Publication: Discovery of agricultural diseases by deep learning and object detection
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Date
2022-01-01
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Abstract
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.
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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
Citation
Karakaya M., Çelebi M. F., Gok A. E., Ersoy S., "DISCOVERY OF AGRICULTURAL DISEASES BY DEEP LEARNING AND OBJECT DETECTION", ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL, cilt.21, sa.1, ss.163-173, 2022