Citation

Lim, Jia Yi and Wee, Yit Yin and Wee, Kuok Kwee (2024) Machine Learning and Image Processing-Based System for Identifying Mushrooms Species in Malaysia. Applied Sciences, 14 (15). p. 6794. ISSN 2076-3417

Text Machine Learning and Image Processing-Based System for Identifying Mushrooms Species in Malaysia.pdf - Published Version Restricted to Repository staff only Download (11MB)

Abstract

Malaysia, a country with a tropical climate characterized by consistent warmth and year-long high humidity, houses the perfect conditions for mushroom growth. Recently, there has been a surge in back-to-nature activities in Malaysia. However, many participants lack prior knowledge about the local flora and fungi, leading to a rise in mushroom poisoning cases, some of which have been fatal. Despite thorough research, there is a notable lack of identification studies specifically focused on mushroom species in Malaysia. Identifying these species is crucial for medical providers to effectively counteract the toxins from ingested mushrooms and also serves as an important educational tool. This study aims to determine the most suitable architecture for mushroom identification, focusing specifically on mushroom species found in Malaysia. A dataset of these mushrooms was curated, augmented, and processed through multiple variants of Vision Transformers (ViTs) and ResNet models, with uniform hyperparameters to ensure fairness. The results indicate that the ViT-L/16 model achieved the highest accuracy at 90.47%

Item Type:	Article
Uncontrolled Keywords:	Mushroom, Machine Learning and Image Processing
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics S Agriculture > SB Plant culture
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Sep 2024 09:01
Last Modified:	02 Sep 2024 09:01
URII:	http://shdl.mmu.edu.my/id/eprint/12934

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