Tomato Leaf Detection, Segmentation, and Extraction in Real-Time Environment for Accurate Disease Detection

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification of plant disease in a real-time environment is a significant challenge for agriculture production. For accurate plant disease detection, precise detection of plant leaves is a meaningful and challenging task for developing smart agricultural systems. Most researchers train and test models on synthetic images. So, when using that model in a real-time scenario, it does not give a satisfactory result because when a model trained on images of leaves is fed with the image of the plant, then its accuracy is affected. In this research work, we have integrated two models, the Segment Anything Model (SAM) with YOLOv8, to detect the tomato leaf of a tomato plant, mask the leaf, and extract the leaf in a real-time environment. To improve the performance of leaf disease detection in plant leaves in a real-time environment, we need to detect leaves accurately. We developed a system that will detect the leaf, mask the leaf, extract the leaf, and then detect the disease in that specific leaf. For leaf detection, the modified YOLOv8 is used, and for masking and extraction of the leaf images from the tomato plant, the Segment Anything Model (SAM) is used. Then, for that specific leaf, an image is provided to the deep neural network to detect the disease.