Deep Learning-Driven Chatbots for Crop Health Monitoring and Agricultural Decision Support

Numerous problems in agriculture, including unpredictable crop yields, disease susceptibility, and the consequences of weather variability, put nutrition and farmer livelihoods at risk. In order to increase agricultural yields, detect diseases early, and provide valuable insights on the Crop Yield Prediction Dataset and Plant Village Dataset, this research provides an AI-powered solution to these issues by integrating deep learning, sophisticated machine learning algorithms, and instantaneous data analysis. The system employs a sophisticated methodology that forecasts temperature, humidity, and conditions for the next five days using the PyOWM API; detects crop diseases using data augmentation and deep learning models such as CNN (accuracy 99.14%), DenseNet-201 (accuracy 99.04%), and Visual Geometry Group-VGG19 (accuracy 97%); and predicts crop yield using models such as Multi-Layer Perceptron-MLP (R2 Score: 0.8242), MLP + Regressor, and Random Forest Regressor achieves the highest R2 Score (0.1789). An AI chatbot that provides farmers with recommendations, disease control methods, and personalised suggestions is part of the technology's real-time help. In order to provide an AI-driven system for weather forecasting, disease detection, yield prediction, and real-time assistance via a chatbot, this project integrates models with high accuracy rates. The user-friendly Streamlit UI is available in Telugu, Hindi, and English, and SQLite handles the secure login and registration procedure.