This project integrates soil moisture sensors, temperature sensors, and weather forecasting data to create an intelligent farming assistant. AI algorithms analyze the soil's moisture content and predict the best irrigation schedule, reducing water wastage while ensuring crops receive adequate hydration. Additionally, computer vision models can detect crop diseases using images captured from drones or cameras. The system can classify plant diseases using deep learning techniques like CNNs (Convolutional Neural Networks) and suggest treatment plans to farmers. By integrating this system with IoT-controlled irrigation and fertilization mechanisms, the project enhances agricultural productivity, reduces costs, and promotes sustainable farming practices.