# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 1 Introduction
[18] utilized the semi-empirical CLAIR model to estimate wheat LAI using ground-based data and imagery from the SPOT (Satellite Pour I'Observation de la Terre) series. All of them proved the value of remote sensing for model calibration and improved regional crop yield estimates. Combining VIs, derived from sources such as Sentinel-2 and Landsat, with artificial intelligence (AI) methods, including machine learning (ML) and deep learning (DL), has greatly increased how accurately yields can be estimated [19; 20]. In many of these studies, indices like the Enhanced Vegetation Index (\(EVI\)) [21; 22] and Normalized Difference Vegetation Index (\(NDVI\)) [23; 24] are commonly used. Nevertheless, a combination of VIs and individual spectral bands has also been utilized in the related literature [25; 26; 27; 28; 29; 30; 31; 32; 33] includingNear-Infrared (NIR), Shortwave Infrared (SWIR), Green Normalized Difference Vegetation Index (\(GNDVI\)), Greenness Index (\(GI\)), Water Index (\(WI\)), Normalized Difference Water Index (\(NDWI\)), and Soil-Adjusted Vegetation Index (\(SAVI\)). ML algorithms, such as Random Forest and XGBoost excel at modeling nonlinear relationships between spectral features and crop yield [34; 35; 36]. Unlike ML, DL and hybrid DL architectures, such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Bidirectional LSTM (Bi-LSTM), AdaBoost-LSTM, and CNN-LSTM, automate feature extraction from satellite time series [37; 38; 39]. Research has mainly focused on maize, wheat, and rice yield prediction [40] due to their status as global staple foods crucial for food security and major agricultural commodities with significant economic impact. The existing extensive data availability further contribute to this emphasis [41; 42; 43]. In contrast, limited research can be found in legumes [44; 45], especially lupin, despite their growing importance in resilient farming systems. Besides, a critical barrier persists: most ML/DL models operate as \"black boxes\" offering no insight into the drivers of their predictions [46]. This hinders stakeholder trust and limits practical adoption, as farmers and policymakers require interpretable evidence to justify interventions [47; 48]. To address these critical gaps, we present an innovative framework that integrates three key components to advance crop yield prediction. First, we combine high-resolution Sentinel-2 imagery with precisely georeferenced grid-scale yield data, achieving spatial precision in ground-truth measurements. Second, we calculate a range of VIs derived from specific combinations of spectral bands. We also utilize individual spectral bands directly, subjecting both VIs and bands to rigorous feature importance analysis. Third, and most innovatively, we incorporate explainable AI (XAI) techniques, namely SHapley Additive exPlanations (SHAP) values, to decode model decision-making processes and identify the specific features driving yield predictions. By concentrating on the understudied legume crop, lupin, while prioritizing both predictive accuracy and interpretability, our work effectively bridges the divide between advanced ML capabilities and practical farm management needs.