Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA.
Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA; Department of Animal Science, The University of Tennessee, Knoxville, TN 37996, USA.
Animal. 2021 Jan;15(1):100059. doi: 10.1016/j.animal.2020.100059. Epub 2020 Dec 10.
Stretching behavior is one of the broiler comfort behaviors that could be used for animal welfare assessment. However, there is currently no methodology for automatic monitoring of stretching behavior under representative production practices. The objectives of this study were to (1) develop a faster region-based convolutional neural network (faster R-CNN) stretching behavior detector for broiler stretching behavior detection, (2) evaluate broiler stretching behaviors under stocking densities (SDs) of 27 (27SD), 29 (29SD), 33 (33SD), and 39 kg/m (39SD) and at weeks 4 and 5 of bird ages, and (3) examine the temporal and spatial distribution of broiler stretching behaviors. The results show that the precision, recall, specificity, and accuracy were over 86% on broiler stretching detection across all SDs and bird ages using the faster R-CNN stretching behavior detector. Broilers spent 230-533 sec stretching every day and showed more stretching behaviors under the 29SD, 33SD, and 39SD in week 4 and under the 29SD and 33SD in week 5, as compared to other SDs. They performed less stretching in a couple of hours after light ON and before light OFF but preferred to stretch in areas with less traffic and disturbance, that is, along the fences and away from the inspection aisle. It is concluded that the stretching behavior detector had acceptable performance in detecting broiler stretching, thus being a useful tool for broiler stretching detection. Broiler stretching behavior is affected by SD and bird age and shows temporal and spatial variations.
伸展行为是肉鸡舒适度行为之一，可用于动物福利评估。然而，目前尚无在代表性生产实践下自动监测伸展行为的方法。本研究的目的是：(1)开发一种更快的基于区域的卷积神经网络（faster R-CNN）肉鸡伸展行为探测器，用于检测肉鸡伸展行为；(2)评估在饲养密度（SD）为 27（27SD）、29（29SD）、33（33SD）和 39kg/m（39SD）以及在 4 周和 5 周龄时肉鸡的伸展行为；(3)研究肉鸡伸展行为的时空分布。结果表明，在所有 SD 和鸟类年龄下，使用更快的 R-CNN 伸展行为探测器对肉鸡伸展检测的精度、召回率、特异性和准确性均超过 86%。肉鸡每天伸展 230-533 秒，在第 4 周，29SD、33SD 和 39SD 以及第 5 周，29SD 和 33SD 下，比其他 SD 下表现出更多的伸展行为。与其他 SD 相比，它们在光照开启后和关闭前的几个小时内伸展较少，但更喜欢在交通和干扰较少的区域伸展，即在围栏附近和远离检查通道。研究结论是，伸展行为探测器在检测肉鸡伸展行为方面具有良好的性能，因此是一种用于肉鸡伸展检测的有用工具。肉鸡的伸展行为受 SD 和鸟类年龄的影响，并表现出时间和空间上的变化。
