Enhancing Crop Yield Prediction Using Random Forest Machine Learning Algorithm
Keywords:
Crop prediction, Crop yield optimization, Data-driven agriculture, Decision support system, Environmental parameters, Kharif, Rabi, Machine Learning in agriculture, Precision farming, Sustainable agriculture, Random forest algorithm, Soil nutrient analysis, Smart farming solutions, Zaid cropsAbstract
Crop Yield Prediction involves the development of a web-based application that integrates machine learning to predict suitable crops for cultivation based on environmental conditions. The application employs a trained Random Forest (RF) model to make accurate predictions based on user inputs such as nitrogen, phosphorus, and potassium levels in the soil, temperature, humidity, pH level, and rainfall. The application is built using the Flask framework, providing a user-friendly interface with routes for user registration, authentication, and crop prediction. A SQLite database is used to manage user data securely, including storing and authenticating credentials. The model's predictions are supplemented with contextual information on crop categories Kharif, Rabi, Zaid, or perennial to guide users effectively. This project aims to assist farmers and agricultural professionals in making data-driven decisions, optimizing crop yield and resource utilization. By combining modern web technologies with machine learning, the system offers an innovative solution to challenges in the agriculture sector.
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