Study of Agribusiness 4.0 – Technologies, challenges and benefits in Agribusiness

Authors

DOI:

https://doi.org/10.33448/rsd-v11i13.35379

Keywords:

Agrobusiness 4.0; Technologies; State of art.

Abstract

A study is presented on Agribusiness 4.0 (Agro 4.0) and its evolution over the years, and how technologies such as the Internet of Things (IoT), Big Data, Remote Sensing and Drones and Machine Learning are being applied in Agro 4.0. The benefits and challenges of technologies and state of the art in Agro 4.0 are also presented. The objective of the work is to carry out bibliographic research on Agro 4.0 and identify the main technologies adopted, as well as benefits and challenges of them. In this qualitative-quantitative review, articles, dissertations and theses were selected from the Scielo and Google Scholar databases, and books were also selected for the work. The study of the present work is justified by the growth of technologies in Agro with a focus on state-of-the artificial intelligence works in this field. In this sense, it is possible to conclude that Agro 4.0 is expanding and in a possible transition to Agro 5.0.

Author Biographies

Vinicyo Luan Chagas de Oliveira, Universidade do Estado de Mato Grosso

Student of the Computer Science course.

Max Robert Marinho, Universidade do Estado de Mato Grosso

Professor Doutor de Ciência da Computação da UNEMAT de Alto Araguaia/Rondonópolis

Daniela Cabral de Oliveira, Universidade do Estado de Mato Grosso

Professor of Mechanical Engineering, working in the Computer Science course.

Mielle Silva Pestana, Universidade do Estado de Mato Grosso

Professor of Mechanical Sciences, working in the Computer Science course.

Sérgio Santos Silva Filho, Universidade do Estado de Mato Grosso

Master Professor in Computer Science, active in the Computer Science course.

Lucas Sperotto, Universidade do Estado de Mato Grosso

Master in Science Professor, active in the Computer Science course.

Fernando Obana, Universidade do Estado de Mato Grosso

Professor of Electrical Engineering, working in the Computer Science course.

References

Aghbashlo, M., Hosseinpour, S. & Mujumdar, A.S. (2015). Application of artificial neural networks (ANNs) in drying technology. A comprehensive review. Drying Technology, 33(12), 1397-1462.

Boursianis, A. D. et al. (2020). Smart Irrigation System for Precision Agriculture - The AREThOU5A IoT Platform. IEEE Sensors Journal.

Chen, J. & Yang, A. (2019). Intelligent Agriculture and Its Key Technologies Based on Internet of Things Architecture. IEEE, 7, 77134-77141.

Clercq, M., Vats, A.& BIEl, A. (2018). Agriculture 4.0: the future of farming technology. World Governement Summit.

Dai, A., Zhou, X. & Wu, Z. (2020). Design of an intelligent controller for a grain dryer: A support vector machine for regression inverse model proportional-integral-derivative controller. Food Science & Nutrition, 8(2), 805-819.

Embrapa. (2020a). Trajetória da agricultura brasileira. https://www.embrapa.br/visao/trajetoria-da-agricultura-brasileira.

Fajardo, M., Whelan, B., Filippi, P. & Bishop, T. (2019). Wheat yield forecast using contextual spatial information. In Precision agriculture’19, Wageningen Academic Publishers, pp.4559-4565.

Fleming, A et al. (2021). Foresighting Australian digital agricultural futures: Applying responsible innovation thinking to anticipate research and development impact under different scenarios. Agricultural Systems, v. 190, n. March, p. 103120. https://doi.org/10.1016/j.agsy.2021.103120>.

Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2015). Vsuf: An R package for variable selection using random forests. The R Journal, 7, 19-33.

Gil, A. C. (2008). Métodos e Técnicas de Pesquisa Social. (6a ed.), Atlas.

Hashem, et al. (2015). The Rise Of “Big Data” On Cloud Computing: Review And Open Research Issues. Information Systems, 47, 98–115.

Jayaraman, et al. (2016). Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt. Sensors, 16, 1884.

Jhan. K., Doshi, A., Patel, P. & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture 1:1-12. DOI: https://doi.org/10.1016/j.aiia.2019.05.004.

Jorgensen, M. H. (2018). Agricultural field production in an ‘industry 4.0’ concept. Agronomy Research, 16(1), 94–102.

Keogh, M. & Henry, M. (2016). The implications of Digital Agriculture and Big data for Australian Agriculture. Ressearch Report, Autralian Farm Institute, Sidney, Autralia.

Klerkx, L., Jakku, E. & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 90–91, 100315.

Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways? Global Food Security, v. 24, n. October 2019, p. 100347. https://doi.org/10.1016/j.gfs.2019.100347>.

Kodan, R., Parmar, P., & Pathania, S. (2020). Internet of Things for Food Sector: Status Quo and Projected Potential. Food Reviews International, 36(6), 584–600.

Kodati, S. & Jeeva, S. (2019). Smart Agricultural using Internet of Things, Cloud and Big Data. International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-10.

Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431–440. https://doi.org/10.1016/J.BUSHOR.2015.03.008.

Lezoche, M. et al. (2020). Agri-food 4.0: A survey of the Supply Chains and Technologies for the Future Agriculture. Computers in Industry, 117, 103187. https://doi.org/10.1016/j.compind.2020.103187>.

Lima, G. C. et al. (2020). Agro 4.0: Enabling agriculture digital transformation through IoT. Revista Ciencia Agronomica, 51(5), 1–20.

Lin, et al. (2017). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. Ieee internet of things journal, vol. 4, no. 5.

Lioutas, et al. (2019). Key Questions On The Use Of Big Data In Farming: An Activity Theory Approach. NJAS - Wageningen Journal of Life Sciences 90–91.

Lisbinski, F. C., Muhl, D. D., Oliveira, L. De, & Coronel, D. A. (2020). Perspectivas e Desafios da Agricultura 4.0 para o Setor Agrícola. Simpósio da ciência do agronegócio, Cepam Agronegócio. https://www.lume.ufrgs.br/bitstream/handle/10183/218601/001122708.pdf?sequence=1#:~:text=Dentre%20as%20perspectivas%20da%20agricultura,evitar%20a%20perda%20da%20biodiversidade.

Massruhá, S, S. F. M., & Leite, A. A. M. M. (2017). Agro 4.0 – Rumo à agricultura digital. JC na Escola Ciência, Tecnologia e Sociedade: Mobilizar o Conhecimento para Alimentar o Brasil, 2017.

Massruhá, S. M. F. S., Leite M. A. de A., Oliveira, S. R. de M., Meira, C. A. A., Luchiari Junior, A., & Bolfe, E. L. (2020). Agricultura digital: Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas. https://www.embrapa.br/busca-de-publicacoes/-/publicacao/1126213/agricultura-digital-pesquisa-desenvolvimento-einovacao-nas-cadeias-produtivas.

Mattetti, M. et al. (2021). Outlining the mission profile of agricultural tractors through CAN-BUS data analytics. Computers and Electronics in Agriculture, v. 184, n. February, p. 106078. https://doi.org/10.1016/j.compag.2021.106078.

Mavridou, E. et al. (2019). Machine vision systems in precision agriculture for crop farming. Journal of Imaging, 5(12).

Mazzetto, F., Gallo, R., & Sacco, P. (2020). Reflections and methodological proposals to treat the concept of “information precision” in smart agriculture practices. Sensors (Switzerland), v. 20, n. 10, p. 1–27.

Medeiros, A. D. de., Pinheiro, D. T., Xavier, W. A., Silva, L. J. da., & Dias, D. C. F. dos S. (2020) Quality classification of Jatropha curcas seeds using radiographic images and machine learning. Industrial Crops and Products 146:112–162. DOI: https://doi.org/10.1016/j.indcrop .2020.112162.

Mekala, M. S., & Viswanathan, P. (2017). A survey: smart agriculture loT with cloud computing. In: 2017 international conference on microelectronic devices, circuits and systems (ICMDCS). IEEE, p.1-7.

Monteleone, S., Moraes, E. A. De, & Faria, B. T. De. (2020). Exploring the Adoption of Precision Agriculture for Irrigation in the Context of Agriculture 4.0: The Key Role of Internet of Things. Sensors.

Moslem A, Younessi-Hmazekhanlu M, Ramazani S H R, & Omidi A H (2019) Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.). Industrial Crops and Products 127(1):185-194. https://doi.org/10.1016/j.indcrop.2018.10.050.

Nayak, P., Rayaguru, K., Bal, L. M., Das, S. & Dash, S. (2021). Modelagem de Rede Neural Artificial da Cinética de Secagem por Ar Quente de Mango Kernel. Revista de Pesquisa Científica e Industrial. V. 80 pp. 750-758.

Oliveira, D. C. de., Barbosa, U. C., Bergland, A. C. R. O, Resende, O., & Oliveira, D. E. C. de. (2022). G-Soja – Website with Prediction on Soybean Classification Using Machine Learning. Revista Engenharia Agrícola. V. 42. DOI: http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210140/2022.

Pillon, C. N. (2017). Dos pós de rocha aos remineralizadores: passado, presente e desafios. In: Congresso Brasileiro de Rochagem, 3., 2017, Pelotas. Anais. Assis: Triunfal Gráfica e Editora, p. 16-23. Editado por Adilson Luis Banberg, Carlos Augusto Posser Silveira, Éder de Souza Martins, Magda Bergmann, Rosane Martinazzo e Suzi Huff Theodoro.

Pison, G. (2019). How many humans tomorrow? The United Nations revises its projections. The Conversation. Waltham. http://theconversation.com/how-many-humans-tomorrow-the-united-nations-revises-its-projections-118938>.

PixForce. Aprendizado de máquina para soluções agrícolas e florestais. PixelFoce, 2018. https://pixforce.com.br/.

Pontes, L. B., & Cavichioli, F. A. (2019). Agricultura de Precisão. SIMTEC - Simpósio de Tecnologia da Fatec Taquaritinga, 5(1), 238-250, 22 dez.

Queiroz, D. M. de., Coelho, A. L. de F., Valente, D. S. M., & Schueller, J. K. (2021). Sensors applied to Digital Agriculture: A review. http://ccarevista.ufc.br/seer/index.php/ccarevista/article/view/7751#:~:text=Sensors%20are%20the%20basis%20of,automate%20the%20prescription%20of%20inputs.

Queiroz, D. M., Coelho, A. L. F., Valente, D. S. M. & Schueller, J. K. (2020). Sensors applied do Digital Agriculture: A review. Revista Ciência Agronômica, v. 51, Special Agriculture 4.0, e20207751. DOI: 10.5935/1806-6690.20200086. Universidade Federal do Ceará, Fortaleza.

Raj, M. et al. (2021). A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. Journal of Network and Computer Applications, 185(5).

Ribeiro, J. G., Marinho, D. Y. & Espinosa, J. W. M. (2018). Agricultura 4.0: Desafios à Produção de Alimentos e Inovações Tecnológicas. Simpósio de Engenharia de Produção. Universidade Federal de Goiás.

Rocha, E. T. B. Da. (2021). Aagricultura 4.0 nas Lavouras: Estudo Multicaso para caracterização em Propriedades Rurais. Dissertação (mestrado) – Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal.

Saffariha M, Jahani A, & Potter D (2020) Seed germination prediction of Salvia limbata under ecological stress in protected areas: an artificial intelligence modeling approach. BMC Ecology 20 (48):1-14. DOI: https://doi.org/10.1186/s12898-020-00316-4

Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(2).

Santana, H. M. de et al. (2019). Evolução histórica da Indústria 4.0 e seus reflexos nos Agronegócios. https://fateclog.com.br/anais/2019/evolu%c3%87%c3%83O%20HIST%c3%93rica%20da%20industria%204.0%20E%20seus%20reflexos%20NO%20agroneg%c3%93cio.pdf.

Schwalbert, et al. (2014). Zonas de manejo: atributos de solo e planta visando a sua delimitação e aplicações na agricultura de precisão. Revista Plantio Direto, Edição 140, p. 21-32.

Silva, J. M. P. & Cavichioli, F. A. (2020). O Uso da Agricultura 4.0 como perspectiva do aumento da Produtividade no Campo. s.l.:DOI: 10.31510/infa.v17i2.1068.

Sordi, V. F. & Vaz, S. C. M. (2020). Os Principais Desafios para a Popularização de Práticas Inovadoras de Agricultura Inteligente. s.l.:Editora Unijuí.

Spancerski, J. S., & Santos, J. A. A. (2021) Previsão da produtividade de arroz: uma aplicação de redes neurais recorrentes LSTM. Revista Cereus 13(2):163-175. DOI: https://doi.org/10.18605/2175-7275/cereus.v13n2p163-175.

Springer. (2020). Precision agriculture. https://www.springer.com/journal/11119/updates/17240272.

Symeonaki, E., Arvanitis, K., & Piromalis, D. (2021). A context-aware middleware cloud approach for integrating precision farming facilities into the IoT toward agriculture 4.0. Applied Sciences (Switzerland), 10(3).

Trivelli, L. et al. (2019). From precision agriculture to Industry 4.0: Unveiling technological connections in the agrifood sector. British Food Journal. [S.l: s.n.].

Tu K, Wen S, Cheng Y, & Zhang T (2021) A non-destructive and highly efficient model for detecting the genuineness of maize variety 'JINGKE 968′using machine vision combined with deep learning. Computers and Electronic in Agriculture. 182

Villafuerte, A., Valadares, F. G., Campolina, G. F., & Silva, M. G. P. (2018). Agricultura 4.0 – Estudo de Inovação Disruptiva no Agronegócio Brasileiro. International Symposium Technological Innovation. ISSN: 2318-3403. 9(1). 150-162. 10.7198/S2318-3403201800010018.

Worldmeter. (2022). Department of economic and social affairs, population division, world population prospects. <http://www.worldometers.info/population/>.

Zaparolli, D. (2020). Agriculture 4.0. Connected Farms. Pesquisa Fapesp magazine. São Paulo, 21(287), 12-20. Available at: <https://revistapesquisa.fapesp.br/wp-content/uploads/2020/01/Pesquisa-287_Completo-2.pdf>. Accesso em: 08 de setembro 2022.

Zeymer, J. S. (2021). Modelagem Matemática dos Fenômenos de Higroscopia e Respiração de Grãos de Soja em diferentes condições de armazenamento. s.l.:s.n. Massruhá.

Zhal, Z. et al. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, n. August 2019, p. 105256. <https://doi.org/10.1016/j.compag.2020.105256>.

Published

09/10/2022

How to Cite

OLIVEIRA, V. L. C. de .; MARINHO, M. R.; OLIVEIRA, D. C. de .; PESTANA, M. S.; SILVA FILHO, S. S.; SPEROTTO, L. .; OBANA, F. . Study of Agribusiness 4.0 – Technologies, challenges and benefits in Agribusiness. Research, Society and Development, [S. l.], v. 11, n. 13, p. e363111335379, 2022. DOI: 10.33448/rsd-v11i13.35379. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/35379. Acesso em: 20 apr. 2024.

Issue

Section

Review Article