Automatic plant recognition using convolutional neural network on malaysian medicinal herbs: the value of data augmentation

Author:

Roslan Noor Aini Mohd,Diah Norizan Mat,Ibrahim Zaidah,Munarko Yuda,Minarno Agus Eko

Abstract

Herbs are an important nutritional source for humans since they provide a variety of nutrients. Indigenous people have employed herbs, in particular, as traditional medicines since ancient times. Malaysia has hundreds of plant species; herb detection may be difficult due to the variety of herb species and their shape and color similarities. Furthermore, there is a scarcity of support datasets for detecting these plants. The main objective of this paper is to investigate the performance of convolutional neural network (CNN) on Malaysian medicinal herbs datasets, real data and augmented data. Malaysian medical herbs data were obtained from Taman Herba Pulau Pinang, Malaysia, and ten kinds of native herbs were chosen. Both datasets were evaluated using the CNN model developed throughout the research. Overall, herbs real data obtained an average accuracy of 75%, whereas herbs augmented data achieved an average accuracy of 88%. Based on these findings, herbs augmented data surpassed herbs actual data in terms of accuracy after undergoing the augmentation technique.

Publisher

Universitas Ahmad Dahlan

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Overcoming Data Limitations in Thai Herb Classification with Data Augmentation and Transfer Learning;Journal of Advanced Computational Intelligence and Intelligent Informatics;2024-05-20

2. A Systematic Review of Medicinal Plant Identification Using Deep Learning;Lecture Notes in Computer Science;2024

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