Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions

In controlled, shaded agricultural environments, conventional remote sensing technologies are often ineffective due to their reliance on direct sunlight and sensitivity to variations in reflectance and absorption. These limitations reduce the effectiveness and precision of image-based leaf area estimation methods, especially in settings such as polytunnel farming, greenhouses, and areas covered by shade netting. Machine learning methods, on the other hand, are used to obtain reliable estimates from complex datasets in various fields, including biology, economics, and engineering. They provide significant practical advantages in applications such as production forecasting and environmental impact analysis in agriculture. This study investigates the use of machine learning models (ElasticNet, Support Vector Regression, Random Forest, Gradient Boosting, and XGBoost) to estimate the leaf area of faba bean cultivars grown under different shading levels. The study used non-destructive morphological traits, such as plant height, leaf number, and leaf density index, as predictive variables. The dataset, incorporated diverse environmental and biological factors, including varying shading intensities, plant densities, and growth stages across different years. The Support Vector Regression (SVR) model achieved the highest predictive accuracy (R² = 0.92, RMSE = 0.07), significantly outperforming the linear ElasticNet model (R² = 0.76). The proposed model provides a low-cost, efficient, and non-destructive approach to leaf area estimation in shaded environments where conventional remote sensing is limited.