Abstract: Agriculture plays a vital role in the economy, and early detection of crop diseases is essential for improving yield and reducing losses. Traditional methods of disease identification rely heavily on manual inspection by experts, which is time-consuming, costly, and often inaccessible to farmers in rural areas. This paper presents Crop Disease Detection, an AI-powered web-based system designed to detect crop diseases from leaf images and predict weatherbased disease risks.

The system utilizes advanced Artificial Intelligence models, including GPT-4o-mini Vision for image-based disease detection and Gemini Flash for real-time weather risk analysis. The proposed solution allows users to upload leaf images, analyze crop health, and receive detailed information such as disease name, severity, symptoms, and treatment recommendations.

In addition, the system integrates weather-based risk prediction using parameters such as temperature, humidity, and rainfall to forecast potential disease outbreaks. The system also provides multilingual support, voice assistance, and a structured database for storing and analyzing historical data.

Experimental results show that the system improves accuracy, reduces manual effort, and provides real-time assistance to farmers. Crop Disease Detection offers a scalable and intelligent solution for modern precision agriculture and can be extended further with IoT integration and advanced predictive analytics.

Keywords: Crop Disease Detection, Artificial Intelligence, Image Processing, Precision Agriculture, Weather Risk Prediction, Machine Learning, Smart Farming, GPT Vision AI, Agricultural Analytics

Downloads: | DOI: 10.17148/IARJSET.2026.13438