Citation

Altabaji, Wassem I. A. E. and Tan, Wooi Haw and Ooi, Chee Pun and Tan, Yi Fei (2023) Identification of Banana Leaf Diseases and Detection. Lecture Notes in Electrical Engineering, 983. pp. 425-434. ISSN 1876-1100

Full text not available from this repository.

Abstract

In modern agriculture, there is a growing interest and attention concentrated on the diagnosis of plant diseases. Recognizing early signs and symptoms of a disease can help to keep outbreaks of the disease on crops to a minimum. Aside from that, most currently available solutions rely on manual identification by a small number of experts, which waste both time and money. Certain artificial intelligence technologies are now being used to provide faster detection and identification of diseases while requiring less work and expenses. This research presents a proposed methodology for the analysis and identification of banana leaf diseases that makes use of Convolutional Neural Networks (CNN). The experimental results demonstrated that the suggested method is capable of providing satisfactory detection and classification of three major banana leaf diseases, namely Cordana, Pestalotiopsis, and Sigatoka, with the validation accuracy of up to 96.24%.

Item Type:	Article
Uncontrolled Keywords:	CNN, Feature extraction, MobileNet, Xception, InceptionV3, Banana leaf
Subjects:	T Technology > TP Chemical technology > TP368-456 Food processing and manufacture
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Jul 2023 01:46
Last Modified:	04 Jul 2023 01:47
URII:	http://shdl.mmu.edu.my/id/eprint/11500

Downloads

Edit (login required)