Monitoring Behaviors of Broiler Chickens at Different Ages with Deep Learning

Abstract

Animal behavior monitoring allows the gathering of animal health information and living habits and is an important technical means in precision animal farming. To quickly and accurately identify the behavior of broilers at different days, we adopted different deep learning behavior recognition models. Firstly, the top-view images of broilers at 2, 9, 16 and 23 days were obtained. In each stage, 300 images of each of the four broilers behaviors (i.e., feeding, drinking, standing, and resting) were segmented, totaling 4800 images. After image augmentation processing, 10,200 images were generated for each day including 8000 training sets, 2000 validation sets, and 200 testing sets. Finally, the performance of different convolutional neural network models (CNN) in broiler behavior recognition at different days was analyzed. The results show that the overall performance of the DenseNet-264 network was the best, with the accuracy rates of 88.5%, 97%, 94.5%, and 90% when birds were 2, 9, 16 and 23 days old, respectively. In addition, the efficient channel attention was introduced into the DenseNet-264 network (ECA-DenseNet-264), and the results (accuracy rates: 85%, 95%, 92%, 89.5%) confirmed that the DenseNet-264 network was still the best overall. The research results demonstrate that it is feasible to apply deep learning technology to monitor the behavior of broilers at different days.

Interindividual distances and orientations of laying hens under 8 stocking densities measured by integrative deep learning techniques

“…The testing performance (accurate and false classification rates) is presented in Table 2 , and the developed classifier finally achieved 99.5% or more accuracy for classifying frames with perching or without perching. Guo et al (2022) also established up to 97% accuracy for classifying broiler behaviors (e.g., drinking, feeding, resting, and standing) at different bird ages by using cropped images. One of the reasons lies in the fact that modern deep learning image classification algorithms are advantageous to understand nonlinear patterns in classification problems ( Li et al, 2021a ).…”

“…Multiple deep learning-based computer vision tasks have been and are increasingly researched to improve efficiency and automation in animal production. Image classification is one of the tasks that can assign an input image to a predefined category or label and was used to identify individual beef cattle ( Li et al, 2022b ) and monitor broiler behaviors at different ages ( Guo et al, 2022 ). Object detection is a vision task that provides a horizontal rectangular bounding box to identify location and extent of each predefined object in an image and was used to detect pecking behaviors of laying hens ( Subedi et al, 2023 ), measure comfort behavior of laying hens ( Sozzi et al, 2023 ), and mark genders of the hemp ducks ( Zheng et al, 2022 ).…”

An enhancement algorithm for head characteristics of caged chickens detection based on cyclic consistent migration neural network

“…With the development of science and technology, an increasing number of researchers are leveraging computer vision technology in the field of poultry breeding, leading to significant advancements ( Wu et al, 2022 ). Such as, Guo et al (2022) developed a convolutional neural network models ( CNN ) to monitor chicken behaviors (i.e., feeding, drinking, standing, and resting). Chen et al (2023) proposed an approach based on deep learning to develop an automatic warning system for anomalous dispersion and movement of chicken flocks in commercial chicken farms.…”

Automatic detection of brown hens in cage-free houses with deep learning methods

“…Computer vision technologies ( CVTs ) have been applied to monitor animals’ behaviors and performance ( Guo et al, 2020 , 2021a , 2021b , 2022 ; Qiao et al, 2022 ; Li et al, 2021 a; Bist et al, 2023a ). The CVT-based methods can avoid human observation errors and monitor animal information quickly, accurately, and efficiently which is of great significance to the large-scale poultry production environments.…”