Regador: APP for coffee water potential estimation
Resumo
In coffee farming, reductions in water availability decrease productivity. Therefore, monitoring the water potential (Ψ), a measure that assesses whether a plant is under water stress, helps management. This article presents the design, development and evaluation of a georeferenced mobile application for coffee farming water potential in the south of Minas Gerais. This work was developed with the premises of Design Science. In particular, in the development of an IT artifact. The research was prescriptive and the artifact was generated as a proof of concept of the system. The analysis of the results was carried out with a quantitative approach. The evaluation of user satisfaction carried out with researchers and coffee growers obtained satisfactory results and validates the importance of the application developed in the decision-making process in the field. The impact is both in the technical and business spheres, since the article contemplates the methodology for developing an artifact that aims to bring scientific research and rural producers closer, as it materializes a model currently available only in specialized journals.
Palavras-chave:
Mobile Application, digital agriculture, geotechnologies, DSRM
Referências
A. G. Albay and Y. Doğan. 2020. A Novel Agriculture Tracking System Using Data Mining Approaches. Avrupa Bilim ve Teknoloji Dergisi -, - (2020), 313–322.
R.N. Athirah, C.Y.N. Norasma, and M.R. Ismail. 2020. Development of an Android Application for Smart Farming in Crop Management. In IOP Conference Series: Earth and Environmental Science, Vol. 540. IOP Publishing, Vancouver, 012074. Issue 1. https://doi.org/10.1088/1755-1315/540/1/012074
J. S. Barroso, M. Pimentel, V. Nunes, and C. Cappelli. 2019. Design Science Research to Design a Conceptual Model about Prosopographic Information Related to Politicians. In Proceedings of the XV Brazilian Symposium on Information Systems(SBSI’19). ACM, Aracaju, Brazil, 1–8. https://doi.org/10.1145/3330204.3330233
L. A. Batista, R. J. Guimarães, F. J. Pereira, G. R. Carvalho, and E. M. Castro. 2010. Anatomia foliar e potencial hídrico na tolerância de cultivares de café ao estresse hídrico. Revista Ciência Agronômica 41 (2010), 475–481. https://doi.org/10.1590/S1806-66902010000300022
W. J. Doll and G. Torkzadeh. 1988. The Measurement of End-User Computing Satisfaction. MIS Q. 12, 2 (1988), 259–274. https://doi.org/10.2307/248851
A. Dresch, D.P. Lacerda, and J.A.V.A. Júnior. 2015. Design Science Research: Método de Pesquisa para Avanço da Ciência e Tecnologia. Bookman Editora, Porto Alegre.
H. El Bilali, F. Bottalico, G. P. Ottomano, and R. Capone. 2020. Information and Communication Technologies for Smart and Sustainable Agriculture. In 30th Scientific-Experts Conference of Agriculture and Food Industry, Muhamed Brka, E. Omanović-Mikličanin, L. Karić, V. Falan, and A. Toroman (Eds.). Springer International Publishing, Cham, 321–334.
A. L. Filardi and A. J. M. Traina. 2008. Montando questionários para medir satisfação usuário:Avaliação de interface de sistema que utiliza técnicas de recuperação de imagens por conteúdo. In Proceedings of the VIII Brazilian Symposium on Human Factors in Computing Systems(IHC ’08). SBC, Porto Alegre/RS, 176–185.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R Moore. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202 (2017), 18–27. https://doi.org/10.25186/cs.v12i2.1176
F.E.A. Horita, F. G. Rocha, L. S. Souza, and G. R. Gonzales. 2020. Design Science in Digital Innovation: A Literature Review. In XVI Brazilian Symposium on Information Systems(SBSI’20). Association for Computing Machinery, New York, NY, USA, Article 28, 7 pages. https://doi.org/10.1145/3411564.3411638
R. Jordan, G. Eudoxie, K. Maharaj, R. Belfon, and M. Bernard. 2016. AgriMaps: Improving site-specific land management through mobile maps. Computers and electronics in agriculture 123 (2016), 292–296.
A. V. Koshkarov. 2018. Machine learning methods in digital agriculture: Algorithms and cases. International Journal of Advanced Studies 8, 1 (2018), 11–26.
Locust. 2021. An open source load testing tool. https://locust.io/
D. A. Maciel, V. A. Silva, H. M.R. Alves, M. M. L. Volpato, J. P.R.A. Barbosa, V. C. O. Souza, et al. 2020. Leaf water potential of coffee estimated by landsat-8 images. Plos one 15, 3 (2020), 1–13. https://doi.org/10.1371/journal.pone.0230013
S. M. F. S. Massruhá and M. A. A. Leite. 2017. Agro 4.0 - Rumo À Agricultura Digital. In JC na Escola Ciência, Tecnologia e Sociedade: mobilizar o conhecimento para alimentar o Brasil. Centro Paula Souza, São Paulo, Chapter 2, 28–35.
J. Mendes, T. M. Pinho, F.S. Neves, J. J. Sousa, et al. 2020. Smartphone Applications Targeting Precision Agriculture Practices—A Systematic Review. Agronomy 10, 6 (2020), 1–44. https://doi.org/10.3390/agronomy10060855
M. Myint, A. Adam, S. Herath, and G. Smith. 2016. Mobile phone applications in management of enuresis: the good, the bad, and the unreliable!Journal of Pediatric Urology 12, 2 (2016), 112.e1–6. https://doi.org/10.1016/j.jpurol.2015.09.011
S.S. Oyelere, J. Suhonen, G. M. Wajiga, and E. Sutinen. 2018. Design, development, and evaluation of a mobile learning application for computing education. Education and Information Technologies 23, 1 (2018), 467–495.
K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee. 2007. A design science research methodology for information systems research. Journal of management information systems 24, 3 (2007), 45–77.
Purwanto and P.B. Hedin. 2020. Measurement of user satisfaction for web-base academic information system using end-user computing satisfaction method. IOP Conference Series: Materials Science and Engineering 909 (12 2020), 012044.
D. P. P. Riffel. 2016. Aplicativo Android para gerenciamento de culturas agrícolas. Curso Superior de Tecnologia em Análise e Desenvolvimento de Sistemas. Universidade Tecnológica Federal do Paraná.
A. C. Silva, A.M. Silva, G. Coelho, F.C. Rezende, and F. A. Sato. 2008. Produtividade e potencial hídrico foliar do cafeeiro Catuaí, em função da época de irrigação. Revista Brasileira de Engenharia Agrícola e Ambiental 12, 1(2008), 21 – 25.
H. R. D. O. Silveira, M. D. O. Santos, V. A. Silva, M. M.L. Volpato, H. M. R. Alves, M. F. Dantas, and G. R. Carvalho. 2015. Relações entre índices de reflectância foliares e potencial hídrico de cafeeiro irrigado e de sequeiro. In Simpósio de Pesquisa dos Cafés do Brasil. Embrapa Café, Curitiba/PR, 1 – 4. http://tot.dti.ufv.br/handle/123456789/3620
L. Simionesei, T. B. Ramos, J. Palma, A. R. Oliveira, and R. Neves. 2020. IrrigaSys: A web-based irrigation decision support system based on open source data and technology. Computers and Electronics in Agriculture 178 (2020), 105822. https://doi.org/10.1016/j.compag.2020.105822
V. C. O. Souza, Y. B. Castro, M. M. V. Paula, and M. M. L. Volpato. 2018. Demarcafé: A Proposal to Support the Mapping of Coffee Areas Using Citizen Science. In Proceedings of the XIV Brazilian Symposium on Information Systems(SBSI’18). ACM, Caxias do Sul, Brazil, 1–8. https://doi.org/10.1145/3229345.3229367
R. Wieringa. 2009. Design Science as Nested Problem Solving. In International Conference on Design Science Research in Information Systems and Technology(DESRIST ’09). ACM, Philadelphia, Pennsylvania, 1–12.
R.N. Athirah, C.Y.N. Norasma, and M.R. Ismail. 2020. Development of an Android Application for Smart Farming in Crop Management. In IOP Conference Series: Earth and Environmental Science, Vol. 540. IOP Publishing, Vancouver, 012074. Issue 1. https://doi.org/10.1088/1755-1315/540/1/012074
J. S. Barroso, M. Pimentel, V. Nunes, and C. Cappelli. 2019. Design Science Research to Design a Conceptual Model about Prosopographic Information Related to Politicians. In Proceedings of the XV Brazilian Symposium on Information Systems(SBSI’19). ACM, Aracaju, Brazil, 1–8. https://doi.org/10.1145/3330204.3330233
L. A. Batista, R. J. Guimarães, F. J. Pereira, G. R. Carvalho, and E. M. Castro. 2010. Anatomia foliar e potencial hídrico na tolerância de cultivares de café ao estresse hídrico. Revista Ciência Agronômica 41 (2010), 475–481. https://doi.org/10.1590/S1806-66902010000300022
W. J. Doll and G. Torkzadeh. 1988. The Measurement of End-User Computing Satisfaction. MIS Q. 12, 2 (1988), 259–274. https://doi.org/10.2307/248851
A. Dresch, D.P. Lacerda, and J.A.V.A. Júnior. 2015. Design Science Research: Método de Pesquisa para Avanço da Ciência e Tecnologia. Bookman Editora, Porto Alegre.
H. El Bilali, F. Bottalico, G. P. Ottomano, and R. Capone. 2020. Information and Communication Technologies for Smart and Sustainable Agriculture. In 30th Scientific-Experts Conference of Agriculture and Food Industry, Muhamed Brka, E. Omanović-Mikličanin, L. Karić, V. Falan, and A. Toroman (Eds.). Springer International Publishing, Cham, 321–334.
A. L. Filardi and A. J. M. Traina. 2008. Montando questionários para medir satisfação usuário:Avaliação de interface de sistema que utiliza técnicas de recuperação de imagens por conteúdo. In Proceedings of the VIII Brazilian Symposium on Human Factors in Computing Systems(IHC ’08). SBC, Porto Alegre/RS, 176–185.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R Moore. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202 (2017), 18–27. https://doi.org/10.25186/cs.v12i2.1176
F.E.A. Horita, F. G. Rocha, L. S. Souza, and G. R. Gonzales. 2020. Design Science in Digital Innovation: A Literature Review. In XVI Brazilian Symposium on Information Systems(SBSI’20). Association for Computing Machinery, New York, NY, USA, Article 28, 7 pages. https://doi.org/10.1145/3411564.3411638
R. Jordan, G. Eudoxie, K. Maharaj, R. Belfon, and M. Bernard. 2016. AgriMaps: Improving site-specific land management through mobile maps. Computers and electronics in agriculture 123 (2016), 292–296.
A. V. Koshkarov. 2018. Machine learning methods in digital agriculture: Algorithms and cases. International Journal of Advanced Studies 8, 1 (2018), 11–26.
Locust. 2021. An open source load testing tool. https://locust.io/
D. A. Maciel, V. A. Silva, H. M.R. Alves, M. M. L. Volpato, J. P.R.A. Barbosa, V. C. O. Souza, et al. 2020. Leaf water potential of coffee estimated by landsat-8 images. Plos one 15, 3 (2020), 1–13. https://doi.org/10.1371/journal.pone.0230013
S. M. F. S. Massruhá and M. A. A. Leite. 2017. Agro 4.0 - Rumo À Agricultura Digital. In JC na Escola Ciência, Tecnologia e Sociedade: mobilizar o conhecimento para alimentar o Brasil. Centro Paula Souza, São Paulo, Chapter 2, 28–35.
J. Mendes, T. M. Pinho, F.S. Neves, J. J. Sousa, et al. 2020. Smartphone Applications Targeting Precision Agriculture Practices—A Systematic Review. Agronomy 10, 6 (2020), 1–44. https://doi.org/10.3390/agronomy10060855
M. Myint, A. Adam, S. Herath, and G. Smith. 2016. Mobile phone applications in management of enuresis: the good, the bad, and the unreliable!Journal of Pediatric Urology 12, 2 (2016), 112.e1–6. https://doi.org/10.1016/j.jpurol.2015.09.011
S.S. Oyelere, J. Suhonen, G. M. Wajiga, and E. Sutinen. 2018. Design, development, and evaluation of a mobile learning application for computing education. Education and Information Technologies 23, 1 (2018), 467–495.
K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee. 2007. A design science research methodology for information systems research. Journal of management information systems 24, 3 (2007), 45–77.
Purwanto and P.B. Hedin. 2020. Measurement of user satisfaction for web-base academic information system using end-user computing satisfaction method. IOP Conference Series: Materials Science and Engineering 909 (12 2020), 012044.
D. P. P. Riffel. 2016. Aplicativo Android para gerenciamento de culturas agrícolas. Curso Superior de Tecnologia em Análise e Desenvolvimento de Sistemas. Universidade Tecnológica Federal do Paraná.
A. C. Silva, A.M. Silva, G. Coelho, F.C. Rezende, and F. A. Sato. 2008. Produtividade e potencial hídrico foliar do cafeeiro Catuaí, em função da época de irrigação. Revista Brasileira de Engenharia Agrícola e Ambiental 12, 1(2008), 21 – 25.
H. R. D. O. Silveira, M. D. O. Santos, V. A. Silva, M. M.L. Volpato, H. M. R. Alves, M. F. Dantas, and G. R. Carvalho. 2015. Relações entre índices de reflectância foliares e potencial hídrico de cafeeiro irrigado e de sequeiro. In Simpósio de Pesquisa dos Cafés do Brasil. Embrapa Café, Curitiba/PR, 1 – 4. http://tot.dti.ufv.br/handle/123456789/3620
L. Simionesei, T. B. Ramos, J. Palma, A. R. Oliveira, and R. Neves. 2020. IrrigaSys: A web-based irrigation decision support system based on open source data and technology. Computers and Electronics in Agriculture 178 (2020), 105822. https://doi.org/10.1016/j.compag.2020.105822
V. C. O. Souza, Y. B. Castro, M. M. V. Paula, and M. M. L. Volpato. 2018. Demarcafé: A Proposal to Support the Mapping of Coffee Areas Using Citizen Science. In Proceedings of the XIV Brazilian Symposium on Information Systems(SBSI’18). ACM, Caxias do Sul, Brazil, 1–8. https://doi.org/10.1145/3229345.3229367
R. Wieringa. 2009. Design Science as Nested Problem Solving. In International Conference on Design Science Research in Information Systems and Technology(DESRIST ’09). ACM, Philadelphia, Pennsylvania, 1–12.
Publicado
16/05/2022
Como Citar
SILVA, Patricia Costa; SOUZA, Vanessa Cristina Oliveira; VOLPATO, Margarete Marin Lordelo; SILVA, Vania Aparecida.
Regador: APP for coffee water potential estimation. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 18. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
