Innovations in agribusiness in the scope of the Ruta Bioceánica
DOI:
https://doi.org/10.20435/inter.v25i1.4239Keywords:
Bioceanic Route, Precision Livestock, Agribusiness, Innovation, TechnologyAbstract
The Bioceanic Route connects Brazil, Paraguay, Argentina and Chile. It can be considered the largest infrastructure project in Latin America and has the potential to reduce the cost of transporting goods between the four countries and continents. In addition, agribusiness is an activity of great economic importance for Brazil and for the state of Mato Grosso do Sul that can be a protagonist in this process. Livestock, in particular, is an important activity in the region, due to its representative value in the economy of this state. Thus, the Bioceânica Route has the potential to boost the development of agribusiness in the region. The highway will facilitate the transport of agricultural products between countries in the region, which will make Brazilian products more competitive in the international market. In addition, the highway will facilitate the entry of new technologies into the agricultural sector, which will help improve farm productivity and efficiency. This article aims to discuss innovations in agribusiness within the scope of the Bioceânica Route, focusing on livestock precision. In addition, we discuss that the Bioceânica Route, with its potential, can be a network that strengthens and intensifies the development of intraregional trade. Indeed, after this contextualization, we highlight the importance of analyzing the creation of Hubs of Innovation and Technologies applied to livestock precision.
References
ASSOCIAÇÃO BRASILEIRA DAS INDÚSTRIAS EXPORTADORAS DE CARNES [ABIEC]. Perfil da pecuária no Brasil: relatório anual. Brasília, DF: ABIEC, ago. 2021.
ABRITA, M. B.; VIGNANDI, R. S.; CENTURIÃO, D. A. S.; RONDINA NETO, A.; PEREIRA, A. P. C.; ESPINDOLA JUNIOR, G.; MARQUES, N.; WEBER, V. A. M.; MACIEL, R. F. Dynamics of local productive arrangements in the municipalities of Mato Grosso do Sul considering the transformations of the Bioceanic Corridor. [S.l.]: PLoS ONE, 2023.
ASATO, T. A.; MARQUES, H. R.; BUZARQUIS, R. M.; BORGES, P. P. Rota de Integração Latino-Americana (RILA) para o desenvolvimento turístico. Interações. Campo Grande, MS, v. 20, n. especial, p. 45–56, 2019.
CAMILO PEREIRA, A. P., ABRITA, M. B.; FONSECA, R. O. Circulação, desenvolvimento econômico e ordenamento territorial: elementos teóricos para análises de pesquisas sobre a Rota de Integração Latino Americana, Confins [Online], 50, 2021. Disponível em: https://journals.openedition.org/confins/37445. Acesso em: 26 jun de 2021.
COMINOTTE, A. et al. Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science, Amsterdam, n. 232, p. 103–904, 2020.
CRESTANA, S.; FRAGALLE, E. A trilha da quinta potência: um primeiro ensaio sobre ciência e inovação, agricultura e instrumentação agropecuária brasileiras. Revista Eixo, Brasília-DF, n. 1, 2012. Doi: http://dx.doi.org/10.19123/eixo.v1i1.8
FORSYTH, D. A.; PONCE, J. Computer vision – a modern approach. Paris: École Normale Superieure, 2012.
GJERGJI, M.; WEBER, V. A. M; SILVA, L. O. C.; GOMES, R. C.; ARAUJO, T. L. A. C.; PISTORI H. Deep learning techniques for beef cattle body weight prediction. In: INTERNATIONAL JOINT CONFERENCE NEURAL NETWORKS (IJCNN). IEEE, 2020. International Joint Conference on Neural Networks (IJCNN), [s.l.], 2020. p. 1–8.
GONÇALVES, D. N.; WEBER, V. A. M.; PISTORI, J. G. B.; GOMES, J. G. B.; ARAUJO, A. V.; PEREIRA, M. F.; GONÇALVES, W. N.; PISTORI, H. Carcass image segmentation using CNN-based methods. Information Processing in Agriculture, [s.l.], 2020.
GONZALEZ, R. C.; WOODS, R. E. Digital Image Processing. 2. ed. [S.l.]: Prentice-Hall, Upper Sadle River, 2002.
GOODFELLOW, I.; BENGIO, Y; COURVILLE, A. Deep Learning. Cambridge: MIT Press, 2016.
HALACHMI, I.; GUARINO, M. Editorial: Precision livestock farming: a ‘per animal’ approach using advanced monitoring technologies. Animal, Cambridge, v. 10, n. 9, 1482–83, 2016. doi: http://dx.doi.org/10.1017/S1751731116001142
HOVE, H. Critiquing sustainable development: a meaningful way of mediating the development impasse? Undercurrent, Seattle, v. 1, n. 1, p. 48–54, 2004.
JAHNE, B.; HAUBECKER, H. Computer vision and applications: a guide for students and practitioners. Academic Press, San Diego, 2000.
JUNG, S.; ARIYUR, K. B. Strategic cattle roundup using multiple quadrotor UAVs. International. Journal of Aeronautical and Space Sciences, Muan, n. 18, p. 315–26, 2017.
LATTIN, J.; CARROL, J. D.; GREEN, P. E. Análise de dados multivariados. CENGAGE Learning, São Paulo, 2011.
LECUN, Y.; BENGIO, Y.; HINTON, G. Deep learning. Nature, England, v. 521, n. 7553, p. 436–44, 2015.
LIU, X.; LIANG, W.; WANG, Y. L. I., S; PEI, M. 3D head pose estimation with convolutional neural network trained on synthetic images. Image Processing (ICIP). IEEE International Conference on, Phoenix, 2016, p. 1289–93, 2016.
MARTINS, M. A. R.; MITISHITA, E. A. Geração de ortofotos com abordagem do georreferenciamento direto de imagens digitais aéreas. Revista Brasileira de Cartografia, Uberlândia, v. 70, n. 1, 2018. ISSN 1808-0936.
MOLDAN, B.; JANOUAKOVÁ, S.; HÁK, T. How to understand and measure environmental sustainability: indicators and targets. Ecological Indicators, Praha, v. 17, p. 4-13, 2012.
MONARD, M. C.; BARANAUSKAS, J. A. Conceitos sobre aprendizado de máquina. São Paulo: Sistemas Inteligentes – fundamentos e aplicações, 2003.
NASIRAHMADI, A.; EDWARDS, S. A.; STURM, B. Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock Science, Amsterdã, v. 202, p. 25–38, 2017.
OLIVEIRA JUNIOR, A. S..; SANT’ANA, D. A.; PACHE, M. C. B.; GARCIA, V.; WEBER, V. A. M.; ASTOLFI, G.; WEBER, F. L.; MENEZES, G. V.; MENEZES, G. K.; ALBUQUERQUE, P. L. F.; COSTA, C. S.; QUEIROZ, E. Q. A.; ROZALES, J. V. A; FERREIRA, M. W.; NAKA, M. H.; PISTORI, H. Fingerlings mass estimation: a comparison between deep and shallow learning algorithms. Smart Agricultural Technology, [s.l.], p. 100020, 2021.
PRUGH, T.; ASSADOURIAN, E. What is sustainability, anyway? World Watch, Washington, v. 16, n. 5, 2003. p. 10–21.
SANT’ANA, D. A.; PACHE, M. C. B.; MARTINS, J.; SOARES, W. P.; MELO, S. L. N.; GARCIA, V.; WEBER, V. A. M.; HEIMBACH, N. S.; MATEUS, R. G.; PISTORI, H. Weighing live sheep using computer vision techniques and regression machine learning. Machine Learning with Applications, p. 100076, 2021.
SARTORI, S.; LATRONICO, F.; CAMPOS, L. M. Sustentabilidade e desenvolvimento sustentável: uma taxonomia no campo da literatura. Ambiente e Sociedade, São Paulo, v. 17, n. 1, 2014.
SZELISKI, R. Computer vision: algorithms and applications. Nova York: Springer, 2010.
WADA, K. labelme: Image Polygonal Annotation with Python. Cambridge, 2016.
WEBER, FL; WEBER, VAM; MENEZES, GV; OLIVEIRA JUNIOR, AS; ALVES, DA; OLIVEIRA, MVO; MATSUBARA, ET; PISTORI, H; ABREU, UGPA. Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks. Computers and Electronics in Agriculture, Amsterdam, v. 175, p. 105548, 2020a.
WEBER, VAM; WEBER, FL; OLIVEIRA, AS; ASTOLFI, G; MENEZES, GV; PORTO, JVA; REZENDE, FPC; MORAES, PH; MATSUBARA, ET; MATEUS, RG; ARAÚJO, TLAC; SILVA, LOC; QUEIROZ, EQA; ABREU, UGPA; GOMES, RCG; PISTORI, H.. Cattle weight estimation using active contour models and regression trees Bagging. Computers and Electronics in Agriculture, Amsterdam, v. 179, p. 105804, 2020b.
WEBER, VAM; WEBER, FL; GOMES, RCG; OLIVEIRA JUNIOR, AS; MENEZES, GV; ABREU, UGPA; BELETE, NAS; PISTORI, H. Prediction of Girolando cattle live weight by means of body measurements extracted from images. Revista Brasileira de Zootecnia, Viçosa-MG, v. 49. 2020c.
WITTEN, I. H.; FRANK, E. Data Mining: practical machine learning tools and techniques. Amsterdam: Morgan Kaufmann, 2005.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Vanessa Weber, Nelagley Marques
This work is licensed under a Creative Commons Attribution 4.0 International License.
Direitos Autorais para artigos publicados nesta revista são do autor, com direitos de primeira publicação para a revista. Em virtude de aparecerem nesta revista de acesso público, os artigos são de uso gratuito, com atribuições próprias, em aplicações educacionais e não-comerciais.
