DESIGN AND IMPLEMENTATION OF AI-POWERED WEB SYSTEM FOR MAIZE DISEASE DETECTION USING MACHINE LEARNING MODEL

This project presents the design and implementation of an AI-powered web platform for maize disease detection and digital farm support called MaizeSense AI. The motivation behind this work stems from the significant impact of maize diseases on food security and the need for accessible, technology-driven solutions for farmers. The system integrates three core modules: a disease detection model developed with MobileNetV2 and implemented in TensorFlow.js, a weather insights service powered by the OpenWeather API, and an AI assistant supported by the Gemini API. The disease detection module enables farmers to upload maize images, which are processed and classified into disease categories with confidence scores, followed by treatment recommendations. The weather insights module provides location-based farming guidance, including irrigation advice, disease prevention tips, and pest management alerts. The AI assistant serves as an interactive tool to answer farming-related questions in real time. Results show that the platform achieved high accuracy in disease detection, reliable weather forecasting, and effective advisory responses, demonstrating its potential as a practical digital agriculture Overall, MaizeSense AI provides an accessible, user-friendly solution that combines artificial intelligence and agricultural knowledge to support farmers, students, and stakeholders in improving maize productivity and sustainability