Tellus: a computational model for the prediction of soil fertility in precision agriculture
Abstract
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing and its integration with the real world. One of the challenges in this area is the use of context awareness. In agriculture, the context can be related to the environment, for example, the chemical and physical aspects that characterize different types of soil over time. This paper proposes a computational model applied in precision agriculture that uses the contexts history to predict soil fertility. The best results were obtained in the prediction of organic matter, with a coefficient of determination (R2) of 0.9102 for root mean square error (RMSE) of 0.49%.
Keywords:
prediction of soil fertility, precision agriculture
References
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Barbosa, J., Tavares, J., Cardoso, I., Alves, B., and Martini, B. (2018). Trailcare: An indoor and outdoor context-aware system to assist wheelchair users. International Journal of Human-Computer Studies, 116:1 – 14.
Bernardi, A., Naime, J., Resende, A., Inamasu, R., and Bassoi, L. (2014). Agricultura de precisão: resultados de um novo olhar. Manual de métodos de análise de solos. EMBRAPA, São Carlos - SP, 2 edition.
Blank, S., Bartolein, C., Meyer, A., Ostermeier, R., and Rostanin, O. (2013). IGreen: A ubiquitous dynamic network to enable manufacturer independent data exchange in future precision farming. Computers and Electronics in Agriculture, 98:109–116.
Concepcion, A. R., Stefanelli, R., and Trinchero, D. (2014). A wireless sensor network platform optimized for assisted sustainable agriculture. In IEEE Global Humanitarian Technology Conference (GHTC 2014), pages 159–165. IEEE.
Costa, N. R., Carvalho, M. d. P. e., Dal Bem, E. A., Dalchiavon, F. C., and Caldas, R. R. (2014). Produtividade de laranja correlacionada com atributos quı́micos do solo visando a zonas especı́ficas de manejo. Pesquisa Agropecuária Tropical, 44(4):391– 398.
Dey, A. K., Abowd, G. D., and Salber, D. (2001). A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Hu- man–Computer Interaction, 16(2-4):97–166.
Ferreira, M. M. C. (2015). Quimiometria: conceitos, métodos e aplicações. Editora da Unicamp, Campinas-SP.
Goap, A., Sharma, D., Shukla, A., and Rama Krishna, C. (2018). An iot based smart irrigation management system using machine learning and open source technologies. Computers and Electronics in Agriculture, 155:41–49.
Griebeler, G., da Silva, L. S., Cargnelutti Filho, A., and Santos, L. d. S. (2016). Avaliação de um programa interlaboratorial de controle de qualidade de resultados de análise de solo. Revista Ceres, 63(3):371–379.
Huong, T. T., Thanh, N. H., Van, N. T., Dat, N. T., Long, N. V., and Marshall, A. J. (2018). Water and energy-efficient irrigation based on markov decision model for precision agriculture. 2018 IEEE Seventh International Conference on Communications and Electronics (ICCE), pages 51–56.
Muñoz, J. D. and Kravchenko, A. (2011). Soil carbon mapping using on-the-go near infrared spectroscopy, topography and aerial photographs. Geoderma, 166(1):102– 110.
Nawar, S. and Mouazen, A. (2019). On-line vis-nir spectroscopy prediction of soil organic carbon using machine learning. Soil and Tillage Research, 190:120 – 127.
Rosa, J. H., Barbosa, J. L. V., Kich, M., and Brito, L. (2015). A Multi-Temporal Context- aware System for Competences Management. International Journal of Artificial Intel- ligence in Education, 25(4):455–492.
Santos, U. J. L., da Rosa Righi, R., and da Costa, C. A. (2018). Compreendendo o desempenho de gerenciadores de contexto para internet das coisas. In 10 o Simpósio Brasileiro de Computação Ubı́qua e Pervasiva (SBCUP), volume 10, Porto Alegre, RS, Brasil. SBC.
SAP AG (2007). Standardized technical architectur e modeling - conceptual and de- sign level. http://www.fmc-modeling.org/download/fmc-and-tam/ SAP-TAM_Standard.pdf Accessed 23-Jan-2019.
Schmitz, M., Martini, D., Kunisch, M., and Mösinger, H.-J. (2009). agroXML Enabling Standardized, Platform-Independent Internet Data Exchange in Farm Management In- formation Systems. In Metadata and Semantics, pages 463–468. Springer US, Boston, MA.
Tedesco, M., Gianello, C., Bissani, C., Bohnen, H., and Volkweiss, S. (1995). Análise de solo, plantas e outros materiais. UFRGS/Departamento de Solos, Porto Alegre, 2 edition.
Treboux, J. and Genoud, D. (2018). Improved machine learning methodology for high precision agriculture. 2018 Global Internet of Things Summit (GIoTS), pages 1–6.
Wetterlind, J., Piikki, K., Stenberg, B., and Söderström, M. (2015). Exploring the pre- dictability of soil texture and organic matter content with a commercial integrated soil profiling tool. European Journal of Soil Science, 66(4):631–638.
Published
2019-07-12
How to Cite
HELFER, Gilson; MARTINI, Bruno ; SANTOS, Ronaldo ; COSTA, Adilson ; BARBOSA, Jorge .
Tellus: a computational model for the prediction of soil fertility in precision agriculture. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 11. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2019.6592.
