A high-precision measuring phenotypic characteristics method for juvenile fish

作者：Xueqian Sun, Jingsen Zhang, Yihan Yin, Suping Liu, Daoliang Li, Yang Wang · 发表于：Smart Agricultural Technology · 年份：2025 · DOI：10.1016/j.atech.2025.101581 · 被引用次数：1 · 研究领域：Water Quality Monitoring Technologies、Fish Biology and Ecology Studies、Fish Ecology and Management Studies

• Developed YOLOv11-CCPose by integrating SMAFormer and ContextGuideFPN with YOLOv11. • Proposed a novel multi-segment measurements method to measure fish with different bending shapes • Integrated a juveniles biomass estimation system with high-precision detection. • It achieved average measurement errors of 1.36%, 3.04%, and 4.21% for length, height, and weight. The rapid swimming and frequent posture changes of juvenile fish have brought great difficulties to the accurate measurement phenotypic characteristics (e.g., length, height, and weight) based on machine vision. This study proposed a novel measurement method for phenotypic characteristic parameters of juvenile spotted knifejaw ( Oplegnathus punctatus ), which combined keypoint detection (YOLOv11-CCPose), multi-segment measurement and weight estimation model. Firstly, ContextGuideFPN module was incorporated in YOLOv11-CCPose to enhance the feature extraction capability for juveniles. And the feature extraction module (C3K2) was improved through the integration of SMAFormer and Convolutional Gated Linear Units (CGLU), significantly improving the accuracy of identifying features. Compared with baseline model, YOLOv11-CCpose achieved improvements of 3.2 % in precision, 2.7 % in recall, 1.8% in mAP@0.5, and 1.6 % in mAP@0.5:0.95. Therefore, it markedly strengthening the multi-scale feature extraction capacity for fast-swimming juveniles with various postures. Secondly, a novel multi-segment measurements method was propos...