Integrating AI Detection and Language Models for Real-Time Pest Management in Tomato Cultivation

yavuz selim Şahin ^1* Nimet Sema Gençer ¹ Hasan Şahin ²

• ¹ Bursa Uludağ University, Bursa, Türkiye
• ² Bursa Technical University, Yıldırım, Bursa, Türkiye

Tomato (Solanum lycopersicum L.) cultivation is crucial globally due to its nutritional and economic value. However, the crop faces significant threats from various pests, including Tuta absoluta, Helicoverpa armigera, and Leptinotarsa decemlineata, among others.These pests not only reduce yield but also increase production costs due to the heavy reliance on pesticides. Traditional pest detection methods are labor-intensive and prone to errors, necessitating the exploration of advanced techniques. This study aims to enhance pest detection in tomato cultivation using AI-based detection and language models. Specifically, it integrates YOLOv8 for detection and segmentation tasks and ChatGPT-4 for generating detailed, actionable insights on the detected pests. YOLOv8 was chosen for its superior performance in agricultural pest detection, capable of processing large volumes of data in real-time with high accuracy. The methodology involved training the YOLOv8 model with images of various pests and plant damage. The model achieved a precision of 98.91%, recall of 98.98%, mAP50 of 98.75%, and mAP50-95 of 97.72% for detection tasks. For segmentation tasks, precision was 97.47%, recall 98.81%, mAP50 99.38%, and mAP50-95 95.99%. These metrics demonstrate significant improvements over traditional methods, indicating the model's effectiveness. The integration of ChatGPT-4 further enhances the system by providing detailed explanations and recommendations based on detected pests. This approach facilitates real-time expert consultation, making pest management accessible to untrained producers, especially in remote areas. The study's results underscore the potential of combining AI-based detection and language models to revolutionize agricultural practices. Future research should focus on training these models with domain-specific data to improve accuracy and reliability.Additionally, addressing the computational limitations of personal devices will be crucial for broader adoption. This integration promises to democratize information access, promoting a more resilient, informed, and environmentally conscious approach to farming.