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Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection
It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural pro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10553313/ https://www.ncbi.nlm.nih.gov/pubmed/37796844 http://dx.doi.org/10.1371/journal.pone.0286732 |
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author | Linfeng, Wang Yong, Liu Jiayao, Liu Yunsheng, Wang Shipu, Xu |
author_facet | Linfeng, Wang Yong, Liu Jiayao, Liu Yunsheng, Wang Shipu, Xu |
author_sort | Linfeng, Wang |
collection | PubMed |
description | It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper. |
format | Online Article Text |
id | pubmed-10553313 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-105533132023-10-06 Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection Linfeng, Wang Yong, Liu Jiayao, Liu Yunsheng, Wang Shipu, Xu PLoS One Research Article It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper. Public Library of Science 2023-10-05 /pmc/articles/PMC10553313/ /pubmed/37796844 http://dx.doi.org/10.1371/journal.pone.0286732 Text en © 2023 Linfeng et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Linfeng, Wang Yong, Liu Jiayao, Liu Yunsheng, Wang Shipu, Xu Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection |
title | Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection |
title_full | Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection |
title_fullStr | Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection |
title_full_unstemmed | Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection |
title_short | Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection |
title_sort | based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10553313/ https://www.ncbi.nlm.nih.gov/pubmed/37796844 http://dx.doi.org/10.1371/journal.pone.0286732 |
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