基于深度学习算法的凡纳滨对虾生长表型测定系统研发及应用
Development and application of a deep learning algorithm-based growth phenotypes measurement system of the Pacific white shrimp(Litopenaeus vannamei)
摘要
Abstract
To address the low efficiency and high error rates associated with manual measurement of growth phenotypes in the Pacific white shrimp(Litopenaeus vannamei),this study developed a dedicated image acquisition box capable of capturing standardized,high-quality side-view images of the shrimp.Utilizing this system,a High-Resolution Network(HRNet)model was employed to identify nine key feature points of the shrimp,enabling the measurement of traits such as body length.Addi-tionally,a Mask Region Convolutional Neural Network(Mask R-CNN)model was utilized for shrimp contour segmentation to calculate body surface area.Regression models incorporating body length and body surface area were subsequently developed to predict body weight.An integrated image processing and data management software was also developed to establish a pre-cise measurement system for the growth phenotypes of L.vannamei.The study found that the HRNet model achieved recogni-tion rates exceeding 98％for all nine feature points,with rates exceeding 99％for seven points.The true values of body length and abdominal segment length were measured using two methods:manual measurement with a ruler and measurement from manually tagged feature points in the images.The predictive accuracy of body length and abdominal segment length was calcu-lated to be 0.91-0.97 and 0.91-0.93,respectively,with average relative errors of 1.39％-4.63％and 2.46％-4.59％.Evaluation against manually segmented shrimp body contours showed that the Mask R-CNN model predicted body surface area with an accuracy of 0.98 and an average relative error of 1.73％.Regression models incorporating variables such as body length,body surface area,and gender were developed to predict body weight,achieving accuracies above 0.94,with the model incorporating both body length and body surface area achieving the highest prediction accuracy(0.97).These results demonstrate that com-puter vision technology combined with deep learning algorithms can accurately measure growth phenotypes,such as body length and body surface area,and predict body weight L.vannamei.This study provides an efficient tool for the accurate and rapid measurement of growth phenotypes in L.vannamei.关键词
凡纳滨对虾/生长表型/深度学习/计算机视觉/测定系统Key words
Litopenaeus vannamei/growth phenotypes/deep learning/computer vision/measurement system分类
农业科技引用本文复制引用
张士薇,陈宝龙,李旭鹏,强光峰,邢群,戚云辉,孔杰,栾生,代平,高广春,孟宪红,罗坤,隋娟,谭建,傅强,曹家旺..基于深度学习算法的凡纳滨对虾生长表型测定系统研发及应用[J].水产学报,2025,49(5):196-208,13.基金项目
国家重点研发计划(2022YFD2400202) （2022YFD2400202）
财政部和农业农村部:国家现代农业产业技术体系专项(CARS-48) （CARS-48）
中国水产科学研究院科技创新团队项目(2020TD26) （2020TD26）
泰山学者工程 （）
广东省"十四五"农业科技创新十大主攻方向揭榜挂帅项目(2022SDZG01) （2022SDZG01）
山东省科技型中小企业创新能力提升工程项目(2023TSGC0744) National Key Research and Development Program of China(2022YFD2400202) （2023TSGC0744）
China Agriculture Research System of MOF and MARA(CARS-48) （CARS-48）
Central Public-interest Scientific Institution Basal Research Fund,CAFS(2020TD26) （2020TD26）
Taishan Scholars Program （）
Open Competition Program of Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province(2022SDZG01) （2022SDZG01）
Shandong Province Science and Technology-oriented Small and Medium-sized Enterprise Innovation Capacity Enhancement Project(2023TSGC0744) （2023TSGC0744）
