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

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

Aditya Singh and James Fletcher

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

Instructions on the Use of Unmanned Aerial Vehicles (UAVs)

Sri Charan Kakarla and Yiannis Ampatzidis

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

Aditya Singh and James Fletcher

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.

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.

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

Sri Charan Kakarla and Yiannis Ampatzidis

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

Rao S. Mylavarapu and Won Suk Daniel Lee

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

James Fletcher and Aditya Singh

Applications of Artificial Intelligence for Precision Agriculture

Yiannis Ampatzidis

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.

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.

A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

Amr Abd-Elrahman, Katie Britt, and Vance Whitaker

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

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

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

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

Rebecca Barocco, Won Suk Lee, and Garret Hortman

Precision Farming Adoption by Florida Citrus Producers: Probit Model Analysis

Brian J. Sevier and Won Suk Lee

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

Arnold W. Schumann and Edward A. Hanlon

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.

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.

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.