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Deep Learning-based Crop Yield Prediction Model for Optimizing Agricultural Productivity and Food Security
Accurate crop yield prediction is vital for optimizing agricultural productivity and ensuring food security. This thesis presents a Crop Prediction System that utilizes advanced machine learning techniques to forecast crop yields. The system analyzes historical weather data, soil conditions, and crop types using deep learning models developed with TensorFlow and Keras. The user interface, created with Flask, enables farmers to input relevant data and receive tailored yield predictions and farming recommendations. This system aims to enhance decision-making in agriculture, reduce risks associated with farming, and promote sustainable agricultural practices, ultimately contributing to improved food security and farmer welfare.
