Agriculture food-tech and Rural Development

Agricultural yield prediction is a critical aspect of optimizing farming practices and ensuring food security. This project focuses on predicting crop yield, pesticide usage, fertilizer requirements, pest detection, and disease detection using deep learning techniques. Yield prediction is modelled using a deep learning algorithm, considering features such as state, area, production, rainwater availability, pesticide usage, and fertilizer application. To predict the required amounts of pesticides and fertilizers, deep learning models are employed. Furthermore, convolution neural networks (CNNs) are used for the detection of pests and plant diseases, enabling proactive interventions to minimize crop damage. By combining predictive analytics with CNNs, this project aims to provide comprehensive support to farmers, improving crop management, resource allocation, and overall agricultural productivity. The use of advanced machine learning algorithms allows for more accurate and timely predictions, ultimately contributing to sustainable agricultural practices.