AI CROP DISEASE REPORT AND SOIL PURITY GENERATOR

Abstract

The rapid growth of agriculture demands intelligent systems to improve crop productivity and soil management. This project, titled “AI-Based Crop Disease Detection and Soil Purity Analysis System”, presents a real-time solution that integrates artificial intelligence with environmental monitoring to assist farmers in making informed decisions. The system utilizes image processing techniques to analyze crop images and detect possible diseases. By applying machine learning concepts, the model classifies crops into categories such as healthy, mildly affected, or severely diseased. In addition, the system evaluates soil conditions by analyzing key parameters such as moisture, pH level, and temperature. Based on these inputs, it determines soil health and provides appropriate recommendations for improvement and maintenance. A user-friendly web interface is developed using Streamlit, allowing users to upload crop images and instantly receive disease predictions along with confidence levels. Simultaneously, simulated or real-time soil data is processed to assess soil quality and suggest preventive measures. This project aims to support precision agriculture by providing an accessible, costeffective, and real-time monitoring tool. It helps reduce crop loss, optimize soil usage, and improve overall agricultural efficiency. The system can be further enhanced by integrating real-time sensors and advanced deep learning models for more accurate predictions.