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Current IssueSpecial IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Integration of Artificial Intelligence in Prediction of Diseases in Animal Farming

V
Vidya Subhash Bhosale1
https://orcid.org/0009-0008-7444-1761
M
Manjushri A. Kadam2,*
https://orcid.org/0000-0001-5665-5987
R
Rajbir Singh3
https://orcid.org/0009-0004-1418-0580
A
Anil R. Shet4
https://orcid.org/0000-0003-3264-8883
M
Md. Mazharunnisa5
https://orcid.org/0000-0002-3046-9294
1School of Engineering and Technology, Pimpari Chinchwad University, Pune-412 106, Maharashtra, India.
2Institute of Management, Bharati Vidyapeeth (Deemed to be University), Kolhapur-416 003, Maharashtra, India.
3Department of Animal Husbandry, IIMT University, Meerut-250 002, Uttar Pradesh, India.
4Department of Biotechnology, KLE Technological University, Hubballi-580 031, Karnataka, India.
5KL Business School, Koneru Lakshmaiah Education Foundation, Guntur-521 180, Andhra Pradesh, India.

ABSTRACT

Background: Animal welfare has become an increasingly important indicator of quality in modern animal farming. Various factors contribute to animal welfare challenges and their early identification and management are essential to minimize economic losses and improve livestock health. Advances in artificial intelligence (AI) and machine learning (ML) have provided new opportunities for monitoring animal welfare and enabling timely disease prediction.

Methods: This study examines the application of AI and ML techniques for predicting and detecting diseases in animals. ML models are trained on large datasets to recognize normal behavioral and health patterns, identify anomalies and generate alerts for potential health issues. The study also reviews the use of modern AI technologies in animal farming, including disease prediction, welfare monitoring and precision livestock management.

Result: The findings indicate that AI-and ML-based approaches can enhance the early detection of animal diseases by continuously analyzing data and identifying abnormal conditions. As these models are trained with larger datasets, their predictive accuracy improves, enabling farmers to detect infectious diseases earlier, monitor barn conditions effectively and make timely management decisions. Overall, AI-driven technologies contribute to improved animal welfare, better farm productivity and reduced economic losses.

REFERENCES


  1. AlZubi, A.A. (2023). Artificial intelligence and its application in the prediction and diagnosis of animal diseases: A review. Indian Journal of Animal Research. 57(10): 1265-1271.  doi: 10.18805/IJAR.BF-1684.

  2. Bagga, T., Ansari, A. H., Akhter, S., Mittal, A. and Mittal, A. (2024). Understanding Indian consumers’ propensity to purchase electric vehicles: An analysis of determining factors in environmentally sustainable transportation. International Journal of Environmental Sciences. 10(1): 1-13.

  3. Bergstra, J. and Bengio, Y. (2012). Random search for hyper- parameter optimization. Journal of Machine Learning Research. 13: 281-305.

  4. Bergstra, J., Yamins, D. and Cox, D.D. (2013). Hyperopt: A Python library for optimizing the hyperparameters of machine learning algorithms. Proceedings of the 12th Python in Science Conference. 13: 13-19. https://doi.org/10.25080/ Majora-8b375195-003.

  5. Botreau, R., Veissier, I., Butterworth, A., Bracke, M.B.M. and Keeling, L.J. (2007). Definition of criteria for overall assessment of animal welfare. Animal Welfare. 16: 225-228.

  6. Breiman, L. (2001). Random forests. Machine Learning. 45: 5-32.

  7. Bundesministerium für Ernährung und Landwirtschaft. (2018). Landwirtschaft Verstehen-Fakten and Hintergründe.

  8. https://www.bmel.de/SharedDocs/Downloads/Broschueren /Landwirtschaf-verstehen.pdf;jsess ionid=4672B911 DF00C56AF28 8442A5EA518E0.1_cid367?__blob= publicationFile.

  9. Bundesverband Rind and Schwein, E.V. (2020). LPA-Rassencodes. https://www.rind-schwein.de/brs-schwein/lparassencodes. html.

  10. Cho, O.H. (2024). An evaluation of various machine learning approaches for detecting leaf diseases in agriculture. Legume Research. 47(4): 619-627. doi: 10.18805/LRF- 787. 

  11. Chung, Y., Oh, S., Lee, J., Park, D., Chang, H.H. and Kim, S. (2013). Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems.  Sensors. 13(10): 12929-12942.

  12. Díaz, J.A.C., Boyle, L.A., Diana, A., Leonard, F.C., Moriarty, J.P., McElroy, M.C., McGettrick, S., Kelliher, D. and Manzanilla, E.G. (2017). Early life indicators predict mortality, illness, reduced welfare and carcass characteristics in finisher pigs. Preventive Veterinary Medicine. 146: 94-102. https:// doi.org/10.1016/j.prevetmed.2017.07.018.

  13. Hai, N.T. and Duong, N.T. (2024). An improved environmental management model for assuring energy and economic prosperity. Acta Innovations. 52: 9-18. https://doi.org/ 10.62441/ActaInnovations.52.2. 

  14. Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H. and Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications. 73: 220-239. https://doi.org/10.1016/ j.eswa.2016.12.035.

  15. Hsu, C.W., Chang, C.C. and Lin, C.J. (2003). A Practical Guide to Support Vector Classification. Technischer Bericht, National Taiwan University.

  16. Lin, C., Weng, R.C. and Keerthi, S.S. (2008). Trust region newton method for logistic regression. Journal of Machine Learning Research. 9(22): 627-650. https://doi.org/ 10.1145/1390681.1390703.

  17. Manteufel, G. and Schön, P.C. (2002). Measuring welfare of pigs by automatic monitoring of stress sounds. Measurement Systems for Animal Data-Bornimer Agrartechnische Berichte. 29: 110-118.

  18. Maltare, N.N., Sharma, D. and Patel, S. (2023). An exploration and prediction of rainfall and groundwater level for the district of Banaskantha, Gujrat, India. International Journal of Environmental Sciences. 9(1): 1-17.

  19. Matthews, S.G., Miller, A.L., Clapp, J., Plötz, T. and Kyriazakis, I. (2016). Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. The Veterinary Journal. 217: 43-51. https://doi.org/ 10.1016/j.tvjl.2016.09.005.

  20. Meshram, V., Patil, K., Meshram, V., Hanchate, D. and Ramkteke, S. (2021). Machine learning in agriculture domain: A state- of-art survey. Artificial Intelligence in the Life Sciences. 1: 100010. https://doi.org/10.1016/j.ailsci.2021.100010.

  21. Nguyen, T.T.  and Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys and Tutorials. 10(4): 56-76. https://doi.org/10.1109/SURV.2008.080406.

  22. Pineau, J., Vincent-Lamarre, P., Sinha, K., Larivière, V., Beygelzimer, A., d’Alché-Buc, F., Fox, E. and Larochelle, H. (2020). Improving reproducibility in machine learning research (A report from the NeurIPS 2019 reproducibility program). arXiv preprint. https://arxiv.org/abs/2003.12206.

  23. Riekert, M., Klein, A., Adrion, F., Hofmann, C. and Gallmann, E. (2020). Automatically detecting pig position and posture by 2D camera imaging and deep learning. Computers and Electronics in Agriculture. 174: 105391. https:// doi.org/10.1016/j. compag.2020.105391.

  24. Tian, T. and Zhu, J. (2015). Max-margin majority voting for learning from crowds. Advances in Neural Information Processing Systems. pp 1621-1629.

  25. Witten, I.H., Frank, E. and Hall, M.A. (2011). Data mining: Practical Machine Learning Tools and Techniques (3rd ed.). Morgan Kaufmann Publishers.
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    Full Research Article

    Integration of Artificial Intelligence in Prediction of Diseases in Animal Farming

    V
    Vidya Subhash Bhosale1
    https://orcid.org/0009-0008-7444-1761
    M
    Manjushri A. Kadam2,*
    https://orcid.org/0000-0001-5665-5987
    R
    Rajbir Singh3
    https://orcid.org/0009-0004-1418-0580
    A
    Anil R. Shet4
    https://orcid.org/0000-0003-3264-8883
    M
    Md. Mazharunnisa5
    https://orcid.org/0000-0002-3046-9294
    1School of Engineering and Technology, Pimpari Chinchwad University, Pune-412 106, Maharashtra, India.
    2Institute of Management, Bharati Vidyapeeth (Deemed to be University), Kolhapur-416 003, Maharashtra, India.
    3Department of Animal Husbandry, IIMT University, Meerut-250 002, Uttar Pradesh, India.
    4Department of Biotechnology, KLE Technological University, Hubballi-580 031, Karnataka, India.
    5KL Business School, Koneru Lakshmaiah Education Foundation, Guntur-521 180, Andhra Pradesh, India.

    ABSTRACT

    Background: Animal welfare has become an increasingly important indicator of quality in modern animal farming. Various factors contribute to animal welfare challenges and their early identification and management are essential to minimize economic losses and improve livestock health. Advances in artificial intelligence (AI) and machine learning (ML) have provided new opportunities for monitoring animal welfare and enabling timely disease prediction.

    Methods: This study examines the application of AI and ML techniques for predicting and detecting diseases in animals. ML models are trained on large datasets to recognize normal behavioral and health patterns, identify anomalies and generate alerts for potential health issues. The study also reviews the use of modern AI technologies in animal farming, including disease prediction, welfare monitoring and precision livestock management.

    Result: The findings indicate that AI-and ML-based approaches can enhance the early detection of animal diseases by continuously analyzing data and identifying abnormal conditions. As these models are trained with larger datasets, their predictive accuracy improves, enabling farmers to detect infectious diseases earlier, monitor barn conditions effectively and make timely management decisions. Overall, AI-driven technologies contribute to improved animal welfare, better farm productivity and reduced economic losses.

    REFERENCES


    1. AlZubi, A.A. (2023). Artificial intelligence and its application in the prediction and diagnosis of animal diseases: A review. Indian Journal of Animal Research. 57(10): 1265-1271.  doi: 10.18805/IJAR.BF-1684.

    2. Bagga, T., Ansari, A. H., Akhter, S., Mittal, A. and Mittal, A. (2024). Understanding Indian consumers’ propensity to purchase electric vehicles: An analysis of determining factors in environmentally sustainable transportation. International Journal of Environmental Sciences. 10(1): 1-13.

    3. Bergstra, J. and Bengio, Y. (2012). Random search for hyper- parameter optimization. Journal of Machine Learning Research. 13: 281-305.

    4. Bergstra, J., Yamins, D. and Cox, D.D. (2013). Hyperopt: A Python library for optimizing the hyperparameters of machine learning algorithms. Proceedings of the 12th Python in Science Conference. 13: 13-19. https://doi.org/10.25080/ Majora-8b375195-003.

    5. Botreau, R., Veissier, I., Butterworth, A., Bracke, M.B.M. and Keeling, L.J. (2007). Definition of criteria for overall assessment of animal welfare. Animal Welfare. 16: 225-228.

    6. Breiman, L. (2001). Random forests. Machine Learning. 45: 5-32.

    7. Bundesministerium für Ernährung und Landwirtschaft. (2018). Landwirtschaft Verstehen-Fakten and Hintergründe.

    8. https://www.bmel.de/SharedDocs/Downloads/Broschueren /Landwirtschaf-verstehen.pdf;jsess ionid=4672B911 DF00C56AF28 8442A5EA518E0.1_cid367?__blob= publicationFile.

    9. Bundesverband Rind and Schwein, E.V. (2020). LPA-Rassencodes. https://www.rind-schwein.de/brs-schwein/lparassencodes. html.

    10. Cho, O.H. (2024). An evaluation of various machine learning approaches for detecting leaf diseases in agriculture. Legume Research. 47(4): 619-627. doi: 10.18805/LRF- 787. 

    11. Chung, Y., Oh, S., Lee, J., Park, D., Chang, H.H. and Kim, S. (2013). Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems.  Sensors. 13(10): 12929-12942.

    12. Díaz, J.A.C., Boyle, L.A., Diana, A., Leonard, F.C., Moriarty, J.P., McElroy, M.C., McGettrick, S., Kelliher, D. and Manzanilla, E.G. (2017). Early life indicators predict mortality, illness, reduced welfare and carcass characteristics in finisher pigs. Preventive Veterinary Medicine. 146: 94-102. https:// doi.org/10.1016/j.prevetmed.2017.07.018.

    13. Hai, N.T. and Duong, N.T. (2024). An improved environmental management model for assuring energy and economic prosperity. Acta Innovations. 52: 9-18. https://doi.org/ 10.62441/ActaInnovations.52.2. 

    14. Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H. and Bing, G. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications. 73: 220-239. https://doi.org/10.1016/ j.eswa.2016.12.035.

    15. Hsu, C.W., Chang, C.C. and Lin, C.J. (2003). A Practical Guide to Support Vector Classification. Technischer Bericht, National Taiwan University.

    16. Lin, C., Weng, R.C. and Keerthi, S.S. (2008). Trust region newton method for logistic regression. Journal of Machine Learning Research. 9(22): 627-650. https://doi.org/ 10.1145/1390681.1390703.

    17. Manteufel, G. and Schön, P.C. (2002). Measuring welfare of pigs by automatic monitoring of stress sounds. Measurement Systems for Animal Data-Bornimer Agrartechnische Berichte. 29: 110-118.

    18. Maltare, N.N., Sharma, D. and Patel, S. (2023). An exploration and prediction of rainfall and groundwater level for the district of Banaskantha, Gujrat, India. International Journal of Environmental Sciences. 9(1): 1-17.

    19. Matthews, S.G., Miller, A.L., Clapp, J., Plötz, T. and Kyriazakis, I. (2016). Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. The Veterinary Journal. 217: 43-51. https://doi.org/ 10.1016/j.tvjl.2016.09.005.

    20. Meshram, V., Patil, K., Meshram, V., Hanchate, D. and Ramkteke, S. (2021). Machine learning in agriculture domain: A state- of-art survey. Artificial Intelligence in the Life Sciences. 1: 100010. https://doi.org/10.1016/j.ailsci.2021.100010.

    21. Nguyen, T.T.  and Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys and Tutorials. 10(4): 56-76. https://doi.org/10.1109/SURV.2008.080406.

    22. Pineau, J., Vincent-Lamarre, P., Sinha, K., Larivière, V., Beygelzimer, A., d’Alché-Buc, F., Fox, E. and Larochelle, H. (2020). Improving reproducibility in machine learning research (A report from the NeurIPS 2019 reproducibility program). arXiv preprint. https://arxiv.org/abs/2003.12206.

    23. Riekert, M., Klein, A., Adrion, F., Hofmann, C. and Gallmann, E. (2020). Automatically detecting pig position and posture by 2D camera imaging and deep learning. Computers and Electronics in Agriculture. 174: 105391. https:// doi.org/10.1016/j. compag.2020.105391.

    24. Tian, T. and Zhu, J. (2015). Max-margin majority voting for learning from crowds. Advances in Neural Information Processing Systems. pp 1621-1629.

    25. Witten, I.H., Frank, E. and Hall, M.A. (2011). Data mining: Practical Machine Learning Tools and Techniques (3rd ed.). Morgan Kaufmann Publishers.
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