Akademik Veri Yönetim Sistemi
2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA 2023, İstanbul, Türkiye, 10 - 11 Mart 2023, cilt.1983 CCIS, ss.422-436
Plant diseases are one of the problems that threaten crop health and yield in agriculture. Various diseases occurring in plants harm human health and economically producers and producer countries. Early diagnosis is very important in order to prevent the damage caused by diseases. For the early detection of these diseases in plants, continuous observation and examination of plants is required. In large agricultural areas, continuous monitoring of the plants by the producers or workers requires long periods of time and causes extra cost increase. In addition, the person who studies plant leaves must be an expert in plant science. A study was carried out to detect diseases by observing plants based on deep learning, which will be a technological solution to all these problems. Yolov5 and Yolov6 algorithms, one of the object recognition algorithms, was used for plant disease diagnosis. After comparing the two algorithms, the highest AP value with 58.4% belongs to the Yolov5-m model, the highest AR value with 69.3% belongs to the Yolov6-s model, and the highest F1 score with 62.4% belongs to the Yolov5-m. With the study, the comparative results of the models of the Yolo algorithms, together with the hyperparameter values, are given. According to the obtained values, it is seen that the small size models give the best performance. The higher performance of the small size models shows that deep learning models can be integrated into a mobile system, enabling rapid plant identification, sustainability in agriculture and cost reduction.