Tag: Yiannis Ampatzidis

Preflight and Flight Instructions on the Use of Unmanned Aerial Vehicles (UAVs) for Agricultural Applications

A drone aircraft in mid-flight. Photo taken 06-14-19.

This 5-page document provides guidance on the appropriate use of unmanned aerial vehicles (UAVs) for agricultural applications in Florida. It contains step-by-step instructions for preparing a UAV for flight, creating a mission path (using flight mission planning apps), and collecting UAV-based data. Written by Sri Charan Kakarla, Leon De Morais Nunes, and Yiannis Ampatzidis, and published by the UF/IFAS Department of Agricultural and Biological Engineering, November 2019.
http://edis.ifas.ufl.edu/ae535

Postflight Data Processing Instructions on the Use of Unmanned Aerial Vehicles (UAVs) for Agricultural Applications

A drone aircraft in mid-flight. Photo taken 06-14-19.

Remote sensing applications for agriculture often require periodically collected high-resolution data, which are difficult to obtain by manned flights or satellite imagery. This 6-page document provides guidance on the use of post-processing software to visualize data collected by unmanned aerial vehicles (UAVs) for agricultural applications. It provides step-by-step instructions for using the data collected from a UAV flight to create several types of maps and indices. Written by Sri Charan Kakarla and Yiannis Ampatzidis, and published by the UF/IFAS Department of Agricultural and Biological Engineering, October 2019.
http://edis.ifas.ufl.edu/ae533

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

Instructions on the Use of Unmanned Aerial Vehicles (UAVs)

Franklin Percival, left, and Peter Ifju, professors at the University of Florida, examine a small plane that can photograph and monitor wildlife and their habitats - Oct. 19, 2004. Controlled by its own on-board computer, the plane stores and downlinks high-quality video and flight data to researchers on the ground. The unmanned aerial vehicle, or UAV, is being developed by UF's College of Engineering in cooperation with UF's Institute of Food and Agricultural Sciences. (AP photo/University of Florida/IFAS/Marisol Amador)

All research and commercial activities involving the use of UAVs must be conducted in compliance with applicable federal and state laws, statutes, and regulations. This new 5-page document provides guidance on the appropriate use of unmanned aerial vehicles (UAVs) or unmanned aircraft systems (UAS) in Florida. Written by Sri Charan Kakarla and Yiannis Ampatzidis, and published by the UF/IFAS Department of Agricultural and Biological Engineering, October 2018.
http://edis.ifas.ufl.edu/ae527