# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 5 Conclusions  
This study presents a novel and interpretable ML framework for crop yield prediction, with a specific focus on an understudied legume crop such lupin. By integrating Sentinel-2 remote sensing data with precisely georeferenced yield data, we assessed the predictive performance of tree-, kernel-, and neural network-based ML algorithms. A thorough data preprocessing pipeline was set up to ensure high-quality data suitable for effective model training. This encompassed steps such as calculation of VIs based on spectral reflectance information, outlier removal using the IQR method, identification and handling of highly correlated features through Spearman analysis, and Z-score normalization. In the direction of addressing potential data imbalance, the SMOTER technique was also applied. Subsequently, six ML models were trained, with hyperparameter optimization and rigorous performance evaluation using GridSearchCV and 5-fold cross-validation.  
It is concluded that the most effective ML model for the present dataset was XGBoost with an \(R^{2}=0.8756\) and robust performance across all evaluation metrics. MLPR and SVR demonstrated comparable but less effective predictive performance with \(R^{2}\) equal to 0.8141 and 0.7933, respectively. In an effort to gain a clearer understanding of the model's decision-making process, an interpretability analysis was conducted on the developed XGBoost model using SHAP. Global interpretability highlighted \(EVI\) and \(NDVI\) as well as two specific spectral bands (\(B03\), \(B12\)) as consistently influential predictors of crop yield.  
In summary, by integrating remote sensing data with XAI techniques the study not only achieved high predictive accuracy, but also offered transparent and interpretable insights into the drivers of crop yield variability. This dual focus addresses a critical barrier in agricultural ML adoption, namely trust and actionable relevance, enabling data-driven decisions that are both scientifically grounded and practically relevant. Future work should focus on validating the model across diverse regions as well as including agronomic variables for a more detailed view of yield drivers. Combining expert input with active learning could also improve model development while minimizing labeling efforts.  
Conceptualization, D.B. and R.B.; methodology, T.P.; software, T.P.; validation, V.M. and T.P.; formal analysis, T.P. and P.B.; investigation, V.M., P.B., C.Z. and G.M.; resources, L.B. and D.B.; data curation, C.Z. and G.M.; writing--original draft preparation, L.B. and T.P.; writing--review and editing, D.B., R.B.; visualization, T.P. and L.B.; supervision, D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed at the corresponding authors.  
This work has been supported by the VALPRO Path project, funded by the European Union's Horizon Europe research and innovation programme within HORIZON-CL6-2021-FARM2 FORK-01-02 under Grant Agreement no. 101059824.  
Authors [PERSON], [PERSON], [PERSON], and [PERSON] were employed by the company farmB Digital Agriculture S.A. [PERSON] was employed by the company Confagricoltura. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.