Article
Agricultural Crop Recommendations Based On Productivity And Season
Agriculture is one of the most important sectors contributing to the economy and food security of many countries. Farmers often face challenges in selecting suitable crops due to changing climatic conditions, soil fertility variations, and lack of accurate agricultural guidance. Incorrect crop selection can result in low productivity, financial losses, and inefficient utilization of resources. This project proposes a Machine Learning-Based Agricultural Crop Recommendation System that recommends suitable crops based on productivity, soil characteristics, and seasonal conditions. The system analyzes parameters such as soil type, temperature, humidity, rainfall, pH level, and historical crop productivity using machine learning algorithms like Decision Tree, Random Forest, and Support Vector Machine (SVM). Based on the analysis, the system predicts the most appropriate crop for cultivation in a particular season and location. The proposed approach improves agricultural productivity, supports datadriven farming decisions, and promotes sustainable agricultural practices.
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