DINOV2-FCS: a model for fruit leaf disease classification and severity prediction
IntroductionThe assessment of the severity of fruit disease is crucial for the optimization of fruit production. By quantifying the percentage of leaf disease, an effective approach to determining the severity of the disease is available. However, the current prediction of disease degree by machine...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1475282/full |
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| author | Chunhui Bai Chunhui Bai Chunhui Bai Lilian Zhang Lilian Zhang Lilian Zhang Lutao Gao Lutao Gao Lutao Gao Lin Peng Lin Peng Lin Peng Peishan Li Peishan Li Peishan Li Linnan Yang Linnan Yang Linnan Yang |
| author_facet | Chunhui Bai Chunhui Bai Chunhui Bai Lilian Zhang Lilian Zhang Lilian Zhang Lutao Gao Lutao Gao Lutao Gao Lin Peng Lin Peng Lin Peng Peishan Li Peishan Li Peishan Li Linnan Yang Linnan Yang Linnan Yang |
| author_sort | Chunhui Bai |
| collection | DOAJ |
| description | IntroductionThe assessment of the severity of fruit disease is crucial for the optimization of fruit production. By quantifying the percentage of leaf disease, an effective approach to determining the severity of the disease is available. However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.MethodsIn light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. DINOV2-FCS employs the DINOv2-B (distilled) backbone feature extraction network to enhance the extraction of features from fruit disease leaf images. In fruit leaf disease classification, for the problem that leaf spots of different diseases have great similarity, we have proposed Class-Patch Feature Fusion Module (C-PFFM), which integrates the local detailed feature information of the spots and the global feature information of the class markers. For the problem that the model ignores the fine spots in the segmentation process, we propose Explicit Feature Fusion Architecture (EFFA) and Alterable Kernel Atrous Spatial Pyramid Pooling (AKASPP), which improve the segmentation effect of the model.ResultsTo verify the accuracy and generalizability of the model, two sets of experiments were conducted. First, the labeled leaf disease dataset of five fruits was randomly divided. The trained model exhibited an accuracy of 99.67% in disease classification, an mIoU of 90.29%, and an accuracy of 95.68% in disease severity classification. In the generalizability experiment, four disease data sets were used for training and one for testing. The mIoU of the trained model reached 83.95%, and the accuracy of disease severity grading was 95.24%.DiscussionThe results demonstrate that the model exhibits superior performance compared to other state-of-the-art models and that the model has strong generalization capabilities. This study provides a new method for leaf disease classification and leaf disease severity prediction for a variety of fruits. Code is available at https://github.com/BaiChunhui2001/DINOV2-FCS. |
| format | Article |
| id | doaj-art-a854faba6a484fe7ac27037fb9ed28b2 |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-a854faba6a484fe7ac27037fb9ed28b22025-08-20T02:19:38ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.14752821475282DINOV2-FCS: a model for fruit leaf disease classification and severity predictionChunhui Bai0Chunhui Bai1Chunhui Bai2Lilian Zhang3Lilian Zhang4Lilian Zhang5Lutao Gao6Lutao Gao7Lutao Gao8Lin Peng9Lin Peng10Lin Peng11Peishan Li12Peishan Li13Peishan Li14Linnan Yang15Linnan Yang16Linnan Yang17College of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Kunming, ChinaCollege of Big Data, Yunnan Agricultural University, Kunming, ChinaYunnan Engineering Technology Research Center of Agricultural Big Data, Kunming, ChinaYunnan Engineering Research Center for Big Data Intelligent Information Processing of Green Agricultural Products, Kunming, ChinaIntroductionThe assessment of the severity of fruit disease is crucial for the optimization of fruit production. By quantifying the percentage of leaf disease, an effective approach to determining the severity of the disease is available. However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.MethodsIn light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. DINOV2-FCS employs the DINOv2-B (distilled) backbone feature extraction network to enhance the extraction of features from fruit disease leaf images. In fruit leaf disease classification, for the problem that leaf spots of different diseases have great similarity, we have proposed Class-Patch Feature Fusion Module (C-PFFM), which integrates the local detailed feature information of the spots and the global feature information of the class markers. For the problem that the model ignores the fine spots in the segmentation process, we propose Explicit Feature Fusion Architecture (EFFA) and Alterable Kernel Atrous Spatial Pyramid Pooling (AKASPP), which improve the segmentation effect of the model.ResultsTo verify the accuracy and generalizability of the model, two sets of experiments were conducted. First, the labeled leaf disease dataset of five fruits was randomly divided. The trained model exhibited an accuracy of 99.67% in disease classification, an mIoU of 90.29%, and an accuracy of 95.68% in disease severity classification. In the generalizability experiment, four disease data sets were used for training and one for testing. The mIoU of the trained model reached 83.95%, and the accuracy of disease severity grading was 95.24%.DiscussionThe results demonstrate that the model exhibits superior performance compared to other state-of-the-art models and that the model has strong generalization capabilities. This study provides a new method for leaf disease classification and leaf disease severity prediction for a variety of fruits. Code is available at https://github.com/BaiChunhui2001/DINOV2-FCS.https://www.frontiersin.org/articles/10.3389/fpls.2024.1475282/fullDINOV2deep learningfruit disease recognitionsemantic segmentationsmart agriculture |
| spellingShingle | Chunhui Bai Chunhui Bai Chunhui Bai Lilian Zhang Lilian Zhang Lilian Zhang Lutao Gao Lutao Gao Lutao Gao Lin Peng Lin Peng Lin Peng Peishan Li Peishan Li Peishan Li Linnan Yang Linnan Yang Linnan Yang DINOV2-FCS: a model for fruit leaf disease classification and severity prediction Frontiers in Plant Science DINOV2 deep learning fruit disease recognition semantic segmentation smart agriculture |
| title | DINOV2-FCS: a model for fruit leaf disease classification and severity prediction |
| title_full | DINOV2-FCS: a model for fruit leaf disease classification and severity prediction |
| title_fullStr | DINOV2-FCS: a model for fruit leaf disease classification and severity prediction |
| title_full_unstemmed | DINOV2-FCS: a model for fruit leaf disease classification and severity prediction |
| title_short | DINOV2-FCS: a model for fruit leaf disease classification and severity prediction |
| title_sort | dinov2 fcs a model for fruit leaf disease classification and severity prediction |
| topic | DINOV2 deep learning fruit disease recognition semantic segmentation smart agriculture |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1475282/full |
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