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Volume 8
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Article

UAV-Borne RGB Imagery and Machine Learning for Estimating Soil Properties and Crop Physiological Traits in Peanut (Arachis hypogaea): A Low-Cost Precision Agriculture Approach

by
Wilson Saltos-Alcivar
1,
Cristhian Delgado-Marcillo
2,
Ezequiel Zamora-Ledezma
3,*,
Carlos A. Rivas
4 and
Henry Antonio Pacheco Gil
2,*
1
Facultad de Posgrado, Universidad Técnica de Manabí, Portoviejo 130150, Ecuador
2
Departamento de Ciencias Agrícolas, Facultad de Ingeniería Agrícola, Universidad Técnica de Manabí, Lodana 13132, Ecuador
3
Laboratorio de Funcionamiento de Agroecosistemas y Cambio Climático FAGROCLIM, Departamento de Ciencias Agrícolas, Facultad de Ingeniería Agrícola, Universidad Técnica de Manabí, Lodana 13132, Ecuador
4
Mediterranean Forest Global Change Observatory, Digitalization and Development in Forestry Ecosystems Laboratory, Department of Forestry Engineering, DigiFoR+-ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV Km. 396, 14071 Córdoba, Spain
*
Authors to whom correspondence should be addressed.
AgriEngineering 2026, 8(5), 177; https://doi.org/10.3390/agriengineering8050177
Submission received: 15 February 2026 / Revised: 19 April 2026 / Accepted: 22 April 2026 / Published: 2 May 2026
(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
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Abstract

Modern agriculture must balance productivity with sustainability. In this context, unmanned aerial vehicles (UAVs) offer flexible, cost-effective tools for crop and soil monitoring in precision agriculture. This study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation. A factorial field experiment with four varieties, two planting densities, and two tillage systems was monitored using high-resolution RGB orthomosaics acquired at key phenological stages. From these images, 17 RGB-based indices were computed and related to soil variables and crop traits using Spearman correlation and two regression algorithms: Random Forest (RF) and k-Nearest Neighbors (KNN). RF models outperformed KNN, with the Red Chromatic Coordinate (RCC) index achieving an R2 of 0.87 for predicting soil organic matter content. Indices such as visible NDVI and the Green Vegetation Index also provided robust estimates of canopy condition and leaf chlorophyll. Overall, the results demonstrate that UAV RGB imagery, processed through simple vegetation indices and RF models, constitutes an effective, low-cost approach for monitoring key agronomic parameters in peanut farming.
Keywords: UAV remote sensing; RGB imagery; vegetation indices; soil organic matter; leaf chlorophyll; machine learning UAV remote sensing; RGB imagery; vegetation indices; soil organic matter; leaf chlorophyll; machine learning

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MDPI and ACS Style

Saltos-Alcivar, W.; Delgado-Marcillo, C.; Zamora-Ledezma, E.; Rivas, C.A.; Pacheco Gil, H.A. UAV-Borne RGB Imagery and Machine Learning for Estimating Soil Properties and Crop Physiological Traits in Peanut (Arachis hypogaea): A Low-Cost Precision Agriculture Approach. AgriEngineering 2026, 8, 177. https://doi.org/10.3390/agriengineering8050177

AMA Style

Saltos-Alcivar W, Delgado-Marcillo C, Zamora-Ledezma E, Rivas CA, Pacheco Gil HA. UAV-Borne RGB Imagery and Machine Learning for Estimating Soil Properties and Crop Physiological Traits in Peanut (Arachis hypogaea): A Low-Cost Precision Agriculture Approach. AgriEngineering. 2026; 8(5):177. https://doi.org/10.3390/agriengineering8050177

Chicago/Turabian Style

Saltos-Alcivar, Wilson, Cristhian Delgado-Marcillo, Ezequiel Zamora-Ledezma, Carlos A. Rivas, and Henry Antonio Pacheco Gil. 2026. "UAV-Borne RGB Imagery and Machine Learning for Estimating Soil Properties and Crop Physiological Traits in Peanut (Arachis hypogaea): A Low-Cost Precision Agriculture Approach" AgriEngineering 8, no. 5: 177. https://doi.org/10.3390/agriengineering8050177

APA Style

Saltos-Alcivar, W., Delgado-Marcillo, C., Zamora-Ledezma, E., Rivas, C. A., & Pacheco Gil, H. A. (2026). UAV-Borne RGB Imagery and Machine Learning for Estimating Soil Properties and Crop Physiological Traits in Peanut (Arachis hypogaea): A Low-Cost Precision Agriculture Approach. AgriEngineering, 8(5), 177. https://doi.org/10.3390/agriengineering8050177

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