Data/hora: 10/06/2024 09:24:21

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	28/10/2014	
Data da última atualização: 	08/01/2020	
Autoria: 	SPERANZA, E. A.; CIFERRI, R. R.; GREGO, C. R.; VICENTE, L. E.	
Afiliação: 	EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO RODRIGUES CIFERRI, UFSCar; CÉLIA REGINA GREGO, CNPM; LUIZ EDUARDO VICENTE, CNPM.	
Título: 	A cluster-based approach to support the delineation of management zones in precision agriculture.	
Ano de publicação: 	2014	
Fonte/Imprenta: 	In: IEEE INTERNATIONAL CONFERENCE ON E-SCIENCE, 10., 2014, Guarujá, SP. Conference proceedings. [S.l.]: Conferente Publishing Services, 2014.	
Páginas: 	p. 119-126.	
DOI: 	DOI 10.1109/eScience.2014.42	
Idioma: 	Inglês	
Conteúdo: 	Abstract-In this paper we propose a cluster-based approach for the delineation of management zones in precision agriculture. The proposed approach was built following the steps of data mining for the clustering task, resulting in a computer application that generates maps of management zones and yield areas, allowing to compare them using known statistical indexes. The basis for this implementation was a model previously published in the literature that uses only historical productivity, soil electrical conductivity and relief data to generate the maps. The main difference of our work with respect to the previous model is the clustering algorithms used in the step of extracting patterns. While the original model uses only the fuzzy c-means algorithm, the model developed in this study uses the GKCluster extension to this algorithm, able to detect clusters with different geometrical shapes. From the tests performed with the new proposed model, we achieved about 76% of correlation between maps of yield and management zones from kappa index, and about 85% of correlation from overall accuracy. The original model reached, according to the authors, a maximum correlation of 49% from kappa index, and 70% from overall accuracy.	
Palavras-Chave: 	Clusterização; Management zones; Mineração de dados espaciais; Spatial data mining.	
Thesagro: 	Agricultura de precisão.	
Thesaurus Nal: 	Cluster analysis; Precision agriculture.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (18024-1) - UPC - AA - DD -  -  -  - DIS