Cargando…
Construction of deep learning-based disease detection model in plants
Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in plant pathology. Thus, an automated system for diseas...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163233/ https://www.ncbi.nlm.nih.gov/pubmed/37147432 http://dx.doi.org/10.1038/s41598-023-34549-2 |
_version_ | 1785037843832242176 |
---|---|
author | Jung, Minah Song, Jong Seob Shin, Ah-Young Choi, Beomjo Go, Sangjin Kwon, Suk-Yoon Park, Juhan Park, Sung Goo Kim, Yong-Min |
author_facet | Jung, Minah Song, Jong Seob Shin, Ah-Young Choi, Beomjo Go, Sangjin Kwon, Suk-Yoon Park, Juhan Park, Sung Goo Kim, Yong-Min |
author_sort | Jung, Minah |
collection | PubMed |
description | Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in plant pathology. Thus, an automated system for disease detecting in crops will play an important role in agriculture by constructing early detection system of disease. To develop this system, construction of a stepwise disease detection model using images of diseased-healthy plant pairs and a CNN algorithm consisting of five pre-trained models. The disease detection model consists of three step classification models, crop classification, disease detection, and disease classification. The ‘unknown’ is added into categories to generalize the model for wide application. In the validation test, the disease detection model classified crops and disease types with high accuracy (97.09%). The low accuracy of non-model crops was improved by adding these crops to the training dataset implicating expendability of the model. Our model has the potential to apply to smart farming of Solanaceae crops and will be widely used by adding more various crops as training dataset. |
format | Online Article Text |
id | pubmed-10163233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101632332023-05-07 Construction of deep learning-based disease detection model in plants Jung, Minah Song, Jong Seob Shin, Ah-Young Choi, Beomjo Go, Sangjin Kwon, Suk-Yoon Park, Juhan Park, Sung Goo Kim, Yong-Min Sci Rep Article Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in plant pathology. Thus, an automated system for disease detecting in crops will play an important role in agriculture by constructing early detection system of disease. To develop this system, construction of a stepwise disease detection model using images of diseased-healthy plant pairs and a CNN algorithm consisting of five pre-trained models. The disease detection model consists of three step classification models, crop classification, disease detection, and disease classification. The ‘unknown’ is added into categories to generalize the model for wide application. In the validation test, the disease detection model classified crops and disease types with high accuracy (97.09%). The low accuracy of non-model crops was improved by adding these crops to the training dataset implicating expendability of the model. Our model has the potential to apply to smart farming of Solanaceae crops and will be widely used by adding more various crops as training dataset. Nature Publishing Group UK 2023-05-05 /pmc/articles/PMC10163233/ /pubmed/37147432 http://dx.doi.org/10.1038/s41598-023-34549-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Jung, Minah Song, Jong Seob Shin, Ah-Young Choi, Beomjo Go, Sangjin Kwon, Suk-Yoon Park, Juhan Park, Sung Goo Kim, Yong-Min Construction of deep learning-based disease detection model in plants |
title | Construction of deep learning-based disease detection model in plants |
title_full | Construction of deep learning-based disease detection model in plants |
title_fullStr | Construction of deep learning-based disease detection model in plants |
title_full_unstemmed | Construction of deep learning-based disease detection model in plants |
title_short | Construction of deep learning-based disease detection model in plants |
title_sort | construction of deep learning-based disease detection model in plants |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163233/ https://www.ncbi.nlm.nih.gov/pubmed/37147432 http://dx.doi.org/10.1038/s41598-023-34549-2 |
work_keys_str_mv | AT jungminah constructionofdeeplearningbaseddiseasedetectionmodelinplants AT songjongseob constructionofdeeplearningbaseddiseasedetectionmodelinplants AT shinahyoung constructionofdeeplearningbaseddiseasedetectionmodelinplants AT choibeomjo constructionofdeeplearningbaseddiseasedetectionmodelinplants AT gosangjin constructionofdeeplearningbaseddiseasedetectionmodelinplants AT kwonsukyoon constructionofdeeplearningbaseddiseasedetectionmodelinplants AT parkjuhan constructionofdeeplearningbaseddiseasedetectionmodelinplants AT parksunggoo constructionofdeeplearningbaseddiseasedetectionmodelinplants AT kimyongmin constructionofdeeplearningbaseddiseasedetectionmodelinplants |