Estimating crop nitrogen status to optimize production and minimize environmental pollution is a major challenge for modern agriculture. The study objective was to develop a multivariate spatiotemporal dynamic clustering approach to generate Nitrogen (N) Management Zones (MZs) in a citrus orchard during the growing season. The research was conducted in four citrus plots in the coastal area of Israel. Five variables were selected to characterize each plot’s spatiotemporal variability of canopy N content. These were split into constant (i.e., elevation, northness, and slope) and non-constant (i.e., canopy N content and tree height) variables. The non-constant data were obtained via bi-monthly imaging campaigns with a multispectral camera mounted on an unmanned aerial vehicle (UAV) throughout the growing season of 2019. The selected variables were then standardized to define the clusters by applying the Getis-Ord Gi* z-score. These were used to develop a spatiotemporal dynamic clustering model using Fuzzy C-means (FCM). Four input variables were investigated in this final stage, including the constant variables only and different combinations of constant and non-constant variables. The support vector machine regression model results for estimating canopy N-content from multispectral images were R2 = 0.771 and RMSE = 0.227. This model was used to predict monthly canopy-level N content and classify the N content levels based on the October N-to-yield content envelope curve. Delineating MZs was followed by the comparison of spatial association among cluster maps. This process may support site-specific and time-specific nitrogen management.
- Multivariate spatial clustering
- Nitrogen status
- Precision fertilization
- Support vector machines
ASJC Scopus subject areas
- Agricultural and Biological Sciences (all)