Citation

Muhammad, Umair and Tan, Wooi Haw and Foo, Yee Loo LeafNet2: A lightweight deep learning model for the classification of leaf diseases. None. (Unpublished)

Text MMUPaper1 SASSP 2022 Muhammad Umair.pdf Restricted to Repository staff only Download (373kB)

Abstract

Artificial intelligence comes very handy for the early detection of leaf diseases, and it is because of that reason we have used the deep learning and utilized the convolutional neural network (CNN) for the classification of lead diseases. The CNN network was adopted from the LeafNet model. We have modified the LeafNet network and named it as LeafNet2. We have achieved a testing accuracy of 96.03% for 38 different plant leave diseases on LeafNet2 model. It is noteworthy that LeafNet network achieved an accuracy 95% respectively for our dataset, we have managed to achieve a better accuracy on LeafNet2 network in comparison with LeafNet model.

Item Type:	Other
Uncontrolled Keywords:	leaf diseases, CNN, image classification, LeafNet2.
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Dr. Sarina Mansor
Date Deposited:	30 Nov 2022 04:18
Last Modified:	30 Nov 2022 04:18
URII:	http://shdl.mmu.edu.my/id/eprint/10660

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