外文摘要：In the broiler-breeding industry, phenotype determination is critical. Leg weight is a fundamental indicator for breeding, and noninvasive testing technology can reduce damage to animals. This study proposes a broiler leg weight estimation system comprising a weight-estimation model and computed tomography (CT) acquisition equipment. The weight-estimation model can automatically process the scan results of live broiler chickens from the CT acquisition equipment. The weight-estimation model comprises an improved youonly-look-once (YOLOv5) segmentation algorithm and a random forest fitting network. The segmentation head was introduced into the YOLOv5 network, combined with a multiscale attention mechanism and an atrous spatial pyramid pooling architecture, and a new network model, YOLO- measuring chicken leg weight (YOLO- MCLW), was proposed to improve segmentation effi- ciency and accuracy. Morphological parameters were extracted from the obtained mask image, and a random forest network was used for fitting. The experiments show that the system exhibited an average absolute error of 7.27 g and an average percentage error of 4.82% in tests on 50 individual legs of 25 broiler chickens. The prediction R2 of broiler chicken legs can reaches 88.98%, the segmentation intersection over union result reaches 95.45%, and 37.04 images are processed per second. This system provides technical support for the part determination of broiler chickens in commercial breeding.
外文关键词：deep learning;computed tomography;weight estimation;phenotype determination
作者：Sun, Ruizhi;Sun, Shulin;Wei, Lei;Chen, Zeqiu;Chai, Yinqian;Wang, Shufan
作者单位：China Agr Univ;Minist Agr
期刊名称：POULTRY SCIENCE
期刊影响因子：0.0
出版年份：2024
出版刊次：103（4）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台