An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification


  • Thavamani Subramani Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, India
  • Vijayakumar Jeganathan Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, India
  • Sruthi Kunkuma Balasubramanian Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, India



Native chicken breed classification, Gender classification, GLCM, PCA, Machine learning algorithms


This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7.


M. K. Padhi, “Importance of Indigenous Breeds of Chicken for Rural Economy and Their Improvements for Higher Production Performance,” Scientifica, vol. 2016, article no. 2604685, 2016.

M. Kumar, S. P. Dahiya, and P. Ratwan, “Backyard Poultry Farming in India: A Tool for Nutritional Security and Women Empowerment,” Biological Rhythm Research, vol. 52, no. 10, pp. 1476-1491, 2021.

R. T. Wilson, “An Overview of Traditional Small-Scale Poultry Production in Low-Income, Food-Deficit Countries,” Annals of Agricultural & Crop Sciences, vol. 6, no. 3, article no. 1077, 2021.

S. Haunshi and U. Rajkumar, “Native Chicken Production in India: Present Status and Challenges,” Livestock Research for Rural Development, vol. 32, no. 11, article no. 181, November 2020.

M. Kanakachari, H. Rahman, R. N. Chatterjee, and T. K. Bhattacharya, “Signature of Indian Native Chicken Breeds: A Perspective,” World’s Poultry Science Journal, vol. 78, no. 2, pp. 421-445, 2022.

S. S. Thavamani, J. Vijayakumar, and K. Sruthi, “GLCM and K-Means Based Chicken Gender Classification,” Smart Technologies, Communication and Robotics, pp. 1-5, October 2021.

H. M. Hafez and Y. A. Attia, “Challenges to the Poultry Industry: Current Perspectives and Strategic Future after the Covid-19 Outbreak,” Frontiers in Veterinary Science, vol. 7, article no. 516, August 2020.

D. K. Dittoe, E. G. Olson, and S. C. Ricke, “Impact of the Gastrointestinal Microbiome and Fermentation Metabolites on Broiler Performance,” Poultry Science, vol. 101, no. 5, article no. 101786, May 2022.

C. Okinda, I. Nyalala, T. Korohou, C. Okinda, J. Wang, T. Achieng, et al., “A Review on Computer Vision Systems in Monitoring of Poultry: A Welfare Perspective,” Artificial Intelligence in Agriculture, vol. 4, pp. 184-208, 2020.

Y. Guo, L. Chai, S. E. Aggrey, A. Oladeinde, J. Johnson, and G. Zock, “A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution,” Sensors, vol. 20, no. 11, article no. 3179, June 2020.

X. Yang, Y. Zhao, G. M. Street, Y. Huang, S. D. Filip To, and J. L. Purswell, “Classification of Broiler Behaviours Using Triaxial Accelerometer and Machine Learning,” Animal, vol. 15, no. 7, article no. 100269, July 2021.

P. Pons, J. Jaen, and A. Catala, “Assessing Machine Learning Classifiers for the Detection of Animals’ Behavior Using Depth-Based Tracking,” Expert Systems with Applications, vol. 86, pp. 235-246, November 2017.

S. M. Derakhshani, M. Overduin, T. G. C. M. van Niekerk, and P. W. G. Groot Koerkamp, “Implementation of Inertia Sensor and Machine Learning Technologies for Analyzing the Behavior of Individual Laying Hens,” Animals, vol. 12, no. 5, article no. 536, March 2022.

N. Sharma, R. Sharma, and N. Jindal, “Machine Learning and Deep Learning Applications-A Vision,” Global Transitions Proceedings, vol. 2, no. 1, pp. 24-28, June 2021.

A. Siddique, S. Shirzaei, A. E. Smith, J. Valenta, L. J. Garner, and A. Morey, “Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies,” Frontiers in Physiology, vol. 12, article no. 712649, 2021.

S. Barbon, A. P. A. Da Costa Barbon, R. G. Mantovani, and D. F. Barbin, “Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification,” Journal of Spectroscopy, vol. 2018, article no. 8949741, 2018.

M. Pitesky, J. Gendreau, T. Bond, and R. Carrasco-Medanic, “Data Challenges and Practical Aspects of Machine Learning-Based Statistical Methods for the Analyses of Poultry Data to Improve Food Safety and Production Efficiency,” CABI Reviews, vol. 2020,, January 2020.

X. Zhuang, M. Bi, J. Guo, S. Wu, and T. Zhang, “Development of an Early Warning Algorithm to Detect Sick Broilers,” Computers and Electronics in Agriculture, vol. 144, pp. 102-113, January 2018.

B. Milosevic, S. Ciric, N. Lalic, V. Milanovic, Z. Savic, I. Omerovic, et al., “Machine Learning Application in Growth and Health Prediction of Broiler Chickens,” World’s Poultry Science Journal, vol. 75, no. 3, pp. 401-410, September 2019.

I. Elawady and C. Özcan, “A New Effective Denoising Filter for High Density Impulse Noise Reduction,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 30, no. 4, article no. 15, 2022.

T. Babu, T. Singh, D. Gupta, and S. Hameed, “Optimized Cancer Detection on Various Magnified Histopathological Colon Imagesbased on DWT Features and FCM Clustering,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 30, no. 1, article no. 1, 2022.

P. K. Bhagat, P. Choudhary, and Kh. Manglem Singh, “A Comparative Study for Brain Tumor Detection in MRI Images Using Texture Features,” Sensors for Health Monitoring, vol. 5, pp. 259-287, 2019.

B. T. Jijo and A. M. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20-28, March 2021.

Y. S. Can, “Stressed or Just Running? Differentiation of Mental Stress and Physical Activityby Using Machine Learning,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 30, no. 1, article no. 21, 2022.

G. Li, X. Hui, Z. Chen, G. D. Chesser Jr, and Y. Zhao, “Development and Evaluation of a Method to Detect Broilers Continuously Walking around Feeder as an Indication of Restricted Feeding Behaviors,” Computers and Electronics in Agriculture, vol. 181, article no. 105982, February 2021.

G. Li, Y. Zhao, Z. Porter, and J. L. Purswell, “Automated Measurement of Broiler Stretching Behaviours under Four Stocking Densities via Faster Region-Based Convolutional Neural Network,” Animal, vol. 15, no. 1, article no. 100059, January 2021.

N. Dhanachandra, K. Manglem, and Y. J. Chanu, “Image Segmentation Using K -Means Clustering Algorithm and Subtractive Clustering Algorithm,” Procedia Computer Science, vol. 54, pp. 764-771, 2015.

Priyanka and D. Kumar, “Feature Extraction and Selection of Kidney Ultrasound Images Using GLCM and PCA,” Procedia Computer Science, vol. 167, pp. 1722-1731, 2020.

Y. Yao, H. Yu, J. Mu, J. Li, and H. Pu, “Estimation of the Gender Ratio of Chickens Based on Computer Vision: Dataset and Exploration,” Entropy, vol. 22, no. 7, article no. 719, July 2020.




How to Cite

Thavamani Subramani, Vijayakumar Jeganathan, and Sruthi Kunkuma Balasubramanian, “An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification”, Proc. eng. technol. innov., vol. 24, pp. 73–86, Apr. 2023.