Advancing Farming Practices: Machine Learning Algorithms for Optimal Crop Selection

This study underscores the critical importance of the agricultural sector for ensuring global food security and economic resilience, emphasizing the role of machine learning (ML)-enhanced crop recommendation systems. These systems are increasingly crucial for guiding farmers in choosing crops that are best suited to their unique soil and environmental conditions. Through the analysis of a comprehensive dataset, including soil composition, climate variations, historical crop yields, and agricultural practices, this research evaluates the performance of several ML algorithms–such as neural networks, decision trees, random forests, and support vector machines–in forecasting the most appropriate crops. Training on historical data allows these algorithms to decipher the complex dynamics between environmental factors and crop results. The success of these models is gauged by metrics including accuracy, precision, recall, and F1-score. This study also delves into the models' interpretability, offering crucial insights to both agricultural practitioners and researchers. Moreover, it presents the creation of a user-friendly web application that applies these ML models to provide personalized crop recommendations, requiring user inputs like geographical location, soil characteristics, and weather data. The findings reveal that ML algorithms can significantly empower farmers with knowledge to select suitable crops, thereby boosting agricultural efficiency, optimizing the use of resources, and enhancing the sustainability of agricultural operations. The research highlights the necessity of selecting the right ML algorithms and preprocessing techniques for achieving superior results. Notably, we have enhanced the accuracy of the random forest algorithm to 99.92% and ADABOOST with the Decision Tree Classifier to 99.02%. This contribution to precision agriculture showcases the practical use of ML in crop recommendation systems as a technological approach to addressing agricultural sector challenges and bolstering the global food supply chain.