Monitoring Behaviors of Broiler Chickens at Different Ages with Deep Learning

Simple Summary Animal behavior in the poultry house could be used as an indicator of health and welfare status. In this study, a convolutional neural network models (CNN) network model was developed to monitor chicken behaviors (i.e., feeding, drinking, standing, and resting). Videos of broilers at different ages were used to build datasets for training the new model, which was compared to several other deep learning frameworks in behavior monitoring. In addition, an attention mechanism module was introduced into the new model to further analyze the influence of attention mechanism on the performance of the network model. This study provides a basis for innovating approach for poultry behavior detection in commercial houses. 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.

[1]  Y. Ying,et al.  Information perception in modern poultry farming: A review , 2022, Comput. Electron. Agric..

[2]  K. Yu,et al.  C3D-ConvLSTM based cow behaviour classification using video data for precision livestock farming , 2022, Comput. Electron. Agric..

[3]  Heping Chen,et al.  A Review of Sensors and Machine Learning in Animal Farming , 2021, 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER).

[4]  Craig Michie,et al.  Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks , 2021, Sensors.

[5]  Salah Sukkarieh,et al.  Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation , 2021, Comput. Electron. Agric..

[6]  I. Veissier,et al.  Developing effective welfare measures for cattle , 2021 .

[7]  John E. Linhoss,et al.  Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review , 2021, Sensors.

[8]  L. Chai,et al.  A Machine Vision-Based Method Optimized for Restoring Broiler Chicken Images Occluded by Feeding and Drinking Equipment , 2021, Animals : an open access journal from MDPI.

[9]  Brano Kusy,et al.  Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation , 2020, Comput. Electron. Agric..

[10]  Rohan Ramanath,et al.  An Attentive Survey of Attention Models , 2019, ACM Trans. Intell. Syst. Technol..

[11]  S. Sukkarieh,et al.  BiGRU-Attention Based Cow Behavior Classification Using Video Data for Precision Livestock Farming , 2021, Transactions of the ASABE.

[12]  Changqing Song,et al.  CNN feature based graph convolutional network for weed and crop recognition in smart farming , 2020, Comput. Electron. Agric..

[13]  Adelumola Oladeinde,et al.  A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution , 2020, Sensors.

[14]  Haiyang Zhang,et al.  Design of Sick Chicken Automatic Detection System Based on Improved Residual Network , 2020, 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).

[15]  Yang Zhao,et al.  Assessment of layer pullet drinking behaviors under selectable light colors using convolutional neural network , 2020, Comput. Electron. Agric..

[16]  Cheng Fang,et al.  Comparative study on poultry target tracking algorithms based on a deep regression network , 2020 .

[17]  Jintao Wang,et al.  A review on computer vision systems in monitoring of poultry: A welfare perspective , 2020 .

[18]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[19]  Tiemin Zhang,et al.  Detection of sick broilers by digital image processing and deep learning , 2019, Biosystems Engineering.

[20]  Xiaoming Xi,et al.  Multi-Gram CNN-Based Self-Attention Model for Relation Classification , 2019, IEEE Access.

[21]  Jian Lian,et al.  Automatic Recognition of Flock Behavior of Chickens with Convolutional Neural Network and Kinect Sensor , 2017, Int. J. Pattern Recognit. Artif. Intell..

[22]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Md. Sumon Shahriar,et al.  Behavior classification of cows fitted with motion collars: Decomposing multi-class classification into a set of binary problems , 2016, Comput. Electron. Agric..

[25]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  R. DeFries,et al.  Agricultural intensification and changes in cultivated areas, 1970–2005 , 2009, Proceedings of the National Academy of Sciences.