FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture
The accurate pest control of pear tree diseases is an urgent need for the realization of smart agriculture, with one of the key challenges being the precise segmentation of pear leaf diseases. However, existing methods show poor segmentation performance due to issues such as the small size of certai...
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1751 |
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| author | Wenyu Wang Jie Ding Xin Shu Wenwen Xu Yunzhi Wu |
| author_facet | Wenyu Wang Jie Ding Xin Shu Wenwen Xu Yunzhi Wu |
| author_sort | Wenyu Wang |
| collection | DOAJ |
| description | The accurate pest control of pear tree diseases is an urgent need for the realization of smart agriculture, with one of the key challenges being the precise segmentation of pear leaf diseases. However, existing methods show poor segmentation performance due to issues such as the small size of certain pear leaf disease areas, blurred edge details, and background noise interference. To address these problems, this paper proposes an improved U-Net architecture, FFAE-UNet, for the segmentation of pear leaf diseases. Specifically, two innovative modules are introduced in FFAE-UNet: the Attention Guidance Module (AGM) and the Feature Enhancement Supplementation Module (FESM). The AGM module effectively suppresses background noise interference by reconstructing features and accurately capturing spatial and channel relationships, while the FESM module enhances the model’s responsiveness to disease features at different scales through channel aggregation and feature supplementation mechanisms. Experimental results show that FFAE-UNet achieves 86.60%, 92.58%, and 91.85% in MIoU, Dice coefficient, and MPA evaluation metrics, respectively, significantly outperforming current mainstream methods. FFAE-UNet can assist farmers and agricultural experts in more effectively evaluating and managing diseases, thereby enabling precise disease control and management. |
| format | Article |
| id | doaj-art-85937fb49abd4c24b8c48a1b9eb9cf7f |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-85937fb49abd4c24b8c48a1b9eb9cf7f2025-08-20T01:48:54ZengMDPI AGSensors1424-82202025-03-01256175110.3390/s25061751FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped ArchitectureWenyu Wang0Jie Ding1Xin Shu2Wenwen Xu3Yunzhi Wu4Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, ChinaAnhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, ChinaAnhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, ChinaAnhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, ChinaAnhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, ChinaThe accurate pest control of pear tree diseases is an urgent need for the realization of smart agriculture, with one of the key challenges being the precise segmentation of pear leaf diseases. However, existing methods show poor segmentation performance due to issues such as the small size of certain pear leaf disease areas, blurred edge details, and background noise interference. To address these problems, this paper proposes an improved U-Net architecture, FFAE-UNet, for the segmentation of pear leaf diseases. Specifically, two innovative modules are introduced in FFAE-UNet: the Attention Guidance Module (AGM) and the Feature Enhancement Supplementation Module (FESM). The AGM module effectively suppresses background noise interference by reconstructing features and accurately capturing spatial and channel relationships, while the FESM module enhances the model’s responsiveness to disease features at different scales through channel aggregation and feature supplementation mechanisms. Experimental results show that FFAE-UNet achieves 86.60%, 92.58%, and 91.85% in MIoU, Dice coefficient, and MPA evaluation metrics, respectively, significantly outperforming current mainstream methods. FFAE-UNet can assist farmers and agricultural experts in more effectively evaluating and managing diseases, thereby enabling precise disease control and management.https://www.mdpi.com/1424-8220/25/6/1751pear leaf diseaseprecise segmentationFFAE-UNetattentionfeature supplementation |
| spellingShingle | Wenyu Wang Jie Ding Xin Shu Wenwen Xu Yunzhi Wu FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture Sensors pear leaf disease precise segmentation FFAE-UNet attention feature supplementation |
| title | FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture |
| title_full | FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture |
| title_fullStr | FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture |
| title_full_unstemmed | FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture |
| title_short | FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture |
| title_sort | ffae unet an efficient pear leaf disease segmentation network based on u shaped architecture |
| topic | pear leaf disease precise segmentation FFAE-UNet attention feature supplementation |
| url | https://www.mdpi.com/1424-8220/25/6/1751 |
| work_keys_str_mv | AT wenyuwang ffaeunetanefficientpearleafdiseasesegmentationnetworkbasedonushapedarchitecture AT jieding ffaeunetanefficientpearleafdiseasesegmentationnetworkbasedonushapedarchitecture AT xinshu ffaeunetanefficientpearleafdiseasesegmentationnetworkbasedonushapedarchitecture AT wenwenxu ffaeunetanefficientpearleafdiseasesegmentationnetworkbasedonushapedarchitecture AT yunzhiwu ffaeunetanefficientpearleafdiseasesegmentationnetworkbasedonushapedarchitecture |