Automated measurement of broiler stretching behaviors under four stocking densities via faster region-based convolutional neural network

Abstract

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/m² (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.

Keywords: Behavior; Deep learning; Management practice; Poultry; Welfare.