Crop Prediction Using Deep Neural Networks

This project introduces a web application based on deep learning that suggests the three most appropriate crops for planting given various factors like temperature, humidity, soil pH, and rainfall. Based on a Multi-Layer Perceptron (MLP) model trained on agricultural data, the system evaluates user inputs and predicts crop suitability along with confidence probabilities. The backend is built with Python and Flask, and the frontend is created with HTML, CSS, and JavaScript. This project supports precision agriculture by improving yield and enabling smart decisions.