Integrating Multinomial Logit and Machine Learning Algorithms to Detect Crop Choice Decision Making

In this study, we explore the influence of environmental and soil factors on crop suitability using statistical models and machine learning techniques. We employ a Multinomial Logit Model (MLM) and predictive models including Random Forest (RF), Gradient Boosting (GB), and Light Gradient Boosting Machine (LightGBM) to assess their impact on rice and maize production. Our findings indicate that nitrogen, rainfall, and humidity significantly enhance rice yields, whereas temperature and soil pH negatively affect it. For maize, nitrogen is beneficial, while potassium, temperature, rainfall, and soil pH are detrimental. The models also highlight the paramount importance of rainfall and humidity in crop selection, with both factors having substantial importance scores across RF, GB, and LightGBM. This data-driven approach achieves over 99% accuracy in crop classification via RF, suggesting a robust framework for agricultural decision-making that can significantly improve crop productivity and sustainability.