Advanced Search
Peeters, A., Agricultural Research Organization, Institute of Soil Water and Environmental Sciences, P.O. Box 6, Bet Dagan, Israel
Zude, M., Leibniz Institute for Agricultural Engineering Potsdam-Bornim E.V., Max-Eyth-Allee 100, Potsdam, Germany
Kathner, J., Leibniz Institute for Agricultural Engineering Potsdam-Bornim E.V., Max-Eyth-Allee 100, Potsdam, Germany
Unlu, M., Faculty of Agriculture, University of Cukurova, C.U. Adana, Turkey
Kanber, R., Faculty of Agriculture, University of Cukurova, C.U. Adana, Turkey
Hetzroni, A., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, P.O. Box 6, Bet Dagan, Israel
Gebbers, R., Leibniz Institute for Agricultural Engineering Potsdam-Bornim E.V., Max-Eyth-Allee 100, Potsdam, Germany
Ben-Gal, A., Agricultural Research Organization, Institute of Soil Water and Environmental Sciences, P.O. Box 6, Bet Dagan, Israel
A combined spatial-aspatial clustering approach for partitioning tree-based data in orchards was developed. The method employs the Getis-Ord Gi∗ statistic applied to the analysis of individual tree data in a grapefruit orchard located near the city of Adana, Turkey. Analyzed tree-variables included yield (total fruit weight per tree) and two possible yield-determining variables, tree size measured as tree trunk circumference (cm) and soil properties measured by the soil apparent electrical conductivity (ECa (mS/m)). Data were collected from 179 trees. The developed method was applied to the analysis of 'hot-spots' (clusters of high data values) and 'cold-spots' (clusters of low data values) in orchards and compared to the k-means clustering algorithm, an aspatial clustering method widely-used in agriculture. The combined method improved results by both discriminating among feature values as well as representing their spatial structure and therefore represents a superior technique for identifying homogenous spatial clusters in orchards. The approach can be used for delineating management zones for optimal precision management of tree crops.
Powered by ClearMash Solutions Ltd -
Volcani treasures
About
Terms of use
A multivariate spatial clustering method for partitioning tree-based orchard data into homogenous zones
Peeters, A., Agricultural Research Organization, Institute of Soil Water and Environmental Sciences, P.O. Box 6, Bet Dagan, Israel
Zude, M., Leibniz Institute for Agricultural Engineering Potsdam-Bornim E.V., Max-Eyth-Allee 100, Potsdam, Germany
Kathner, J., Leibniz Institute for Agricultural Engineering Potsdam-Bornim E.V., Max-Eyth-Allee 100, Potsdam, Germany
Unlu, M., Faculty of Agriculture, University of Cukurova, C.U. Adana, Turkey
Kanber, R., Faculty of Agriculture, University of Cukurova, C.U. Adana, Turkey
Hetzroni, A., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, P.O. Box 6, Bet Dagan, Israel
Gebbers, R., Leibniz Institute for Agricultural Engineering Potsdam-Bornim E.V., Max-Eyth-Allee 100, Potsdam, Germany
Ben-Gal, A., Agricultural Research Organization, Institute of Soil Water and Environmental Sciences, P.O. Box 6, Bet Dagan, Israel
A multivariate spatial clustering method for partitioning tree-based orchard data into homogenous zones
A combined spatial-aspatial clustering approach for partitioning tree-based data in orchards was developed. The method employs the Getis-Ord Gi∗ statistic applied to the analysis of individual tree data in a grapefruit orchard located near the city of Adana, Turkey. Analyzed tree-variables included yield (total fruit weight per tree) and two possible yield-determining variables, tree size measured as tree trunk circumference (cm) and soil properties measured by the soil apparent electrical conductivity (ECa (mS/m)). Data were collected from 179 trees. The developed method was applied to the analysis of 'hot-spots' (clusters of high data values) and 'cold-spots' (clusters of low data values) in orchards and compared to the k-means clustering algorithm, an aspatial clustering method widely-used in agriculture. The combined method improved results by both discriminating among feature values as well as representing their spatial structure and therefore represents a superior technique for identifying homogenous spatial clusters in orchards. The approach can be used for delineating management zones for optimal precision management of tree crops.
Scientific Publication
You may also be interested in