Use of Artificial Intelligence in Livestock: Literature review

Authors

DOI:

https://doi.org/10.33448/rsd-v12i4.40777

Keywords:

Agribusiness; Productive efficiency; Management; Technology.

Abstract

The use of technological solutions improves the efficiency and sustainability of agricultores system, such as the use of artificial intelligence (AI), which is an interdisciplinary field that can change the agricultural paradigm to something different from the current one. AI-powered solutions not only allow producers to do more with less, they also improve quality and ensure crops reach market faster. Given the relevance of the topic and the information presented, this integrative literature review aims to address and highlight the importance and possibility of using Artificial Intelligence in Livestock. A bibliographic review was carried out to collect information and data available on the use and application of artificial intelligence in livestock, where the search and compilation of data occurred through the search tools of Google, Google Scholar, SCIELO - Scientific Electronic Library and Ministry of Agriculture, Livestock and Supply. When observing the use of artificial intelligence in livestock, we can infer that its implementation provides the possibility of remote identification and counting of animals, animal behavior and the formation of a database, more precisely, of an individual animal or a property, allowing to the producer and responsible technician to outline strategies that maximize production and reduce costs, in addition to developing a sustainable business.

References

ABIEC - Associação Brasileira das Indústrias Exportadoras de Carne. (2022). Beef Report – Perfil da Pecuária no Brasil. http://abiec.com.br/publicacoes/beef-report-2022/.

Abade, A., de Campos, M. D., Porto, L. F., de Farias Coelho, Y., de Moura Sousa, Y., & Nespolo, J. P. (2016). A construçao otimizada de um drone para aplicaçoes na agricultura e pecuária de precisao. Anais da Escola Regional de Informática da Sociedade Brasileira de Computação (SBC)–Regional de Mato Grosso, 7.

Abdulridha, J., Ampatzidis, Y., Ehsani, R. e De Castro, A. I. (2018) Evaluating the performance of spectral features e multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado. Computers and Electronics in Agriculture. 155. 203-211.

Alreshidi, E. (2019) Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI). International Journal of Advanced Computer Science and Applications (IJACSA). 10(5).93-102.

Alvares, L. O., Bogorny, V., Kuijpers, B., de Macedo, J. A. F., Moelans, B., & Vaisman, A. (2007, November). A model for enriching trajectories with semantic geographical information. In Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems.1-8.

Anderson, D. M. (2010). Geospatial methods and data analysis for assessing distribution of grazing livestock. In Proceedings of the 4th Grazing Livestock Nutrition Conference. 9-10. Western Section American Society of Animal Science Champaign, IL, USA.

Barbedo, J. G. A., Koenigkan, L. V., Santos, T. T., & Santos, P. M. (2019). A study on the detection of cattle in UAV images using deep learning. Sensors, 19(24), 5436.

Baena, S., Boyd, D. S., & Moat, J. (2018). UAVs in pursuit of plant conservation-Real world experiences. Ecological informatics, 47, 2-9.

Bah, M. D., Afiance, A., & Canals, R. (2017, November). Weeds detection in UAV imagery using SLIC and the hough transform. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). 1-6.

Bannerjee, G., Sarkar, U., Das, S. & Ghosh, I. (2018) Artificial Intelligence in Agriculture: A Literature Survey. International Journal of Scientific Research in Computer Science Applications and Management Studies (IJSRCSAMS). 7(3).1-6.

Berckmans, D. (2014). Precision livestock farming technologies for welfare management in intensive livestock systems. Rev. Sci. Tech,33(1), 189-196.

Borchers, M. R., & Bewley, J. M. (2015). An assessment of producer precision dairy farming technology use, prepurchase considerations, and usefulness. Journal of dairy science, 98(6), 4198-4205.

Brennecke, K. (2007). Fracionamento de carboidratos e proteínas e a predição da proteína bruta e suas frações e das fibras em detergentes neutro e ácido de Brachiaria brizantha cv. Marandu por uma rede neural artificial (Doctoral dissertation, Universidade de São Paulo).

Berry, D. P. & Crowley, J. J. (2013). Cell Biology Symposium: Genetics of feed efficiency in dairy and beef cattle. Journal of Animal Science. 91: 1594–1613.

Bruinsma, J. (2009). The Resource Outlook to 2050: By how much do land, water and crop yields need to increase by 2050?. In Proceedings of the FAO Expert Meeting. How to Feed the World in 2050. Food and Agriculture Organization of the United Nations, Economic and Social Development Department. Rome, Italy: FAO. 2-29.

Cáceres, E. N.; Pistore, H.; Turine, M. A. S.; Pires, P. P.; Soares, C. O.; & Carromeu, C. (2011). Computational precision livestock - position paper. In: II Workshop of the Brazilian Institute for Web Science Research. Rio de Janeiro.

Charania, I. e Li, X. (2020) Smart farming: Agriculture’s shift from a labor intensive to technology native industry. Internet of Things. 9. 1-15.

Chamoso, P., Raveane, W., Parra, V., & González, A. (2014). UAVs applied to the counting and monitoring of animals. In Ambient intelligence-software and applications. 71-80.

Chimakurthi, V. N. S. S. (2017). Risks of Multi-Cloud Environment: Micro Services Based Architecture and Potential Challenges. ABC Research Alert, 5(3).

Chimakurthi, V. N. S. S. (2018). Emerging of Virtual Reality (VR) Technology in Education and Training. Asian Journal of Humanity, Art and Literature, 5(2), 157-166.

Chimakurthi, V. N. S. S. (2019). Implementation of Artificial Intelligence Policy in the Field of Livestock and Dairy Farm. American Journal of Trade and Policy, 6(3), 113-118.

Cominotte, A., Fernandes, A. F. A., Dorea, J. R. R., Rosa, G. J. M., Ladeira, M. M., van Cleef, E. H. C. B., & Neto, O. M. (2020). Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science, 232, 103904.

Cunha, P. L. P., Cunha, C. S., & Alves, P. F. (2014) Revisão bibliográfica sistemática integrativa: a pesquisa baseada em evidências. Revista Anima Educação. 6(3):1-63.

De Clercq, M., Vats, A. & Biel, A. (2018) Agriculture 4.0: The future of farming technology. The World Government Summit and Oliver Wyman, pp. 1-30.

Dharmaraj, V. e Vijayanand, C. (2018) Artificial Intelligence (AI) in Agriculture. International Journal of Current Microbiology and Applied Sciences.7(12). 2122-2128.

Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern Classification 2. ed. 605 Third Avenue.

Eckelkamp, E. A., & Bewley, J. M. (2020). On-farm use of disease alerts generated by precision dairy technology. Journal of dairy science, 103(2), 1566-1582.

Elahi, E., Weijun, C., Zhang, H. & Nazeer, M. (2019) Agricultural intensification and damages to human health in relation to agrochemicals: Application of artificial intelligence. Land Use Policy. 83. 461-474.

Eli-Chukwu, N. C. (2019) Applications of Artificial Intelligence in Agriculture: A Review. Engineering, Technology & Applied Science Research. 9(4). 4377-4383.

EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária. (2012). EMBRAPA GADO DE LEITE - Panorama do Leite. Ano 6. n. 65. Juiz de Fora, MG.

FAO - Organização das Nações Unidas para a Alimentação e a Agricultura. (2017). The future of food and agriculture – Trends and challenges. Rome: Food and Agriculture Organization of the United Nations.

Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and electronics in agriculture, 145, 311-318.

Gallucci, S., Fiocchi, S., Bonato, M., Chiaramello, E., Tognola, G., & Parazzini, M. (2022). Exposure Assessment to Radiofrequency Electromagnetic Fields in Occupational Military Scenarios: A Review. International Journal of Environmental Research and Public Health, 19(2), 920.

Ghotbaldini, H., Mohammadabadi, M., Nezamabadi-pour, H., Babenko, O. I., Bushtruk, M. V., & Tkachenko, S. V. (2019). Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed. Acta Scientiarum. Animal Sciences, 41.

Gianola, D., Okut, H., Weigel, K. A., & Rosa, G. J. (2011). Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC genetics, 12(1), 1-14.

Goedde, L., Katz, J., Menard, A., & Revellat, J. (2020) Agriculture’s connected future: How technology can yield new growth. McKinsey & Company. October, 1-10.

Gomes, R. A., Monteiro, G. R., Assis, G. J. F., Busato, K. C., Ladeira, M. M., & Chizzotti, M. L. (2016). Estimating body weight and body composition of beef cattle trough digital image analysis. Journal of Animal Science, 94(12), 5414-5422.

Gonzalez, L. F., Montes, G. A., Puig, E., Johnson, S., Mengersen, K., & Gaston, K. J. (2016). Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors, 16(1), 97.

Guarino, M., Norton, T., Berckmans, D., Vranken, E., & Berckmans, D. (2017). A blueprint for developing and applying precision livestock farming tools: A key output of the EU-PLF project. Animal Frontiers, 7(1), 12-17.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.

Kaab, A., Sharifi, M., Mobli, H., Nabavi-Pelesaraei, A., & Chau, K. (2019). Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production. Science of the Total Environment, 664, 1005-1019.

Kashiha, M., Bahr, C., Ott, S., Moons, C. P., Niewold, T. A., Ödberg, F. O., & Berckmans, D. (2014). Automatic weight estimation of individual pigs using image analysis. Computers and Electronics in Agriculture, 107, 38-44.

Khakurel, J., Penzenstadler, B., Porras, J., Knutas, A., & Zhang, W. (2018). The rise of artificial intelligence under the lens of sustainability. Technologies, 6(4), 100.

Kouadio, L., Deo, R. C., Byrareddy, V., Adamowski, J. F., Mushtaq, S. & Nguyen, V. P. (2018) Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Computers and Electronics in Agriculture. 155. 324-338.

Kunze, L., Hawes, N., Duckett, T., Hanheide, M., & Krajník, T. (2018). Artificial intelligence for long-term robot autonomy: A survey. IEEE Robotics and Automation Letters, 3(4), 4023-4030.

Lakshmi, V. & Corbett, J. (2020) How Artificial Intelligence Improves Agricultural Productivity and Sustainability: A Global Thematic Analysis. In Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS’20). Maui, Hawaii: ScholarSpace. 5202-5211

Lal, P. P., Prakash, A. A., Chand, A. A., Prasad, K. A., Mehta, U., Assaf, M. H. & Mamun, K. A. (2022). IoT integrated fuzzy classification analysis for detecting adulterants in cow milk. Sensing and Bio-Sensing Research, 36, 100486.

Laloë, D. (2019). Artificial Intelligence and Livestock New data, new approaches.

Li, D., & Du, Y. (2017). Artificial intelligence with uncertainty. CRC press. Beijing, China: CRC Press, Taylor e Francis Group, 9(4), 4377–4383.

Lin, F., Zhang, D., Huang, Y., Wang, X. & Chen, X. (2017) Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features. Sustainability. 9 (8). 1-14.

Lomba, L. F. D., Jesus, L., Rubinsztejn, H. K. S., Gonda, L., & Pires, P. P. (2015). O uso de inteligência artificial na identificação do comportamento bovino. In X Congresso Brasileiro de Agroinformática, Anais Ponta Grossa: UFMS.

Marr, B. (2019) What is AI?. www.bernardmarr.com/default.asp?contentID=963.

Martiskainen, P., Järvinen, M., Skön, J. P., Tiirikainen, J., Kolehmainen, M., & Mononen, J. (2009). Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Applied animal behaviour science, 119(1-2), 32-38.

Massruhá, S. M. F. S., Leite, M. D. A., Oliveira, S. D. M., Meira, C. A. A., Luchiari Junior, A., & Bolfe, E. (2020). Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas. Embrapa Agricultura Digital-Livro científico (ALICE).

Mewes, J. (2018) Artificial Intelligence And Its Uses In Ag Irrigation. AgriTech Tomorrow. (October) Disponível em:https://www.agritechtomorrow.com/article/2018/10/artificial-intelligence-and-its-uses-in-ag-irrigation/11094.

Murase, H. (2000). Artificial intelligence in agriculture. Computers and Electronics in Agriculture, 29. 1-2.

Nadimi, E. S., Søgaard, H. T., & Bak, T. (2008). ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosystems engineering, 100(2), 167-176.

Neethirajan, S. (2021). The use of artificial intelligence in assessing affective states in livestock. Frontiers in Veterinary Science, 879.

Norton, T., Chen, C., Larsen, M. L. V., & Berckmans, D. (2019). Precision livestock farming: Building ‘digital representations’ to bring the animals closer to the farmer.Animal,13(12), 3009-3017.

Nourizadeh, M., Kalantari, E., & Habiba, S. (2018). Modeling of Tehran residents attitude to GMFs using structural equations.

Olejnik, K., Popiela, E., & Opaliński, S. (2022). Emerging Precision Management Methods in Poultry Sector. Agriculture, 12(5), 718.

Pacheco, V. M. (2019). Desenvolvimento de classificador de conforto térmico para bovinos de leite utilizando modelagem computacional e termografia de infravermelho (Doctoral dissertation, Universidade de São Paulo).

Palma, A. T., Bogorny, V., Kuijpers, B., & Alvares, L. O. (2008). A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the 2008 ACM symposium on Applied computing 863-868.

Panpatte, D. G. (2018) Artificial Intelligence in Agriculture: An Emerging Era of Research. In proceedings of the 31st Canadian Conference on Artificial Intelligence. [Online]. Anand, India: ResearchGate, 1-8. https://www.researchgate.net/publication/328555978_Artificial_Intelligence_in_Agriculture_An_Emerging_Era_of_Research.

Pantazi, X. E., Moshou, D. & Bochtis, D. (2020a) Sensors in agriculture. Intelligent Data Mining and Fusion Systems in Agriculture. 1st ed. San Diego, United States: Elsevier Science Publishing Co Inc. p. 1-15.

Pantazi, X. E., Moshou, D. & Bochtis, D. (2020b) Artificial intelligence in agriculture. Intelligent Data Mining and Fusion Systems in Agriculture. 1st ed. San Diego, United States: Elsevier Science Publishing Co Inc. p. 17-101.

Pathak, J., Hunt, B., Girvan, M., Lu, Z., & Ott, E. (2018). Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Physical review letters, 120(2), 024102.

Patel, H., Samad, A., Hamza, M., Muazzam, A., & Harahap, M. K. (2022). Role of Artificial Intelligence in Livestock and Poultry Farming. Sinkron: jurnal dan penelitian teknik informatika, 7(4), 2425-2429.

Prudkin, G., & Breunig, F. (2019). Drones e ciência: teoria e aplicações metodológicas. Santa Maria, RS: FACOS-UFMS.

Rayome, A. D. (2019) How AI could save the environment, Tech Republic. https://www.techrepublic.com/article/how-ai-could-save-the-environment/

Revanth. (2019). Towards Future Farming: How Artificial Intelligence is transforming the Agriculture Industry. https://www.wipro.com/holmes/towards-future-farming-how-artificial-intelligence-is-transforming-the-agriculture-industry/

Russell, S. & Norvig, P. (2010) Artificial Intelligence: A Modern Approach. 3rd ed. New Jersey: Pearson Education

Rouhiainen, L. (2018). Artificial Intelligence: 101 things you must know today about our future: Lasse Rouhiainen.

Santos, T. T., de Souza, L. L., dos Santos, A. A., & Avila, S. (2020). Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association. Computers and Electronics in Agriculture, 170, 105247.

Sarker, M. N. I., Wu, M., Chanthamith, B., Yusufzada, S., Li, D. & Zhang, J. (2019) Big Data Driven Smart Agriculture: Pathway for Sustainable Development. In Proceedings of the 2nd International Conference on Artificial Intelligence and Big Data Big (ICAIBD). Chengdu, China: IEEE. 60-65.

Scheibe, K. M., & Gromann, C. (2006). Application testing of a new three-dimensional acceleration measuring system with wireless data transfer (WAS) for behavior analysis. Behavior research methods, 38(3), 427-433.

Schönfeld, M. V., Heil, R. & Bittner, L. (2018) Big Data on a Farm – Smart Farming. In Hoeren, T. and Kolany-Raiser, B. (Eds.). Big Data in Context. Germany: SpringerBriefs in Law. 109-120.

Sharma, B., & Koundal, D. (2018). Cattle health monitoring system using wireless sensor network: a survey from innovation perspective. IET Wireless Sensor Systems, 8(4), 143-151.

Tullo, E., Fontana, I., Diana, A., Norton, T., Berckmans, D., & Guarino, M. (2017). Application note: Labelling, a methodology to develop reliable algorithm in PLF. Computers and Electronics in Agriculture, 142, 424-428.

Wang, Y., Yang, W., Winter, P., & Walker, L. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering, 100(1), 117-125.

Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., ... & Chau, D. H. P. (2020). CNN explainer: learning convolutional neural networks with interactive visualization. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1396-1406.

Yaacoub, J. P., Noura, H., Salman, O., & Chehab, A. (2020). Security analysis of drones systems: Attacks, limitations, and recommendations.Internet of Things,11, 100218.

Yu, X., Wang, J., Kays, R., Jansen, P. A., Wang, T., & Huang, T. (2013). Automated identification of animal species in camera trap images. EURASIP Journal on Image and Video Processing, 2013(1), 1-10.

Downloads

Published

27/03/2023

How to Cite

SILVA, A. M. da .; SANTOS, F. K. dos .; MACHADO, P. B. .; BERGHAHN, L. G. .; CAMPOS, G. P. de .; ARAÚJO, C. V. de; ARAÚJO, S. I.; MENEZES, F. L. de . Use of Artificial Intelligence in Livestock: Literature review. Research, Society and Development, [S. l.], v. 12, n. 4, p. e6612440777, 2023. DOI: 10.33448/rsd-v12i4.40777. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/40777. Acesso em: 21 nov. 2024.

Issue

Vol. 12 No. 4

Section

Agrarian and Biological Sciences

License

Copyright (c) 2023 Adriana Moreira da Silva; Francieli Kovalski dos Santos; Pamella Biazi Machado; Luísa Götz Berghahn; Gilberto Pedroso de Campos; Cláudio Vieira de Araújo; Simone Inoe Araújo; Flávio Luiz de Menezes

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.