Cargando…
Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network
Plant leaf diseases are closely related to people’s daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806268/ https://www.ncbi.nlm.nih.gov/pubmed/31557958 http://dx.doi.org/10.3390/s19194161 |
_version_ | 1783461590420422656 |
---|---|
author | Hang, Jie Zhang, Dexiang Chen, Peng Zhang, Jun Wang, Bing |
author_facet | Hang, Jie Zhang, Dexiang Chen, Peng Zhang, Jun Wang, Bing |
author_sort | Hang, Jie |
collection | PubMed |
description | Plant leaf diseases are closely related to people’s daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep learning-based method to identify and classify plant leaf diseases. The proposed method can take the advantages of the neural network to extract the characteristics of diseased parts, and thus to classify target disease areas. To address the issues of long training convergence time and too-large model parameters, the traditional convolutional neural network was improved by combining a structure of inception module, a squeeze-and-excitation (SE) module and a global pooling layer to identify diseases. Through the Inception structure, the feature data of the convolutional layer were fused in multi-scales to improve the accuracy on the leaf disease dataset. Finally, the global average pooling layer was used instead of the fully connected layer to reduce the number of model parameters. Compared with some traditional convolutional neural networks, our model yielded better performance and achieved an accuracy of 91.7% on the test data set. At the same time, the number of model parameters and training time have also been greatly reduced. The experimental classification on plant leaf diseases indicated that our method is feasible and effective. |
format | Online Article Text |
id | pubmed-6806268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68062682019-11-07 Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network Hang, Jie Zhang, Dexiang Chen, Peng Zhang, Jun Wang, Bing Sensors (Basel) Article Plant leaf diseases are closely related to people’s daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep learning-based method to identify and classify plant leaf diseases. The proposed method can take the advantages of the neural network to extract the characteristics of diseased parts, and thus to classify target disease areas. To address the issues of long training convergence time and too-large model parameters, the traditional convolutional neural network was improved by combining a structure of inception module, a squeeze-and-excitation (SE) module and a global pooling layer to identify diseases. Through the Inception structure, the feature data of the convolutional layer were fused in multi-scales to improve the accuracy on the leaf disease dataset. Finally, the global average pooling layer was used instead of the fully connected layer to reduce the number of model parameters. Compared with some traditional convolutional neural networks, our model yielded better performance and achieved an accuracy of 91.7% on the test data set. At the same time, the number of model parameters and training time have also been greatly reduced. The experimental classification on plant leaf diseases indicated that our method is feasible and effective. MDPI 2019-09-25 /pmc/articles/PMC6806268/ /pubmed/31557958 http://dx.doi.org/10.3390/s19194161 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hang, Jie Zhang, Dexiang Chen, Peng Zhang, Jun Wang, Bing Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network |
title | Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network |
title_full | Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network |
title_fullStr | Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network |
title_full_unstemmed | Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network |
title_short | Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network |
title_sort | classification of plant leaf diseases based on improved convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6806268/ https://www.ncbi.nlm.nih.gov/pubmed/31557958 http://dx.doi.org/10.3390/s19194161 |
work_keys_str_mv | AT hangjie classificationofplantleafdiseasesbasedonimprovedconvolutionalneuralnetwork AT zhangdexiang classificationofplantleafdiseasesbasedonimprovedconvolutionalneuralnetwork AT chenpeng classificationofplantleafdiseasesbasedonimprovedconvolutionalneuralnetwork AT zhangjun classificationofplantleafdiseasesbasedonimprovedconvolutionalneuralnetwork AT wangbing classificationofplantleafdiseasesbasedonimprovedconvolutionalneuralnetwork |