An Enhanced Deep Neural Network Framework for Accurate Tomato Disease Recognition in Real-time Environment

Agricultural production is a critical sector that directly impacts the financial and social well-being of a society. The identification of plant diseases in a real-time environment is a significant challenge for agriculture production. Conventional disease detection methods, which depend significantly on manual inspection, are time-consuming, labour-intensive, and susceptible to human error. Furthermore, many recently developed models struggle in real-time scenarios because their accuracy is compromised when trained on isolated leaf images but then used to analyse entire plants. To tackle these issues, this research offers an advanced, automated system from tomato leaf segmentation and disease detection to the automatic spray prescription in real-time environment. This research presents an integrated system to address these issues, focusing on tomato plants. In first part of the research after deeply analysing the YOLO (You Only Look Once) models we integrate two models, the YOLOv8 with SAM (Segment Anything