Monitoring the ramp use of cage-free laying hens with deep learning technologies.

IF 4.2 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Bidur Paneru, Xiao Yang, Anjan Dhungana, Samin Dahal, Casey W Ritz, Woo Kim, Tianming Liu, Lilong Chai
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引用次数: 0

Abstract

Mislaid eggs are management and economic challenges in Cage-free (CF) housing systems, producing about 10-15 % floor eggs, although approximately 40 % of table egg laying hens are now in CF production systems. Ramps may ease the use of nesting boxes by minimizing physical extension to hens, thereby reducing floor eggs, and increasing the number of eggs laid in the nest box. The objective of our study was to develop a deep learning method for monitoring hens' ramp use and the potential impact of ramp access to the nest box on the number of floor eggs and the number of eggs laid in the nest boxes. A total of 600 Lohmann LSL Lite hens were raised from day 1 to 413 in three identical research rooms (7.3 m L × 6.1 m W × 3 m H) following the Lohmann LSL Lite management guide. Each study room had four nest boxes placed at the four corners of the room. Two nest boxes were provided with ramp access (R), and two nest boxes had no ramp access (NR) in each study room and were replicated among three study rooms. The ramp use was video recorded at 15 frames per second (fps). We trained two You Only Look Once (YOLO) models, YOLOv5u and YOLO11, object detection models for 200 epochs each. A total of 2,000 images were used for training (70 %), validation (20 %), and testing (10 %) for the model. All models achieved a precision, recall, and mean average precision at 0.50 intersection over union (mAP@0.50) of at least 0.94. YOLO11n(nano) achieved the highest precision (0.9940), recall (0.9934), and mAP@0.50 (0.9848). Our best model provides a baseline for automatic ramp use detection with 0.99 precision. Ramp access did not lower the floor egg production statistically (p = 0.5468). Across bird weeks, ramp access (R) and no ramp access (NR) revealed opposite patterns in floor and nest-box egg production. Ramp access generally resulted in significantly higher floor egg production in several weeks (52, 54, 58), but also produced higher nest-box egg counts in other weeks (49, 51, 52, 54, 56, 58), whereas no-ramp access showed more nest-box access in weeks when ramp access floor egg were high (48, 50, 53, 55, 57, 59). Overall, ramp access did not consistently increase or decrease egg laying in either location, but shifted the proportion of eggs between the floor and nest-boxes depending on bird age (week). Future studies are warranted to investigate the effect of ramp use on nesting behavior and floor eggs from egg laying to the end of the laying cycle. Data on floor eggs and nest box eggs with ramp access to the nest box from commercial aviary systems, as well as the CF system, also need to be compared.

利用深度学习技术监测散养蛋鸡的坡地使用情况。
错产蛋是无笼(CF)饲养系统的管理和经济挑战,尽管目前约有40%的桌上蛋鸡采用无笼(CF)饲养系统,但仍产生约10- 15%的地板蛋。坡道可以通过最大限度地减少母鸡的物理延伸来减轻巢箱的使用,从而减少地板上的鸡蛋,并增加在巢箱中产下的鸡蛋数量。本研究的目的是开发一种深度学习方法,用于监测母鸡对坡道的使用情况,以及坡道进入窝箱对地板鸡蛋数量和窝箱中鸡蛋数量的潜在影响。按照罗曼LSL Lite管理指南,从第1天到第413天,在3个相同的研究室内(7.3 m L × 6.1 m W × 3 m H)饲养600只罗曼LSL Lite母鸡。每个自习室都有四个巢箱,放在房间的四个角落。每个自习室设置2个有坡道通道(R)的巢箱和2个没有坡道通道(NR)的巢箱,并在3个自习室中复制。坡道的使用以每秒15帧(fps)的速度进行视频记录。我们训练了两个YOLO (You Only Look Once)模型YOLOv5u和YOLO11,这两个模型分别训练了200 epoch的目标检测模型。总共有2000张图像用于模型的训练(70%)、验证(20%)和测试(10%)。所有模型的精度、召回率和平均平均精度在0.50交叉点上达到至少0.94 (mAP@0.50)。YOLO11n(nano)获得了最高的精密度(0.9940)、召回率(0.9934)和mAP@0.50(0.9848)。我们最好的模型为0.99精度的自动斜坡使用检测提供了基线。坡道饲喂并未显著降低蛋鸡的产蛋量(p = 0.5468)。在整个鸟周内,坡道通道和无坡道通道在地面和巢箱产蛋方面表现出相反的模式。坡道通道通常会在几周内显著提高地面产蛋量(52,54,58),但在其他周(49,51,52,54,56,58)也会产生更高的巢箱蛋数,而无坡道通道在坡道通道地面产蛋量高的周(48,50,53,55,57,59)显示更多的巢箱卵。总体而言,坡道通道并没有持续地增加或减少产卵量,而是根据鸟龄(周)在地板和巢箱之间改变了蛋的比例。未来的研究需要进一步研究坡道对产卵行为和产蛋周期结束时的地板蛋的影响。还需要比较从商业鸟舍系统和CF系统进入巢箱的地板蛋和巢箱蛋的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Poultry Science
Poultry Science 农林科学-奶制品与动物科学
CiteScore
7.60
自引率
15.90%
发文量
0
审稿时长
94 days
期刊介绍: First self-published in 1921, Poultry Science is an internationally renowned monthly journal, known as the authoritative source for a broad range of poultry information and high-caliber research. The journal plays a pivotal role in the dissemination of preeminent poultry-related knowledge across all disciplines. As of January 2020, Poultry Science will become an Open Access journal with no subscription charges, meaning authors who publish here can make their research immediately, permanently, and freely accessible worldwide while retaining copyright to their work. Papers submitted for publication after October 1, 2019 will be published as Open Access papers. An international journal, Poultry Science publishes original papers, research notes, symposium papers, and reviews of basic science as applied to poultry. This authoritative source of poultry information is consistently ranked by ISI Impact Factor as one of the top 10 agriculture, dairy and animal science journals to deliver high-caliber research. Currently it is the highest-ranked (by Impact Factor and Eigenfactor) journal dedicated to publishing poultry research. Subject areas include breeding, genetics, education, production, management, environment, health, behavior, welfare, immunology, molecular biology, metabolism, nutrition, physiology, reproduction, processing, and products.
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