Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Pennisetum glaucum)

Abstract

Digital tools and non-destructive monitoring techniques are crucial for real-time evaluations of crop output and health in sustainable agriculture, particularly for precise above-ground biomass (AGB) computation in pearl millet ( Pennisetum glaucum ). This study employed a transfer learning approach using pre-trained convolutional neural networks (CNNs) alongside shallow machine learning algorithms (Support Vector Regression, XGBoost, Random Forest Regression) to estimate AGB. Smartphone-based RGB imaging was used for data collection, and Shapley additive explanations (SHAP) methodology evaluated predictor importance. The SHAP analysis identified Normalized Green-Red Difference Index (NGRDI) and plant height as the most influential features for AGB estimation. XGBoost achieved the highest accuracy (R 2 = 0.98, RMSE = 0.26) with a comprehensive feature set, while CNN-based models also showed strong predictive ability. Random Forest Regression performed best with the two most important features, whereas Support Vector Regression was the least effective. These findings demonstrate the effectiveness of CNNs and shallow machine learning for non-invasive AGB estimation using cost-effective RGB imagery, supporting automated biomass prediction and real-time plant growth monitoring. This approach can aid small-scale carbon inventories in smallholder agricultural systems, contributing to climate-resilient strategies.

Plant phenotyping relevance

スマートフォンRGB画像から植物の地上部バイオマスを推定する画像・機械学習手法が研究の中心であり、特徴量比較と精度評価も実施しているため、植物フェノタイピング手法として含める。

Code and data availability

The paper describes smartphone RGB images, AGB measurements, SegVeg/VegAnn segmentation, and ML models, but provides no public deposit of its own phenotype data, images, code, or trained models. The Colab URL is a generic platform reference, and VegAnn/SegVeg are cited prior-work assets, not this paper's assets. No de-

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