Abstract
Technological advances in computer vision, mechatronics, artificial intelligence and machine learning have enabled the development and implementation of remote sensing technologies for plant/weed/pest/disease identification and management. They provide a unique opportunity for developing intelligent agricultural systems for precision applications. Herein, the Artificial Intelligence (AI) and Machine Learning concepts are described, and several examples are presented to demonstrate the application of the AI in agriculture.
Available on EDIS at: https://edis.ifas.ufl.edu/ae529
References
Abdulridha, J., Y. Ampatzidis, R. Ehsani, and A. de Castro. 2018. "Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado." Computers and Electronics in Agriculture 155: 203-2011. https://doi.org/10.1016/j.compag.2018.10.016
Ampatzidis, Y., L. D. Bellis, and A. Luvisi. 2017. "iPathology: Robotic applications and management of plants and plant diseases." Sustainability 9(6): 1010. https://doi.org/10.3390/su9061010
Ampatzidis, Y. and A. C. Cruz. 2018. "Plant disease detection utilizing artificial intelligence and remote sensing." In International Congress of Plant Pathology (ICPP) 2018: Plant Health in a Global Economy, July 29-August 3. Boston, MA.
Ampatzidis, Y., A. C. Cruz, R. Pierro, A. Materazzi, A. Panattoni, L. De Bellis, and A. Luvisi. 2018a. "Vision-based system for detecting grapevine yellow diseases using artificial intelligence." In XXX International Horticultural Congress, II International Symposium on Mechanization, Precision Horticulture, and Robotics, 12-16 August, 2018. Istanbul, Turkey.
Ampatzidis, Y., P. A. Stansly, and V. H. Meirelles. 2018b. "Automated systems and methods for monitoring and mapping insects in orchards." U.S. provisional patent application No. 62/696,089.
Blue River Technology. 2018. "Optimize Every Plant." Accessed December 14, 2018. http://www.bluerivertechnology.com
Cruz, A. C., A. El-Kereamy, and Y. Ampatzidis. 2018. "Vision-based grapevine Pierce's disease detection system using artificial intelligence." In ASABE Annual International Meeting, July 29-August 1. Detroit, MI. https://doi.org/10.13031/aim.201800148
Cruz, A. C., A. Luvisi, L. De Bellis, and Y. Ampatzidis. 2017. "X-FIDO: an effective application for detecting olive quick decline syndrome with novel deep learning methods." Frontiers, Plant Sci.
https://doi.org/10.3389/fpls.2017.01741
Guan, Z. and F. Wu. 2018. Modeling the Choice between Foreign Guest Workers or Domestic Workers. Working Paper. Gainesville: University of Florida Institute of Food and Agricultural Sciences.
Guan, Z., F. Wu, F. M. Roka, and A. Whidden. 2015. "Agricultural labor and immigration reform." Choices 30(4): 1-9.
Harvest CROO Robotics. 2018. "Harvest CROO Robotics." Accessed December 14, 2018. http://harvestcroorobotics.com
Luvisi, A., Y. Ampatzidis, and L. D. Bellis. 2016. "Plant pathology and information technology: Opportunity and uncertainty in pest management." Sustainability 8(8): 831. https://doi.org/10.3390/su8080831
Roka, F. M., S. Simnitt, and D. Farnsworth. 2017. "Pre-employment costs associated with H-2A agricultural workers and the effects of the '60-minute rule.'" International Food and Agribusiness Management Review 20(3): 335-346. https://doi.org/10.22434/IFAMR2016.0033
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