Abstract:
In this study, we employ advanced data-driven techniques to investigate the complex relationships between the
yields of five major crops and various geographical and spatiotemporal features in Senegal. We analyze how
these features influence crop yields by utilizing remotely sensed data. Our methodology incorporates clustering
algorithms and correlation matrix analysis to identify significant patterns and dependencies, offering a compre-
hensive understanding of the factors affecting agricultural productivity in Senegal. To optimize the model's
performance and identify the optimal hyperparameters, we implemented a comprehensive grid search across
four distinct machine learning regressors: Random Forest, Extreme Gradient Boosting (XGBoost), Categorical
Boosting (CatBoost), and Light Gradient-Boosting Machine (LightGBM). Each regressor offers unique functional-
ities, enhancing our exploration of potential model configurations. The top-performing models were selected
based on evaluating multiple performance metrics, ensuring robust and accurate predictive capabilities. The re-
sults demonstrated that XGBoost and CatBoost perform better than the other two. We introduce synthetic crop
data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural datasets.
By achieving high similarity scores with real-world data, our synthetic samples enhance model robustness,
mitigate overfitting, and provide a viable solution for small dataset issues in agriculture. Our approach distin-
guishes itself by creating a flexible model applicable to various crops together. By integrating five crop datasets
and generating high-quality synthetic data, we improve model performance, reduce overfitting, and enhance re-
alism. Our findings provide crucial insights for productivity drivers in key cropping systems, enabling robust
recommendations and strengthening the decision-making capabilities of policymakers and farmers in data-
scarce regions.
