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AI – BASED CROP YEILD PREDICTION AND SMART FARMING
Agriculture plays a vital role in ensuring food security and supporting economic development. However, predicting crop yield accurately is a challenging task due to variations in soil conditions, rainfall, temperature, humidity, and other environmental factors. Traditional farming methods mainly depend on farmers' experience and historical practices, which may not always provide reliable predictions. To address these challenges, the proposed AI-Based Crop Yield Prediction and Smart Farming system utilizes Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict crop yield and provide smart farming recommendations. The system collects agricultural parameters such as soil type, rainfall, temperature, humidity, and crop information from the user and processes them using a trained machine learning model. Based on the analysis, it predicts the expected crop yield and suggests suitable farming practices, including crop selection, irrigation planning, fertilizer usage, and pest management. The application is developed using Python, Flask, HTML, CSS, and Machine Learning technologies, providing a simple and user-friendly web interface. The proposed system improves prediction accuracy, reduces manual effort, optimizes resource utilization, and supports sustainable farming practices. It serves as an effective decision-support tool for farmers, agricultural researchers, and organizations by enhancing productivity and promoting data-driven agriculture.
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