Tag: Precision Agriculture

Applications of Artificial Intelligence for Precision Agriculture

Real-time citrus detection using YOLO (a real-time AI object detection algorithm) on an NVidia Jetson TX2 board (Graphics Processing Unit, GPU). These results are achieved by using just 150 pictures to train the AI-based system.

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, and disease identification and management. They provide a unique opportunity for the development of intelligent agricultural systems for precision applications. This 5-page document discusses the concepts of artificial intelligence (AI) and machine learning and presents several examples to demonstrate the application of AI in agriculture. Written by Yiannis Ampatzidis, and published by the UF/IFAS Department of Agricultural and Biological Engineering, December 2018.
http://edis.ifas.ufl.edu/ae529

AE467 Grower Expectations of New Technologies for Applications in Precision Horticulture

AE467, a 4-page article by Reza Ehsani, Sindhuja Sankaran, and Cristian Dima, reports the results of a needs assessment focus meeting. It provides a synopsis of perceived apple and orange growers’ needs, expectations, and concerns related to the new technologies being developed for various precision horticulture applications. Published by the UF Department of Agricultural and Biological Engineering, October 2010.
http://edis.ifas.ufl.edu/ae467

AE466 Increasing Field Efficiency of Farm Machinery Using GPS

AE466, a 3-page illustrated fact sheet by Reza Ehsani, shows how Global Positioning Systems data can provide very useful information about the efficiency of agricultural equipment. Published by the UF Department of Agricultural and Biological Engineering, August 2010.
http://edis.ifas.ufl.edu/ae466

AE438 GPS Accuracy for Tree Scouting and Other Horticultural Uses

AE438, a 7-page illustrated fact sheet by Reza Ehsani, Sherrie Buchanon, and Masoud Salyani, provides citrus producers using GPS for citrus greening disease scouting with some simple explanations of the causes of GPS error and the level of accuracy that can be expected from different classes of GPS receivers. Published by the UF Department of Agricultural and Biological Engineering, January 2009.
http://edis.ifas.ufl.edu/AE438

AE444 Variable Rate Technology for Florida Citrus

AE444, a 5-page illustrated fact sheet by Reza Ehsani, Arnold Schumann, and Masoud Salyani, describes this important site-specific management component of precision agriculture which provides economic benefits to growers while reducing the application of agrochemicals. Includes references. Published by the UF Department of Agricultural and Biological Engineering, January 2009.
http://edis.ifas.ufl.edu/AE444