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- W4200571805 abstract "Past studies of plant disease and pest recognition used classification methods that presented a singular recognition result to the user. Unfortunately, incorrect recognition results may be output, which may lead to further crop damage. To address this issue, there is a need for a system that suggest several candidate results and allow the user to make the final decision. In this study, we propose a method for diagnosing plant diseases and identifying pests using deep features based on transfer learning. To extract deep features, we employ pre-trained VGG and ResNet 50 architectures based on the ImageNet dataset, and output disease and pest images similar to a query image via a k-nearest-neighbor algorithm. In this study, we use a total of 23,868 images of 19 types of hot-pepper diseases and pests, for which, the proposed model achieves accuracies of 96.02 and 99.61%, respectively. We also measure the effects of fine-tuning and distance metrics. The results show that the use of fine-tuning-based deep features increases accuracy by approximately 0.7-7.38%, and the Bray-Curtis distance achieves an accuracy of approximately 0.65-1.51% higher than the Euclidean distance." @default.
- W4200571805 created "2021-12-31" @default.
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- W4200571805 date "2021-12-16" @default.
- W4200571805 modified "2023-09-30" @default.
- W4200571805 title "Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning" @default.
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- W4200571805 doi "https://doi.org/10.3389/fpls.2021.724487" @default.
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