atuВестник Алматинского технологического университетаThe Journal of Almaty Technological University2304-568X2710-0839АО "АТУ"10.48184/2304-568X-2024-2-103-111atu-2226Research ArticleТЕХНОЛОГИЯ ПИЩЕВОЙ И ПЕРЕРАБАТЫВАЮЩЕЙ ПРОМЫШЛЕННОСТИFOOD AND PROCESSING INDUSTRY TECHNOLOGYДостижения технологии систем искусственного интеллекта – анализа изображений (IA) для комплексной оценки качества кормов домашних животныхAdvancements in artificial intelligence-imaging analysis (IA) systems technology for comprehensive quality evaluation of pet food productshensive Quality Evaluation of Pet Food ProductsКумар ШармаРишавKumar SharmaRishav

 Матхура, Уттар-Прадеш, 281001, Индия 

Mathura, Uttar Pradesh, 281001, India 

rishavvet42@gmail.comУниверситет ветеринарных наук штата Уттар Прадеш Пандит Дин Даял УпадхьяяИндияDepartment of Livestock Products Technology, U.P. Pt. Deen Dayal Upadhyaya Veterinary Science UniversityIndia2024170620241442103111Copyright © Кумар Шарма Р., 20242024Кумар Шарма Р.Kumar Sharma R.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vestnik-atu.kz/jour/article/view/2226

Растущий спрос на высококачественные корма для домашних животных и необходимость соблюдения строгих стандартов безопасности привели к поиску и разработке технологий, позволяющих точно и быстро оценить качество этих продуктов. Одной из таких технологий являются системы Imaging Analysis (IA), которые не требуют измельчения продукта для анализа, обеспечивают автоматизацию и экономическую эффективность для удовлетворения этих растущих требований. Системы анализа изображений (IA) электронно воспроизводят визуальное восприятие человека, обеспечивая точную и эффективную оценку изображений. Обширные исследования выявили их потенциал и продемонстрировали успешное применение для проверки и сортировки кормов для домашних животных. В этом обзоре представлены фундаментальные компоненты системы Imaging Analysis, а также рассмотрены их преимущества и недостатки. Кроме того, в статье рассматриваются методы обработки изображений и дается всесторонний анализ последних достижений и потенциальных возможностей их применения для оценки качества кормов для домашних животных.

The increasing demand for high-quality pet food products and the need for strict safety standards have led to the exploration and development of technologies that can accurately and quickly assess the quality of these products. One such technology is Imaging Analysis (IA) systems, which offers automation, non-destructiveness, and costeffectiveness to meet these evolving requirements. Imaging Analysis (IA) systems electronically replicate human visual perception, enabling precise and efficient evaluation of images. Extensive research has highlighted its potential and demonstrated successful applications in examining and grading pet food products. This review paper introduces the fundamental components of computer vision systems, while also discussing their advantages and disadvantages. Additionally, it explores image processing techniques and provides a comprehensive analysis of recent advancements and potential applications in evaluating the quality of pet food products.

корм для домашних животныхсистемы анализа изображений (IA)технологии оценки качестваавтоматизацияобработка изображенийPet foodImaging Analysis (IA) systemsquality assessment technologiesautomationimage processingReferencesBrosnan, T. and Sun, D.W. (2004). Improving quality inspection of food products by computer visiona review. Journal of Food Engineering, 61: 3-16.Brosnan, T. and Sun, D.W. (2004). Improving quality inspection of food products by computer visiona review. Journal of Food Engineering, 61: 3-16.Bukhari, S.N.H., Jain, A. and Haq E. (2021). “Machine learning based ensemble model for zika virus Tcell epitope prediction,” Journal of Healthcare Engineering, vol. 2021, Article ID 9591670, 10 pages, 2021.Bukhari, S.N.H., Jain, A. and Haq E. (2021). “Machine learning based ensemble model for zika virus Tcell epitope prediction,” Journal of Healthcare Engineering, vol. 2021, Article ID 9591670, 10 pages, 2021.Gunasekaran, S. (2001). Non-destructive food evaluation techniques to analyse properties and quality. Food Science and Technology (vol. 105), New York: Marcel Decker.Gunasekaran, S. (2001). Non-destructive food evaluation techniques to analyse properties and quality. Food Science and Technology (vol. 105), New York: Marcel Decker.Krutz, G.W., Gibson, H.G., Cassens, D.L. and Zhang, M. (2000). Colour vision in forest and wood engineering. Landwards, 55: 2-9.Krutz, G.W., Gibson, H.G., Cassens, D.L. and Zhang, M. (2000). Colour vision in forest and wood engineering. Landwards, 55: 2-9.Kumar R, Goswami M, Pathak V. Innovations in pet nutrition: investigating diverse formulations and varieties of pet food: mini review. MOJ Food Process Technols. 2024;12(1):86‒89. DOI: 10.15406/mojfpt.2024.12.00302Kumar R, Goswami M, Pathak V. Innovations in pet nutrition: investigating diverse formulations and varieties of pet food: mini review. MOJ Food Process Technols. 2024;12(1):86‒89. DOI: 10.15406/mojfpt.2024.12.00302Kumar R, Goswami M. Harnessing poultry slaughter waste for sustainable pet nutrition: a catalyst for growth in the pet food industry. J Dairy Vet Anim Res. 2024;13(1):31‒33. DOI: 10.15406/jdvar.2024.13.00344Kumar R, Goswami M. Harnessing poultry slaughter waste for sustainable pet nutrition: a catalyst for growth in the pet food industry. J Dairy Vet Anim Res. 2024;13(1):31‒33. DOI: 10.15406/jdvar.2024.13.00344Kumar, R., & Goswami, M. (2024). Feathered nutrition: unlocking the potential of poultry byproducts for healthier pet foods. Acta Scientific Veterinary Sciences. http://dx.doi.org/10.31080/ASVS.2024.06.0868Kumar, R., & Goswami, M. (2024). Feathered nutrition: unlocking the potential of poultry byproducts for healthier pet foods. Acta Scientific Veterinary Sciences. http://dx.doi.org/10.31080/ASVS.2024.06.0868Kumar, R., & Goswami, M. (2024). Optimizing Pet Food Formulations with Alternative Ingredients and Byproducts. Acta Scientific Veterinary Sciences. http://dx.doi.org/10.31080/ASVS.2024.06.0869Kumar, R., & Goswami, M. (2024). Optimizing Pet Food Formulations with Alternative Ingredients and Byproducts. Acta Scientific Veterinary Sciences. http://dx.doi.org/10.31080/ASVS.2024.06.0869Kumar, R., & Sharma, A. (2024). Review of Pet Food Packaging in the US Market: Future Direction Towards Innovation and Sustainability. Annual Research & Review in Biology, 39(6), 16–30. https://doi.org/10.9734/arrb/2024/v39i62085Kumar, R., & Sharma, A. (2024). Review of Pet Food Packaging in the US Market: Future Direction Towards Innovation and Sustainability. Annual Research & Review in Biology, 39(6), 16–30. https://doi.org/10.9734/arrb/2024/v39i62085Kumar, R., Goswami, M. and Pathak, V. (2023). Enhancing Microbiota Analysis, Shelf-life, and Palatability Profile in Affordable Poultry Byproduct Pet Food Enriched with Diverse Fibers and Binders. J. Anim. Res., 13(05): 815-831. DOI: 10.30954/2277-940X.05.2023.24Kumar, R., Goswami, M. and Pathak, V. (2023). Enhancing Microbiota Analysis, Shelf-life, and Palatability Profile in Affordable Poultry Byproduct Pet Food Enriched with Diverse Fibers and Binders. J. Anim. Res., 13(05): 815-831. DOI: 10.30954/2277-940X.05.2023.24Kumar, R., Goswami, M., & Pathak, V. (2024). Gas Chromatography Based Analysis of Fatty Acid Profiles in Poultry Byproduct-Based Pet Foods: Implications for Nutritional Quality and Health Optimization. Asian Journal of Research in Biochemistry, 14(4), 1–17. https://doi.org/10.9734/ajrb/2024/v14i4289Kumar, R., Goswami, M., & Pathak, V. (2024). Gas Chromatography Based Analysis of Fatty Acid Profiles in Poultry Byproduct-Based Pet Foods: Implications for Nutritional Quality and Health Optimization. Asian Journal of Research in Biochemistry, 14(4), 1–17. https://doi.org/10.9734/ajrb/2024/v14i4289Kumar, R., Goswami, M., Pathak, V., & Singh, A. (2024). Effect of binder inclusion on poultry slaughterhouse byproducts incorporated pet food characteristics and palatability. Animal Nutrition and Feed Technology, 24(1), 177-191. DOI: 10.5958/0974-181X.2024.00013.1Kumar, R., Goswami, M., Pathak, V., & Singh, A. (2024). Effect of binder inclusion on poultry slaughterhouse byproducts incorporated pet food characteristics and palatability. Animal Nutrition and Feed Technology, 24(1), 177-191. DOI: 10.5958/0974-181X.2024.00013.1Kumar, R., Goswami, M., Pathak, V., Bharti, S.K., Verma, A.K., Rajkumar, V. and Patel, P. 2023. Utilization of poultry slaughter byproducts to develop cost effective dried pet food. Anim. Nutr. Technol., 23: 165-174. DOI: 10.5958/0974-181X.2023.00015.XKumar, R., Goswami, M., Pathak, V., Bharti, S.K., Verma, A.K., Rajkumar, V. and Patel, P. 2023. Utilization of poultry slaughter byproducts to develop cost effective dried pet food. Anim. Nutr. Technol., 23: 165-174. DOI: 10.5958/0974-181X.2023.00015.XKumar, R., Goswami, M., Pathak, V., Verma, A.K. and Rajkumar, V. 2023. Quality improvement of poultry slaughterhouse byproductsbased pet food with incorporation of fiber-rich vegetable powder. Explor. Anim. Med. Res., 13(1): 54-61. DOI: 10.52635/eamr/13.1.54-61Kumar, R., Goswami, M., Pathak, V., Verma, A.K. and Rajkumar, V. 2023. Quality improvement of poultry slaughterhouse byproductsbased pet food with incorporation of fiber-rich vegetable powder. Explor. Anim. Med. Res., 13(1): 54-61. DOI: 10.52635/eamr/13.1.54-61Kumar, R., Thakur, A., & Sharma, A. (2023). Comparative prevalence assessment of subclinical mastitis in two crossbred dairy cow herds using the California mastitis test. J Dairy Vet Anim Res, 12(2), 98-102. http://dx.doi.org/10.15406/jdvar.2023.12.00331Kumar, R., Thakur, A., & Sharma, A. (2023). Comparative prevalence assessment of subclinical mastitis in two crossbred dairy cow herds using the California mastitis test. J Dairy Vet Anim Res, 12(2), 98-102. http://dx.doi.org/10.15406/jdvar.2023.12.00331Kumar., et al. “Promoting Pet Food Sustainability: Integrating Slaughterhouse By-products and Fibrous Vegetables Waste". Acta Scientific Veterinary Sciences 6.5 (2024): 07-11. DOI: 10.31080/ASVS.2024.06.0871Kumar., et al. “Promoting Pet Food Sustainability: Integrating Slaughterhouse By-products and Fibrous Vegetables Waste". Acta Scientific Veterinary Sciences 6.5 (2024): 07-11. DOI: 10.31080/ASVS.2024.06.0871Liu, D., Ma, J., Sun, D.W., Pu, H., Gao, W., Qu, J. and Zeng, X.A. (2014). Prediction of Color and pH of Salted Porcine Meats Using Visible and NearInfrared Hyperspectral Imaging. Food Bioprocess Technology, 7(11):3100 – 3108.Liu, D., Ma, J., Sun, D.W., Pu, H., Gao, W., Qu, J. and Zeng, X.A. (2014). Prediction of Color and pH of Salted Porcine Meats Using Visible and NearInfrared Hyperspectral Imaging. Food Bioprocess Technology, 7(11):3100 – 3108.Mahalik, N.P. and Nambiar, A.N. 2010. Robotic Automation in Dairy and Meat Processing Sector for Hygienic Processing and Enhanced Production. Trends in food packaging and manufacturing systems and technology. Trends in Food Science & Technology, 21(3): 117-128. http://dx.doi.org/10.1016/j.tifs.2009.12.006.Mahalik, N.P. and Nambiar, A.N. 2010. Robotic Automation in Dairy and Meat Processing Sector for Hygienic Processing and Enhanced Production. Trends in food packaging and manufacturing systems and technology. Trends in Food Science & Technology, 21(3): 117-128. http://dx.doi.org/10.1016/j.tifs.2009.12.006.Mery, D., Pedreschi, F., Soto, A. (2013). Automated Design of a Computer Vision System for Visual Food Quality Evaluation. Food Bioprocess Technology, 6(8): 2093-2108Mery, D., Pedreschi, F., Soto, A. (2013). Automated Design of a Computer Vision System for Visual Food Quality Evaluation. Food Bioprocess Technology, 6(8): 2093-2108Misimi, E., Oye, E.R., Eilertsen, A., Mathiassen, J.R., Asebo, O.B., Gjerstad, T., Buljo, J. and Skotheim, O. (2016). GRIBBOT– Robotic 3D vision-guided harvesting of chicken fillets. Computer and Electronic Agriculture, 121: 84-100.Misimi, E., Oye, E.R., Eilertsen, A., Mathiassen, J.R., Asebo, O.B., Gjerstad, T., Buljo, J. and Skotheim, O. (2016). GRIBBOT– Robotic 3D vision-guided harvesting of chicken fillets. Computer and Electronic Agriculture, 121: 84-100.Park, B. (2016). Quality Evaluation of Poultry Carcasses. Computer Vision Technology for Food Quality Evaluation, Chapter, 9, pp. 213-218.Park, B. (2016). Quality Evaluation of Poultry Carcasses. Computer Vision Technology for Food Quality Evaluation, Chapter, 9, pp. 213-218.Storbeck, F. and Daan, B. (2001). Fish species recognition using computer vision and a neural network. Fisheries Research, 51: 11-15.Storbeck, F. and Daan, B. (2001). Fish species recognition using computer vision and a neural network. Fisheries Research, 51: 11-15.Tarbell, K.A. and Reid, J.F. (1991). A computer vision system for characterizing corn growth and development. Transactions of the ASAE, 34(5), 2245–2249.Tarbell, K.A. and Reid, J.F. (1991). A computer vision system for characterizing corn growth and development. Transactions of the ASAE, 34(5), 2245–2249.Valous, N.A., Mendoza, F. and Sun, D.W. (2010). Emerging Non-Contact Imaging, Spectroscopic and Colorimetric Technologies for Quality Evaluation and Control of Hams: A review. Trends in Food Science & Technology, 21(1): 26-43.Valous, N.A., Mendoza, F. and Sun, D.W. (2010). Emerging Non-Contact Imaging, Spectroscopic and Colorimetric Technologies for Quality Evaluation and Control of Hams: A review. Trends in Food Science & Technology, 21(1): 26-43.Vithu, P. and Moses, J.A. (2016). Machine Vision System for Food Grain Quality Evaluation: A review. Trends in Food Science & Technology, 56:13-20Vithu, P. and Moses, J.A. (2016). Machine Vision System for Food Grain Quality Evaluation: A review. Trends in Food Science & Technology, 56:13-20Wu, D., Sun, DW. 2013. Colour Measurements by Computer Vision for Food Quality Control – A review. Trends in Food Science & Technology, 29(1): 5-20.Wu, D., Sun, DW. 2013. Colour Measurements by Computer Vision for Food Quality Control – A review. Trends in Food Science & Technology, 29(1): 5-20.Wu, X., Liang, X., Wang, Y. Wu, B. and Sun, J. (2022), Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review. Foods 11, 3713. https://doi.org/10.3390/foods11223713.Wu, X., Liang, X., Wang, Y. Wu, B. and Sun, J. (2022), Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review. Foods 11, 3713. https://doi.org/10.3390/foods11223713.Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J. and Liu, C. (2014). Principles, Developments and Applications of Computer Vision for External Quality Inspection of Fruits and Vegetables: A review. Food Research International 62: 326-343.Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J. and Liu, C. (2014). Principles, Developments and Applications of Computer Vision for External Quality Inspection of Fruits and Vegetables: A review. Food Research International 62: 326-343.

The authors declare that there are no conflicts of interest present.