Smart AgroAid: AI-Driven Web-based Crop Disease Detection and Management System

Crop diseases pose a serious danger to global food security and cause large financial losses in the agricultural sector. In order to provide accurate diagnosis and treatment, this study introduces a web-based crop disease detection system that integrates environmental data and sophisticated picture processing. The system analyzes leaf pictures and uses convolutional neural networks (CNNs) to classify plant illnesses. To improve diagnostic precision, environmental elements including soil characteristics, temperature, and humidity are also taken into account. Farmers input leaf photos using the system's user-friendly web interface, and they will receive personalized treatment recommendations along with real-time disease diagnosis findings. This system's sturdy design guarantees scalability and accessibility, especially in rural and resource-constrained locations, and is inspired by earlier research combining ML and AI for agricultural applications. This approach aims to boost output, reduce crop losses, and promote sustainable agricultural practices by providing useful insights and automating disease detection. The multilingual solution ensures inclusion for varied farming communities by automating disease identification and offering actionable insights.