Comments: 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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Bibliographic Details
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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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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