Precision Agriculture

How to Avoid Common Problems with Leaf Wetness Sensor Installation and Maintenance

T. B. Onofre, C. W. Fraisse, N. A. Peres, and J. McNair

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

Sri Charan Kakarla and Yiannis Ampatzidis

Parameters for Site-Specific Soil Phosphorus Loss Modeling from Soil Test Data

Biswanath Dari, Vimala D. Nair, and Willie G. Harris

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part III: Best Practices for Efficient Aerial Surveying

Aditya Singh and James Fletcher

UF/IFAS Nutrient Management Series: Soil Sampling Strategies for Precision Agriculture

Rao S. Mylavarapu and Won Suk Daniel Lee

Yield Mapping Hardware Components for Grains and Cotton Using On-the-Go Monitoring Systems

Rebecca Barocco, Won Suk Lee, and Garret Hortman

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

Sri Charan Kakarla, Leon De Morais Nunes, and Yiannis Ampatzidis

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part I: Overview

James Fletcher and Aditya Singh

Precision Farming Adoption by Florida Citrus Producers: Probit Model Analysis

Brian J. Sevier and Won Suk Lee

Instructions on the Use of Unmanned Aerial Vehicles (UAVs)

Sri Charan Kakarla and Yiannis Ampatzidis

Applications of Artificial Intelligence for Precision Agriculture

Yiannis Ampatzidis

Nutrition of Florida Citrus Trees, 3 Edition: Chapter 5. Precision Agriculture for Citrus Nutrient Management

Arnold W. Schumann and Edward A. Hanlon

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part II: Platforms and Payloads

Aditya Singh and James Fletcher

Understanding Artificial Intelligence: What It Is and How It Is Used in Agriculture

Daeun Choi, Omeed Mirbod, Uchechukwu Ilodibe, and Steven Kinsey

The primary goal of this article is to provide background knowledge and terms that frequently come up in other articles about AI as well as in general use. The target audiences of this series include the general public, Extension educators, and farmers who want to know more about AI systems and their applications. This series will help readers understand the opportunities that AI brings to agriculture as smart technologies continue to grow in the market. Written by Daeun Choi, Omeed Mirbod, Uchechukwu Ilodibe, and Steven Kinsey, and published by the UF/IFAS Department of Agricultural and Biological Engineering, October 2023.

Variable Rate Irrigation Technology: A Step-by-Step Guide to Field Implementation

Haimanote Bayabil, Niguss Solomon Hailegnaw, and Vivek Sharma

This fact sheet explores variable rate irrigation (VRI) technology and its components and provides specific examples of field-scale applications of VRI technology with step-by-step guidelines for data collection, data interpretation, developing VRI prescription maps, and uploading maps for implementation. Written by Haimanote Bayabil, Niguss Solomon Hailegnaw, and Vivek Sharma, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2025.

Centering and Engaging Farmers in Technology Development to Facilitate Innovation Adoption: Designing Your Approach

Shiala M. Naranjo and Kathryn A. Stofer

Researchers, engineers, scientists, and program designers can use the information from this publication to engage better with farmers and to ensure programs and products from research are beneficial and easily adopted. This publication shares background on the importance of engagement and several high-level strategies for consideration as you approach engagement, especially for the first time, illustrated by the work of the IoT4Ag Center. Overall, the strategies center the farmer, addressing how to design engagement around audience needs, understand externalities, and determine shared values, highlighted by a case study of how engagement has gone wrong. Written by Shiala M. Naranjo and Kathryn A. Stofer, and published by the UF/IFAS Department of Agricultural Education and Communication, September 2025.

Variable Rate Technology and Its Application in Precision Agriculture

Vivek Sharma, Uday Bhanu Prakash Vaddevolu, Shiva Bhambota, Yiannis Ampatzidis, Haimanote Bayabil, and Aditya Singh

The main aim of this publication is to discuss the concept of variable rate technology (VRT), and its components associated with variable rate application of water, fertilizer, and other agricultural inputs. This publication also provides an example of the control system for variable rate application of agricultural inputs in row and tree crops. Written by Vivek Sharma, Uday Bhanu Prakash Vaddevolu, Shiva Bhambota, Yiannis Ampatzidis, Haimanote Bayabil, and Aditya Singh, and published by the UF/IFAS Department of Agricultural and Biological Engineering, January 2025.

Unlocking the Potential of Technology: Estimating Nitrogen Requirement in Corn Using Optical Sensors

Lakesh K. Sharma, Diego A. H. de S. Leitão, and Hardeep Singh

In the domain of agriculture, the application of optical sensing technology has become increasingly prevalent in the assessment of corn grain production. However, it is notable that comprehensive research endeavors addressing the efficacy of employing the Normalized Difference Vegetation Index (NDVI) as a predictive tool for corn grain yield in the specific context of Florida remain limited. To address this gap in knowledge, a dedicated study focusing on nitrogen (N) rates was meticulously designed in 2022. The primary objective of this study was to provide critical insights and empirical evidence to both our Extension agents and the community of corn farmers. Its overarching aim is to elucidate the practical utility of NDVI sensors in forecasting crop output and facilitating precise estimations of N requirements.

Approaches to Consider for Site-Specific Field Mapping

Lakesh K. Sharma and Yiannis Ampatzidis

This publication discusses the general concept of site-specific farming, i.e., dividing a farm into several smaller management parcels instead of considering the whole farm as a single unit. This publication provides information on methods available that could be incorporated into the farming system for smart decision-making, for example, varying nutrient applications. The publication also discusses Normalized Difference Vegetative Index (NDVI) maps, Global Positioning System (GPS) units, yield monitors, and electrical conductivity maps.

Glossary of Site-Specific Nutrient Management Terms

Lauri Baker, Jay Capasso, Osvaldo Gargiulo, Lauren Geiss, Emma Matcham, Kelly Morgan, Ricky Telg, and Lincoln Zotarelli

This glossary provides clear, unified definitions for terms and is intended to help aid communication with nutrient management professionals across the state, especially when sharing new site-specific nutrient management recommendations. This glossary should help lessen confusion on how the listed terms are defined and interpreted, thereby helping to improve communication within and across disciplines. Written by Lauri Baker, Jay Capasso, Osvaldo Gargiulo, Lauren Geiss, Emma Matcham, Kelly Morgan, Ricky Telg, and Lincoln Zotarelli, and published by the UF/IFAS Department of Agronomy, April 2025.

Impact of In-Row Planting Patterns on Reniform Nematodes Using Precision Agriculture in Cotton

D. L. Wright, S. George, I. Small, T. Stansly, and Z. Grabau

This article is intended to inform cotton producers and associated agricultural professionals of a practical method to help manage plant-parasitic nematodes in cotton. Written by D. L. Wright, S. George, I. Small, T. Stansly, and Z. Grabau, and published by the UF/IFAS Agronomy Department, revised September 2022.

Types of Unmanned Aerial Vehicles (UAVs), Sensing Technologies, and Software for Agricultural Applications

Sri Charan Kakarla and Yiannis Ampatzidis

This publication provides detailed information about the most used UAVs, sensing technologies, and software for agricultural applications. Written by Sri Charan Kakarla and Yiannis Ampatzidis, and published by the UF/IFAS Department of Agricultural and Biological Engineering, October 2021.