Enhancing Agricultural Decision Making with Machine Learning

Agriculture is the backbone of the Indian economy, but the farmers are unable to pick the right crop because of multi-dimensional environmental conditions such as soil type, climate, and market demand. This research solves this issue by developing an intelligent crop suggestion system using machine learning to provide precise, data-driven crop suggestions. The system takes into account variables such as soil fertility, weather, and market demand to maximize productivity and minimize losses. The system used various machine learning algorithms such as KNN, Decision Trees, Random Forest, and Naive Bayes, and Random Forest gave the best accuracy of 99.89% for a test sample of 9,240 samples out of a total of 46,200 records. The model delivered high precision, recall, and F1-scores for 22 types of crops to make consistent recommendations with fluctuating climatic conditions. Ensemble learning was also used to enhance accuracy by taking multiple algorithmic outputs and using them to make more precise decisions. A web application was developed with six key features: AI-based crop prediction, data analytics, community support, multilingual support, best agriculture practices, and crop analytics. Multilingual support allows accessibility by farmers with different linguistic backgrounds, and the community feature allows knowledge sharing and collaboration. The outcome of this research is a viable and scalable solution empowering Indian farmers with technology-driven knowledge to maximize crop selection, enhance sustainability, and enhance rural economic growth. By bridging the gap between advanced machine learning techniques and real-world agriculture needs, the system offers a data-driven solution to enhance farm productivity and enhance informed decision-making in modern agriculture.