Prediction of Chicken Diseases by Transfer Learning Method

Abstract

With the development of computing technologies, artificial intelligence is used in a wide range of areas, from engineering to healthcare. In this study, it was aimed to predict chicken diseases with transfer learning. For this purpose, a ready-made data set was studied. This data set contains fecal photographs of healthy chickens diagnosed with Coccidiosis, Newcastle and Salmonella diseases. The data set has been subjected to necessary pre-processing such as size readjustment. Subsequently, the data set, which was then subjected to pre-processing, was divided into 70% and 30% as training and testing. To solve the disease classification problem, a network was created by adding fully connected layers to ResNet50, InceptionV3, InceptionResNetV2, Xception and MobileNetV2 architectures. The weights of the architectures mentioned in these networks were selected as ImageNet and were not trained. Then, networks containing these architectures were trained using the training data set. The trained networks were validated with the test data set and accuracy rates of 32.7%, 80.6%, 85.2%, 89.2% and 90.7% were obtained, respectively. According to these results, MobileNetV2 was used in the proposed artificial neural network architecture since the best result was calculated using the MobileNetV2 architecture. The proposed artificial neural network architecture was trained with the same training set and validation was carried out with the same test data set. After these procedures, the true prediction rate of the proposed architecture for the test data set was calculated as 92.1%. Also, F1 score of the proposed architecture was measured 0.923. Additionally, thanks to the deconvolution layer used in the proposed architecture, network sizes have been reduced by approximately 50%. Thanks to this reduction, the training time is shortened and it becomes easier to implement it on embedded systems in future studies. As a result, the diseases of chickens were predicted largely accurately with the transfer learning method.

Keywords

• Transfer learning
• deconvolution
• neural network

References

1. [1] U. Uslu and O. Ceylan, “Serbest Dolaşımlı üretim Sisteminde Yetiştirilen hubbard isa red-ja broyler tavuklardaki Sekal Koksidiyozun Tedavisinde toltrazuril’in (COC-CIDE®) Etkinliğinin Değerlendirilmesi,” Dicle Üniversitesi Veteriner Fakültesi Dergisi, vol. 13, no. 2, pp. 135–138, 2020. doi:10.47027/duvetfd.816477.
2. [2] A. Mimbay, “Newcastle hastalığının korunma ve kontrolü,” Etlik Veteriner Mikrobiyoloji Dergisi ,vol. 5 no. 1-2-3, pp. 128-137.
3. [3] H. Yardımcı, and A. Aksoy, “Tavuklarda Salmonella infeksiyonlarının kontrolü,” Etlik Veteriner Mikrobiyoloji Dergisi , vol. 25, no. 2, pp. 63-72.
4. [4] M. C. Bingol and O. Aydogmus, “Practical application of a safe human-robot interaction software,” Industrial Robot: the international journal of robotics research and application, vol. 47, no. 3, pp. 359–368, 2020. doi:10.1108/ir-09-2019-0180.
5. [5] B. Gürkan and A. Çifçi, "Eritematöz Skuamöz Hastalıkların Teşhisinde Makine Öğrenme Algoritmalarının Etkisi." Journal of Intelligent Systems: Theory and Applications vol. 4, no. 2, pp. 195-202, 2021.
6. [6] B. Gürkan, "Makine öğrenmesi algoritmaları kullanarak erken dönemde diyabet hastalığı riskinin araştırılması." Journal of Intelligent Systems: Theory and Applications, vol. 4, no. 1, pp.55-64, 2021.
7. [7] N. E. M. Khalifa, M. H. N. Taha, D. Ezzat Ali, A. Slowik and A. E. Hassanien, "Artificial Intelligence Technique for Gene Expression by Tumor RNA-Seq Data: A Novel Optimized Deep Learning Approach," IEEE Access, vol. 8, pp. 22874-22883, 2020, doi: 10.1109/ACCESS.2020.2970210.
8. [8] K. İsmail and A. Çifci. "An effective and fast solution for classification of wood species: A deep transfer learning approach." Ecological Informatics, vol. 69, pp. 195-202, 2022.

9. [9] L. Zu, X. Chu, Q. Wang, Y. Ju and M. Zhang, "Joint Feature Target Detection Algorithm of Beak State Based on YOLOv5," IEEE Access, vol. 11, pp. 64458-64467, 2023, doi: 10.1109/ACCESS.2023.3275432.
10. [10] W. Zhu, Y. Peng and B. Ji, "An Automatic Dead Chicken Detection Algorithm Based on SVM in Modern Chicken Farm," 2009 Second International Symposium on Information Science and Engineering, Shanghai, China, 2009, pp. 323-326, doi: 10.1109/ISISE.2009.62.
11. [11] M. A. A. A. Bakar, P. J. Ker, S. G. H. Tang, H. J. Lee and B. S. Zainal, "Classification of Unhealthy Chicken based on Chromaticity of the Comb," 2022 IEEE International Conference on Computing (ICOCO), Kota Kinabalu, Malaysia, 2022, pp. 1-5, doi: 10.1109/ICOCO56118.2022.10031812.
12. [12] Y. Guo et al., “Detecting broiler chickens on litter floor with the Yolov5-CBAM Deep Learning Model,” Artificial Intelligence in Agriculture, vol. 9, pp. 36–45, 2023. doi:10.1016/j.aiia.2023.08.002.
13. [13] B.-L. Chen et al., “Developing an automatic warning system for anomalous chicken dispersion and movement using Deep Learning and machine learning,” Poultry Science, vol. 102, no. 12, p. 103040, 2023. doi:10.1016/j.psj.2023.103040.
14. [14] M. C. Bingol and O. Aydogmus, “Development of a Human-Robot Interaction System for Industrial Applications,” Balkan Journal of Electrical and Computer Engineering (BAJECE), vol. 1, no. 1, pp. 1–10, 2023.
15. [15] H. Afridi, M. Ullah, Ø. Nordbø, A. G. Larsgard and F. Alaya Cheikh, "Leveraging Transfer Learning for Analyzing Cattle Front Teat Placement," 2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2023, pp. 1-6, doi: 10.1109/IPTA59101.2023.10320080.
16. [16] R. N. Shebiah and S. Arivazhagan, "Deep Learning Based Image Analysis for Classification of Foot and Mouth Disease in Cattle," 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2023, pp. 701-705, doi: 10.1109/ICIRCA57980.2023.10220765.
17. [17] W. Hao et al., “A novel jinnan individual cattle recognition approach based on mutual attention learning scheme,” Expert Systems with Applications, vol. 230, p. 120551, 2023. doi:10.1016/j.eswa.2023.120551
18. [18] Allandclive, “Chicken disease image classification,” Kaggle, https://www.kaggle.com/datasets/allandclive/chicken-disease-1 (accessed Nov. 24, 2023).
19. [19] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. doi:10.1109/cvpr.2016.90.
20. [20] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. doi:10.1109/cvpr.2016.308.
21. [21] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-V4, inception-resnet and the impact of residual connections on learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1, 2017. doi:10.1609/aaai.v31i1.11231.
22. [22] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. doi:10.1109/cvpr.2017.195.
23. [23] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/cvpr.2018.00474.