Precision Agriculture
Geographic Information System (GIS)/Global Positioning System (GPS), Remote Sensing, Unmanned Aerial Vehicles
UF/IFAS ABE precision agriculture and automation extension programs include the development of an educational program to promote adoption and evaluation of state-of-the-art precision agriculture machinery, equipment, and techniques with a goal of improving the profitability and environmental sustainability of Florida’s specialty crop industry. Topic areas include smart machinery, GIS/GPS applications, remote sensing, UAV (Unmanned Aerial Vehicles), Artificial Intelligence (AI), machine vision and robotics, wireless sensors network, Internet of Things (IoT), and mechatronics.
UF/IFAS EDIS Publications
- Instructions on the Use of Unmanned Aerial Vehicles (UAVs)
- Precision Farming Adoption by Florida Citrus Producers: Probit Model Analysis
- Yield Mapping Hardware Components for Grains and Cotton Using On-the-Go Monitoring Systems
- More EDIS Publications
Use the dropdown arrows below to learn more about our Extension faculty.
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Dr. Yiannis Ampatzidis - Precision Agriculture/Automation
Dr. Yiannis Ampatzidis
Assistant Professor
Precision Agriculture/Automation
239-658-3451
i.ampatzidis@ufl.edu- Unmanned Aerial Vehicle (UAV) in agriculture, real-time sensing, disease detection, variable-rate technology, and GIS/GPS systems for site-specific management practices
- Precision farming technologies, precision irrigation
- Smart machines and systems in agriculture, Internet of Things
- Artificial Intelligence, machine learning, and machine vision
- Automation and robotics for specialty crops
- Farm machinery, mechanization for specialty crops
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Dr. Aditya Singh - Remote Sensing and Spectroscopy
Dr. Aditya Singh
Assistant Professor
Remote Sensing and Spectroscopy
352-294-6739
aditya01@ufl.edu | ROG 261- Remote sensing, spectroscopy
- Develop methods for landscape-scale assessments of forest health using remotely sensed imagery and environmental data
- Develop new tools, techniques, and instrumentation for efficient detection of nutrient status and stress in agricultural crops due to pests and diseases