Definição de zonas de manejo utilizando algoritmo de agrupamento fuzzy c-means com variadas métricas de distâncias

• Autor(a) principal: Fontana, Fabiane Sorbar
• Data de Publicação: 2017
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: http://tede.unioeste.br/handle/tede/3764
• topic: Agricultura de precisão; Unidades de manejo; Métodos de agrupamento de dados; Clusterização; Data grouping methods; Precision agriculture; Management units; Clustering; CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA

Precision Agriculture (AP) uses technologies aimed at increasing productivity and reducing environmental impact through localized application of agricultural inputs. In order to make AP economically feasible, it is essential to improve current methodologies, as well as to propose new ones, such as the design of management areas (MZs) from productivity data, topographic, and soil attributes, among others, to determine which are heterogeneous subareas among themselves in the same area. In this context, the main objective of this research was to evaluate three distance metrics (Diagonal, Euclidian, and Mahalanobis) through FUZME and SDUM software (for the definition of management units) using the fuzzy c-means algorithm, and, at a further moment, to evaluate the cultures of soybeans and corn, as well as the association between them. On the first scientific paper, using data corresponding to four distinct areas, the three metrics with original and normalized data associated with soybean yield were evaluated. For area A, the Diagonal and Mahalanobis distances exempted the need for normalization of the variables, presenting areas that were identical for both versions. After the normalization of the data, the Euclidian distance presented a better delineation in its MZs for area A. For areas B, C, and D it was not possible to reach conclusions regarding the best performance, since only one variable was used for the process of MZs, and that has directly influenced the results. On the second scientific paper, data corresponding to three distinct areas were applied to analyze the use of soybean and corn yields, as well as the association between them, in the selection of variables to define MZs. Based on the variables available for each of the areas, the selection was carried out using the spatial correlation method, considering, for each one of the areas, the three target yields (soybean, corn, and soybean+corn). The type of productivity used demonstrated two different outcomes: first in the variable selection process, where its alternation resulted in different selections for the same area, and second, in the evaluation of the defined MZs, where even when the same variables were selected in the definition of the MZs, the performances of the MZs were different. After the validation methods applied, it was verified that the best target yield was soybean+corn, reasserting the idea of being better to use these two cultures, together, when defining the MZs of an area with rotating crops of soybean and corn.