Discovery of agricultural diseases by deep learning and object detection - ÇELEBİ, MEHMET FATİH; ERSOY, SEZGİN (My University, 2022)
In this study deep learning and object detection models for image-based plant disease recognition have been carried. Trained models were tested on pictures and in real-time with a video camera for five different diseases in tomato leaves. Object detection algorithm was implemented from the personal computer, and deep learning models were applied via Google Colab. Real-time object detection was achieved in the developed model with YOLOv5 algorithm with the highest accuracy of 93.38% in validation accuracy and 94.48% in training accuracy with the highest value of 92.96% in precision. Furthermore, it has been observed that YOLOv5 algorithm gives faster and more accurate results than the previous versions of YOLO.
Keywords: Tarımsal Bilimler; Çevre Mühendisliği; Mühendislik ve Teknoloji; Agricultural Sciences; Environmental Engineering; Engineering and Technology; ÇEVRE BİLİMLERİ; Çevre / Ekoloji; Tarım ve Çevre Bilimleri (AGE); ENVIRONMENTAL SCIENCES; ENVIRONMENT/ECOLOGY; Agriculture & Environment Sciences (AGE); Aquatic Science; Nature and Landscape Conservation; Environmental Science (miscellaneous); Physical Sciences; Life Sciences; agricultural disease; deep learning; disease detection; object detection