Detection the quality of raisin in real time by deep learning techniques

Deep learning has a very advanced usage area and diversity, as well as the growth of computational power, deep learning algorithms have provided remarkable results in the field of agriculture. Deep learning is used in agriculture analysis of crops, pests, weeds, irrigation, fertilization, weather (climatization), farmland analysis, soil analysis and autonomous machines used in agriculture. In the study, using deep learning techniques, the conformity of Sultana raisin samples in TS 3411 to the standard was determined with a real-time system. The raisin dataset consists of three classes as 1.Quality, 2.Quality and Grape Stalk and 10544 images. In the study, train were made with YOLOv5, YOLOv6, YOLOv7, YOLOX, FCOS and TOOD models. In the train, the lowest TOOD with 0.018 of the total loss value, TOOD with 84.6% for mAPs and YOLOv5 with 88.7% for mAP^(50:05:95) were obtained. However, since YOLOv6 has higher fps in real-time detection, YOLOv6 is preferred in real-time detection.