Utilização de métodos de interpolação e agrupamento para definição de unidades de manejo em agricultura de precisão

• Ano de defesa: 2014
• Autor(a) principal: Schenatto, Kelyn
• Orientador(a): Souza, Eduardo Godoy de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual do Oeste do Parana
• Programa de Pós-Graduação: Programa de Pós-Graduação "Stricto Sensu" em Engenharia Agrícola
• Departamento: Engenharia
• País: BR
• Palavras-chave em Português: krigagem; zonas de manejo; K-Means; Fuzzy C-Means
• Palavras-chave em Inglês: krigagem; management zones; K-Means; Fuzzy C-Means
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
• Link de acesso: http://tede.unioeste.br:8080/tede/handle/tede/2637

Despite the benefits offered by the technology of precision agriculture (PA), the necessity of dense sampling grids and use of sophisticated equipment for the soil and plant handling make it financially unfeasible in many cases, especially for small producers. With the aimof making viable the PA, the definition of management zones (MZ) consists in dividing the plotin subregions that have similar physicochemical features, where it is possible to work in the conventional manner (without site-specific input application), differing them from the other sub-regions of the field. Thus we use concepts from PA, but adapting some procedures to the reality of the producer, not requiring the replacement of machinery traditionally used.Therefore, yield is usually correlated with physical and chemical properties through statistical and geostatistical methods, and attributes are selected to generate thematic maps, which are then used to define the MZ. In the generation of thematic maps step, are commonly used traditional interpolation methods (Inverse Distance - ID , inverse of the square distance - ISD, and kriging - KRI), and it is important to assess if the quality of thematic maps generated influences in the MZ drafting process and can not justify the interpolation data using robust methods such as KRI. Thus, the present study aimed to evaluate three interpolation methods (ID , ISD and KRI ) for generation of thematic maps used in the generation of MZ by clustering methods K-Means and Fuzzy C-Meas, in two experimental areas (9.9 ha and 15.5 ha), and been used data from four seasons (three crops of soybeans and one of corn). The KRI interpolation and ID showed similar UM. The agreement between the maps decreased when an increase in the number of classes, but with greater intensity with the Fuzzy C-Means method. Clustering algorithms K-Means and Fuzzy C-Means performed similar division on two UM. The best interpolation method was KRI following the ID, what justifies the choice of a more robust interpolation (KRI) to generate UM