AI Crop Analysis with Roboflow

An automated system for detecting crop diseases, particularly in tomatoes, combines Roboflow's RF-DETR model and Claude's AI reasoning to provide actionable treatment recommendations, aiming to mitigate global economic losses of $220 billion annually due to plant diseases. The tutorial guides users through training a custom detection model using the Tomato Fruit Disease Detection dataset, deploying the model via Roboflow Workflows, and integrating Claude Sonnet 4.5 to transform disease detections into comprehensive guidance, including treatment options, cost analysis, and prevention strategies. The RF-DETR Small model achieves a 74.8% mAP@50, balancing precision and recall, ensuring reliable identification of diseased fruits, with recommendations for organic and chemical treatments, cultural practices, and economic impact analysis. The workflow, ready for production deployment, highlights the integration of detection and reasoning to convert raw images into structured, actionable agricultural insights, emphasizing the potential for scaling to additional crops and diseases, automated alerts, and seamless integration into farm management systems.