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• W4385459393 abstract "Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses." @default.
• W4385459393 created "2023-08-02" @default.
• W4385459393 creator A5002198775 @default.
• W4385459393 creator A5091443836 @default.
• W4385459393 date "2023-08-01" @default.
• W4385459393 modified "2023-10-18" @default.
• W4385459393 title "An Analysis of Plant Diseases Identification Based on Deep Learning Methods" @default.
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• W4385459393 doi "https://doi.org/10.5423/ppj.oa.02.2023.0034" @default.
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