GreenHarvest: Data-Driven Crop Yield Prediction and Eco-Friendly Fertilizer Guidance for Sustainable Agriculture

In India, agriculture is considered a prominent source of income. The most common issue in Indian agriculture is farmers choosing the wrong crop and not utilizing the right fertilizer for their soil. This will result in a major decrease in their production. Green harvest helps farmers to grow an ideal crop using data like soil attributes, soil kinds, and crop yield statistics. Fertilizer suggestions are also made based on site-specific characteristics. This makes crop selection errors less frequent, and productivity rises. This issue is resolved by creating a recommendation system using machine learning models with a majority voting method that uses Random Forest, K nearest neighbor (KNN), and Support Vector Machine (SVM) as learners to accurately and effectively recommend a crop for the site-specific parameters. It is identified that Random Forest gives better accuracy than KNN and Support Vector in providing accurate results as essential instruments in precision agriculture, crop yield prediction, and fertilizer advice to help farmers increase crop output while reducing wasteful use of resources and environmental impact. It also helps future researchers to use Random Forest as an accurate machine learning algorithm in this field.