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Identification of management zones in precision agriculture: An evaluation of alternative cluster analysis methods

Gavioli, Alan, de Souza, Eduardo Godoy, Bazzi, Claudio Leones, Schenatto, Kelyn, Betzek, Nelson Miguel
Biosystems engineering 2019 v.181 pp. 86-102
agricultural land, algorithms, analysis of variance, cluster analysis, corn, economic sustainability, learning, precision agriculture, soybeans, variance, Brazil
The definition of management zones (MZs) in agricultural fields has been suggested as an economically viable approach to precision agriculture. The most used methods for this task are the cluster analysis algorithms Fuzzy C-means (FCM) and K-means. However, some studies have presented that these algorithms may not provide the best classes to define MZs. Considering that these studies presented comparisons of only a few clustering methods, the objective of our research was to evaluate 20 algorithms for defining MZs, including more than 10 methods that have not been investigated in the literature for this purpose. The following algorithms were evaluated: Average Linkage, Bagged Clustering, Centroid Linkage, Clara, Complete Linkage, Diana, Fanny, FCM, Fuzzy C-shells, Hard Competitive Learning, Hybrid Hierarchical Clustering, K-means, McQuitty's Method, Median Linkage, Neural Gas, Partitioning Around Medoids, Single Linkage, Spherical K-means, Unsupervised Fuzzy Competitive Learning and Ward's Method. The evaluation was conducted with data obtained between 2010 and 2015 from three commercial agricultural fields cultivated with soya bean and maize in Brazil. The results of the analysis of variance suggested a division of the three fields into two classes with significantly different yields and a division of one of the fields into three classes. These divisions were satisfactorily performed using 17 algorithms, but McQuitty's Method and Fanny were considered to be the best choices because they produced the largest reductions in the variance of the yield in the three fields. In addition, they generated classes with high internal homogeneity and delimited MZs without spatial fragmentation.