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Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification

Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A...

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Autores principales: Hu, Xiaobo, Wang, Rujing, Du, Jianming, Hu, Yimin, Jiao, Lin, Xu, Taosheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953136/
https://www.ncbi.nlm.nih.gov/pubmed/36844049
http://dx.doi.org/10.3389/fpls.2023.1091600
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author Hu, Xiaobo
Wang, Rujing
Du, Jianming
Hu, Yimin
Jiao, Lin
Xu, Taosheng
author_facet Hu, Xiaobo
Wang, Rujing
Du, Jianming
Hu, Yimin
Jiao, Lin
Xu, Taosheng
author_sort Hu, Xiaobo
collection PubMed
description Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions. Following this idea, we present a novel Class-Attention-based Lesion Proposal Convolutional Neural Network (CALP-CNN), which utilizes a class response map to locate the main lesion object and propose discriminative lesion details. Specifically, the CALP-CNN firstly locates the main lesion object from the complex background through a class object location module (COLM) and then applies a lesion part proposal module (LPPM) to propose the discriminative lesion details. With a cascade architecture, the CALP-CNN can simultaneously address the interference from the complex background and the misclassification of similar diseases. A series of experiments on a self-built dataset of field strawberry diseases is conducted to testify the effectiveness of the proposed CALP-CNN. The classification results of the CALP-CNN are 92.56%, 92.55%, 91.80% and 91.96% on the metrics of accuracy, precision, recall and F1-score, respectively. Compared with six state-of-the-art attention-based fine-grained image recognition methods, the CALP-CNN achieves 6.52% higher (on F1-score) than the sub-optimal baseline MMAL-Net, suggesting that the proposed methods are effective in identifying strawberry diseases in the field.
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spelling pubmed-99531362023-02-25 Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification Hu, Xiaobo Wang, Rujing Du, Jianming Hu, Yimin Jiao, Lin Xu, Taosheng Front Plant Sci Plant Science Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions. Following this idea, we present a novel Class-Attention-based Lesion Proposal Convolutional Neural Network (CALP-CNN), which utilizes a class response map to locate the main lesion object and propose discriminative lesion details. Specifically, the CALP-CNN firstly locates the main lesion object from the complex background through a class object location module (COLM) and then applies a lesion part proposal module (LPPM) to propose the discriminative lesion details. With a cascade architecture, the CALP-CNN can simultaneously address the interference from the complex background and the misclassification of similar diseases. A series of experiments on a self-built dataset of field strawberry diseases is conducted to testify the effectiveness of the proposed CALP-CNN. The classification results of the CALP-CNN are 92.56%, 92.55%, 91.80% and 91.96% on the metrics of accuracy, precision, recall and F1-score, respectively. Compared with six state-of-the-art attention-based fine-grained image recognition methods, the CALP-CNN achieves 6.52% higher (on F1-score) than the sub-optimal baseline MMAL-Net, suggesting that the proposed methods are effective in identifying strawberry diseases in the field. Frontiers Media S.A. 2023-01-26 /pmc/articles/PMC9953136/ /pubmed/36844049 http://dx.doi.org/10.3389/fpls.2023.1091600 Text en Copyright © 2023 Hu, Wang, Du, Hu, Jiao and Xu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Hu, Xiaobo
Wang, Rujing
Du, Jianming
Hu, Yimin
Jiao, Lin
Xu, Taosheng
Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification
title Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification
title_full Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification
title_fullStr Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification
title_full_unstemmed Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification
title_short Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification
title_sort class-attention-based lesion proposal convolutional neural network for strawberry diseases identification
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953136/
https://www.ncbi.nlm.nih.gov/pubmed/36844049
http://dx.doi.org/10.3389/fpls.2023.1091600
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