Leaf Classification for Crop Pests and Diseases in the Compressed Domain
Crop pests and diseases have been the main cause of reduced food production and have seriously affected food security. Therefore, it is very urgent and important to solve the pest problem efficiently and accurately. While traditional neural networks require complete processing of data when processin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/48 |
| _version_ | 1797406280075182080 |
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| author | Jing Hua Tuan Zhu Jizhong Liu |
| author_facet | Jing Hua Tuan Zhu Jizhong Liu |
| author_sort | Jing Hua |
| collection | DOAJ |
| description | Crop pests and diseases have been the main cause of reduced food production and have seriously affected food security. Therefore, it is very urgent and important to solve the pest problem efficiently and accurately. While traditional neural networks require complete processing of data when processing data, by compressed sensing, only one part of the data needs to be processed, which greatly reduces the amount of data processed by the network. In this paper, a combination of compressed perception and neural networks is used to classify and identify pest images in the compressed domain. A network model for compressed sampling and classification, CSBNet, is proposed to enable compression in neural networks instead of the sensing matrix in conventional compressed sensing (CS). Unlike traditional compressed perception, no reduction is performed to reconstruct the image, but recognition is performed directly in the compressed region, while an attention mechanism is added to enhance feature strength. The experiments in this paper were conducted on different datasets with various sampling rates separately, and our model was substantially less accurate than the other models in terms of trainable parameters, reaching a maximum accuracy of 96.32%, which is higher than the 93.01%, 83.58%, and 87.75% of the other models at a sampling rate of 0.7. |
| first_indexed | 2024-03-09T03:24:07Z |
| format | Article |
| id | doaj.art-0d3374edc161473e9cb4735da287f3fa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:24:07Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0d3374edc161473e9cb4735da287f3fa2023-12-03T15:03:38ZengMDPI AGSensors1424-82202022-12-012314810.3390/s23010048Leaf Classification for Crop Pests and Diseases in the Compressed DomainJing Hua0Tuan Zhu1Jizhong Liu2School of Software, Jiangxi Agricultural University, Nanchang 330045, ChinaSchool of Software, Jiangxi Agricultural University, Nanchang 330045, ChinaSchool of Mechatronics Engineering, Nanchang University, Nanchang 330031, ChinaCrop pests and diseases have been the main cause of reduced food production and have seriously affected food security. Therefore, it is very urgent and important to solve the pest problem efficiently and accurately. While traditional neural networks require complete processing of data when processing data, by compressed sensing, only one part of the data needs to be processed, which greatly reduces the amount of data processed by the network. In this paper, a combination of compressed perception and neural networks is used to classify and identify pest images in the compressed domain. A network model for compressed sampling and classification, CSBNet, is proposed to enable compression in neural networks instead of the sensing matrix in conventional compressed sensing (CS). Unlike traditional compressed perception, no reduction is performed to reconstruct the image, but recognition is performed directly in the compressed region, while an attention mechanism is added to enhance feature strength. The experiments in this paper were conducted on different datasets with various sampling rates separately, and our model was substantially less accurate than the other models in terms of trainable parameters, reaching a maximum accuracy of 96.32%, which is higher than the 93.01%, 83.58%, and 87.75% of the other models at a sampling rate of 0.7.https://www.mdpi.com/1424-8220/23/1/48agricultural imagesimage classificationneural networkscompressed domain |
| spellingShingle | Jing Hua Tuan Zhu Jizhong Liu Leaf Classification for Crop Pests and Diseases in the Compressed Domain Sensors agricultural images image classification neural networks compressed domain |
| title | Leaf Classification for Crop Pests and Diseases in the Compressed Domain |
| title_full | Leaf Classification for Crop Pests and Diseases in the Compressed Domain |
| title_fullStr | Leaf Classification for Crop Pests and Diseases in the Compressed Domain |
| title_full_unstemmed | Leaf Classification for Crop Pests and Diseases in the Compressed Domain |
| title_short | Leaf Classification for Crop Pests and Diseases in the Compressed Domain |
| title_sort | leaf classification for crop pests and diseases in the compressed domain |
| topic | agricultural images image classification neural networks compressed domain |
| url | https://www.mdpi.com/1424-8220/23/1/48 |
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