Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data  

作　　者：Mohammad Amin Razavi A.Pouyan Nejadhashemi Babak Majidi Hoda S.Razavi Josue Kpodo Rasu Eeswaran Ignacio Ciampitti P.V.Vara Prasad 

机构地区：[1]School of Electrical and Computer Engineering,University of Tehran,Tehran,Iran [2]Department of Biosystems and Agricuttural Engineering,Michigan State University,MI,USA [3]Department of Computer Engineering,Khatam University,Tehran,Iran [4]Department of Computer Science and Engineering,Michigan State University,East Lansing,MI,USA [5]Department of Agronomy,Faculty of Agriculture,University of Jafina,Kilinochch,Sri Lanka [6]Department of Agronomy,Kansas State University,Manhattan,KS,UA [7]Feed the Future Sustainable Intensification Innovation Lab,Kansas State University,Manhattan,KS,UA

出　　处：《Artificial Intelligence in Agriculture》2024年第4期99-114,共16页农业人工智能(英文)

摘　　要：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 comprehensive 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 functionalities,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 results 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 distinguishes 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 realism.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 datascarce regions.

关 键 词：Crop yield prediction Variational auto encoder Pattern recognition on spatiotemporal and physiographical variables Synthetic tabular data generation Ensemble learning 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]