Mobile Application for Medicinal Plants Recognition from Leaf Image Using Convolutional Neural Network

Sugiarto, David and Siswantoro, Joko and Naufal, Mohammad Farid and Idrus, Bahari (2023) Mobile Application for Medicinal Plants Recognition from Leaf Image Using Convolutional Neural Network. Indonesian Journal of Information Systems (IJIS), 5 (2). pp. 43-56. ISSN 2623-0119; E-ISSN 2623-2308

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Abstract

Indonesia is a country that has thousands of plant types that can be used as traditional medicine. However, some people have not utilized this potential optimally due to the lack of knowledge about medicinal plants' types, benefits, and substances. Therefore, there is a need to develop an application that can identify medicinal plants that grow in Indonesia and provide information about the benefits and content of the substances contained in them. In this study, medicinal plants will be recognized using a mobile application from leaf images based on a pre-trained convolutional neural network (CNN) with a transfer learning technique. Three pre-trained CNN architectures, namely VGG-16, MobileNetV2, and DenseNet-121, are explored for medicinal plant recognition. Hyperparameter tuning is performed at the fully connected layer of all architectures with 20 possible modifications to find the best model. The experimental results on 24 types of medicinal plants show that the model based on MobileNetV2 achieves the best classification accuracy of 97.74%. The best model is obtained by modifying the fully connected layer of MobileNetV2 into three dense layers with the number of neurons 736, 448, and 928, respectively. After the application recognizes the types of medicinal plants, information about the benefits and substances contained in them is displayed to the user.

Item Type:	Article
Uncontrolled Keywords:	medicinal plants recognition; leaf image; convolutional neural network; transfer learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Joko Siswantoro
Date Deposited:	24 Feb 2023 02:02
Last Modified:	11 Oct 2024 09:21
URI:	http://repository.ubaya.ac.id/id/eprint/43490

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