Detection of Bovine Species on Image Using Machine Learning Classifiers

Year 2024, , 137 - 148, 01.03.2024

Ali Tezcan Sarızeybek , Ali Hakan Isık

https://doi.org/10.35378/gujs.1203685

Abstract

There are too many cattle in the world and too many breeds of cattle. For someone who is new to cattle breeding, it may be difficult to tell which species their cattle are. In some cases, even an experienced person may not understand the breeds of two cattle that are similar in appearance. In this study, the aim is to classify the cattle species with image processing methods and mobile applications written in Flutter and TensorFlow Lite. For classifying breeds, The VGG-16 algorithm was used for feature extraction. XGBoost and Random Forest algorithms were used for classification and the combined versions of the two methods were compared. In addition, SMOTE algorithm and image augmentation algorithms were used to prevent the imbalance of the dataset, the performance results of the combined versions of the two methods were compared. Images of different cattle species from different farms were obtained and the dataset was prepared, different image processing models were trained, the trained models were tested and the performance analyses were made. As a result of performance tests, it is obtained that the best model is VGG16+Random Forest+SMOTE+Augmentation with 88.77% accuracy result for this study. In the mobile application, first the cattle is detected with a pre-trained object detection model, and then the breed classification of the cattle on the image is made with image classification model.

Keywords

SMOTE, VGG16, XGBoost, Random forest, Cow classification

References

• [1] Santoni, M. M., Sensuse, D. I., Arymurthy, A. M., Fanany, M. I., “Cattle race classification using gray level co-occurrence matrix convolutional neural networks”, Procedia Computer Science, 59, 493-502, (2015).
• [2] Ou, Y., Wu, X., Qian, H., Xu, Y., “A real time race classification system”, IEEE International Conference on Information Acquisition, 6, (2005).
• [3] T. Sutojo, P. S. Tirajani, D. R. Ignatius Moses Setiadi, C. A. Sari and E. H. Rachmawanto, "CBIR for classification of cow types using GLCM and color features extraction," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 182-187, (2017).
• [4] Bello, R. W., Talib, A. Z., Mohamed, A. S. A., Olubummo, D. A., Otobo, F. N., “Image-based Individual Cow Recognition Using Body Patterns”. Image, 11(3), (2020).
• [5] Jwade, S. A., Guzzomi, A., Mian, A., “On farm automatic sheep breed classification using deep learning”, Computers and Electronics in Agriculture, 167, 105055, (2019).
• [6] de Miranda Almeida, R. M., Chen, D., da Silva Filho, A. L., Brandao, W. C., “Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study”, ICEIS, 660-667, (2021).
• [7] David, R., Duke, J., Jain, A., Reddi, V. J., Jeffries, N., Li, J., Warden, P., “Tensorflow lite micro: Embedded machine learning on tinyml systems”, Proceedings of Machine Learning and Systems, 3, 800-811, (2020).
• [8] Simonyan, K., Zisserman, A., “Very deep convolutional networks for large-scale image recognition”, arXiv preprint, arXiv:1409.1556, (2014).
• [9] Shermin, T., Teng, S. W., Murshed, M., Lu, G., Sohel, F., Paul, M., “Enhanced transfer learning with imagenet trained classification layer”, Pacific-Rim Symposium on Image and Video Technology, 142-155, (2019).
• [10] Tammina, S., “Transfer learning using vgg-16 with deep convolutional neural network for classifying images”, International Journal of Scientific and Research Publications, 9(10): 143-150, (2019).
• [11] Srivastava, S., Kumar, P., Chaudhry, V., Singh, A., “Detection of ovarian cyst in ultrasound images using fine-tuned VGG-16 deep learning network”, SN Computer Science, 1(2): 1-8, (2020).
• [12] Rawat, J., Logofătu, D., Chiramel, S., “Factors affecting accuracy of convolutional neural network using VGG-16”, International Conference on Engineering Applications of Neural Networks, 251-260, (2020).
• [13] Kumar, A., Shaikh, A. M., Li, Y., Bilal, H., Yin, B., “Pruning filters with L1-norm and capped L1- norm for CNN compression”, Applied Intelligence, 51(2): 1152-1160, (2021).
• [14] Breiman, L., “Random forests”, Machine Learning, 45(1): 5-32, (2001).
• [15] Biau, G., Scornet, E., “A random forest guided tour”, Test, 25(2): 197-227, (2006).
• [16] Pal, M., “Random forest classifier for remote sensing classification”, International Journal of Remote Sensing, 26(1): 217-222, (2005).
• [17] Segal, M. R., “Machine learning benchmarks and random forest regression”, Center for Bioinformatics and Molecular Biostatistics, (2004).
• [18] Didavi, A. B., Agbokpanzo, R. G., Agbomahena, M., “Comparative study of Decision Tree, Random Forest and XGBoost performance in forecasting the power output of a photovoltaic system”, 4th International Conference on Bio-Engineering for Smart Technologies, 1-5, (2021).
• [19] Oshiro, T. M., Perez, P. S., Baranauskas, J. A., “How many trees in a random forest?”, International Workshop on Machine Learning and Data Mining in Pattern Recognition, 154-168, (2012).
• [20] Kulkarni, V. Y., Sinha, P. K., “Pruning of random forest classifiers: A survey and future directions”, 2012 International Conference on Data Science & Engineering, 64-68, (2012).
• [21] Chen, T., Guestrin, C., “Xgboost: A scalable tree boosting system”, Proceedings of the 22nd acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785-794, (2016).
• [22] Brownlee, J., “XGBoost With python: Gradient boosted trees with XGBoost and scikit-learn”, Machine Learning Mastery, (2016).
• [23] Priscilla, C. V., Prabha, D. P., “Influence of optimizing XGBoost to handle class imbalance in credit card fraud detection”, 2020 Third International Conference on Smart Systems and Inventive Technology, 1309-1315, (2020).
• [24] Yadav, D. C., Pal, S., “Analysis of heart disease using parallel and sequential ensemble methods with feature selection techniques: heart disease prediction”, International Journal of Big Data and Analytics in Healthcare, 6(1): 40-56, (2021).
• [25] Dhaliwal, S. S., Nahid, A. A., Abbas, R., “Effective intrusion detection system using XGBoost”, Information, 9(7): 149, (2018).
• [26] Davagdorj, K., Pham, V. H., Theera-Umpon, N., Ryu, K. H., “XGBoost-based framework for smoking-induced noncommunicable disease prediction”, International Journal of Environmental Research and Public Health, 17(18), 6513, (2020).
• [27] Sahin, E. K., “Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest”, SN Applied Sciences, 2(7): 1-17, (2020).