IDENTIFICATION OF MANAGEMENT ZONES IN PRECISION AGRICULTURE: AN EVALUATION OF ALTERNATIVE CLUSTER ANALYSIS METHODS
Autores: ALAN GAVIOLI, EDUARDO GODOY DE SOUZA, CLAUDIO LEONES BAZZI, KELYN SCHENATTO, NELSON MIGUEL BETZEK
Periódico:BIOSYSTEMS ENGINEERING, V. 181, p. 86-102, 2019.
ABSTRACT:
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.
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.
KEYWORDS: Agricultural management units, Cluster analysis, Precision agriculture, Principal component analysis, Site-specific management.