Region-aggregated attention CNN for disease detection in fruit images.

<h4>Background</h4>Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy...

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Autores principales:	Chang Hee Han, Eal Kim, Tan Nhu Nhat Doan, Dongil Han, Seong Joon Yoo, Jin Tae Kwak
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9224d388654740718fb25ea241f04112
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spelling	oai:doaj.org-article:9224d388654740718fb25ea241f041122021-12-02T20:13:33ZRegion-aggregated attention CNN for disease detection in fruit images.1932-620310.1371/journal.pone.0258880https://doaj.org/article/9224d388654740718fb25ea241f041122021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258880https://doaj.org/toc/1932-6203<h4>Background</h4>Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy and health. Herein, we propose an improved design of the disease detection system for plant images.<h4>Methods</h4>Built upon the two-stage framework of object detection neural networks such as Mask R-CNN, the proposed network involves three types of extensions, including the addition of additional level of feature pyramids to improve the exploration and proposal of candidate regions, the aggregation of feature maps from all levels of feature pyramids per candidate region to fully exploit the information from feature pyramids, and the introduction of a squeeze-and-excitation block to the construction of feature pyramids and the aggregated feature maps to improve the representation of feature maps.<h4>Results</h4>The proposed network was evaluated using 74 images of infected apple fruits. In 3-fold cross-validation, the proposed network achieved averaged precision (AP) of 72.26, AP at 0.5 threshold of 88.51 and AP at 0.75 threshold of 82.30. In the comparative experiments, the proposed network outperformed the other competing networks. The utility of the three extensions was also demonstrated in comparison to Mask R-CNN.<h4>Conclusions</h4>The experimental results suggest that the proposed network could identify and localize the symptom of the disease with high accuracy, leading to an early diagnosis and treatment of the disease, and thus holding the potential for improving crop yield and quality.Chang Hee HanEal KimTan Nhu Nhat DoanDongil HanSeong Joon YooJin Tae KwakPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258880 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chang Hee Han Eal Kim Tan Nhu Nhat Doan Dongil Han Seong Joon Yoo Jin Tae Kwak Region-aggregated attention CNN for disease detection in fruit images.
description	<h4>Background</h4>Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy and health. Herein, we propose an improved design of the disease detection system for plant images.<h4>Methods</h4>Built upon the two-stage framework of object detection neural networks such as Mask R-CNN, the proposed network involves three types of extensions, including the addition of additional level of feature pyramids to improve the exploration and proposal of candidate regions, the aggregation of feature maps from all levels of feature pyramids per candidate region to fully exploit the information from feature pyramids, and the introduction of a squeeze-and-excitation block to the construction of feature pyramids and the aggregated feature maps to improve the representation of feature maps.<h4>Results</h4>The proposed network was evaluated using 74 images of infected apple fruits. In 3-fold cross-validation, the proposed network achieved averaged precision (AP) of 72.26, AP at 0.5 threshold of 88.51 and AP at 0.75 threshold of 82.30. In the comparative experiments, the proposed network outperformed the other competing networks. The utility of the three extensions was also demonstrated in comparison to Mask R-CNN.<h4>Conclusions</h4>The experimental results suggest that the proposed network could identify and localize the symptom of the disease with high accuracy, leading to an early diagnosis and treatment of the disease, and thus holding the potential for improving crop yield and quality.
format	article
author	Chang Hee Han Eal Kim Tan Nhu Nhat Doan Dongil Han Seong Joon Yoo Jin Tae Kwak
author_facet	Chang Hee Han Eal Kim Tan Nhu Nhat Doan Dongil Han Seong Joon Yoo Jin Tae Kwak
author_sort	Chang Hee Han
title	Region-aggregated attention CNN for disease detection in fruit images.
title_short	Region-aggregated attention CNN for disease detection in fruit images.
title_full	Region-aggregated attention CNN for disease detection in fruit images.
title_fullStr	Region-aggregated attention CNN for disease detection in fruit images.
title_full_unstemmed	Region-aggregated attention CNN for disease detection in fruit images.
title_sort	region-aggregated attention cnn for disease detection in fruit images.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9224d388654740718fb25ea241f04112
work_keys_str_mv	AT changheehan regionaggregatedattentioncnnfordiseasedetectioninfruitimages AT ealkim regionaggregatedattentioncnnfordiseasedetectioninfruitimages AT tannhunhatdoan regionaggregatedattentioncnnfordiseasedetectioninfruitimages AT dongilhan regionaggregatedattentioncnnfordiseasedetectioninfruitimages AT seongjoonyoo regionaggregatedattentioncnnfordiseasedetectioninfruitimages AT jintaekwak regionaggregatedattentioncnnfordiseasedetectioninfruitimages
_version_	1718374779019329536

Region-aggregated attention CNN for disease detection in fruit images.

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