Quantifying Nesting Behavior Metrics of Broiler Breeder Hens with Computationally Efficient Image Processing Algorithms and Big Data Analytics

Abstract

Nesting behaviors are important to understand facility design, resource allowance, animal welfare, and the health of broiler breeder hens. How to automatically extract informative nesting behavior metrics of broiler breeder hens remains a question. The objective of this work was to quantify the nesting behavior metrics of broiler breeder hens using computationally efficient image algorithms and big data analytics. Here, 20 broiler breeder hens and 1–2 roosters were raised in an experimental pen, and four pens equipped with six-nest-slot nest boxes were used for analyzing the nesting behaviors of broiler hens over the experimental period. Cameras were installed on the top of the nest boxes to monitor the hens’ behaviors, such as the time spent in the nest slot, frequency of visits to the nest slot, simultaneous nesting pattern, hourly time spent by the hens in each nest slot, and time spent before and after feed withdrawal, and videos were continuously recorded for nine days for nine hours a day when the hens were 56 weeks of age. Image processing algorithms, including template matching, thresholding, and contour detection, were developed and applied to quantify the hen nesting behavior metrics frame by frame. The results showed that the hens spent significantly different amounts of time and frequencies in different nest slots (p < 0.001). A decrease in the time spent in all nest slots from 1 pm to 9 pm was observed. The nest slots were not used 60.1% of the time. Overall, the proposed method is a helpful tool to quantify the nesting behavior metrics of broiler breeder hens and support precision broiler breeder management.

Keywords

• poultry
• automatic behavior recognition
• animal welfare
• artificial intelligence