Year: 2024
Type: article
Abstract: To meet the demands for rapid and accurate appearance inspection in potato sorting, this study proposes a potato appearance detection algorithm based on an improved version of YOLOv8. MobileNetV4 is employed to replace the YOLOv8 backbone network, and a triple attention mechanism is introduced to the neck network along with the Inner-CIoU loss function to accelerate convergence and enhance the accuracy of potato appearance detection. Experimental results demonstrate that the proposed YOLOv8 model achieves precision, recall, and mean average precision of 91.4%, 87.7%, and 93.7% respectively on the test set. Compared to YOLOv5s, YOLOv7tiny, and the original base network, it exhibits minimal memory usage while improving the [email protected] by 1.1, 0.9, and 0.3 percentage points respectively, providing a reference for potato quality inspection.
Source: INMATEH Agricultural Engineering
Authors: Huan ZHANG, Zhen LIU, Ranbing YANG, Zhiguo PAN, Zhaoming SU, Xinlin LI, Zeyang LIU, Chuanmiao SHI, Shuai WANG, Hongzhu WU
Institutions: Qingdao Agricultural University, Hainan University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4405666602