FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features
Accurate diagnosis of pear tree nutrient deficiency symptoms is vital for the timely adoption of fertilization and treatment. This study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow feature fusion for diagnosing nutrient deficiency...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/9/4507 |
| _version_ | 1797601644872990720 |
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| author | Yi Song Li Liu Yuan Rao Xiaodan Zhang Xiu Jin |
| author_facet | Yi Song Li Liu Yuan Rao Xiaodan Zhang Xiu Jin |
| author_sort | Yi Song |
| collection | DOAJ |
| description | Accurate diagnosis of pear tree nutrient deficiency symptoms is vital for the timely adoption of fertilization and treatment. This study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow feature fusion for diagnosing nutrient deficiency symptoms in pear leaves. First, the shallow features of nutrient-deficient pear leaf images are extracted using manual feature extraction methods, and the depth features are extracted by the deep network model. Second, the shallow features are fused with the depth features using serial fusion. In addition, the fused features are trained using three classification algorithms, F-Net, FC-Net, and FA-Net, proposed in this paper. Finally, we compare the performance of single feature-based and fusion feature-based identification algorithms in the nutrient-deficient pear leaf diagnostic task. The best classification performance is achieved by fusing the depth features output from the ConvNeXt-Base deep network model with shallow features using the proposed FA-Net network, which improved the average accuracy by 15.34 and 10.19 percentage points, respectively, compared with the original ConvNeXt-Base model and the shallow feature-based recognition model. The result can accurately recognize pear leaf deficiency images by providing a theoretical foundation for identifying plant nutrient-deficient leaves. |
| first_indexed | 2024-03-11T04:06:33Z |
| format | Article |
| id | doaj.art-11b636ea25ae44198abe385effd38b3e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:06:33Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-11b636ea25ae44198abe385effd38b3e2023-11-17T23:45:23ZengMDPI AGSensors1424-82202023-05-01239450710.3390/s23094507FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow FeaturesYi Song0Li Liu1Yuan Rao2Xiaodan Zhang3Xiu Jin4College of Information and Computer Science, Anhui Agricultural University, Hefei 230001, ChinaCollege of Horticulture, Anhui Agricultural University, Hefei 230001, ChinaCollege of Information and Computer Science, Anhui Agricultural University, Hefei 230001, ChinaCollege of Information and Computer Science, Anhui Agricultural University, Hefei 230001, ChinaCollege of Information and Computer Science, Anhui Agricultural University, Hefei 230001, ChinaAccurate diagnosis of pear tree nutrient deficiency symptoms is vital for the timely adoption of fertilization and treatment. This study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow feature fusion for diagnosing nutrient deficiency symptoms in pear leaves. First, the shallow features of nutrient-deficient pear leaf images are extracted using manual feature extraction methods, and the depth features are extracted by the deep network model. Second, the shallow features are fused with the depth features using serial fusion. In addition, the fused features are trained using three classification algorithms, F-Net, FC-Net, and FA-Net, proposed in this paper. Finally, we compare the performance of single feature-based and fusion feature-based identification algorithms in the nutrient-deficient pear leaf diagnostic task. The best classification performance is achieved by fusing the depth features output from the ConvNeXt-Base deep network model with shallow features using the proposed FA-Net network, which improved the average accuracy by 15.34 and 10.19 percentage points, respectively, compared with the original ConvNeXt-Base model and the shallow feature-based recognition model. The result can accurately recognize pear leaf deficiency images by providing a theoretical foundation for identifying plant nutrient-deficient leaves.https://www.mdpi.com/1424-8220/23/9/4507nutrient deficienciesdeep learningfeature fusionconvolutional neural networksattention mechanism |
| spellingShingle | Yi Song Li Liu Yuan Rao Xiaodan Zhang Xiu Jin FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features Sensors nutrient deficiencies deep learning feature fusion convolutional neural networks attention mechanism |
| title | FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features |
| title_full | FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features |
| title_fullStr | FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features |
| title_full_unstemmed | FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features |
| title_short | FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features |
| title_sort | fa net a fused feature for multi head attention recoding network for pear leaf nutritional deficiency diagnosis with visual rgb image depth and shallow features |
| topic | nutrient deficiencies deep learning feature fusion convolutional neural networks attention mechanism |
| url | https://www.mdpi.com/1424-8220/23/9/4507 |
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