Integrating AI detection and language models for real-time pest management in Tomato cultivation

Tomato (<i>Solanum lycopersicum</i> L.) cultivation is crucial globally due to its nutritional and economic value. However, the crop faces significant threats from various pests, including <i>Tuta absoluta</i>, <i>Helicoverpa armigera</i>, and <i>Leptinotarsa decemlineata</i>, 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.