Research on the Detection of Stellera Chamaejasme Flower Based on Deep Learning

Jieteng Jiang, Shuo Dong, Chunmei Li, Yihan Ma

2022

Abstract

Under the dual impact of global climate change and human activities in recent decades, the grassland vegetation in the Three-River Source area is seriously degraded, and accurate evaluation of grassland is the primary condition for ecological protection. Therefore, using intelligent means to evaluate grassland is the first step in ecological protection. In this paper, the most widely used target detection algorithms FasterRCNN, SSD and Yolov3-SPP are used to detect the degradation indicator grass species of Stellera chamaejasme flower. The experimental results are compared and analyzed, and the characteristics of the three target detection algorithms and their performance in the detection of degraded indicator grass species are discussed.

Paper Citation

in Harvard Style

Jiang J., Dong S., Li C. and Ma Y. (2022). Research on the Detection of Stellera Chamaejasme Flower Based on Deep Learning. In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI; ISBN 978-989-758-620-0, SciTePress, pages 824-830. DOI: 10.5220/0011768400003607

in Bibtex Style

@conference{icpdi22,
author={Jieteng Jiang and Shuo Dong and Chunmei Li and Yihan Ma},
title={Research on the Detection of Stellera Chamaejasme Flower Based on Deep Learning},
booktitle={Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI},
year={2022},
pages={824-830},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011768400003607},
isbn={978-989-758-620-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI
TI - Research on the Detection of Stellera Chamaejasme Flower Based on Deep Learning
SN - 978-989-758-620-0
AU - Jiang J.
AU - Dong S.
AU - Li C.
AU - Ma Y.
PY - 2022
SP - 824
EP - 830
DO - 10.5220/0011768400003607
PB - SciTePress