Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models.

Potato as a staple food, plays a crucial role in ensuring a sustainable food supply and mitigating poverty and malnutrition in various regions across the globe. India, specifically holding the second position in global potato production, plays a significant role in the global potato market. The accurate prediction of potato yield is crucial for addressing global food security and sustainable farming practices. Looking into these facts, this study focused on seven districts in Uttar Pradesh, India, viz, Agra, Aligarh, Etawah, Farrukhabad, Firozabad, Hathras and Kannauj. This research aims to compare the performance of five machine learning models-Elastic Net (ELNET), Random Forest, Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) to identify the most effective approach for potato yield forecasting. Time series data spanning 16 years (2005-2021) was collected for seven districts in Uttar Pradesh, including potato yields and weather variables. The data was detrended, processed into weather indices, and split into 70% for training and 30% for testing. Each district was treated as a unique case, with models trained and validated independently. The ANN model demonstrated superior performance with the highest R