AGRIBOT

Abstract

The integration of Artificial Intelligence with modern farming practices improves crop management accuracy and supports better decision-making for farmers. The proposed AGRIBOT system combines soil data analysis and image-based disease detection to provide real-time, precise farming advice. Using XGBoost, the system predicts suitable crops based on soil composition, pH level, and environmental factors. Meanwhile, ResNet, a deep learning model, classifies plant diseases from captured images, helping farmers identify and address problems early. AGRIBOT works as an interactive chatbot, allowing farmers to easily input data or upload images and get instant feedback through a simple interface. The system boosts productivity by optimizing crop selection, reducing losses from diseases, and promoting sustainable farming practices. Experimental results show that using AI models in farming decisions significantly improves accuracy and reliability compared to traditional methods.