Application of data grouping methods into vegetation indices to generate management zone maps
Abstract
This article aims to evaluate the impact of using different agricultural indices to define management zones using pre-processing techniques to prepare the data for the use of clustering algorithms on spatial data from a cotton plantation. The methodology involved acquiring agricultural indices data by drone, pre-processing including removing not a number values, creating tables of comma separated values and selecting indices based on correlation. The Fuzzy C-Means algorithm was implemented to create maps of management zones with four groups, identifying flaws in the plantation. With this, we visually obtained areas with different health levels for cultivation.
Keywords:
Management zones, Fuzzy C-Means, precision agriculture, pre-processing, vegetation indices
References
Bottega, E. L. (2014). Use of site-specific management for soybean production in the Brazilian Savannah. 2014. 92 f. Tese (Doutorado em Construções rurais e ambiência; Energia na agricultura; Mecanização agrícola) - Universidade Federal de Viçosa, Viçosa.
Nenos, L. et al. (2021) Machine learning in agriculture: A comprehensive updated review. Sensors, v. 21, n. 11, p. 3758.
Rodrigues, L. D. S; PEREIRA, D. R. (2021) Aprendizado de Máquina Aplicado Em Imagem Ndvi Para Previsão Da Produtividade Da Cana-De-Açúcar. In: Colloquium Exactarum. ISSN: 2178-8332. p. 82-98.
Devi, N. B.; Kavida, A. C. RETRACTED ARTICLE (2021): Pre-processing on remotely sensed data with unsupervised classification analysis. J Ambient Intell Human Comput 12, 6825–6839.
Hopewell, K. (2013) New protagonists in global economic governance: Brazilian agribusiness at the WTO. New Political Economy, v. 18, n. 4, p. 603-623.
Javed, T. et al. (2021) Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data. Science of the total environment, v. 759, p. 143530.
Li, S. et al. High-quality vegetation index product generation (2021): A review of NDVI time series reconstruction techniques. International Journal of Applied Earth Observation and Geoinformation, v. 105, p. 102640.
Lindblom, J. et al. (2017). Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies. Precision agriculture, v. 18, p. 309-331.
Meneses, K. C. D. (2021). Rumo à agricultura inteligente: previsão de produtividade agrícola com dados agrometeorológicos usando machine learning.
Muruganantham, P. et al.(2022). A systematic literature review on crop yield prediction with deep learning and remote sensing. Remote Sensing, v. 14, n. 9, p. 1990.
Nallan, S. et al. (2014). Geospatial data pre-processing on watershed datasets: A GIS approach.
RUß, G. (2012). Spatial data mining in precision agriculture. Tese de Doutorado. Universitätsbibliothek.
Sharma, A. et al. (2020). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, v. 9, p. 4843-4873.
Santos, R. T. D. (2014). Um modelo de referência para o processo de definição de zonas de manejo em agricultura de precisão. Tese de Doutorado. Universidade de São Paulo.
Valente, D. S. M. (2010). Desenvolvimento de um sistema de apoio à decisão para definir zonas de manejo em cafeicultura de precisão.
Nenos, L. et al. (2021) Machine learning in agriculture: A comprehensive updated review. Sensors, v. 21, n. 11, p. 3758.
Rodrigues, L. D. S; PEREIRA, D. R. (2021) Aprendizado de Máquina Aplicado Em Imagem Ndvi Para Previsão Da Produtividade Da Cana-De-Açúcar. In: Colloquium Exactarum. ISSN: 2178-8332. p. 82-98.
Devi, N. B.; Kavida, A. C. RETRACTED ARTICLE (2021): Pre-processing on remotely sensed data with unsupervised classification analysis. J Ambient Intell Human Comput 12, 6825–6839.
Hopewell, K. (2013) New protagonists in global economic governance: Brazilian agribusiness at the WTO. New Political Economy, v. 18, n. 4, p. 603-623.
Javed, T. et al. (2021) Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data. Science of the total environment, v. 759, p. 143530.
Li, S. et al. High-quality vegetation index product generation (2021): A review of NDVI time series reconstruction techniques. International Journal of Applied Earth Observation and Geoinformation, v. 105, p. 102640.
Lindblom, J. et al. (2017). Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies. Precision agriculture, v. 18, p. 309-331.
Meneses, K. C. D. (2021). Rumo à agricultura inteligente: previsão de produtividade agrícola com dados agrometeorológicos usando machine learning.
Muruganantham, P. et al.(2022). A systematic literature review on crop yield prediction with deep learning and remote sensing. Remote Sensing, v. 14, n. 9, p. 1990.
Nallan, S. et al. (2014). Geospatial data pre-processing on watershed datasets: A GIS approach.
RUß, G. (2012). Spatial data mining in precision agriculture. Tese de Doutorado. Universitätsbibliothek.
Sharma, A. et al. (2020). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, v. 9, p. 4843-4873.
Santos, R. T. D. (2014). Um modelo de referência para o processo de definição de zonas de manejo em agricultura de precisão. Tese de Doutorado. Universidade de São Paulo.
Valente, D. S. M. (2010). Desenvolvimento de um sistema de apoio à decisão para definir zonas de manejo em cafeicultura de precisão.
Published
2023-11-28
How to Cite
RUVER, Fábio S.; SANTOS, Raul T.; MARTINS, Claudia A..
Application of data grouping methods into vegetation indices to generate management zone maps. In: REGIONAL SCHOOL ON INFORMATICS OF MATO GROSSO (ERI-MT), 12. , 2023, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 176-186.
ISSN 2447-5386.
DOI: https://doi.org/10.5753/eri-mt.2023.236619.
