YOLO-Sp: A Novel Transformer-Based Deep Learning Model for Achnatherum splendens Detection

Yuzhuo Zhang, Tianyi Wang (), Yong You, Decheng Wang, Dongyan Zhang, Yuchan Lv, Mengyuan Lu and Xingshan Zhang
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Yuzhuo Zhang: College of Engineering, China Agricultural University, Beijing 100083, China
Tianyi Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Yong You: College of Engineering, China Agricultural University, Beijing 100083, China
Decheng Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Dongyan Zhang: National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
Yuchan Lv: College of Engineering, China Agricultural University, Beijing 100083, China
Mengyuan Lu: College of Engineering, China Agricultural University, Beijing 100083, China
Xingshan Zhang: College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2023, vol. 13, issue 6, 1-18

Abstract: The growth of Achnatherum splendens ( A. splendens ) inhibits the growth of dominant grassland herbaceous species, resulting in a loss of grassland biomass and a worsening of the grassland ecological environment. Therefore, it is crucial to identify the dynamic development of A. splendens adequately. This study intended to offer a transformer-based A. splendens detection model named YOLO-Sp through ground-based visible spectrum proximal sensing images. YOLO-Sp achieved 98.4% and 95.4% AP values in object detection and image segmentation for A. splendens , respectively, outperforming previous SOTA algorithms. The research indicated that Transformer had great potential for monitoring A. splendens . Under identical training settings, the AP value of YOLO-Sp was greater by more than 5% than that of YOLOv5. The model’s average accuracy was 98.6% in trials conducted at genuine test sites. The experiment revealed that factors such as the amount of light, the degree of grass growth, and the camera resolution would affect the detection accuracy. This study could contribute to the monitoring and assessing grass plant biomass in grasslands.

Keywords: Achnatherum splendens; deep learning; grassland environment; image segmentation; transformer model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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