上传
深度学习
卷积神经网络
计算机科学
鉴定(生物学)
人工智能
预警系统
比例(比率)
机器学习
人机交互
生态学
地理
万维网
地图学
电信
生物
作者
Zhuolei Yang,Zheming Fan,Chenyu Niu,Pei-Xin Li,Hongjie Zhong
出处
期刊:Journal of advances in mathematics and computer science
[Sciencedomain International]
日期:2023-03-29
卷期号:38 (6): 39-53
被引量:2
标识
DOI:10.9734/jamcs/2023/v38i61767
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI