AI-Driven Crop Disease Detection and Market Price Prediction System for Sustainable Farming
Vinothini T1, Madhusudhan N2, Nadish M3, Nirmal Kumar K4
1 Computer Science and Engineering Adhiyamaan College of Engineering
2 Computer Science and Engineering Adhiyamaan College of Engineering
3 Computer Science and Engineering Adhiyamaan College of Engineering
4 Computer Science and Engineering Adhiyamaan College of Engineering
Abstract - Agricultural productivity is highly affected by plant diseases, unpredictable market prices, and limited expert guidance for farmers. This project presents an AI-powered intelligent farming assistant that detects crop diseases from leaf images, predicts real-time market prices, and generates customized recommendations to improve crop health and maximize yield. The system uses machine learning and computer vision to identify diseases such as blight, wilt, and nutrient deficiencies with high accuracy. A price prediction model analyzes historical and current market trends to help farmers plan profitable sales. Additionally, the platform provides preventive measures, pesticide suggestions, irrigation schedules, and fertilizer recommendations based on crop conditions and weather data. Designed as a mobile-friendly application, this solution empowers farmers with early disease diagnosis, cost-effective decision-making, and continuous monitoring of crop growth. The proposed system improves farm productivity, reduces losses, and supports sustainable agricultural practices, making it a valuable tool for modern smart farming.
Key Words: AI-Driven Crop Disease Detection System, smart farming, crop disease detection, machine learning, computer vision, market price prediction, precision agriculture, agricultural analytics, cloud-based platform, React Native, Node.js, Python, MongoDB, image processing, real-time monitoring, crop health analysis, fertilizer recommendation, pesticide suggestion, irrigation management, weather-based advisory, sustainable farming, yield improvement, decision support
