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

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.

Plant phenotyping relevance

UAV RGB画像と機械学習による作物生理形質・キャノピー状態・葉緑素の推定手法を中心に評価しており、植物表現型取得・推定が実質的な貢献である。

Code and data availability

The supplied blocks describe UAV RGB orthomosaics, 17 spectral indices, and RF/KNN models, but contain no data or code availability statement, no public repository deposit, and no authors' URL for imagery, phenotype data, or analysis scripts. Appendix C presents raw spectral index values only as tables within the paper

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