Explainable Deep Learning Reproduces a ‘Professional Eye’ on the Diagnosis of Internal Disorders in Persimmon Fruit
Abstract Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been applied mainly for effective and rapid p...
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Veröffentlicht in: | Plant and cell physiology 2020-12, Vol.61 (11), p.1967-1973 |
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container_end_page | 1973 |
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container_issue | 11 |
container_start_page | 1967 |
container_title | Plant and cell physiology |
container_volume | 61 |
creator | Akagi, Takashi Onishi, Masanori Masuda, Kanae Kuroki, Ryohei Baba, Kohei Takeshita, Kouki Suzuki, Tetsuya Niikawa, Takeshi Uchida, Seiichi Ise, Takeshi |
description | Abstract
Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been applied mainly for effective and rapid phenotyping. In this study, beyond simple optimizations of phenotyping, we propose an application of deep neural networks to make an image-based internal disorder diagnosis that is hard even for experts, and to visualize the reasons behind each diagnosis to provide biological interpretations. Here, we exemplified classification of calyx-end cracking in persimmon fruit by using five convolutional neural network models with various layer structures and examined potential analytical options involved in the diagnostic qualities. With 3,173 visible RGB images from the fruit apex side, the neural networks successfully made the binary classification of each degree of disorder, with up to 90% accuracy. Furthermore, feature visualizations, such as Grad-CAM and LRP, visualize the regions of the image that contribute to the diagnosis. They suggest that specific patterns of color unevenness, such as in the fruit peripheral area, can be indexes of calyx-end cracking. These results not only provided novel insights into indexes of fruit internal disorders but also proposed the potential applicability of deep neural networks in plant biology. |
doi_str_mv | 10.1093/pcp/pcaa111 |
format | Article |
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Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been applied mainly for effective and rapid phenotyping. In this study, beyond simple optimizations of phenotyping, we propose an application of deep neural networks to make an image-based internal disorder diagnosis that is hard even for experts, and to visualize the reasons behind each diagnosis to provide biological interpretations. Here, we exemplified classification of calyx-end cracking in persimmon fruit by using five convolutional neural network models with various layer structures and examined potential analytical options involved in the diagnostic qualities. With 3,173 visible RGB images from the fruit apex side, the neural networks successfully made the binary classification of each degree of disorder, with up to 90% accuracy. Furthermore, feature visualizations, such as Grad-CAM and LRP, visualize the regions of the image that contribute to the diagnosis. They suggest that specific patterns of color unevenness, such as in the fruit peripheral area, can be indexes of calyx-end cracking. These results not only provided novel insights into indexes of fruit internal disorders but also proposed the potential applicability of deep neural networks in plant biology.</description><identifier>ISSN: 1471-9053</identifier><identifier>EISSN: 1471-9053</identifier><identifier>DOI: 10.1093/pcp/pcaa111</identifier><identifier>PMID: 32845307</identifier><language>eng</language><publisher>Japan: Oxford University Press</publisher><subject>Deep Learning ; Diospyros - anatomy & histology ; Flowers - anatomy & histology ; Fruit - anatomy & histology ; Image Interpretation, Computer-Assisted ; Neural Networks, Computer ; Plant Diseases</subject><ispartof>Plant and cell physiology, 2020-12, Vol.61 (11), p.1967-1973</ispartof><rights>The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com 2020</rights><rights>The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c423t-ac0076856d96c6672f36009811b559e96d8db1083c0da06e47e5c41341b747163</citedby><cites>FETCH-LOGICAL-c423t-ac0076856d96c6672f36009811b559e96d8db1083c0da06e47e5c41341b747163</cites><orcidid>0000-0001-9993-8880</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1584,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32845307$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Akagi, Takashi</creatorcontrib><creatorcontrib>Onishi, Masanori</creatorcontrib><creatorcontrib>Masuda, Kanae</creatorcontrib><creatorcontrib>Kuroki, Ryohei</creatorcontrib><creatorcontrib>Baba, Kohei</creatorcontrib><creatorcontrib>Takeshita, Kouki</creatorcontrib><creatorcontrib>Suzuki, Tetsuya</creatorcontrib><creatorcontrib>Niikawa, Takeshi</creatorcontrib><creatorcontrib>Uchida, Seiichi</creatorcontrib><creatorcontrib>Ise, Takeshi</creatorcontrib><title>Explainable Deep Learning Reproduces a ‘Professional Eye’ on the Diagnosis of Internal Disorders in Persimmon Fruit</title><title>Plant and cell physiology</title><addtitle>Plant Cell Physiol</addtitle><description>Abstract
Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been applied mainly for effective and rapid phenotyping. In this study, beyond simple optimizations of phenotyping, we propose an application of deep neural networks to make an image-based internal disorder diagnosis that is hard even for experts, and to visualize the reasons behind each diagnosis to provide biological interpretations. Here, we exemplified classification of calyx-end cracking in persimmon fruit by using five convolutional neural network models with various layer structures and examined potential analytical options involved in the diagnostic qualities. With 3,173 visible RGB images from the fruit apex side, the neural networks successfully made the binary classification of each degree of disorder, with up to 90% accuracy. Furthermore, feature visualizations, such as Grad-CAM and LRP, visualize the regions of the image that contribute to the diagnosis. They suggest that specific patterns of color unevenness, such as in the fruit peripheral area, can be indexes of calyx-end cracking. These results not only provided novel insights into indexes of fruit internal disorders but also proposed the potential applicability of deep neural networks in plant biology.</description><subject>Deep Learning</subject><subject>Diospyros - anatomy & histology</subject><subject>Flowers - anatomy & histology</subject><subject>Fruit - anatomy & histology</subject><subject>Image Interpretation, Computer-Assisted</subject><subject>Neural Networks, Computer</subject><subject>Plant Diseases</subject><issn>1471-9053</issn><issn>1471-9053</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kDFPwzAQhS0EolCY2JEnhIQKdp04zoighUqVqBDMkeNcilFiBzsRdOvPgL_XX4KrFsTEcLobvvd07yF0QsklJSm7alQTRkpK6Q46oFFCBymJ2e6fu4cOvX8lhCSCkX3UY0MRxYwkB-h99NFUUhuZV4BvARo8BemMNnP8CI2zRafAY4lXy8-ZsyV4r62RFR4tYLX8wtbg9iUItZwb67XHtsQT04JbM7faW1eA81gbPAtb13UQjF2n2yO0V8rKw_F299HzePR0cz-YPtxNbq6nAxUNWTuQKvzMRcyLlCvOk2HJOCGpoDSP4xRSXogip0QwRQpJOEQJxCqiLKJ5EsJz1kfnG9-Q5a0D32a19gqqShqwnc-GEUtYKkQsAnqxQZWz3jsos8bpWrpFRkm2bjoLTWfbpgN9ujXu8hqKX_an2gCcbQDbNf86fQP6s4kY</recordid><startdate>20201223</startdate><enddate>20201223</enddate><creator>Akagi, Takashi</creator><creator>Onishi, Masanori</creator><creator>Masuda, Kanae</creator><creator>Kuroki, Ryohei</creator><creator>Baba, Kohei</creator><creator>Takeshita, Kouki</creator><creator>Suzuki, Tetsuya</creator><creator>Niikawa, Takeshi</creator><creator>Uchida, Seiichi</creator><creator>Ise, Takeshi</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9993-8880</orcidid></search><sort><creationdate>20201223</creationdate><title>Explainable Deep Learning Reproduces a ‘Professional Eye’ on the Diagnosis of Internal Disorders in Persimmon Fruit</title><author>Akagi, Takashi ; Onishi, Masanori ; Masuda, Kanae ; Kuroki, Ryohei ; Baba, Kohei ; Takeshita, Kouki ; Suzuki, Tetsuya ; Niikawa, Takeshi ; Uchida, Seiichi ; Ise, Takeshi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c423t-ac0076856d96c6672f36009811b559e96d8db1083c0da06e47e5c41341b747163</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Deep Learning</topic><topic>Diospyros - anatomy & histology</topic><topic>Flowers - anatomy & histology</topic><topic>Fruit - anatomy & histology</topic><topic>Image Interpretation, Computer-Assisted</topic><topic>Neural Networks, Computer</topic><topic>Plant Diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Akagi, Takashi</creatorcontrib><creatorcontrib>Onishi, Masanori</creatorcontrib><creatorcontrib>Masuda, Kanae</creatorcontrib><creatorcontrib>Kuroki, Ryohei</creatorcontrib><creatorcontrib>Baba, Kohei</creatorcontrib><creatorcontrib>Takeshita, Kouki</creatorcontrib><creatorcontrib>Suzuki, Tetsuya</creatorcontrib><creatorcontrib>Niikawa, Takeshi</creatorcontrib><creatorcontrib>Uchida, Seiichi</creatorcontrib><creatorcontrib>Ise, Takeshi</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Plant and cell physiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Akagi, Takashi</au><au>Onishi, Masanori</au><au>Masuda, Kanae</au><au>Kuroki, Ryohei</au><au>Baba, Kohei</au><au>Takeshita, Kouki</au><au>Suzuki, Tetsuya</au><au>Niikawa, Takeshi</au><au>Uchida, Seiichi</au><au>Ise, Takeshi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Explainable Deep Learning Reproduces a ‘Professional Eye’ on the Diagnosis of Internal Disorders in Persimmon Fruit</atitle><jtitle>Plant and cell physiology</jtitle><addtitle>Plant Cell Physiol</addtitle><date>2020-12-23</date><risdate>2020</risdate><volume>61</volume><issue>11</issue><spage>1967</spage><epage>1973</epage><pages>1967-1973</pages><issn>1471-9053</issn><eissn>1471-9053</eissn><abstract>Abstract
Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been applied mainly for effective and rapid phenotyping. In this study, beyond simple optimizations of phenotyping, we propose an application of deep neural networks to make an image-based internal disorder diagnosis that is hard even for experts, and to visualize the reasons behind each diagnosis to provide biological interpretations. Here, we exemplified classification of calyx-end cracking in persimmon fruit by using five convolutional neural network models with various layer structures and examined potential analytical options involved in the diagnostic qualities. With 3,173 visible RGB images from the fruit apex side, the neural networks successfully made the binary classification of each degree of disorder, with up to 90% accuracy. Furthermore, feature visualizations, such as Grad-CAM and LRP, visualize the regions of the image that contribute to the diagnosis. They suggest that specific patterns of color unevenness, such as in the fruit peripheral area, can be indexes of calyx-end cracking. These results not only provided novel insights into indexes of fruit internal disorders but also proposed the potential applicability of deep neural networks in plant biology.</abstract><cop>Japan</cop><pub>Oxford University Press</pub><pmid>32845307</pmid><doi>10.1093/pcp/pcaa111</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0001-9993-8880</orcidid><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
subjects | Deep Learning Diospyros - anatomy & histology Flowers - anatomy & histology Fruit - anatomy & histology Image Interpretation, Computer-Assisted Neural Networks, Computer Plant Diseases |
title | Explainable Deep Learning Reproduces a ‘Professional Eye’ on the Diagnosis of Internal Disorders in Persimmon Fruit |
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