Classification of Animal Behaviour Using Deep Learning Models
Abstract Damage to crops by animal intrusion is one of the biggest threats to crop yield. People who stay near forest areas face a major issue with animals. The most significant task in deep learning is animal behaviour classification. This article focuses on the classification of distinct animal behaviours such as sitting, standing, eating etc. The proposed system detects animal behaviours in real time using deep learning-based models, namely, convolution neural network and transfer learning. Specifically, 2D-CNN, VGG16 and ResNet50 architectures have been used for classification. 2D-CNN, «VGG-16» and «ResNet50» have been trained on the video frames displaying a range of animal behaviours. The real time behaviour dataset contains 682 images of animals eating, 300 images of animas sitting and 1002 images of animals standing, therefore, there is a total of 1984 images in the training dataset. The experiment shows good accuracy results on the real time dataset, achieving 99.43 % with Resnet50 compared to 2D CNN ,VGG19 and VGG166.
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Billah, Masum, et al.«Real-time goat face recognition using convolutional neural network». Computers and Electronics in Agriculture, 194, 106730.
Bimantoro, M. Z., & Emanuel, A. W. R. (2021, April). Sheep Face Classification using Convolutional Neural Network. In 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) (pp. 111-115). IEEE.
Brandes, S., Sicks, F., & Berger, A. (2021). Behaviour classification on giraffes (Giraffa camelopardalis) using machine learning algorithms on triaxial acceleration data of two commonly used GPS devices and its possible application for their management and conservation. Sensors, 21(6), 2229.
Chandrakar, R., Raja, R., & Miri, R. (2021). Animal detection based on deep convolutional neural networks with genetic segmentation. Multimedia Tools and Applications, 1-14.
Chen, G., Sun, P., & Shang, Y. (2017, November). Automatic fish classification system using deep learning. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 24-29). IEEE.
Dar, A. S., & Palanivel, S. (2022). Real-time face authentication system using stacked deep autoencoder for facial reconstruction. International Journal of Thin Film Science and Technology, 11(1), 9.
Dar, S. A., & Palanivel, S. (2021). Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System. New Approaches in Commerce, Economics, Engineering, Humanities, Arts, Social Sciences and Management: Challenges and Opportunities, 143.
Debauche, O., Elmoulat, M., Mahmoudi, S., Bindelle, J., & Lebeau, F. (2021). Farm animals’ behaviors and welfare analysis with AI algorithms: A review. Revue d'Intelligence Artificielle, 35(3).
Deng, X., Yan, X., Hou, Y., Wu, H., Feng, C., Chen, L., ... & Shao, Y. (2021). DETECTION OF BEHAVIOUR AND POSTURE OF SHEEP BASED ON YOLOv3. INMATEH-Agricultural Engineering, 64(2).
Favorskaya, M., & Pakhirka, A. (2019). Animal species recognition in the wildlife based on muzzle and shape features using joint CNN. Procedia Computer Science, 159, 933-942.
Ferrarini, Alessandro, and Marco Gustin. «Introducing a new tool to derive animal behaviour from GPS data without ancillary data: The red-footed falcon in Italy as a case study». Ecological Informatics (2022): 101645.
Fogarty, E. S., Swain, D. L., Cronin, G. M., Moraes, L. E., & Trotter, M. (2020). Behaviour classification of extensively grazed sheep using machine learning. Computers and Electronics in Agriculture, 169, 105175.
Ghosh, P., Mustafi, S., Mukherjee, K., Dan, S., Roy, K., Mandal, S. N., & Banik, S. (2021). Image-Based Identification of Animal Breeds Using Deep Learning. In Deep Learning for Unmanned Systems (pp. 415-445).
Indhumathi, j., & balasubramanian, m. (2022). Real time video based human suspicious activity recognition using deep learning.
Kamminga, Jacob W., et al. «Generic online animal activity recognition on collar tags». Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 2017.
Manasa, K., Paschyanti, D. V., Vanama, G., Vikas, S. S., Kommineni, M., & Roshini, A. (2021, July). Wildlife surveillance using deep learning with YOLOv3 model. In 2021 6th International Conference on Communication and Electronics Systems (ICCES) (pp. 1798-1804). IEEE.
Neena, A., & Geetha, M. (2018). Image classification using an ensemble-based deep CNN. In Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 3 (pp. 445-456). Springer Singapore.
Nguyen, Hung, et al. «Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring». 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2017.
Prudhivi, L., Narayana, M., Subrahmanyam, C., Krishna, M. G., & Chavan, S. (2023, March). Animal Species Image Classification. In 2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP) (pp. 1-5). IEEE.
Qassim, H., Verma, A., & Feinzimer, D. (2018, January). Compressed residual-VGG16 CNN model for big data places image recognition. In 2018 IEEE 8th annual computing and communication workshop and conference (CCWC) (pp. 169-175). IEEE.
Qian, S., Ning, C., & Hu, Y. (2021, March). MobileNetV3 for image classification. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) (pp. 490-497). IEEE.
Qiao, Y., Guo, Y., Yu, K., & He, D. (2022). C3D-ConvLSTM based cow behaviour classification using video data for precision livestock farming. Computers and Electronics in Agriculture, 193, 106650.
Saini, Deepak, et al. «Automated knee osteoarthritis severity classification using three‐stage preprocessing method and VGG16 architecture». International Journal of Imaging Systems and Technology (2023).
Sakai, K., Oishi, K., Miwa, M., Kumagai, H., & Hirooka, H. (2019). Behavior classification of goats using 9-axis multi sensors: The effect of imbalanced datasets on classification performance. Computers and Electronics in Agriculture, 166, 105027.
Schneider, S., Taylor, G. W., & Kremer, S. (2018, May). Deep learning object detection methods for ecological camera trap data. In 2018 15th Conference on computer and robot vision (CRV) (pp. 321-328). IEEE.
Shabbir, Amsa, et al. «Satellite and scene image classification based on transfer learning and fine tuning of ResNet50». Mathematical Problems in Engineering 2021 (2021): 1-18.
Simonyan, K., & Zisserman, A. (2020). Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556 (09 2014). URL https://arxiv. org/abs/1409.1556. Accessed: February.
Sowmya, M., Balasubramanian, M., & Vaidehi, K. (2023). Human Behavior Classification using 2D–Convolutional Neural Network, VGG16 and ResNet50. Indian Journal of Science and Technology, 16(16), 1221-1229.
Vehkaoja, A., Somppi, S., Törnqvist, H., Cardó, A. V., Kumpulainen, P., Väätäjä, H., ... & Vainio, O. (2022). Description of movement sensor dataset for dog behavior classification. Data in Brief, 40, 107822.
Wang, H. (2020, April). Garbage recognition and classification system based on convolutional neural network VGG16. In 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) (pp. 252-255). IEEE.
Wang, Xihao, Peihan Li, and Chengxi Zhu. «Classification of Wildlife Based on Transfer Learning». 2020 The 4th International Conference on Video and Image Processing. 2020.
Williams, Lauren R., et al. «Application of accelerometers to record drinking behaviour of beef cattle». Animal Production Science, 59(1), 122-132.
Wu, D., Wang, Y., Han, M., Song, L., Shang, Y., Zhang, X., & Song, H. (2021). Using a CNN-LSTM for basic behaviors detection of a single dairy cow in a complex environment. Computers and Electronics in Agriculture, 182, 106016.
Yang, Qiumei, and Deqin Xiao. «A review of video-based pig behavior recognition». Applied Animal Behaviour Science, 233, 105146.
Yudin, D., Sotnikov, A., & Krishtopik, A. (2019). Detection of big animals on images with road scenes using deep learning. In 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) (pp. 100-1003). IEEE.
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Sowmya, M., Balasubramanian, M., & Vaidehi, K. (2024). Classification of Animal Behaviour Using Deep Learning Models. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31638. https://doi.org/10.14201/adcaij.31638
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