Maize leaf disease identification based on WG-MARNet
In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of noise, background interference,...
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| Veröffentlicht in: | PloS one 2022-04, Vol.17 (4), p.e0267650-e0267650 |
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| creator | Li, Zongchen Zhou, Guoxiong Hu, Yaowen Chen, Aibin Lu, Chao He, Mingfang Hu, Yahui Wang, Yanfeng |
| description | In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of noise, background interference, and low detection accuracy of maize leaf disease images. To begin, a processing layer called Wavelet threshold guided bilateral filtering (WT-GBF) based on the WG-MARNet model is employed to reduce image noise and perform high and low-frequency decomposition of the input image using WT-GBF. This increases the input image's resistance to environmental interference and feature extraction capability. Secondly, for the multiscale feature fusion technique, an average down-sampling and tiling method is employed to increase feature representation and limit the risk of overfitting. Then, on high and low-frequency multi-channel, an attenuation factor is introduced to optimize the performance instability during training of the deep network. Finally, when the convergence and accuracy are compared, PRelu and Adabound are used instead of the Relu activation function and the Adam optimizer. The experimental results revealed that our method's average recognition accuracy was 97.96%, and the detection time for a single image was 0.278 seconds. The average detection accuracy has been increased. The method lays the groundwork for the precise control of maize diseases in the field. |
| doi_str_mv | 10.1371/journal.pone.0267650 |
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This method can solve the problems of noise, background interference, and low detection accuracy of maize leaf disease images. To begin, a processing layer called Wavelet threshold guided bilateral filtering (WT-GBF) based on the WG-MARNet model is employed to reduce image noise and perform high and low-frequency decomposition of the input image using WT-GBF. This increases the input image's resistance to environmental interference and feature extraction capability. Secondly, for the multiscale feature fusion technique, an average down-sampling and tiling method is employed to increase feature representation and limit the risk of overfitting. Then, on high and low-frequency multi-channel, an attenuation factor is introduced to optimize the performance instability during training of the deep network. Finally, when the convergence and accuracy are compared, PRelu and Adabound are used instead of the Relu activation function and the Adam optimizer. The experimental results revealed that our method's average recognition accuracy was 97.96%, and the detection time for a single image was 0.278 seconds. The average detection accuracy has been increased. The method lays the groundwork for the precise control of maize diseases in the field.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0267650</identifier><identifier>PMID: 35483023</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Attenuation ; Background noise ; Biology and Life Sciences ; Computer and Information Sciences ; Corn ; Crop yields ; Deep learning ; Disease ; Disease detection ; Diseases ; Engineering ; Feature extraction ; Filtration ; Identification methods ; Interference ; Leaves ; Machine learning ; Management ; Medical imaging ; Neural networks ; Neural Networks, Computer ; Noise ; Noise reduction ; Pesticides ; Physical Sciences ; Plant diseases ; Plant Leaves ; Research and Analysis Methods ; Tiling ; Vision systems ; Zea mays</subject><ispartof>PloS one, 2022-04, Vol.17 (4), p.e0267650-e0267650</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Li et al 2022 Li et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-b387d08b2bd118a3b87017f52a731f873c53ecdbe54b82b73227ecf52cc9b353</citedby><cites>FETCH-LOGICAL-c692t-b387d08b2bd118a3b87017f52a731f873c53ecdbe54b82b73227ecf52cc9b353</cites><orcidid>0000-0002-5142-4845</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050012/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050012/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35483023$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Zongchen</creatorcontrib><creatorcontrib>Zhou, Guoxiong</creatorcontrib><creatorcontrib>Hu, Yaowen</creatorcontrib><creatorcontrib>Chen, Aibin</creatorcontrib><creatorcontrib>Lu, Chao</creatorcontrib><creatorcontrib>He, Mingfang</creatorcontrib><creatorcontrib>Hu, Yahui</creatorcontrib><creatorcontrib>Wang, Yanfeng</creatorcontrib><title>Maize leaf disease identification based on WG-MARNet</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of noise, background interference, and low detection accuracy of maize leaf disease images. To begin, a processing layer called Wavelet threshold guided bilateral filtering (WT-GBF) based on the WG-MARNet model is employed to reduce image noise and perform high and low-frequency decomposition of the input image using WT-GBF. This increases the input image's resistance to environmental interference and feature extraction capability. Secondly, for the multiscale feature fusion technique, an average down-sampling and tiling method is employed to increase feature representation and limit the risk of overfitting. Then, on high and low-frequency multi-channel, an attenuation factor is introduced to optimize the performance instability during training of the deep network. Finally, when the convergence and accuracy are compared, PRelu and Adabound are used instead of the Relu activation function and the Adam optimizer. The experimental results revealed that our method's average recognition accuracy was 97.96%, and the detection time for a single image was 0.278 seconds. The average detection accuracy has been increased. The method lays the groundwork for the precise control of maize diseases in the field.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Attenuation</subject><subject>Background noise</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Corn</subject><subject>Crop yields</subject><subject>Deep learning</subject><subject>Disease</subject><subject>Disease detection</subject><subject>Diseases</subject><subject>Engineering</subject><subject>Feature extraction</subject><subject>Filtration</subject><subject>Identification methods</subject><subject>Interference</subject><subject>Leaves</subject><subject>Machine learning</subject><subject>Management</subject><subject>Medical imaging</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Noise</subject><subject>Noise reduction</subject><subject>Pesticides</subject><subject>Physical Sciences</subject><subject>Plant 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WG-MARNet</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-04-28</date><risdate>2022</risdate><volume>17</volume><issue>4</issue><spage>e0267650</spage><epage>e0267650</epage><pages>e0267650-e0267650</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of noise, background interference, and low detection accuracy of maize leaf disease images. To begin, a processing layer called Wavelet threshold guided bilateral filtering (WT-GBF) based on the WG-MARNet model is employed to reduce image noise and perform high and low-frequency decomposition of the input image using WT-GBF. This increases the input image's resistance to environmental interference and feature extraction capability. Secondly, for the multiscale feature fusion technique, an average down-sampling and tiling method is employed to increase feature representation and limit the risk of overfitting. Then, on high and low-frequency multi-channel, an attenuation factor is introduced to optimize the performance instability during training of the deep network. Finally, when the convergence and accuracy are compared, PRelu and Adabound are used instead of the Relu activation function and the Adam optimizer. The experimental results revealed that our method's average recognition accuracy was 97.96%, and the detection time for a single image was 0.278 seconds. The average detection accuracy has been increased. The method lays the groundwork for the precise control of maize diseases in the field.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35483023</pmid><doi>10.1371/journal.pone.0267650</doi><tpages>e0267650</tpages><orcidid>https://orcid.org/0000-0002-5142-4845</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Attenuation Background noise Biology and Life Sciences Computer and Information Sciences Corn Crop yields Deep learning Disease Disease detection Diseases Engineering Feature extraction Filtration Identification methods Interference Leaves Machine learning Management Medical imaging Neural networks Neural Networks, Computer Noise Noise reduction Pesticides Physical Sciences Plant diseases Plant Leaves Research and Analysis Methods Tiling Vision systems Zea mays |
| title | Maize leaf disease identification based on WG-MARNet |
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