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- W4386900532 abstract "Pine nematode disease, also named Pine wilt disease (PWD), has become a major disaster for the global forestry, devastating a large number of pine forests in the world and leading to great economic losses. In this paper, the small-sample forest image data is obtained through forest fire surveillance cameras and pre-processed by some augmentation methods. Then an automatic screening method for PWD based on the TransUNet network is proposed. This paper uses a small amount of image data captured by forest fire surveillance cameras instead of unmanned aerial vehicle (UAV) images. We perform pre-processing operations on the datasets, such as haze removal, clipping, and image filling. Multiple loss functions were selected, and a new loss function based on precision and recall named PR loss function, is developed in this paper. The results demonstrate that the proposed model has relatively high accuracy, with precision, recall, and F1 score of about 0.870, 0.756, and 0.807. Compared with many spectrum analysis methods and deep learning models, our proposed method can obtain better performance under small-sample and low-resolution restrictions." @default.
- W4386900532 created "2023-09-21" @default.
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- W4386900532 date "2023-06-16" @default.
- W4386900532 modified "2023-09-27" @default.
- W4386900532 title "Automatic Image Screening of Pine Wilt Disease Based on TransUNet" @default.
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- W4386900532 doi "https://doi.org/10.1109/iccsse59359.2023.10245235" @default.
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