Normalization of data for delineating management zones
Autor: | Kelyn Schenatto, Humberto Martins Beneduzzi, Alan Gavioli, Claudio Leones Bazzi, Nelson Miguel Betzek, Eduardo Godoy de Souza |
---|---|
Rok vydání: | 2017 |
Předmět: |
0106 biological sciences
Normalization (statistics) Forestry 04 agricultural and veterinary sciences Horticulture Standard score 01 natural sciences Fuzzy logic Standard deviation Computer Science Applications Database normalization Euclidean distance Statistics 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Variance reduction Cluster analysis Agronomy and Crop Science 010606 plant biology & botany Mathematics |
Zdroj: | Computers and Electronics in Agriculture. 143:238-248 |
ISSN: | 0168-1699 |
Popis: | Management zones (MZs) are a viable economic alternative to variable-rate application (VRA) based on prescription maps; however, unlike the latter, MZs can employ conventional machinery. The use of management zones (MZs) is considered an economically viable alternative because of its low initial cost and high return in economic and environmental benefits. Data clustering techniques and the Fuzzy C-Means algorithm are the most widely used processes for delineating MZs. The most common similarity measurement used is Euclidean distance; however, because the algorithm is sensitive to the range of the input variables, these variables are typically normalized dividing the value by the standard deviation, maximum value, average, or data set range. The objective of this study was to assess the influence of data normalization methods for delineating MZs. The experiment was conducted in three experimental fields with 9.9, 15.0, and 19.8 ha, located in Southern Brazil between 2010 and 2014. The variables used for delineating MZs were selected using spatial correlation statistics and data were normalized using methods of standard score, range, and average. The MZs were delineated using the Fuzzy C-Means algorithm, which created two, three, and four clusters. The normalization methods were evaluated by five indices (modified partition entropy [MPE], fuzziness performance index [FPI], variance reduction [VR], smoothness index [SI], and kappa), and ANOVA. It was found that when the MZs delineation uses more than one variable with different scales in the clustering process using Euclidean distance, normalization is required. The range method was considered the overall best normalization method. |
Databáze: | OpenAIRE |
Externí odkaz: |