Tomato cultivation is a cornerstone of global agriculture, yet it faces significant challenges from a variety of diseases that can drastically reduce yield and quality. Traditional methods of disease detection, which rely on manual inspection, are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study presents an advanced, automated system for tomato disease detection and spray prescription using an enhanced YOLOv9 (You Only Look Once) model. By leveraging advanced deep learning techniques, the proposed system accurately identifies and detects nine tomato leaf diseases in real-time by making efficient, precise, and accurate decisions. This YOLOv9 model is modified for detecting tomato leaf diseases and optimized for getting higher accuracy and efficiency. The system automatically prescribes the spray based on detected disease, which helps in reducing pesticide use, along with the environmental impact. This system helps in maximizing crop health and yield. After testing the system on the test dataset and real-time images, the results demonstrate the system’s accuracy and efficiency, achieving a detection accuracy of 97% and spray prescription accuracy of 94%. Integrating a YOLOv9 with a spray prescription system provides a sustainable, cost-effective solution for managing tomato plant diseases. Implementing this system on edge devices paves the way for more extensive precision agriculture applications. By integrating advanced technology with real-world agricultural needs, this work makes a contribution and a global effort to ensure food security and ecological farming practices.
Islam et al. (2026) studied this question.