Staff View: Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation

Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation

Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of t...

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Main Authors:	Islam, Md Shofiqul, Sultana, Sunjida, Farid, Fahmid Al, Islam, Md Nahidul, Rashid, Mamunur, Bari, Bifta Sama, Noramiza, Hashim, Mohd Nizam, Husen
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/40154/1/Multimodal%20hybrid%20deep%20learning%20approach%20to%20detect%20tomato%20leaf.pdf http://umpir.ump.edu.my/id/eprint/40154/ https://doi.org/10.3390/s22166079 https://doi.org/10.3390/s22166079
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spelling	my.ump.umpir.401542024-02-07T07:17:27Z http://umpir.ump.edu.my/id/eprint/40154/ Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation Islam, Md Shofiqul Sultana, Sunjida Farid, Fahmid Al Islam, Md Nahidul Rashid, Mamunur Bari, Bifta Sama Noramiza, Hashim Mohd Nizam, Husen QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants. MDPI 2022-08 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40154/1/Multimodal%20hybrid%20deep%20learning%20approach%20to%20detect%20tomato%20leaf.pdf Islam, Md Shofiqul and Sultana, Sunjida and Farid, Fahmid Al and Islam, Md Nahidul and Rashid, Mamunur and Bari, Bifta Sama and Noramiza, Hashim and Mohd Nizam, Husen (2022) Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation. Sensors, 22 (6079). pp. 1-31. ISSN 1424-8220. (Published) https://doi.org/10.3390/s22166079 https://doi.org/10.3390/s22166079
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Islam, Md Shofiqul Sultana, Sunjida Farid, Fahmid Al Islam, Md Nahidul Rashid, Mamunur Bari, Bifta Sama Noramiza, Hashim Mohd Nizam, Husen Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation
description	Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants.
format	Article
author	Islam, Md Shofiqul Sultana, Sunjida Farid, Fahmid Al Islam, Md Nahidul Rashid, Mamunur Bari, Bifta Sama Noramiza, Hashim Mohd Nizam, Husen
author_facet	Islam, Md Shofiqul Sultana, Sunjida Farid, Fahmid Al Islam, Md Nahidul Rashid, Mamunur Bari, Bifta Sama Noramiza, Hashim Mohd Nizam, Husen
author_sort	Islam, Md Shofiqul
title	Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation
title_short	Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation
title_full	Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation
title_fullStr	Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation
title_full_unstemmed	Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation
title_sort	multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation
publisher	MDPI
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/40154/1/Multimodal%20hybrid%20deep%20learning%20approach%20to%20detect%20tomato%20leaf.pdf http://umpir.ump.edu.my/id/eprint/40154/ https://doi.org/10.3390/s22166079 https://doi.org/10.3390/s22166079
_version_	1822924119022239744
score	13.232414

Multimodal hybrid deep learning approach to detect tomato leaf disease using attention based dilated convolution feature extractor with logistic regression classiﬁcation

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