Show simple item record

AuthorChowdhury, Muhammad E. H.
AuthorRahman, Tawsifur
AuthorKhandakar, Amith
AuthorAyari, Mohamed A.
AuthorKhan, Aftab U.
AuthorKhan, Muhammad S.
AuthorAl-Emadi, Nasser
AuthorReaz, Mamun B.
AuthorIslam, Mohammad T.
AuthorAli, Sawal H.
Available date2023-04-17T06:57:45Z
Publication Date2021
Publication NameAgriEngineering
ResourceScopus
URIhttp://dx.doi.org/10.3390/agriengineering3020020
URIhttp://hdl.handle.net/10576/41979
AbstractPlants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
SponsorFunding: The open-access publication of this article was funded by the Qatar National Library and this work was made possible by HSREP02-1230-190019 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherMDPI
Subjectautomatic plant disease detection
classification
CNN
deep learning
segmentation of leaves
smart agriculture
TitleAutomatic and Reliable Leaf Disease Detection Using Deep Learning Techniques
TypeArticle
Pagination294-312
Issue Number2
Volume Number3
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record