An algorithmic model for recognizing healthy wheat seeds based on YOLOv8

doi:10.1117/12.3031325

	An algorithmic model for recognizing healthy wheat seeds based on YOLOv8
	Wei, Dong; Liang, Xiyin
	2024
会议名称	3rd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024
会议录名称	Proceedings of SPIE - The International Society for Optical Engineering
卷号	13181
会议日期	January 26, 2024 - January 28, 2024
会议地点	Beijing, China
会议录编者/会议主办者	Academic Exchange Information Centre (AEIC)
出版者	SPIE
摘要	Seed quality is a key factor in wheat germination rate, total yield and other indicators, and is of great importance for food security. For a long time, wheat seed selection and breeding have been heavily dependent on manual experience selection, which is characterized by high labor costs, unstable detection accuracy and high subjectivity. In this paper, we propose a machine vision recognition algorithm model WSEED-YOLOV8 for healthy wheat seeds improved from YOLO V8, which significantly improves the model's ability to extract features for wheat seed categories and whether wheat seeds suffer from Sunn Pest (Eurygaster integriceps) damage by introducing Deformable Convolution V2 (DCNv2) and encapsulating it in the C2f_DCNv2 module instead of the second, third and fourth C2f modules in YOLO V8n. By introducing the Polarized Self Attention mechanism module, with a slight increase in the parameters, it reduces the omission of false detections in the case of dense stacking of wheat seeds in the same image; and by using the Shape Loss Shape-IoU function as the IoU calculation function of the bounding box, which improves the target detection ability in the case of tilted wheat placement. The experimental results show that the WSEED-YOLOV8 wheat healthy seed recognition algorithm model achieves a recognition precision of 95% on the same wheat seed dataset, which is 15.9% higher than the YOLO V8n baseline precision. An 11% improvement in recognition precision was achieved with a similar number of parameters as the YOLO V8s baseline. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
关键词	Food supply Image enhancement Seed Wages Algorithm model Algorithmic model Deformable convolution v2 Germination rates Key factors Recognition algorithm Seed quality Sunn pest Wheat seeds YOLO v8
DOI	10.1117/12.3031325
收录类别	EI
语种	英语
EI入藏号	20243216833544
EI主题词	Image recognition
EI分类号	821.4 Agricultural Products ; 822.3 Food Products ; 912.4 Personnel
原始文献类型	Conference article (CA)
EISSN	1996-756X
ISSN	0277-786X
文献类型	会议论文
条目标识符	https://ir.nwnu.edu.cn/handle/39RV6HYL/98796
专题	实体学院_物理与电子工程学院
通讯作者	Liang, Xiyin
作者单位	College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou; 730070, China
第一作者单位	物理与电子工程学院
通讯作者单位	物理与电子工程学院
第一作者的第一单位	物理与电子工程学院
推荐引用方式 GB/T 7714	Wei, Dong,Liang, Xiyin. An algorithmic model for recognizing healthy wheat seeds based on YOLOv8[C]//Academic Exchange Information Centre (AEIC):SPIE,2024.