Suppr超能文献

利用机器视觉技术追踪笼养蛋鸡的沙浴行为。

Tracking dustbathing behavior of cage-free laying hens with machine vision technologies.

作者信息

Paneru Bidur, Bist Ramesh, Yang Xiao, Chai Lilong

机构信息

Department of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.

Department of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.

出版信息

Poult Sci. 2024 Dec;103(12):104289. doi: 10.1016/j.psj.2024.104289. Epub 2024 Aug 31.

Abstract

Dustbathing (DB) is a functionally important maintenance behavior in birds that clean plumage, realigns feather structures, removes feather lipids, which helps to remove parasites and prevents feathers from becoming too oily. Among different natural behaviors birds perform in cage-free (CF) housing, DB is one of the important behavior related to bird welfare. Earlier studies have identified DB behavior using manual method such as counting number of DB bouts, and duration of DB bouts from video recordings. The manual detection of DB behavior is time-consuming, sometimes prone to errors, and have limitations. Therefore, an automated precision monitoring method is needed to detect DB behavior in laying hens from an early age in CF housing environment. The objectives of this study were to (1) develop and test a deep learning model for detecting DB behavior and find out the optimal model; and (2) assess the performance of the optimal model in detecting DB behavior at different growing phases. In this study, deep learning models, i.e., YOLOv7-DB, YOLOv7x-DB, YOLOv8s-DB and YOLOv8x-DB, networks, were developed, trained, and compared in tracking DB behavior in 4 CF rooms each with 200 hens (W-36 Hy-Line). Results indicate that the YOLOv8x-DB model outperform all other models on detecting DB behavior with a precision of 93.4%, recall of 91.20%, and mean average precision (mAP@0.50) of 93.70%. All models performed with over 90% detection precision; however, model performance was affected by equipment like drinking lines, perches, and feeders. Based on the optimal model (YOLOv8x-DB), DB detection precision was highest during grower phase (precision of 96.80%, recall of 97.10%, mAP@0.50 of 98.60%, and mAP@0.50-0.95 of 79.10% followed by prelay, layers, developer, and peaking phases. This study provides a reference for poultry and egg producers that DB behavior can be detected automatically with precision of at least 89% or more using optimal model at any growing phase of laying hens.

摘要

沙浴是鸟类一项具有重要功能的维持行为,它能清洁羽毛、重新排列羽毛结构、去除羽毛脂质,有助于清除寄生虫并防止羽毛过于油腻。在笼养(CF)环境中鸟类所表现出的不同自然行为中,沙浴是与鸟类福利相关的重要行为之一。早期研究通过手动方法识别沙浴行为,例如从视频记录中统计沙浴次数和沙浴时长。手动检测沙浴行为耗时且有时容易出错,存在局限性。因此,需要一种自动化的精确监测方法来在笼养环境中从雏鸡阶段开始检测蛋鸡的沙浴行为。本研究的目的是:(1)开发并测试用于检测沙浴行为的深度学习模型并找出最优模型;(2)评估最优模型在不同生长阶段检测沙浴行为的性能。在本研究中,开发、训练并比较了深度学习模型,即YOLOv7-DB、YOLOv7x-DB、YOLOv8s-DB和YOLOv8x-DB网络,用于跟踪4个笼养房间中每个房间200只母鸡(W-36海兰鸡)的沙浴行为。结果表明,YOLOv8x-DB模型在检测沙浴行为方面优于所有其他模型,其精确率为93.4%,召回率为91.20%,平均精度均值(mAP@0.50)为93.70%。所有模型的检测精确率均超过90%;然而,模型性能受到饮水管、栖木和喂食器等设备的影响。基于最优模型(YOLOv8x-DB),育成期沙浴检测精确率最高(精确率为96.80%,召回率为97.10%,mAP@0.50为98.60%,mAP@0.50-0.95为79.10%),其次是预产期、产蛋期、发育期和高峰期。本研究为家禽和蛋类生产者提供了参考,即在蛋鸡的任何生长阶段,使用最优模型可以精确地自动检测沙浴行为,精确率至少达到89%或更高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51f1/11426045/b309257a4f89/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验