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precision agriculture (source )

Peeters, A. - TerraVision Lab, Midreshet Ben-Gurion, 8499000, Israel.  
Termin, D. - Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Rishon-Le’Zion, 7528809, Israel; Faculty of Civil and Environmental Engineering, Technion – Israel Institute of Technology, Haifa, 32000, Israel.  
Linker, R. - Faculty of Civil and Environmental Engineering, Technion – Israel Institute of Technology, Haifa, 32000, Israel.


 

Site-specific agricultural management (SSM) relies on identifying within-field spatial variability and is used for variable rate input of resources. Precision agricultural management commonly attempts to integrate multiple datasets to determine management zones (MZs), homogenous units within the field, based on spatial characteristics of environmental and crop properties (i.e., terrain, soil, vegetation conditions). This study compared several multivariate spatial clustering methods to determine MZs for precision nitrogen fertilization in a citrus orchard. Six variables, namely normalized difference vegetation index, crop water stress index, digital surface model, slope, elevation and aspect, were used to characterize spatial variability within four plots. Six clustering model composites were compared, each including some or all of the following components: (1) spatial representation of the data (e.g., Getis Ord Gi*); (2) variable weights based on their relative contribution; and (3) clustering methods, including different extensions of K-means and hierarchical clustering algorithms. The fuzzy K-means algorithm applied to the weighted spatial representation was found to generate MZs with similar numbers of trees, while the K-means algorithm applied over the spatial representation generated MZs that were more continuous over space, with minimum fragmentation. Spatial variability was not constant across the orchard and among the different variables. Management of the sub-units, or plots, using spatial representation rather than the measured values, is proposed as a more suitable platform for agricultural practices. SSM is dependent upon available variable rate application technologies. Future development of fertilizer application for individual trees will require adjusting the statistical approach to support tree-specific management. The suggested model composite is flexible and may be composed of different models for delineating plot-specific MZs.

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A comparison between spatial clustering models for determining N-fertilization management zones in orchards

Peeters, A. - TerraVision Lab, Midreshet Ben-Gurion, 8499000, Israel.  
Termin, D. - Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Rishon-Le’Zion, 7528809, Israel; Faculty of Civil and Environmental Engineering, Technion – Israel Institute of Technology, Haifa, 32000, Israel.  
Linker, R. - Faculty of Civil and Environmental Engineering, Technion – Israel Institute of Technology, Haifa, 32000, Israel.


 

A comparison between spatial clustering models for determining N-fertilization management zones in orchards

Site-specific agricultural management (SSM) relies on identifying within-field spatial variability and is used for variable rate input of resources. Precision agricultural management commonly attempts to integrate multiple datasets to determine management zones (MZs), homogenous units within the field, based on spatial characteristics of environmental and crop properties (i.e., terrain, soil, vegetation conditions). This study compared several multivariate spatial clustering methods to determine MZs for precision nitrogen fertilization in a citrus orchard. Six variables, namely normalized difference vegetation index, crop water stress index, digital surface model, slope, elevation and aspect, were used to characterize spatial variability within four plots. Six clustering model composites were compared, each including some or all of the following components: (1) spatial representation of the data (e.g., Getis Ord Gi*); (2) variable weights based on their relative contribution; and (3) clustering methods, including different extensions of K-means and hierarchical clustering algorithms. The fuzzy K-means algorithm applied to the weighted spatial representation was found to generate MZs with similar numbers of trees, while the K-means algorithm applied over the spatial representation generated MZs that were more continuous over space, with minimum fragmentation. Spatial variability was not constant across the orchard and among the different variables. Management of the sub-units, or plots, using spatial representation rather than the measured values, is proposed as a more suitable platform for agricultural practices. SSM is dependent upon available variable rate application technologies. Future development of fertilizer application for individual trees will require adjusting the statistical approach to support tree-specific management. The suggested model composite is flexible and may be composed of different models for delineating plot-specific MZs.

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