A YOLOv8n-T and ByteTrack-Based Dual-Area Tracking and Counting Method for Cucumber Flowers
Year: 2025
Type: article
Abstract: Accurate counting of cucumber flowers using intelligent algorithms to monitor their sex ratio is essential for intelligent facility agriculture management. However, complex greenhouse environments impose higher demands on the precision and efficiency of counting algorithms. This study proposes a dual-area counting algorithm based on an improved YOLOv8n-Track (YOLOv8n-T) and ByteTrack cascaded framework. This method accomplishes the cucumber flower counting task by detecting flower targets, tracking them frame-by-frame, and validating the count through dual-area counting. The YOLOv8n-T incorporates a Coordinate Attention (CA) mechanism and lightweight modules while optimizing the loss function, thereby improving floral feature extraction capabilities and reducing computational complexity. By integrating the ByteTrack tracking algorithm with a dual-area counting strategy, the robustness of flower counting in dynamic environments is strengthened. Experimental results show that the improved YOLOv8n-T achieves mAP and F1 scores of 86.9% and 82.1%, surpassing YOLOv8n by 3% and 2.6%, respectively, with a 0.3 G reduction in model parameters. The integrated framework achieves a detection accuracy of 82.4% for cucumber flower counting. This research provides a new method for monitoring cucumber flower sex ratios in facility agriculture, promoting the development of intelligent agricultural management.
Source: Agronomy
Authors: Liyang Su, Shujuan Zhang, Hongtu Zhang, Xiangsen Meng, Xiongkui He
Institutions: China Agricultural University, Shanxi Agricultural University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,137
Funder: China Agricultural University
OpenAlex ID: W4412512958