.
Fu, H., M. Karkee, L. He, J. Duan, J. Li, and Q. Zhang, 2020. Bruise patterns of fresh market apples caused by fruit-to-fruit impact. Agronomy, 10(1), Article 59. (http://doi.org/10.3390/agronomy 10010059).
Fu, L., Y. Majeed, X. Zhang, M. Karkee and Q. Zhang, 2020. Faster R-CNN-based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting. Biosystems Engineering, 197: 245-256. http://doi.org/10.1016/j.biosystemseng.2020.07.007.
Gao, F., L. Fu, X. Zhang, Y. Majeed, R. Li, M. Karkee and Q. Zhang. 2020. Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN. Computers and Electronics in Agriculture, 176, 105634. http://doi.org/10.1016/j.compag.2020.105634.
Gao, Z., R. A. Naidu, Q. Zhang, and L. R. Khot. 2020. Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging. Computers and Electronics in Agriculture, 179, 105807 https://doi.org/10.1016/j.compag.2020.105807
Khot, L. R.*. 2020. Transitioning from precision to decision horticulture: technology landscape. ISHS Acta Horticulturae 1279, XXX International Horticultural Congress IHC2018: VII Conference on Landscape and Urban Horticulture, IV Conference on Turfgrass Management and Science for Sports Fields and II Symposium on Mechanization, Precision Horticulture, and Robotics, 43:1, https://doi.org/10.17660/ActaHortic.2020.1279.29
Long, Y., M. Li, D. Gao, Z. Zhang, H. Sun, and Q. Zhang, 2020. Chlorophyll content detection based on image segmentation by plant spectroscopy. Spectroscopy and Spectral Analysis, 40(7): 2253-2258. https://doi.org/10.3964/j.issn. 1000-0593(2020)07-2253-06.
Majeed, Y., J. Zhang, X. Zhang, L. Fu, M. Karkee, Q. Zhang, and M.D. Whiting, 2020. Deep learning based segmentation for automated training of apple trees on trellis wires. Computers and Electronics in Agriculture, 170. Article 105277. (https://doi.org/10.1016/J.COMPAG.2020.105277).
Majeed, Y., M. Karkee, Q. Zhang, L. Fu, and M.D. Whiting, 2020. Determining grapevine cordon shape for automated green shoot thinning using semantic segmentation-based deep learning networks. Computers and Electronics in Agriculture, 171. Article 105308. (https://doi.org/10.1016/J.COMPAG.2020.105308).
Majeed, Y., M. Karkee, and Q. Zhang, 2020. Estimating the trajectories of vine cordons in full foliage canopies for automated green shoot thinning in vineyards. Computers and Electronics in Agriculture, 176. Article 105671. (https://doi.org/10.1016/j.compag.2020.105671).
Zhang, J., M. Karkee, Q. Zhang, X. Zhang, Y. Majeed, L. FU, and S. Wang, 2020. Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting. Computers and Electronics in Agriculture, 173. Article 105384. (doi: 10.1016/J.COMPAG.2020.105384).
Zhang, X., L. He, J. Zhang, M.D. Whiting, M. Karkee and Q. Zhang, 2020. Determination of key canopy parameters for mass mechanical apple harvesting using supervised machine learning and principal component analysis (PCA). Biosystems Engineering, 193: 247-263. (doi: 10.1016/j.biosystemseng.2020.03.006).
Zhang, X., He, L., Karkee, M., Whiting, M. D., and Zhang, Q. 2020. Field Evaluation of Targeted Shake-and-Catch Harvesting Technologies for Fresh Market Apple. Transactions of the ASABE, 2020. doi: 10.13031/trans.13779.
Ranjan, R., L. R. Khot, R. Troy Peters, M. R. Salazar-Gutierrez and G. Shi. 2020. In-field crop physiology sensing aided real-time apple fruit surface temperature monitoring for sunburn prediction. Computers and Electronics in Agriculture, 157: 105558. https://doi.org/10.1016/j.compag.2020.105558.
Santiago, W. E., N. J. Leite, B. J. Teruel, M. Karkee, and C. A.M. Azania. 2019. Evaluation of bag-of-features (BoF) technique for weed management in sugarcane production. Australian Journal Crop of Science, 13(11):1819-1825.
Sinha, R., R. Ranjan, H. Y. Bahlol, L. R. Khot, G.–A. Hoheisel and M. Grieshop. 2020. Development and performance evaluation of a pneumatic spray delivery based solid set canopy delivery system for high-density apple orchard. Transactions of the ASABE, 62(6): 37-48. https://doi.org/10.13031/trans.13411
Sinha, R., R. Ranjan, G. Shi, G.-A. Hoheisel, M. Grieshop and L. R. Khot. 2020. Solid set canopy delivery system for efficient agrochemical delivery in modern architecture apple and grapevine canopies. Acta Horticulturae 1269: II International Symposium on Innovative Plant Protection in Horticulture, 277-286. https://doi.org/10.17660/ActaHortic.2020.1269.38
Wang, B., R. Ranjan, L. R. Khot and R. Troy Peters. 2020. Smartphone application‐enabled apple fruit surface temperature monitoring tool for in‐field and real‐time sunburn susceptibility prediction. Sensors, 20, 608. https://doi:10.3390/s20030608

Thesis/Dissertations
Gao, Zongmei (2020). Spectral imaging based non-contact detection of biotic and abiotic stress in berry crops. Ph.D. Dissertation. April 2020, Washington State University.
Majeed, Yaqoob (2020). Machine Vision System for the Automated Green Shoot Thinning in Vineyards. Ph.D. Dissertation. April 2020, Washington State University.
Zhang, Xin (2020). Study of Canopy-Machine Interaction in Mass Mechanical Harvest of Fresh Market Apples. Ph.D. Dissertation. March 2020, Washington State University.

Conference Paper and Presentations
Anura P. Rathnayake, G. A. Hoheisel and L. R. Khot. 2020. A PWM based retrofit controller for optimized spray applications in perennial specialty crops. Paper No. 2001023, ASABE 2020 Virtual Annual International Meeting, July 12-15, 2020 (Oral Presentation).
Bhattarai, U. Automated Blossom Detection in Apple Trees using Deep Learning. Twenty First IFAC World Congress, Berlin, Germany, July 12-17, 2020 (Virtual).
Bhusal, S., Bhattarai, U., and Karkee, M. 2019. Improving Pest Bird Detection in a Vineyard Environment Using Super-Resolution and Deep Learning. IFAC-PapersOnLine, 52(30), 18-23.
Fu, H., J. Duan, M. Karkee, L. He, H. Xia, J. Li and Q. Zhang. 2019. Effect of shaking amplitude and capturing height on mechanical harvesting of fresh market apples. IFAC-PapersOnLine, 52(30), 306-311.
Majeed, Y., Karkee, M., Zhang, Q. Fu, L. and Whiting, M.D. 2019. A study on the detection of visible parts of cordons using deep learning networks for the automated green shoot thinning in vineyards. IFAC-PapersOnLine, 52(30), pp.82-86.
Ranjan, R., R. Sinha, L. R. Khot, G. A. Hoheisel, M. Grieshop and M. Ledebhur. 2020. Effect of emitter modifications on spraying attributes of a pneumatic spray delivery based solid set canopy delivery system configured for high-density apple orchard. Paper No. 2000164, ASABE 2020 Virtual Annual International Meeting, July 12-15, 2020 (Oral Presentation).
Ranjan, R., L. R. Khot, R. T. Peters, and M. R. Salazar-Gutierrez. 2020. Field evaluation of visible-infrared and microclimate sensing aided crop physiology sensing system for apple sunburn management. Paper No. 2000165, ASABE 2020 Virtual Annual International Meeting, July 12-15, 2020 (Oral Presentation).
You, A., F. Sukkar, R. Fitch, M. Karkee, and J.R. Davidson. 2020. An efficient planning and control framework for pruning fruit trees. IEEE International Conference on Robotics and Automation. May 31 – Aug 31, 2020 (Virtual).
Zhang, Q. 2019. Digital Agriculture: Opportunities and Challenges, A View of Automation. WSU Digital Agriculture Summit, December 4-6, 2020 (Invited Keynote Speech).
Zhang, X., Fu, L., Karkee, M., Whiting, M. D., & Zhang, Q. 2019. Canopy segmentation using ResNet for mechanical harvesting of apples. IFAC-PapersOnLine, 52 (30), 306-311.

Other Products
Khot, L., R. Sinha, G.-A. Hoheisel, and Matthew Grieshop. 2019. Solid set canopy delivery system for WA vineyards. Washington State University - Viticulture and Enology Extension News, Spring 2019. http://wine.wsu.edu/extension/viticulture-enology-news-veen/.

West Virginia
Kirkpatrick, D., Rice, K., Ibrahim, A. Fleischer, S., Tooker, J., Tabb, A., Medeiros, H., Morrison, W., Leskey, T. 2020. The Influence of Marking Methods on Mobility, Survivorship, and Field Recovery of Halyomorpha halys (Hemiptera: Pentatomidae) Adults and Nymphs. Environmental Entomology, nvaa095, https://doi.org/10.1093/ee/nvaa095 .
Zhu, J., Teolis, S., Biassou, N., Tabb, A., Jabin, P., and Lavi, O. 2020. Tracking the adaptation and compensation processes of patients brain arterial network to an evolving glioblastoma.IEEE Transactions on Pattern Analysis & Machine Intelligence (accepted, currently preprint).
Feldmann, M. J., Hardigan, M. A., Famula, R. A., López, C. M., Tabb, A., Cole, G. S., Knapp, S. J. 2020. Multi-Dimensional Machine Learning Approaches for Fruit Shape Phenotyping in Strawberry. GigaScience. https://doi.org/10.1093/gigascience/giaa030

Code releases
Tabb, A. “Data and Code from: Using cameras for precise measurement of two-dimensional plant features: CASS.” Zenodo, 2020. http://doi.org/10.5281/zenodo.3677473
Tabb, A. and Feldmann, M. J. "Data and Code from: Calibration of Asynchronous Camera Networks: CALICO," (Version v.1) [Data set]. Zenodo, 2019. http://doi.org/10.5281/zenodo.3520866

09/20/2021

Arizona

Siemens, M.C., Godinez, Jr., V. & Gayler, R.R. 2021. Centimeter Scale Resolution Spot Sprayer for Precision In-Row Weed Control. In Proc. 73rd Annual California Weed Science Society 73:44. Salinas, Calif.: California Weed Science Society.

Siemens, M.C. Godinez, Jr., V., Bahr, N. & Fennimore, S.A. 2021. Development and evaluation of a novel band-steam applicator for controlling soilborne pathogens and weeds in lettuce. ASABE Paper No. 2100185. St. Joseph, Mich.: ASABE.

California

Rotta, N.M., Curry, S., Han, J., Reconco, R., Spang, E., Ristenpart, W., & Donis-González, I.R. 2021. A comprehensive analysis of operations and mass flows in postharvest processing of washed coffee. Resources, Conservation and Recycling 170: 105554.

Félix-Palomares, L., & & Donis-González, I.R. 2021. Optimization and Validation of Rancimat Operational Parameters to Determine Walnut Oil Oxidative Stability. Processes 9 (4): 651.

Kilinya Mayanja, I., Coates, M.C., Niederholzer, F., & Donis-González, I.R. 2021. Development of a Stockpile Heated and Ambient Air Dryer (SHAD) for Freshly Harvested Almonds. Applied Engineering in Agriculture. 37(3): 417-425. (doi: 10.13031/aea.14364).

Donis-González, I.R., Bergman, S.M., Sideli, G.M. ,Slaughter, D.C., & Crisosto, C.H. 2020. Color vision system to assess English walnut (Juglans Regia) kernel pellicle color. Postharvest Biology and Technology 167 (111199): 1-11.

Su, W-H, Fennimore, S.A., Slaughter, D.C. 2020. Development of a systemic crop signalling system for automated real-time plant care in vegetable crops. Biosystems Engineering. Vol. 193: 62-74.

Raja, R., Nguyen, T.T., Vuong, V.L., Slaughter, D.C., Fennimore, S.A., 2020. RTD-SEPs: Real-time detection of stem emerging points and classification of crop-weed for robotic weed control in producing tomato. Biosystems Engineering. Vol. 195: 152-171.

Arikapudi R., Vougioukas, S.G. (2021). Robotic Tree-fruit Harvesting with Telescoping Arms: A study of Linear Fruit Reachability under Geometric Constraints. IEEE Access. (9): 17114-17126 https://doi.org/10.1109/ACCESS.2021.3053490

Fei, Z., Vougioukas, S.G. (2021). Co-Robotic Harvest-aid Platforms: Real-time Control of Picker Lift Heights to Maximize Harvesting Efficiency. Computers and Electronics in Agriculture. (180): 105894. https://doi.org/10.1016/j.compag.2020.105894

Araujo, G. D. M., Khorsandi, F., & Abdullah, A. (2021). Ability of Youth to Activate Agricultural All-Terrain Vehicles’ Main Controls. In 2021 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

Araujo, G. D. M., Khorsandi, F., Kabakibo, S., & Kreylos, O. (2021). Can youth reach agricultural all-terrain vehicle controls? In 2021 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

Khorsandi, F., Ayers, P. D., Myers, M., Oesch, S., & White, D. J. (2021). Engineering Control Technologies to Protect Operators in Agricultural All-Terrain Vehicle Rollover Incidents. Journal of Agricultural Safety and Health, 0.

Khorsandi, F., Ayers, P., Denning, G., Jennissen, C., Jepsen, D., Myers, M., ... & White, D. J. (2020). Hazard control methods to improve agricultural all-terrain vehicle safety. Journal of agromedicine, 1-16.

Chou, H. Y., Khorsandi, F., & Vougioukas, S. G. (2020). Developing and testing a gps-based steering control system for an autonomous all-terrain vehicle. In 2020 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

Fei, Z., Olenskyj, A., Bailey, B., and Earles. J.M. (accepted). Enlisting 3D crop models and GANs for more data efficient and generalizable fruit detection. International Conference on Computer Vision (ICCV), 7th workshop on Computer Vision in Plant Phenotyping and Agriculture.

New York

Ertai Liu, Kaitlin M Gold, David Combs, Lance Cadle-Davidson, Yu Jiang. 2021. Deep Learning-based Autonomous Downy Mildew Detection and Severity Estimation in Vineyards. 2021 ASABE Annual International Virtual Meeting, Paper # 2100486. doi:10.13031/aim.202100486.

Rodrigo Borba Onofre, David M Gadoury, Arne Stensvand, Andrew Bierma, Mark S Rea, and Natalia A. Peres. 2021. Use of Ultraviolet Light to Suppress Powdery Mildew in Strawberry Fruit Production Fields. Plant Disease. https://doi.org/10.1094/PDIS-04-20-0781-RE

Florida

Publications

Costa L., McBreen J., Ampatzidis Y., Guo J., Reisi Gahrooei M., Babar A., 2021. Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes. Precision Agriculture (accepted).

Costa L., Ampatzidis Y., Rohla C., Maness N., Cheary B., Zhang L., 2021. Measuring pecan nut growth utilizing machine vision and deep learning for the better understanding of the fruit growth curve. Computers and Electronics in Agriculture, 181, 105964, doi.org/10.1016/j.compag.2020.105964.

Costa L., Archer L., Ampatzidis Y., Casteluci L., Caurin G.A.P., Albrecht U., 2021. Determining leaf stomatal properties in citrus trees utilizing machine vision and artificial intelligence. Precision Agriculture 22, 1107-1119, https://doi.org/10.1007/s11119-020-09771-x.

Kim, W.-S., D.-H. Lee, Y.-J. Kim, T. Kim, W. S. Lee, and C.-H. Choi. 2021. Stereo-vision-based crop height estimation for agricultural robots. Computers and Electronics in Agriculture 181: 105937. https://doi.org/10.1016/j.compag.2020.105937.

Kim, W. S., W. S. Lee, and Y. J. Kim. 2020. A Review of the applications of the Internet of Things (IoT) for agricultural automation. J. Biosyst. Eng. 45: 385–400. https://doi.org/10.1007/s42853-020-00078-3.

Nunes L., Ampatzidis Y., Costa L., Wallau M., 2021. Horse foraging behavior detection using sound recognition techniques and artificial intelligence. Computers and Electronics in Agriculture, 183, 106080, doi.org/10.1016/j.compag.2021.106080.

Swarup, A., W. S. Lee, N. Peres, and C. Fraisse. 2020. Strawberry plant wetness detection using color and thermal imaging. J. of Biosystems Engineering. 45: 409-421. https://doi.org/10.1007/s42853-020-00080-9.

Uyeh, D. D., J. Kim, S. Lohumi, T. Park, B.-K. Cho, S. Woo, W. S. Lee, and Y. Ha. 2021. Rapid and non-destructive monitoring of moisture content in livestock feed using a global hyperspectral model. Animals 11, 1299. https://doi.org/10.3390/ani11051299.

Vijayakumar V., Costa L., Ampatzidis Y., 2021. Prediction of citrus yield with AI using ground-based fruit detection and UAV imagery. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021, 2100493, doi:10.13031/aim.202100493.

Xie, C. and W. S. Lee. 2021. Detection of citrus black spot symptoms using spectral reflectance. Postharvest Biology and Technology 180: 111627. https://doi.org/10.1016/j.postharvbio.2021.111627.

Zhou, C., W. S. Lee, O. E. Liburd, I. Aygun, J. K. Schueller, and I. Ampatzidis. 2021. Smartphone-based tool for two-spotted spider mite detection in strawberry. ASABE Paper No. 2100558. St. Joseph, MI.: ASABE.

Zhou, X., Y. Ampatzidis, W. S. Lee, and S. Agehara. 2021. Postharvest strawberry bruise detection using deep learning. ASABE Paper No. 2100458. St. Joseph, MI.: ASABE.

Presentations

Abdulridha J., Ampatzidis Y., Qureshi J., Batuman O., Kakarla S., 2021. Detecting and monitoring the progress of downy mildew disease in watermelon by utilizing UAV–based hyperspectral imaging and machine learning. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Adosoglou G., Park S., Ampatzidis Y., Pardalos P., 2021. A high-level task planning of autonomous robots with multi-dimensional loading constraints for precision weed management under field variability. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Ampatzidis Y., 2021. AI applications in specialty crops. 2021 Virtual ASABE Annual International Meeting, Special Session - Processing Systems AI and Data Science Application in Food and Biological Material Processing, July 11-14, 2021.

Ampatzidis Y., 2021. Automation, artificial intelligence and robotics in strawberry production. 9th International Strawberry Symposium (ISHS – ISS2021), May 1-5, 2021. Keynote Speaker.

Costa L., Ampatzidis Y., Shukla S., 2021. Citrus fruit maturity prediction utilizing UAV multispectral imaging and machine learning. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Vijayakumar V., Archer L., Ampatzidis Y., Albrecht U., Batuman O., 2021. An automated delivery system for therapeutic materials using needle-based trunk injection to treat HLB affected Citrus. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Vijayakumar V., Partel V., Ampatzidis Y., Silwal A., Kantor G., 2021. Autonomous smart sprayer for precision weed management using machine vision and AI. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Extension-Outreach

Dr. Ampatzidis (Invited talks):

Emerging Technologies and AI for BMP. UF/IFAS Water Wednesdays. February 24, 2021.

Artificial Intelligence in Agriculture. UF EGN1935-FOAI (31730) - Freshman Engineering: Frontiers of AI (virtual lecture). February 9, 2021.

Applications of Artificial Intelligence in Precision Agriculture. Central District Ag BMP virtual meeting. February 3, 2021.

Drones, artificial intelligence, and the future of pest management in vegetable crops. Annual vegetable growers virtual meeting. The Oregon processed vegetable commission. January 25, 2021.

Saving Citrus with NVIDIA. NVIDIA Podcast #75 (https://www.storagereview.com/podcast/podcast-75-saving-citrus-with-nvidia-ai; 45 min), January 2021.

Kentucky

Dvorak, J., Pampolini, L., Jackson, J., Seyyedhasani, H., Goff, B., Sama, M. (2021). Predicting Quality and Yield of Growing Alfalfa from a UAV. Transactions of the ASABE. 64(1): 63-72. doi: 10.13031/trans.13769

Minch, C.*, Dvorak, J., Jackson, J., & Sheffield, S. T. (2021). Creating a Field-Wide Forage Canopy Model Using UAVs and Photogrammetry Processing. Remote Sensing, 13(13), 2487. MDPI AG. http://dx.doi.org/10.3390/rs13132487

Mississippi

Lucas Gay, Filip To, Ruixiu Sui: Moisture Determination of Cotton in Static Conditions Via Capacitive Sensing. Belt-wide Cotton Conference Proceedings, Cotton Engineering Systems, #20434, 3.5.2021

Joshua Tandio, Filip To , Ruixiu Sui: Bench Top Plastic Contaminant Detection in Cotton Using Deep Learning Neural Network Trained with Images Taken under 4 Lighting Colors, Belt-wide Cotton Conference Proceedings, Cotton Engineering Systems, #20420, 3.5.2021

Oregon

Western FarmPress, April 10, 2019, Startup States Push Precision Viticulture

Good Fruit Grower, May 22, 2019, The Margins of Mechanization: Oregon State University economist assesses the costs and benefits of mechanizing vineyard tasks. The Margins of Mechanization

OWRI and Washington Wine Commission sponsored webinar, June 11, 2019, titled "Can Mechanizing Vineyard Tasks Make you Money?" Webinar Recording

National Grape Research Alliance Newsletter, June 2019: The True Cost of Mechanization

Pennsylvania

Journal Articles

Mirbod, O., Choi, D., Thomas, R. and He, L. 2021. Overcurrent-driven LEDs for consistent image colour and brightness in agricultural machine vision applications. Computers and Electronics in Agriculture, 187, 106266.

Yuan, W., & Choi, D. (2021). UAV-Based Heating Requirement Determination for Frost Management in Apple Orchard. Remote Sensing, 13(2), 273.

Zahid, A., He, L., Choi, D., Schupp, J. and Heinemann, P. 2021. Investigation of Branch Accessibility with a Robotic Pruner for Pruning Apple Trees. Transactions of the ASABE, 64(5).

Zahid, A., Mahmud, M.S., He, L., Heinemann, P., Choi, D. and Schupp, J. 2021. Technological advancements towards developing a robotic pruner for apple trees: A review. Computers and Electronics in Agriculture, 189, 106383.

Huang, M., He, L., Choi, D., Pecchia, J. and Li, Y. 2021. Picking dynamic analysis for robotic harvesting of Agaricus bisporus mushrooms. Computers and Electronics in Agriculture, 185, 106145.

Huang, M., Jiang, X., He, L., Choi, D., Pecchia, J. and Li, Y. 2021. Development of A Robotic Harvesting Mechanism for Button Mushrooms. Transactions of the ASABE, 64(2), 565-575.

Mahmud, M.S., Zahid, A., He, L., Choi, D., Krawczyk, G., Zhu, H. and Heinemann, P. 2021. Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications. Computers and Electronics in Agriculture, 182, 106053.

Jiang, X. and He, L. 2021. Investigation of Effective Irrigation Strategies for High-Density Apple Orchards in Pennsylvania. Agronomy, 11(4), 732.

Mahmud, M.S., Zahid, A., He, L. and Martin, P. 2021. Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits. Sensors, 21(9), p.3262.

Thesis/Dissertations

Zahid, Azlan (2021). Development of a robotic manipulator for pruning apple trees. PhD dissertation. August 2021. Pennsylvania State University.

Zhang, Haozhe (2021). Internet of things (IoT)-based precision irrigation with LoRaWAN technology applied to vegetable production. MS thesis. May 2021. Pennsylvania State University.

Mirbod, Omeed (2021). In-field imaging of apple orchards using stereo vision for improved fruit size and yield estimation. MS thesis. May 2021. Pennsylvania State University.

Extension Talks (Long He)

Internet of Things (IoT) for Precision Irrigation Management in Tree Fruit Orchards. February 4, 2021. 2021 Cornell NYS Tree Fruit Conference.

Precision Irrigation Systems for Tree Fruit Orchards-2020 Season Updates. February 11, 2021. 2021 Mid-Atlantic Fruit and Vegetable Convention.

Developing Sensor-Based Smart Irrigation Systems for Vegetable Crops. February 11, 2021. 2021 Mid-Atlantic Fruit and Vegetable Convention.

Potential of Using Robotic Systems on Crop Load Management for Apples. On-Line, 234 Participants. (February 22, 2021). 2021 Penn State Extension Winter Fruit School.

Washington

Journal Articles

Arai, R., S. Sakai, A. Tatsuoka, and Q. Zhang, 2021. Analytical, experimental, and numerical investigation of energy in hydraulic cylinder dynamics of agriculture scale excavators. Energies, Accepted, In Press, https://doi.org/10.3390/en1010000

Chandel, A. K., B. Molaei, L. R. Khot, R. T. Peters, C. O. Stöckle, and P. W. Jacoby. 2021. High-resolution spatiotemporal water use mapping of surface and direct-root-zone drip irrigated grapevines using UAS-based thermal and multispectral remote sensing. Remote Sensing, 13, 954. https://doi.org/10.3390/rs13050954

Chandel , A. K., B. Molaei, L. R. Khot, R. T. Peters, and C. O. Stöckle. 2020. High resolution geospatial evapotranspiration mapping of irrigated field crops using multispectral and thermal infrared imagery with METRIC energy balance model. Drones, 4(3), 52. https://doi.org/10.3390/drones4030052

Chandel , A., L. R. Khot, and L.-X. Yu. 2021. Alfalfa (Medicago sativa L.) crop vigor and yield characterization using high-resolution aerial multispectral and thermal infrared imaging technique. Computers and Electronics in Agriculture, 182, 105999. https://doi.org/10.1016/j.compag.2021.105999

Kothawade , G., S. Sankaran, A. A. Bates, B. K. Schroeder and L. R. Khot. 2020. Feasibility of volatile biomarker‐based detection of Pythium leak in postharvest stored potato tubers using field asymmetric ion mobility spectrometry. Sensors, 20(24), 7350. https://doi.org/10.3390/s20247350

Kothawade, G., A. K. Chandel , L. R. Khot, S. Sankaran, A. A. Bates, and B. Schroeder. 2021. Field asymmetric ion mobility spectrometry for pre-symptomatic rot detection in stored Ranger Russet and Russet Burbank potatoes. Postharvest Biology and Technology, 181, 111679 https://doi.org/10.1016/j.postharvbio.2021.111679

Majeed, Y., M. Karkee, Q. Zhang, L. Fu, and M.D. Whiting, 2021. Development and performance evaluation of a machine vision system and an integrated prototype for automated green shoot thinning in vineyards. Journal of Field Robotics, 38: 898-916. https://doi.org/10.1002/rob.22013

Marzougui, A., Y. Ma, R. J. McGee, L. R. Khot, and S. Sankaran. 2020. Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in Lentil. Plant Phenomics, 20, Article ID 2393062. https://doi.org/10.34133/2020/2393062

Marzougui, A., Y. Ma, R. J. McGee, L. R. Khot, and S. Sankaran. 2020. Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil [Dataset]. Zenodo. http://doi.org/10.5281/zenodo.4018168

McCoy, M. L., G.-A. Hoheisel, L. R. Khot, and M. M. Moyer. 2021. Assessment of three commercial over-the-row sprayer technologies in Eastern Washington vineyards. American Journal of Enology and Viticulture, https://doi.org/10.5344/ajev.2021.20058.

Molaei, B., A. Chandel, R.T. Peters, L.R. Khot, and J.Q. Vargas. 2021. Investigating lodging in Spearmint with overhead sprinklers compared to drag hoses using the texture feature from low altitude RGB imagery. Information Processing in Agriculture, https://doi.org/10.1016/j.inpa.2021.02.003

Ranjan , R.; R. Sinha , L.R. Khot, G.-A. Hoheisel, M. Grieshop, and M. Ledebuhr. 2021. Spatial distribution of spray from a solid set canopy delivery system in a high-density apple orchard retrofitted with modified emitters. Applied Sciences, 11, 709. https://doi.org/10.3390/app11020709

Ranjan , R., L. R. Khot, R. Troy Peters, M. R. Salazar-Gutierrez and G. Shi . 2020. In-field crop physiology sensing aided real-time apple fruit surface temperature monitoring for sunburn prediction. Computers and Electronics in Agriculture, 157: 105558. https://doi.org/10.1016/j.compag.2020.105558

Rathnayake, A. P., A. Chandel, M. Schrader, G.-A. Hoheisel and L. R. Khot. 2021. Spray patterns and perceptive canopy interaction assessment of commercial airblast sprayers used in Pacific Northwest perennial specialty crop production. Computers and Electronics in Agriculture, 184, 106097 https://doi.org/10.1016/j.compag.2021.106097

Rathnayake, A. P., A. Chandel, M. Schrader, G.-A. Hoheisel and L. R. Khot. 2021. Air-assisted velocity profiles and perceptive canopy interactions of commercial airblast sprayers used in Pacific Northwest perennial specialty crop production. CIGR e-journal, Accepted, In Press.

Rathnayake, A. P., L. R. Khot, G. A. Hoheisel, H. W. Thistle, M. E. Teske, and M. J. Willett. 2021. Downwind spray drift assessment for airblast sprayer applications in a modern apple orchard system. Transactions of the ASABE, 64(2): 601-613. https://doi.org/10.13031/trans.14324

Sinha, R., J. Quiros Vargas, L. R. Khot and S. Sankaran. 2021. High resolution aerial photogrammetry based 3D mapping of fruit crop canopies for precision inputs management. Information Processing in Agriculture, https://doi.org/10.1016/j.inpa.2021.01.006.

Worasit, S., A. Marzougui, S. Sankaran, L. R. Khot, A. A. Bates, and B. Schroeder. 2021. Identification of volatile biomarkers for high-throughput sensing of soft rot and Pythium leak diseases in stored potatoes. Food Chemistry, Accepted, In Press.

Zhang, X., He, L., Karkee, M., Whiting, M. D., and Zhang, Q. 2020. Field Evaluation of Targeted Shake-and-Catch Harvesting Technologies for Fresh Market Apple. Transactions of the ASABE, 63(6): 1759-1771. https://doi.org/10.13031/trans.13779

Thesis/Dissertations

Chandel, Abhilash (2021). Small unmanned aerial system based remote sensing to map geospatial water use of field and perennial specialty crops. April 2021. Washington State University.

Ranjan, Rakesh (2021). Sensing integrated automated solid set canopy delivery system for crop loss management in deciduous fruits and grapevines. April 2021. Washington State University.

Books and Book Chapters

Huang, Y. and Q. Zhang, 2021. Agricultural Cybernetics. Springer, ISBN: 978-3-030-72102-2, (255 pp).

He, Y., P. Nie, Q. Zhang and F. Liu, 2021. Agricultural Internet of Things: Technologies and Applications. Springer, ISBN: 978-3-030-65701-7, (439 pp).

Karkee, M. and Q. Zhang, 2021. Fundamentals of Agricultural and Field Robotics. Springer, ISBN: 978-3-030-70399-8, (455 pp).

Karkee, M., Q. Zhang, and A. Silwal, 2021. Chapter 4. Agricultural Robots for Precision Agricultural Tasks in Tree Fruit Orchards. In: Bechar, A. (ed). Innovation in Agricultural Robotics for Precision Agriculture. Springer (26 pp).

Zhang, Q. and M. Karkee, 2021. Chapter 1. Agricultural Robotics: An Introduction. In: Karkee, M. & Q. Zhang (eds.). Fundamentals of Agricultural and Field Robotics. Springer (10 pp).

Karkee, M., B. Santosh, and Q. Zhang, 2021. Chapter 3. 3D Sensing Techniques and Systems. In: Karkee, M. & Q. Zhang (eds.). Fundamentals of Agricultural and Field Robotics. Springer (39 pp).

Zhang, X., Q. Zhang, M. Karkee, and M.D. Whiting, 2021. Chapter 16. Machinery-Canopy Interactions in Tree Fruit Crops. In: Karkee, M. & Q. Zhang (eds.). Fundamentals of Agricultural and Field Robotics. Springer (28 pp).

He, Y., Q. Zhang, and P. Nie, 2021. Chapter 1. Introduction of Agricultural IoT. In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (21 pp).

He, Y., Y. Tang, Q. Zhang, and Y. Zhao, 2021. Chapter 2. Agricultural IoT Standardization and System Applications. In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (17 pp).

Zhang, Q., Y. He, P. Nie, and S. Xiao, 2021. Chapter 3. Data Communication and Networking Technologies. In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (56 pp).

Liu, F., Y. He, Q. Zhang, W. Wang, and T. Shen, 2021. Chapter 5. Crop Information Sensing Technology. In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (32 pp).

Fang, H., Y. He, Q. Zhang, J. Zhang, and Y. Shi, 2021. Chapter 6. Field Condition Sensing Technology. In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (28 pp).

Nie, P., Q. Zhang, and Y. He, 2021. Chapter 10. IoT Management of Field Crops and Orchards. In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (12 pp).

Rovira-Más, F., Q. Zhang, and V. Saiz-Rubio, 2020. Chapter 11. Mechatronics and Intelligent Systems in Agricultural Machinery. In: Holden, N. M., Wolfe, M. L., Ogejo, J. A., & Cummins, E. J. (Ed.), Introduction to Biosystems Engineering. Virginia Tech Publishing.

08/01/2022

Arizona

Book Chapter

Fennimore, S.A. & Siemens, M.C. 2022. Mechanized weed management in vegetable crops. In Encyclopedia of Smart Agricultural Technologies, ed. Qin Zhang. (accepted)

Outreach Publications

Siemens, M.C. 2022. New Weeding Technologies for the 2022 Growing Season – Article II. 1 July 13. Vol. 14, Issue 14. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. New Weeding Technologies for the 2022 Growing Season. 1 June 29. Vol. 13, Issue 13. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. 2022 International Robotic Ag Technologies. 1 June. Vol. 13, Issue 11. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. 2022 Automated Technology Field Day – Salinas, CA : UC Cooperative Extension. 18 May. Vol. 13, Issue 10. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. Control of Palmer Amaranth with Finger Weeders Shows Good Promise in Texas A&M Cotton Studies. 4 May. Vol. 13, Issue 9. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. Specialty Crop Agricultural Robotics and Technology Forum. 20 April. Vol. 13, Issue 8. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. Finger Weeder Removing Large Palmer Amaranth Plant in Cotton Video. 6 April. Vol. 13, Issue 7. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. 2nd Generation Prototype Band-Steam Machine – Initial Testing. 22 March. Vol. 13, Issue 6. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. Pre-Plant Injection of Steam for Controlling Soilborne Pathogens and In-Row Weeds: Summary of Trial Results. 9 March. Vol. 13, Issue 5. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. “Innovations in Weed Control Technologies” Session and Field Demo at 2022 Southwest Ag Summit. 23 February. Vol. 13, Issue 4. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. Band-Steam Applicator Field Demo and Trial Results – 2022 Southwest Ag Summit. 9 February. Vol. 13, Issue 3. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. Use of Steam for Post Emergent Weed Control. 26 January. Vol. 13, Issue 2. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2022. Control of Fusarium Wilt with Band-Steam – Trials Show Mixed Results. 12 January. Vol. 13, Issue 1. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2021. Automated Thinning Machine Performance – Vigilance Important. 15 December. Vol. 12, Issue 25. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2021. Camera Guided Shift Hitch and Finger Weeders. 1 December. Vol. 12, Issue 24. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2021. Commercial Autonomous Ag Field Robots - Update. 17 November. Vol. 12, Issue 23. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2021. Automated In-Row Weeders Impress in Weedy Fields. 3 November. Vol. 12, Issue 22. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Siemens, M.C. 2021. 2nd Automated Weeding Technologies Field Demo – Update. 06 October. Vol. 12, Issue 20. Tucson, Ariz.: University of Arizona, Yuma Agricultural Center.

Invited Presentations

Siemens, M.C. 2022. “New” Technologies and Innovations for Improved Weed Control. New Mexico Chile Conference, February 1.

California

Perez-Ruiz, M., Slaughter, DC. 2021. Development of a precision 3-row synchronized transplanter. Biosystems Engineering. Vol 206: 67-78.

Anokye-Bempah, L, Phetpan, K, Slaughter, D.C. & Donis-González, I.R. 2022. Design, calibration, and validation of a green coffee inline moisture content estimation system using time-domain reflectometry (TDR). Submitted for publication; Journal of Food Engineering.

Peng, C., Vougioukas, S., Slaughter, D., Fei, Z., Arikapudi, R. (2022) A strawberry harvest-aiding system with crop-transport corobots: Design, development, and field evaluation. Journal of Field Robotics.

Chou, H-Y., Khorsandi, F., Vougioukas, S.G., Fathallah, F. (2022). Developing and Evaluating an Autonomous Agricultural All-Terrain Vehicle for Field Experimental Rollover Simulations. Computers and Electronics in Agriculture (194) 106735.

Avigal, Y., Wong, W., Presten, M., Theis, M., Aeron, S., Deza, A., Sharma, S., Parikh, R., Oehme, S., Carpin, S., Viers, J., Vougioukas, S., Goldberg, K. (2022). Simulating Polyculture Farming to Learn Automation Policies for Plant Diversity and Precision Irrigation. IEEE Transactions on Automation Science and Engineering. 19(3):1352-1364.

Fei, Z., Vougioukas, S.G. (2022). Row-sensing Templates: A Generic 3D Sensor-based Approach to Robot Localization with Respect to Orchard Row Centerlines. Journal of Field Robotics, 1-27.

Abdelmoneim, A. Daccache, R. Khadra, M. Bhanot, G. Dragonetti (2021). Internet of Things (IoT) for double ring infiltrometer automation. Computers and Electronics in Agriculture, Volume 188, September 2021, 106324

Florida

Patel, A., W. S. Lee, N. A. Peres, and C. W. Fraisse. 2021. Strawberry plant wetness detection using computer vision and deep learning. Smart Agricultural Technology 1, 2021, 100013, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2021.100013

Yun, C., H.-J. Kim, C.-W. Jeon, M. Gang, W. S. Lee, and J. G. Han. 2021. Stereovision-based ridge-furrow detection and tracking for auto-guided cultivator. Computers and Electronics in Agriculture 191, 2021, 106490, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2021.106490.

Puranik, P., W. S. Lee, N. Peres, F. Wu, A. Abd-Elrahman, and S. Agehara. 2021. Strawberry flower and fruit detection using deep learning for developing yield prediction models. In the Proceedings of the 13th European Conference on Precision Agriculture (ECPA), July 19-22, 2021, Budapest, Hungary.

Zhou, X., W. S. Lee, Y. Ampatzidis, Y. Chen, N. Peres, and C. Fraisse. 2021. Strawberry maturity classification from UAV and near-ground imaging using deep learning. Smart Agricultural Technology 1, 2021, 100001, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2021.100001.

Michigan

Guyer, D.E. Mechanical Harvesting. In: Advanced Automation for Tree Fruit Orchards and Vineyards. Eds: Vougioukas and Zhang. Springer Media. (at publisher)

Guyer, D.E. Advances in Image-based Identification and Analysis of Crop Insect Pests. In: Advances in Monitoring of Native and Invasive Insect Crop Pests. Eds: Fountain and Pope. Burleigh Dodds Publishing. (in final review)

Mississippi

Publications

He, L., Zhang, X., & Zahid, A. (2022). Chapter 3 – Mechanical management of modern planar fruit tree canopies. In Advanced Automation for Tree Fruit Orchards and Vineyards (Vougioukas, S. G., & Zhang, Q. ed.), Springer Book Series: Agriculture Automation and Control. (In Press)

Lu, S., Chen, W., Zhang, X., & Karkee, M. (2022). Canopy-attention-YOLOv4-based immature/mature apple fruit detection on dense-foliage tree architectures for early crop load estimation. Computers and Electronics in Agriculture, 193, 106696.

Upadhyaya, P., Karkee, M., Kashetri, S., & Zhang, X. (2022). Automated lag phase detection in wine grapes. In Proceedings of the 15^th International Conference on Precision Agriculture (unpaginated, online). Monticello, IL: International Society of Precision Agriculture.

Peng, H., Zhong, J., Liu, H., Li, J., Yao, M., & Zhang, X. (2022). ResDense-focal-DeepLabv3+ enabled litchi branch semantic segmentation for robotic harvesting. Available at SSRN.

Barnes E. M., G. Morgan, K. Hake, J. Devine, R. Kurtz, G. Ibendahl, A. Sharda, G. Rains, J. Snider, J. M. Maja, J. A. Thomasson, Y. Lu, H. Gharakhani, J. Griffin, E. Kimura, R. Hardin, T. Raper, S. Young, K. Fue, M. Pelletier, J. Wanjura, and G. Holt. 2021. Opportunities for robotic systems and automation in cotton production. AgriEngineering 3(2):339-362; doi.org/10.3390/agriengineering3020023.

Gharakhani, H., J. A. Thomasson, and Y. Lu. 2022. An end-effector for robotic cotton harvesting. Smart Agricultural Technology 2(1):1-11.

Presentations

Zhang, X. & Regmi, A. MLCAS2021 Crop yield prediction challenge. 3^rd International Workshop on Machine Learning for Cyber-Agricultural Systems, online (11/2/2021–11/3/2021) (invited talk)

Zhang, X. Study of canopy-machine interaction in mass mechanical harvest of fresh market apples. 30^th Members’ Meeting of the Club of Bologna (Agriculture mechanization vision for the future: The Club of Bologna thirty years of contribution for improve its diffusion and sustainability), Bologna, Italy (10/22/2021–10/23/2021) (invited talk)

Zhang, X., Liu, X., Lu, S., & Karkee, M. Deepsort-YOLOv3: Robust on-the-go grape bunch video tracking for yield estimation throughout the growth season. ASABE AIM, Houston, TX (7/17/2022–7/20/2022)

Chakraborty, M., Pourreza, A., Zhang, X., Jafarbiglu, H., & Shackel, K. A. Almond bloom mapping at the tree level for early yield forecasting. ASABE AIM, Houston, TX (7/17/2022–7/20/2022)

Upadhyaya, P., Zhang, X., Lu, S., & Karkee, M. Smartphone-app for crop load estimation and lag phase detection in wine grapes. 15^th International Conference on Precision Agriculture (ICPA), Minneapolis, MN (6/26/2022–6/29/2022)

Pennsylvania

Journal Publications:

Zhang, H., He, L., Di Gioia, F., Choi, D., Elia, A. and Heinemann, P., 2022. LoRaWAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato. Smart Agricultural Technology, 2, p.100053.

Yuan, W., Choi, D., Bolkas, D., Heinemann, P.H. and He, L., 2022. Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification. International Journal of Remote Sensing, 43(8), pp.3106-3130.

Zahid, A., Mahmud, M.S., He, L., Schupp, J., Choi, D. and Heinemann, P., 2022. An Apple Tree Branch Pruning Analysis. HortTechnology, 32(2), pp.90-98.

Hussain, M., He, L., Schupp, J. and Heinemann, P., 2022. Green Fruit Removal Dynamics for Robotic Green Fruit Thinning End-Effector Development. Journal of the ASABE (in press).

Xiao, D., Pan, Y., Feng, J., Yin, J., Liu, Y. and He, L., 2022. Remote sensing detection algorithm for apple fire blight based on UAV multispectral image. Computers and Electronics in Agriculture, 199, p.107137.

Mahmud, M.S., Zahid, A., He, L., Choi, D., Krawczyk, G. and Zhu, H., 2021. LiDAR-sensed tree canopy correction in uneven terrain conditions using a sensor fusion approach for precision sprayers. Computers and Electronics in Agriculture, 191, p.106565.

Book Chapter:

He, L., Zahid, A. and Mahmud, M.S., Robotic Tree Fruit Harvesting: Status, Challenges, and Prosperities. Sensing, Data Managing, and Control Technologies for Agricultural Systems, p.299-332.

Extension Publication:

He, L., Choi, D., & Pecchia, J. (2021). "Investigation of computer vision system and robotic picking mechanism for button mushroom harvesting." Mushroom News.

He, L. (2021). Introduction of automatic irrigation systems for tree fruit orchards. Penn State Extension.

He, L., Shannon, T., & Mahmud, M. S. (2021). Unmanned aerial vehicle-based crop scouting in fruit trees. Penn State Extension.

Thesis/Dissertations:

Md Sultan Mahmud (2022). Study of core technologies in tree canopy parameter measurements for development an advanced precision sprayer. PhD Dissertation. June 2022. Pennsylvania State University.

Wenan Yuan (2022). Development of a UAV-based multi-dimensional mapping framework for precise and convenient frost management in apple orchard. PhD Dissertation. May 2022. Pennsylvania State University.

Tennessee

Oleksak, K., Wu, Y., Abella, M., Wang, Z., & Gan, H. (2021). Trajectory Optimization of Unmanned Aerial Vehicles for Wireless Communication with Ground Terminals. In AIAA Scitech 2021 Forum (p. 0709).

Daniel, A., Wu, Y., Wang, Z., & Gan, H. (2021). Trajectory Optimization of Unmanned Aerial Vehicles for Wireless Coverage under Time Constraint. In AIAA Scitech 2021 Forum (p. 1581).

Rice, C., McDonald, S., Gan, H., Lee, W.S., Chen, Y., Shi, Y., Wang, Z. (2022) Perception, path planning, and flight control for drone-enabled autonomous pollination system. Computers and Electronics in Agriculture (under review)

Texas

Publications:

Ahamed M. S., Sultan, M., Monfet, D., Rahman, M.S., Zhang, Y., Zahid, A., Aleem, M., Achour, Y., and Ahsan, T. M. A. 2022. Thermal environment controls and sustainability challenges in indoor vertical farming, Journal of Cleaner Production [Under review]

Ojo, M., and Zahid, A. 2022. Deep learning and its potential in controlled environment agriculture, Applied Intelligence [Under review]

Presentations:

Ojo, M., Zahid, A. 2022. Automatic crop disease scouting system based on deep neural networks model. In 2022 ASABE Annual International Virtual Meeting Houston TX (Presentation)

Zahid, A. 2021. Robotics and intelligent systems for controlled environment agriculture, In 3^rd Annual Controlled Environment Agriculture Conference, Dallas TX (Presentation)

Washington

Journal Articles

Bhattarai, U., & Karkee M. (2022). A Weakly Supervised Approach for Flower/Fruit Counting in Apple Orchards. Computers in Industry, 138, 103635. https://doi.org/10.1016/j.compind.2022.103635

Bhusal, S., U. Bhattarai, M. Karkee, Y. Majeed, & Q. Zhang. (2022). Automated execution of pest bird deterrence system using a programmable unmanned aerial vehicle (UAV). Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.106972.

Gao, Z., Y. Zhao, G.-A. Hoheisel, L.R. Khot, and Q. Zhang, 2021. Blueberry bud freeze damage detection using optical sensors: Identification of spectral features through hyperspectral imagery. Journal of Berry Research, https://doi.org/10.3233/JBR-211506.

Guo, J., Karkee, M., Yang, Z., Fu, H., Li, J., Jiang, Y., ... & Duan, J. (2021). Discrete element modeling and physical experiment research on the biomechanical properties of banana bunch stalk for postharvest machine development. Computers and Electronics in Agriculture, 188, 106308.

Guo, J., Karkee, M., Yang, Z., Fu, H., Li, J., Jiang, Y., ... & Duan, J. (2021). Research of simulation analysis and experimental optimization of banana de-handing device with self-adaptive profiling function. Computers and Electronics in Agriculture, 185, 106148.

Lohan, S. K., Narang, M. K., Singh, M., Singh, D., Sidhu, H. S., Singh, S., Dixit, A.K, & Karkee, M. (2021). Design and development of remote-control system for two-wheel paddy transplanter. Journal of Field Robotics. https://doi.org/10.1002/rob.22045

Lohan, S. K., Narang, M. K., Singh, M., Khadatkar, A., & Karkee, M. (2021). Actuating force required for operating various controls of walk-behind type paddy transplanter leading to development of remotely operated system. Journal of Agricultural Safety and Health, 27(2):87-103. DOI:10.13031/jash.14186

Lu, S., Chen, W., Zhang, X., & Karkee, M. (2022). Canopy-attention-YOLOv4-based immature/mature apple fruit detection on dense-foliage tree architectures for early crop load estimation. Computers and Electronics in Agriculture, 193, 106696

Rathnayake, A.P., A. Chandel, M. Schrader, G.-A. Hoheisel and L.R. Khot. 2022. Air-assisted velocity profiles and perceptive canopy interactions of commercial airblast sprayers used in Pacific Northwest perennial specialty crop production. CIGR e-journal, 24(1): 73-89.

Zhou, Z., G. Diverres, C. Kang, S. Thapa, M. Karkee, Q. Zhang, & M. Keller. 2022. Ground-Based Thermal Imaging for Assessing Crop Water Status in Grapevines over a Growing Season. Agronomy, 12(2), 322.

US Patents

Zhang, X., C. Mo, M.D. Whiting, and Q. Zhang, 2021. Plant-based compositions for the protection of plants from cold damage. Publication No. US 2021/0029896 A1 (Feb 4, 2021).

07/31/2023

Arizona

Peer-reviewed Publications

Raja, R., Slaughter, D.C., Fennimore, S.A. & Siemens, M.C. 2023. Real-time control of high-resolution micro-jet sprayer integrated with machine vision for precision weed control. Biosystems Eng. 228: 31-48.

Guerra, N., Fennimore, S.A., Siemens, M.C., & Goodhue, R.E. 2022. Band steaming for weed and disease control in leafy greens and carrots. Sci. 57(11): 1453-1459.

Patents

Heun J.T., Andrade-Sanchez P., & Sanyal D. 2023. Low-Cost Electronic Monitoring System of High Temporal Resolution In-Situ Soil Respiration. TLA Invention Disclosure UA23-221. Tucson, Ariz: Tech Launch Arizona, University of Arizona.

Bahr, N. A., Siemens, M.C., Godinez, Jr., & Fennimore, S.A. 2022. Method and apparatus for applying steam for soil disinfestation. TLA Invention Disclosure UA22-190. Tucson, Ariz: Tech Launch Arizona, University of Arizona.

Book Chapters

Fennimore, S.A. & Siemens, M.C. 2023. Mechanized weed management in vegetable crops. In Encyclopedia of Smart Agricultural Technologies, ed. Q. Zhang. Cham, Switzerland: Springer, Cham. https://doi.org/10.1007/978-3-030-89123-7_244-2.

California

Peer-reviewed Publications

Tang M, Sadowski DL, Peng C, Vougioukas SG, Klever B, Khasla SDS, Brown PH and Jin Y. (2023). Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network. Front. Plant Sci. 14:1070699. https://doi.org/10.3389/fpls.2023.1070699

Peng, C., Fei, Z., Vougioukas, SG. (2023). GNSS-Free End-of-Row Detection and Headland Manoeuvring for Orchard Navigation Using a Depth Camera. Machines 11(1), 84. https://doi.org/10.3390/machines11010084.

Ghafoor, A., F. A. Khan, F. Khorsandi, M. A. Khan, H. M. Nauman, and M. U. Farid. Development and Evaluation of a Prototype Self-Propelled Crop Sprayer for Agricultural Sustainability in Small Farms. Sustainability, (2022), 14: 9204.

Khan, F. A., A. Ghafoor, M. A. Khan, M. U. Chattha, and F. Khorsandi. Parameter Optimization of Newly Developed Self-Propelled Variable Height Crop Sprayer Using Response Surface Methodology (RSM) Approach. Agriculture, (2022), 12(3):408.

Chou, H. Y., F. Khorsandi, S. G.Vougioukas, F. A.Fathallah . Developing and evaluating an autonomous agricultural all-terrain vehicle for field. experimental rollover simulations. Computers and Electronics in Agriculture, (2022), 194: 106735.

Khorsandi, F., G. D. M. Araujo, and F. Fathallah. A systematic review of youth and all-terrain vehicles safety in agriculture. Journal of Agromedicine, (2022), 1-23.

Araujo, G. D. M., F. Khorsandi, and F.A. Fathallah. Forces Required to Operate Controls on Agricultural All-Terrain Vehicles: Implications for Youth. Journal of Ergonomics, (2022), 1-15.

Dos Santos FFL, de Queiroz DM, Valente DSM, Khorsandi F, de Moura Araújo G. Analysis of Different Electric Current Frequencies in Soil Apparent Conductivity. Journal of Biosystems Engineering, (2023), 1-14.

Gibbs, J., C. Sheridan, F. Khorsandi*, A. Yoder. Emphasizing Safe Engineering Design Features of Quad Bikes in Agricultural Safety Programs. JASH, (2023), 29(2): 121-127.

Araujo, G. D. M., F. Khorsandi, F. A. Fathallah. Reach Evaluation to Operate Controls on Agricultural All-Terrain Vehicles: Implications for Young Operators. Journal of Safety Research, (2023), 84: 353-363.

Sirapoom Peanusaha, Alireza Pourreza, Yuto Kamya, Matthew Fidelibus. Grape Nitrogen retrieval by hyperspectral sensing–Part I: leaf level. Under review in the Journal of Remote Sensing of Environment.

Yuto Kamya, Alireza Pourreza, Sirapoom Peanusaha, Hamid Jafarbiglu, Matthew Fidelibus. Grape Nitrogen retrieval by hyperspectral sensing–Part II: canopy level. In-preparation.

Ahmadi A., Daccache A., Snyder R., Suvočarev K. (2022). Meteorological driving forces of reference evapotranspiration and their trends in California, Science of The Total Environment, 157823.

Emami, M.; Ahmadi, A.; Daccache, A.; Nazif, S.; Mousavi, S.-F.; Karami, H. County-Level Irrigation Water Demand Estimation Using Machine Learning: Case Study of California. Water 2022, 14, 1937.

Florida

Peer-reviewed Publications

Ghoveisi, H., M. Kadyampakeni, J. Qureshi, and L. Diepenbrock. 2023. Water use efficiency in young citrus trees on metalized UV reflective mulch compared to bare ground. Water 2023, 15, 2098. https://doi.org/10.3390/w15112098

Kwakye, S. and M. Kadyampakeni. 2023. Impact of deficit irrigation on growth and water relations of HLB-affected citrus trees under greenhouse conditions. Water 15, 2085. https://doi.org/10.3390/w15112085

Brewer, M. 2023. Citrus row-middle management using cover crops for suppressing weeds and improving soil. Ph.D. Dissertation, University of Florida, Gainesville, FL.

Teshome F.T., Bayabil H.K., Hoogenboom G., Schaffer B., Singh A., Ampatzidis Y., 2023. Unmanned Aerial Vehicle (UAV) Imaging and Machine Learning Applications for Plant Phenotyping. Computers and Electronics in Agriculture, 212, 108064, https://doi.org/10.1016/j.compag.2023.108064.

Zhou C., Lee W.S., Liburd O.E., Aygun I., Zhou X., Pourreza A., Schueller J.K., Ampatzidis Y., 2023. Detecting Two-spotted Spider Mites and Predatory Mites in Strawberry Using Deep Learning. Smart Agricultural Technology, 100229, https://doi.org/10.1016/j.atech.2023.100229.

Javidan S.M., Banakar A., Vakilian K.A., Ampatzidis Y., 2023. Tomato leaf diseases classification using image processing and weighted ensemble learning. Agronomy Journal, http://doi.org/10.1002/agj2.21293.

Hariharan J., Ampatzidis Y., Abdulridha J., Batuman O., 2023. An AI-based Spectral Data Analysis Process for Recognizing Unique Plant Biomarkers and Disease Features. Computers and Electronics in Agriculture, 204, 107574, https://doi.org/10.1016/j.compag.2022.107574.

Momeny M., Neshat A.A., Jahanbakhshi A., Bakhtoor M.., Ampatzidis Y., Radeva P., 2023. Grading and fraud detection of Saffron via learning-to-augment incorporated inception-v4 CNN. Food Control, 109554, https://doi.org/10.1016/j.foodcont.2022.109554.

Panta S., Zhou B., Zhu L., Maness N., Rohla C., Costa L., Ampatzidis Y., Fontainer C., Kaur A., Zhang, L., 2023. Selecting non-linear mixed effect model for growth and development of pecan nut. Scientia Horticulturae, 309, 111614, https://doi.org/10.1016/j.scienta.2022.111614.

Poudyal C., Sandhu H., Ampatzidis Y., Odero D.C., Arbelo O.C., Cherry R.H., Costa L., 2023. Prediction of morho-physiological traits in sugarcane using aerial imagery and machine learning. Smart Agricultural Technology, 100104, https://doi.org/10.1016/j.atech.2022.100104.

Vijayakumar V., Ampatzidis Y., Costa L., 2023. Tree-level Citrus Yield Prediction Utilizing Ground and Aerial Machine Vision and Machine Learning. Smart Agricultural Technology, 100077, https://doi.org/10.1016/j.atech.2022.100077.

Javidan S.M., Banakar A., Vakilian K.A., Ampatzidis Y., 2023. Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. Smart Agricultural Technology, 100081, https://doi.org/10.1016/j.atech.2022.100081.

Momeny M., Jahanbakhshi A., Neshat A.A., Hadipour-Rokni R., Zhang Y-D., Ampatzidis Y., 2022. Detection of citrus black spot disease and ripeness level in orange fruit using robust and generalized deep CNN based on learning-to-augment strategy. Ecological Informatics, 101829, https://doi.org/10.1016/j.ecoinf.2022.101829.

Zhou X., Ampatzidis Y., Lee W.S., Zhou C., Agehara S., Schueller J.K., 2022. Deep learning-based postharvest strawberry bruise detection under UV and incandescent light. Computers and Electronics in Agriculture, 22, 107389, https://doi.org/10.1016/j.compag.2022.107389.

Longchamps L., Tisseyre B., Taylor J., Sagoo L, Momin Md.A., Fountas S., Manfrini L., Ampatzidis Y., Schueller K.J., Khosla R., 2022. Yield sensing technologies for perennial and annual horticultural crops: a review. Precision Agriculture, https://doi.org/10.1007/s11119-022-09906-2.

Poudyal C., Costa L., Sandhu H., Ampatzidis Y., Odero D.C., Arbelo O.C., Cherry R.H., 2022. Sugarcane yield prediction and genotype selection using UAV-based hyperspectral imaging and machine learning. Agronomy Journal, doi.org/10.1002/agj2.21133.

Abdulridha J., Ampatzidis Y., Qureshi J., Roberts P., 2022. Identification and classification of downy mildew development stages in watermelon utilizing aerial and ground remote sensing and machine learning. Frontiers in Plant Science, 13, 791018, https://doi.org/10.3389/fpls.2022.791018.

Costa L., McBreen J., Ampatzidis Y., Guo J., Reisi Gahrooei M., Babar A., 2022. Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes. Precision Agriculture, 23(2), 622-642, https://doi.org/10.1007/s11119-021-09852-5.

Costa L., Kunwar S., Ampatzidis Y., Albrecht U., 2022. Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning. Precision Agriculture, 23(3), 854-875, https://doi.org/10.1007/s11119-021-09864-1.

Mirbod, O., Choi, D., Heinemann, P. H., Marini, R. P., & He, L. (2023). On-tree apple fruit size estimation using stereo vision with deep learning-based occlusion handling. Biosystems Engineering, 226, 27-42. (open access)

Yuan, W., Choi, D., Bolkas, D., Heinemann, P. H., & He, L. (2022). Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification. International Journal of Remote Sensing, 43(8), 3106-3130.

Yuan, W., Choi, D., & Bolkas, D. (2022). GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees. Computers and Electronics in Agriculture, 197, 106966.

Zhang, H., He, L., Di Gioia, F., Choi, D., Elia, A., & Heinemann, P. (2022). LoRaWAN based Internet of Things (IoT) System for Precision Irrigation in Plasticulture Fresh-market Tomato. Smart Agricultural Technology, 100053.

Zhou, X., Y. Ampatzidis, W. S. Lee, C. Zhou, S. Agehara, and J. K. Schueller. 2022. Deep learning-based postharvest strawberry bruise detection under UV and incandescent light. Computers and Electronics in Agriculture 202 (2022) 107389. https://doi.org/10.1016/j.compag.2022.107389.

Patel, A.M., W. S. Lee, and N. A. Peres. 2022. Imaging and deep learning based approach to leaf wetness detection in strawberry. Sensors 2022, 22, 8558. https://doi.org/10.3390/s22218558.

Thesis/Dissertation

Uthman, Q.O. 2023. Management of huanglongbing-affected “Valencia” sweet oranges in sandy soils of central Florida: sorption kinetics and equilibria, uptake, and leaching of nutrients and imidacloprid in a Florida sandy soil. D. Dissertation, University of Florida, Gainesville, FL.

Michigan

Peer-reviewed Publications

Dang, F., Chen, D., Lu, Y., Li, Z., 2023. YOLOWeeds: a novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture 205, 107655.

Rahman, A., Lu, Y., Wang, H., 2023. Performance evaluation of deep learning object detectors for weed detection for cotton. Smart Agricultural Technology 3, 100126.

Mississippi

Peer-reviewed Publications

Lucas Gay, Filip To, Joe Thomas, Sean Donohoe, “Inline Real-Time Moisture Sensing System for Gin Cotton”, Proceedings of National Cotton Council Belt-wide Cotton Conferences, New Orleans, January 10-12, 2023.

Zhang, X., Thayananthan, T., Usman, M., Liu, W., & Chen, Y. (2023, June). Multi-ripeness level blackberry detection using YOLOv7 for soft robotic harvesting. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII (Vol. 12539, pp. 85-96). SPIE. https://doi.org/10.1117/12.2663367

Chakraborty, M., Pourreza, A., Zhang, X., Jafarbiglu, H., Shackel, K. A., & DeJong, T. (2023). Early almond yield forecasting by bloom mapping using aerial imagery and deep learning. Computers and Electronics in Agriculture. (Accepted)

Peng, H., Zhong, J., Liu, H., Li, J., Yao, M., & Zhang, X. (2023). ResDense-focal-DeepLabV3+ enabled litchi branch semantic segmentation for robotic harvesting. Computers and Electronics in Agriculture, 206, 107691. https://doi.org/10.1016/j.compag.2023.107691

Lu, S., Liu, X., He, Z., Zhang, X., Liu, W., & Karkee, M. (2022). Swin-transformer-YOLOv5 for real-time wine grape bunch detection. Remote Sensing, 14(22), 5853. https://doi.org/10.3390/rs14225853

Book Chapters:

Zhang, X. (2023). Robotics and Automation Technologies: Plant-machine interface. In Encyclopedia of Smart Agriculture Technologies (Zhang, Q. ed.), Springer. https://doi.org/10.1007/978-3-030-89123-7_124-1

He, L., Zhang, X., & Zahid, A. (2023). Chapter 2 – Mechanical management of modern planar fruit tree canopies. In Advanced Automation for Tree Fruit Orchards and Vineyards (Vougioukas, S. G., & Zhang, Q. ed.), Springer Book Series: Agriculture Automation and Control. https://doi.org/10.1007/978-3-031-26941-7_2

New York

Peer-reviewed Publications

Liu, E., Gold, K., Cadle-Davidson, L., Combs, D., & Jiang, Y. (2022, October). Near Real-Time Vineyard Downy Mildew Detection and Severity Estimation. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 9187-9194). IEEE.

Kanaley, K., Paul, A., Combs, D., Liu, E., Jiang, Y., & Gold, K. (2022, December). Mapping Winegrape Disease Epidemics with SkySat and PlanetScope Imagery. In AGU Fall Meeting Abstracts (Vol. 2022, pp. IN43A-05).

Pennsylvania

Peer-reviewed Publications

Hussain, M., He, L., Heinemann, P., & Schupp, J. (2022). Green fruit removal dynamics for robotic green fruit thinning end-effector development. Journal of ASABE 65(4), 779-788.

Mahmud, M. S., Zahid, A., He, L., Zhu, H., Heinemann, P., Choi, D., & Krawczyk, G. (2022). Development of an automatic airflow control system for precision sprayers based on tree canopy density. Journal of ASABE 65(6), 1225-1240.

Mu, X., He, L., Heinemann, P., Schupp, J., & Karkee, M. (2023). Mask R-CNN based king flowers identification for precise apple pollination. Smart Agricultural Technology 4, 100151.

Mahmud, M. S., He, L., Heinemann, P., Choi, D., & Zhu, H. (2023). Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications. Smart Agricultural Technology 4(100153).

Hussain, M., He, L., Schupp, J., Lyons, D., & Heinemann, P. (2023). Green fruit segmentation and orientation estimation for robotic green fruit thinning of apples. Computers and Electronics in Agriculture 207, 107734.

Yuan, W., Hua, W., Heinemann, P.H. & He, L. (2023). UAV photogrammetry-based apple orchard blossom density estimation and mapping. Horticulturae, 9(2), 266.

Mahmud, M.S., He, L., Zahid, A., Heinemann, P., Choi, D., Krawczyk, G. & Zhu, H., 2023. Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management. Computers and Electronics in Agriculture, 209, 107862.

Book Chapter:

He, L., Zhang, X., & Zahid, A. (2023). Mechanical Management of Modern Planar Fruit Tree Canopies. In Book: Advanced Automation for Tree Fruit Orchards and Vineyards. Cham: Springer International Publishing.

He, L. (2022). Variable rate Technologies for Precision Agriculture. In Zhang, Q. (eds) Encyclopedia of Smart Agriculture Technologies. Springer, Cham. ISBN/ISSN: 978-3-030-89123-7

Gohil, H & He, L. (2023). Precision Irrigation for Orchards. In: Zhang, Q. (eds) Encyclopedia of Smart Agriculture Technologies. Springer, Cham. ISBN/ISSN: 978-3-030-89123-7_193-1

Thesis and Dissertation:

Kittiphum Pawikhum (2022). Design of end-effectors for thinning apples in the green fruit stage. MS Thesis. The Pennsylvania State University.

Rashmi Sahu (2023). Development of vision system and end-effector for automatic bud thinning of apple tree: early crop load management. MS Thesis. The Pennsylvania State University.

Tennessee

Peer-reviewed Publications

Rice, C. R., McDonald, S. T., Shi, Y., Gan, H., Lee, W. S., Chen, Y., & Wang, Z. (2022). Perception, Path Planning, and Flight Control for a Drone-Enabled Autonomous Pollination System. Robotics, 11(6), 144.

Rice, C.R., Gan, H., Wang, Z. (2023). Real-Time Path Planning and Collision-Free Flight

Control for Drone-Assisted Autonomous Pollination Systems. Information Processing in Agriculture. (under review).

Texas

Peer-reviewed Publications

Ojo, M. O., and Zahid, A. 2023. Improving deep learning classifiers performance via preprocessing and class imbalance approaches in a plant disease detection pipeline. Agronomy, 13, 887.

Mahmud, M.S., Zahid, A., and Das, A.K. 2023. Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects. Sensors, 23, 1818. https://doi.org/10.3390/s23041818

Ojo, M. O., and Zahid, A. 2022. Deep learning in controlled environment agriculture: A review of recent advancements, challenges, and prospects, Sensors, 22(20), 7965

Book Chapters

Zahid, A., and Mahmud, M.S. 2023. LiDAR Sensing and its Applications in Agriculture. Q. Zhang (ed.), Encyclopedia of Smart Agriculture Technologies, Springer Nature Switzerland

Washington

Peer-reviewed Publications

Molaei, B., A. K. Chandel, R. Troy Peters, L. R. Khot, A. Khan, F. Maureira, and C. Stockle. 2023. Investigating the application of artificial hot and cold reference surfaces for improved ETc estimation using the UAS-METRIC energy balance model. Agricultural Water Management, 284, 108346. https://doi.org/10.1016/j.agwat.2023.108346.

Chandel^l, A.K., M.M. Moyer, M. Keller, L.R. Khot, and G-A. Hoheisel. 2022. Soil and climate geographic information system data-derived risk mapping for grape phylloxera in Washington state. Frontiers in Plant Science, 13, 827393-827393 https://doi.org/10.3389/fpls.2022.827393

Chandel, A.K., A.P. Rathnayake, and L.R. Khot. 2022. Mapping apple canopy attributes using aerial multispectral imagery for precision crop inputs management. Acta Horticulturae. 1346, 537-546, https://10.17660/ActaHortic.2022.1346.68

Chandel, A. K., Amogi, B., Khot, L., Stockle, C. O., and R. T. Peters. 2022. Digitizing Crop Water Use with Data-Driven Approaches. Resource Magazine, 29(4), 14--16.

Kalyanaraman*, A., M. Burnett, A. Fern, L. Khot, and J. Viers. 2022. Special report: The AgAID AI institute for transforming workforce and decision support in agriculture. Computers and Electronics in Agriculture, 197, 106944

McCoy, M. L., G.-A. Hoheisel, L. R. Khot, and M. M. Moyer*. 2022. Adjusting air-assistance and nozzle style for optimized airblast sprayer use in eastern Washington vineyards. Catalyst: Discovery into Practice, 6(1): 9-19 https://doi:10.5344/catalyst.2021.21001 (Featured on cover page)

Molaei, B., A. Chandel^l, R.T. Peters*, L.R. Khot, and J.Q. Vargas^l. Investigating lodging in Spearmint with overhead sprinklers compared to drag hoses using the texture feature from low altitude RGB imagery. Information Processing in Agriculture, 9(2): 335-341 https://doi.org/10.1016/j.inpa.2021.02.003

Molaei, B., R.T. Peters*, L.R. Khot, and C. Stockle. 2022. Assessing suitability of auto-selection of hot and cold anchor pixels of the UAS-metric model for developing crop water use maps. Remote Sensing, 14(18), 4454; https://doi.org/10.3390/rs14184454

Ranjan^l, R., R. Sinha^l, L.R. Khot*, and M. Whiting. 2022. Thermal-RGB imagery and in-field weather sensing derived sweet cherry wetness prediction model. Scientia Horticulturae, 294, 110782 https://doi.org/10.1016/j.scienta.2021.110782

Rathnayake^l, A. P., A. Chandel^l, M. Schrader^l, G.-A. Hoheisel and L. R. Khot*. 2022. Air-assisted velocity profiles and perceptive canopy interactions of commercial airblast sprayers used in Pacific Northwest perennial specialty crop production. CIGR e-journal, 24 (1) 7039.

Rathnayake^l, R. K Sahni^l, L. R. Khot*, G.-A. Hoheisel and H. Zhu. 2022. Intelligent sprayer spray rates optimization to efficiently apply chemicals in modern apple orchards. Journal of the ASABE, 65 (6): 1-10. https://doi.org/10.13031/ja.14654

Sahni^l, R. K, R. Ranjan^l, L. R. Khot*, G.-A. Hoheisel and M. Grieshop. 2022. Reservoir units optimization in pneumatic spray delivery-based fixed spray system for large-scale commercial adaptation. Sustainability, 14, 10843. https://doi.org/10.3390/su141710843

Schrader^l, M.J., A.P. Rathnayake^l, and L. R. Khot. 2022. Horticultural oil thermotherapy delivery system for perennial specialty crops: a-proof-of-concept and preliminary results. Applied Engineering in Agriculture, 38(2), 461-468.

Schrader^l, M.J., P. Smytheman, E.H. Beers, and L.R. Khot*. 2022. An open-source low-cost imaging system plug-in for pheromone traps aiding remote insect pest population monitoring in fruit crops. Machines, 10(1), 52. https://doi.org/10.3390/machines10010052

Sinha^l, R., J. Quiros Vargas^l, S. Sankaran and L. R. Khot*. 2022. High resolution aerial photogrammetry based 3D mapping of fruit crop canopies for precision inputs management. Information Processing in Agriculture,9(1): 11-23 https://doi.org/10.1016/j.inpa.2021.01.006.

Worasit, S., A. Marzougui, A. A. Bates, B. Schroeder, L. R. Khot and S. Sankaran*. 2022. Identification of volatile biomarkers for high-throughput sensing of soft rot and Pythium leak diseases in stored potatoes. Food Chemistry, 370, 130910 https://doi.org/10.1016/j.foodchem.2021.130910

Kang, C., Diverres, G., Karkee, M., Zhang, Q., & Keller, M. (2023). Decision-support system for precision regulated deficit irrigation management for wine grapes. Computers and Electronics in Agriculture, 208, 107777.

Borrenpohl, D., & Karkee, M. (2023). Automated pruning decisions in dormant sweet cherry canopies using instance segmentation. Computers and Electronics in Agriculture, 207, 107716.

Bayano-Tejero, S., Karkee, M., Rodríguez-Lizana, A., & Sola-Guirado, R. R. (2023). Estimation of harvested fruit weight using volume measurements with distance sensors: A case study with olives in a big box. Computers and Electronics in Agriculture, 205, 107620.

Mu, X., He, L., Heinemann, P., Schupp, J., & Karkee, M. (2023). Mask R-CNN based apple flower detection and king flower identification for precision pollination. Smart Agricultural Technology, 4, 100151.

Bayano-Tejero, S., Karkee, M., Rodríguez-Lizana, A., & Sola-Guirado, R. R. (2023). Estimation of harvested fruit weight using volume measurements with distance sensors: A case study with olives in a big box. Computers and Electronics in Agriculture, 205, 107620

Lu, S., Liu, X., He, Z., Zhang, X., Liu, W., & Karkee, M. (2022). Swin-Transformer-YOLOv5 for Real-Time Wine Grape Bunch Detection. Remote Sensing, 14(22), 5853.

Guo, J., Duan, J., Yang, Z., & Karkee, M. (2022). De-Handing Technologies for Banana Postharvest Operations—Updates and Challenges. Agriculture, 12(11), 1821.

Thesis/Dissertations

Borrenpohl, D. (2023). Automated Pruning Decisions in Dormant Sweet Cherry Canopies using Instance Segmentation. MS Thesis, Washington State University.

Bhattarai, U. (2023). Robotic Blossom Thinning System for Tree Fruit Crops. PhD Dissertation, Washington State University.

Kang, C. (2023). Decision-Support System for Water Stress Assessment and Deficit Irrigation Management in Wine Grapes. PhD Dissertation, Washington State University.

Books and Book Chapters

Sahni, R.K., and R. Khot* 2023. Fixed spray systems for perennial specialty crops. In: Zhang, Q. (eds) Encyclopedia of Smart Agriculture Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-89123-7_195-1

Karkee, M., Majeed, Y., & Zhang, Q. (2023). Advanced Technologies for Crop-Load Management. In Advanced Automation for Tree Fruit Orchards and Vineyards(pp. 119-149) (Editors Stavros Vougioukas and Qin Zhang). Cham: Springer International Publishing.

Karkee, M., &Silwal, A. (2023). Robotic Fruit Harvesting. In: Zhang, Q. (eds) Encyclopedia of Smart Agriculture Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-89123-7_139-1