Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture
| Autor: | SPERANZA, E. A., CIFERRI, R. R. |
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| Přispěvatelé: | EDUARDO ANTONIO SPERANZA, CNPTIA, RICARDO RODRIGUES CIFERRI, UFSCar. |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice) Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
| Popis: | This paper describes experiments performed using diff erent approaches for spatial data clustering, aiming to assist the delineation of management classes in Precision Agriculture (PA). These approaches were established from the partitional clustering algorithm Fuzzy c-Means (FCM), traditionally used in PA, and from the hierarchical clustering algorithm HACC-Spatial, especially designed for PA. We also performed experiments using diff erent clustering ensembles approaches, evaluating their behavior to achieve consensus solutions from individual clusterings obtained from attribute splitting or using exclusively FCM or HACC-Spatial. The achieved results exhibited some diff erences between FCM and HACC-Spatial, mainly for the visualization of management classes in the form of maps. The HACCSpatial algorithm achieved, in general, better results when compared to FCM and ensembles approaches. Regarding the consensus clusterings provided by ensembles, we can point out the attempt to achieve agreement results which most closely matches the original clusterings, decreasing or increasing the stratifi cation of the management classes maps. Made available in DSpace on 2018-11-13T23:58:24Z (GMT). No. of bitstreams: 1 UsingensemblesSperanzaGeoinfo2017.pdf: 934144 bytes, checksum: 576fcfe3ad51c7a83f4a68536ec8f972 (MD5) Previous issue date: 2018-11-13 Título equivalente em português: Utilizando ensembles com abordagens de agrupamento espacial para o delineamento de classes de manejo em agricultura de precisão. Edição especial de papers selecionados que foram apresentados no GEOINFO 2016. |
| Databáze: | OpenAIRE |
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