PRODUCTS

These tools have been made available for broad use, and include a k-means clustering algorithm for the creation of soil management zones, and a conditioned Latin Hypercube Sampling (cLHS) algorithm to help sample and characterise variability. The tools have been designed to be used by agronomists and farmers to optimise soil sample locations and improve overall soil management. In order to work effectively, both tools should operate together; the k-means clustering run first to create soil management zones, and the cLHS run against those zones. This allows a feedback loop to help ensure that the data used in the cLHS algorithm is reflective of the soil.

Using the Tools

Create a Farm

A user is given access to a new farm when their account is created. This is the container for all paddocks, zones and sample information. 

Create a Paddock

Once a user has identified the location of their farm, they can either draw a single paddock or load a shape or KML file using the ‘+’ button.

Setup paddock data

After a user creates a paddock, they enter a name and select the calendar month in which the crop is at peak biomass. We use this to filter the date range when selecting NDVI imagery at the next step. 

Run a farm survey

The farm survey will gather all publicly available datasets and processes it. These datasets can be used in the next step to create soil management zones.

Zoning the farm

To create zones, the user is given the option to zone the entire farm or individual paddocks (as outlined above). They select the number of zones and how many samples to take across each zone. They can also change the weighting applied to each dataset. The number of samples per zone and the metrics that you choose will determine the suggested location for you to take the samples. The cLHS algorithm is automatically run against these zones.