Variable Rate Technology and Its Application in Precision Agriculture
Spatiotemporal variation of corn normalized difference vegetation index (NDVI) captured using an unmanned aircraft system (UAS) at the UF/IFAS North Florida Research and Education Center, Suwannee Valley (UF/IFAS NFREC-SV), Live Oak, FL. Credits: Vivek Sharma, UF/IFAS.
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Keywords

variable rate application
precision agriculture

Categories

How to Cite

Sharma, Vivek, Uday Bhanu Prakash Vaddevolu, Shiva Bhambota, Yiannis Ampatzidis, Haimanote Bayabil, and Aditya Singh. 2025. “Variable Rate Technology and Its Application in Precision Agriculture: AE607, 1 2025”. EDIS 2025 (1). Gainesville, FL. https://doi.org/10.32473/edis-ae607-2025.

Abstract

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.

https://doi.org/10.32473/edis-ae607-2025
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References

Ampatzidis, Y. 2018. “Applications of Artificial Intelligence for Precision Agriculture: AE529, 12/2018.” EDIS 2018 (6). https://doi.org/10.32473/edis-ae529-2018

Campos, J., M. Gallart, J. Llop, P. Ortega, R. Salcedo, and E. Gil. 2020. “On-Farm Evaluation of Prescription Map-Based Variable Rate Application of Pesticides in Vineyards.” Agronomy 10 (1): 102. https://doi.org/10.3390/agronomy10010102

Costa, L., S. Kunwar, Y. Ampatzidis, and U. Albrecht. 2022. “Determining Leaf Nutrient Concentrations in Citrus Trees Using UAV Imagery and Machine Learning.” Precision Agriculture:1–22. https://doi.org/10.1007/s11119-021-09864-1

Costa, L., J. McBreen, Y. Ampatzidis, J. Guo, M. R. Gahrooei, and M. A. Babar. 2022. “Using UAV-Based Hyperspectral Imaging and Functional Regression to Assist in Predicting Grain Yield and Related Traits in Wheat Under Heat-Related Stress Environments for the Purpose of Stable Yielding Genotypes.” Precision Agriculture 23 (2): 622–642. https://doi.org/10.1007/s11119-021-09852-5

Grisso, R. D., M. M. Alley, W. E. Thomason, D. L. Holshouser, and G. T. Roberson. 2011. “Precision Farming Tools: Variable-Rate Application.” Virginia Cooperative Extension. https://www.researchgate.net/publication/309121121_Precision_farming_tools_Variable-rate_application

Kakarla, S. C., and Y. Ampatzidis. 2021. “Types of Unmanned Aerial Vehicles (UAVs), Sensing Technologies, and Software for Agricultural Applications: AE565, 10/2021.” EDIS 2021 (5). https://doi.org/10.32473/edis-ae565-2021

Lark, R. M., and J. V. Stafford. 1997. “Classification as a First Step in the Interpretation of Temporal and Spatial Variation of Crop Yield.” Annals of Applied Biology 130 (1): 111–121. https://doi.org/10.1111/j.1744-7348.1997.tb05787.x

Mylavarapu, R. S., and W. S. D. Lee. 2020. “UF/IFAS Nutrient Management Series: Soil Sampling Strategies for Precision Agriculture: SL 190, 02/2020.” EDIS. https://edis.ifas.ufl.edu/ss402

O’Shaughnessy, S. A., S. R. Evett, P. D. Colaizzi, M. A. Andrade, T. H. Marek, D. M. Heeren, F. R. Lamm, and J. L. LaRue. 2019. “Identifying Advantages and Disadvantages of Variable Rate Irrigation: An Updated Review.” Applied Engineering in Agriculture 35 (6): 837–852. https://doi.org/10.13031/aea.13128

Partel, V., L. Costa, and Y. Ampatzidis. 2021. “Smart Tree Crop Sprayer Utilizing Sensor Fusion and Artificial Intelligence.” Computers and Electronics in Agriculture 191:106556. https://doi.org/10.1016/j.compag.2021.106556

Partel, V., S. C. Kakarla, and Y. Ampatzidis. 2019. “Development and Evaluation of a Low-Cost and Smart Technology for Precision Weed Management Utilizing Artificial Intelligence.” Computers and Electronics in Agriculture 157:339–350. https://doi.org/10.1016/j.compag.2018.12.048

Raun, W. R., J. B. Solie, R. K. Taylor, D. B. Arnall, C. J. Mack, and D. E. Edmonds. 2008. “Ramp Calibration Strip Technology for Determining Midseason Nitrogen Rates in Corn and Wheat.” Agronomy Journal 100 (4): 1088–1093. https://doi.org/10.2134/agronj2007.0288N

Sharma, V., D. R. Rudnick, and S. Irmak. 2013. “Development and Evaluation of Ordinary Least Squares Regression Models for Predicting Irrigated and Rainfed Maize and Soybean Yields.” Transactions of the ASABE 56 (4): 1361–1378. https://doi.org/10.13031/trans.56.9973

United States Department of Agriculture (USDA). 2023. “Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms.” Economic Information Bulletin No. (EIB-248). https://ers.usda.gov/publications/pub-details/?pubid=105893

Vijayakumar, V., Y. Ampatzidis, and L. Costa. 2023. “Tree-Level Citrus Yield Prediction Utilizing Ground and Aerial Machine Vision and Machine Learning.” Smart Agricultural Technology 3:100077. https://doi.org/10.1016/j.atech.2022.100077

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