This project presents a full-stack web platform that integrates YOLO-based object detection with two large language models, DeepSeek and Qwen, for intelligent diagnosis of watermelon pests in smart agriculture. The system uses YOLO to detect visual symptoms from field images, then leverages the LLMs to provide contextual analysis and treatment recommendations. This multimodal approach represents a significant step forward in agricultural AI, combining the strengths of computer vision and natural language processing. The platform is designed for real-world deployment in field conditions, making it highly practical for farmers and agritech companies. The integration of multiple AI models demonstrates a trend towards more sophisticated, domain-specific AI solutions. For developers, this project offers insights into building similar systems for other crops or agricultural challenges. The commercial potential is substantial, as precision agriculture continues to grow globally.
A novel integration of YOLO with DeepSeek and Qwen LLMs for diagnosing watermelon pests, showcasing multimodal AI in agriculture.