The case study of the Mevo-Beitar vineyard in Israel provides the user with an example of the application process from data input to output, including collected data, data processing, data analysis and outputs. A detailed description of the case study, the methods and algorithms can be found in the following papers: "Peeters et al. (under review), A spatial machine learning (SML) model for predicting crop water stress index (CWSI) to increase in-season frequency of water status maps for precision irrigation of wine-grape vineyards" and Bahat et al., 2021, Remote Sensing, In-season interactions between vine vigor, water status and wine quality in terrain-based management-zones in a ‘Cabernet Sauvignon' vineyard.
The case study is a commercial wine-grape vineyard located 10 km from Jerusalem, next to the
settlement of Mevo Beitar (coordinates 31o43’ N; 35o06’ E). The area has a Mediterranean climate
(mean maximum summer temperature 29.4oC and mean minimum winter temperature 6.4oC) and a mean
annual precipitation of 540 mm (between October - May). Topography is highly variable with
sloping hills and an elevation of 675-900 m. A wadi crosses the vineyard from east to west. The
vineyard consists of 5522 Vitis vinifera L. ‘Cabernet Sauvignon’ vines on a 2.5 ha area. Spacing
in the vineyard is 1.5 m between vines and 3 m between rows.
A variety of plant and environmental data was collected using in-situ measurements, data logging (wired and wireless sensors) and UAV (drones) collected data.
Collected data included:
Data collected for sample trees such as: stem water potential (SWP), trunk circumference and tree trunk growth, canopy temperatures with infrared thermometer sensors (IRT) and tree canopy RGB imagery.
Continuous data collected for the entire vineyard such as, thermal and multispectral imaging with drones, soil properties e.g. apparent electrical conductivity (ECa) and soil volumetric water content, meteorological data, topographical data including digital surface and digital elevation models (DSM, DEM) and yield. Crop related indices CWSI and normalized difference vegetation index (NDVI) were derived from the thermal and multispectral images, respectively. The topographic wetness index (TWI) was derived from the DEM.
Only data that was spatially continuous, such as topographical data (slope, aspect, TWI), ECa, NDVI and CWSI, is used to develop/run the model. All data is organized in a raster format, with each raster representing an attribute (such as slope, NDVI, TWI etc.) and linked to a specific location; i.e. each vine location is assigned the value of its associated raster. For each location the x,y coordinates are derived as well. The final output dataset includes a csv file where each column represents a variable. In the vineyard case study, in addition to point (vine) based calculations, mean calculations of CWSI were calculated for each of the 20 equally sized (30 x 30 m) management cells used for application of variable rate irrigation (VRI) and representing management zones.
Data is analyzed using machine learning methods and spatial statistical methods in order to
recognize spatial patterns and correlations among variables and examine which variables most
affect plant water status. Based on the analysis a prediction machine learning based model
is developed.
Model outputs include a CWSI prescription map with CWSI predictions per tree point
and a prescription map per management cell with CWSI values averaged per cell (or MZ).
Water4Crop is a comprehensive model for predicting crop water stress.
It provides users with output prescription maps that can help them understand
the spatial distribution of crop water stress on a sub-field level. However,
accurate and meaningful outputs largely depend on the accurate representation of
the orchard and on sufficient spatial coverage. Users should test the model outputs
against real field data before developing water management recommendations.
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