The sensors and software behind the soil intelligence

Our cloud platform crunches data from our soil sensors integrated with multiple layers, to provide the best insights on when, where and how much to irrigate and fertilize and the best crop protection plan to follow.

Platform Overview

Precise Weather
Soil Sensors
Soil Mapping
Hydraulic Models
Aerial Imagery
Topography Maps
User Input
Crop Models

Soil Sensors

Each sensor collects moisture, temperature and electrical conductivity (EC) at multiple depths. Intervals of data measurement and transmission to the CropX cloud can be remotely configured and adjusted to each crop’s unique needs.
All of the data is geo-tagged based on GPS coordinates creating geospatial time series for all measured data.

Moisture:
Measurement of volumetric water content (VWC) values via ADR sensors. Moisture values are converted from electric impedance to VWC levels using a proprietary self-calibration method. Moisture values have an accuracy of +/- 1% across a range of 0-50% VWC.

Temperature:
Temperatures are measured with an accuracy of +/- 0.5°C (max) and an operating range of -10°C to +70°C.
Each unit also measures the internal temperature of the unit above ground, which can help with increasing the precision of weather data.

Electric Conductivity (EC):
Measurement in decisiemens/m, with an operating range of 0-5 decisiemens/m (bulk), representing the soil salinity level, which can be used to manage crop salinity regime.

Precise Weather

CropX is using various ag-specific weather data services to obtain precise weather information relevant to the users and CropX’s algorithms.
For each location CropX chooses the best relevant data source, according to various parameters.

Weather information includes air temperature, humidity, wind speed, evapotranspiration (ET), precipitation, min and max temperatures and more.

One-week forcasts are presented and all data can be exported.

Aerial Imagery

For each field, we pull high-resolution aerial images from several sources, using many ag-related indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), True Color (RGB) and other relevant indices.

Images are collected from several satellites, which allow CropX to retrieve a new image for every field every two to three days in average.

Topography Maps

Topography has a huge impact on the flow of water and nutrients in the soil and in the field.
CropX uses Digital Elevation Maps (DEM) retrieved from various sources, to provide high resolution.

Soil Mapping

Soil type and texture are essential to the correct calculation of precise moisture levels, irrigation regime, hydraulic properties and organic matter.
We use publicly-available soil maps and allow for upload of customers’ existing EM and GIS Spatial maps, and users can input data on soil specification, so that the platform can analyze these parameters.

Hydraulic Models

CropX Sensor provides a series of geospatial-temporal moisture level measurements, iterating periodically over time for each specific depth in which CropX Sensor is equipped with an electrode.
In order to make those depth-specific measurements into a continuous sensor, capturing an accurate view of the soil’s water content, a highly precise hydraulic model is being used.

This model enables CropX to simulate the soil’s water content starting from the ground level downwards, going down as deep as two meters below the root-zone as well as providing historical analysis and future prediction.

Using the continuous properties which are being calculated as part of the model, CropX can improve the precision of the field irrigation plan in order to reduce water usage.

Furthermore, the model can be used to control fertilizers distribution in the soil, as well as monitoring the plant’s uptake rate, at the exact depth of the crop’s effective root zone.

Crop Models

CropX uses many crop models in order to learn and understand the behavior of its supported crops from all the relevant aspects – water uptake, growth stages, nutrient uptake, water and salinity stresses and more.

The infrastructure developed allows CropX to manage these models on a regional basis, understating that Corn growth in Arizona’s arid climate may differ significantly than Corn growing in Australia’s tepid climate.

Our machine learning algorithms constantly adapt these models to the actual growth of the supported crops, thus refining these models continuously.

Currently supported crops include:

Sweet Potato, Wheat, Sugar Cane, Cotton, Maize, Soy, Sunflower, Peas, Quinoa, Green Beans, Carrot, Sugar Beet, Tef, Sorghum, Tomato, Rice, Corn, Barley, Potato, Winter Wheat, Onion, Canola, Alfalfa and Grass Lands, with additional 20 crop types and varieties currently in the pipeline.

User Input

In order to provide its customers with meaningful insights, alerts and information, CropX collects information from its end users, relevant to CropX offering, including field size and location, irrigation system and parameters, crop type and related info (such as growth cycle, planting date, expected GDD), irrigation management thresholds, and more. This data too is then geo-tagged and correlated with units’ data.