Development of Invasive Plant Recognition System Based on Deep Learning

Yang, Zhuolei and Fan, Zheming and Niu, Chenyu and Li, Peixin and Zhong, Hongjie (2023) Development of Invasive Plant Recognition System Based on Deep Learning. Journal of Advances in Mathematics and Computer Science, 38 (6). pp. 39-53. ISSN 2456-9968

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Abstract

A major ecological issue that has seriously harmed both human society and the environment is the invasion of alien plants. To stop the invasion of alien plants, it is crucial to create an effective and precise monitoring and early warning system. In this situation, deep learning and computer vision have significant potential to enhance plant monitoring on a wide scale. This study suggests a deep learning-based approach for identifying invasive plants. The user interface is developed as a mobile application (APP). The identification result can be acquired in 1 to 2 seconds after downloading the plant image from the APP, uploading it to the server, and using the convolutional neural network (CNN). The system had an average accuracy of 90.39% on the test set thanks to data augmentation and enhanced networks. The deep learning-based invasive plant identification system created in this study has demonstrated through experiments that it may effectively support botanical research and ecological environment monitoring.

Item Type: Article
Subjects: GO for STM > Mathematical Science
Depositing User: Unnamed user with email support@goforstm.com
Date Deposited: 04 Apr 2023 10:06
Last Modified: 05 Feb 2024 04:23
URI: http://archive.article4submit.com/id/eprint/489

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