Optimizing Agricultural Productivity: A Data-Driven Ensemble Model for Crop Recommendation Based on Site-Specific Characteristics and Weather Conditions in India

India’s economy and employment are significantly impacted by agriculture. Indian farmers frequently make the mistake of selecting the incorrect crop for the characteristics of their land. The effect is a decrease in productivity. Careful crop selection is necessary for farmers to provide high-quality harvests. We have discovered a solution to the farmers’ dilemma. Here, we introduce an ensemble model that uses a majority voting approach recommendation system to provide extremely precise crop recommendations for parameters unique to each site, such as soil nutrients (nitrogen, phosphorus, potassium, and pH level) and local weather conditions (temperature, humidity, and rainfall). The methods we use to do this include Decision Tree, Random Forest, K-Nearest Neighbors, and Naive Bayes.