Machine learning for managing soil constraints

WHY THIS PROJECT IS NEEDED

This project aims to find the best ways to manage multiple soil constraints such as sodicity, acidity, and salinity to help farmers make informed soil management decisions to maximise productivity and profitability.

While there are different ways to manage constraints in isolation, it is difficult to know which method to use and when, due to high variability in the responsiveness of soils to ameliorants where multiple soil constraints exist.

In short: The project uses data and a computer-based approach to predict which management and amelioration practices will work best for a particular soil, to enhance soil productivity and profitability for farmers.

Project focus

To address this, the project is developing and testing a computer-based approach (using a knowledge-guided machine learning framework) that incorporates scientific understanding and learnings from existing data. This will then be used to predict which combinations of soil management will work best for a particular soil that is affected by multiple constraints, under specific weather and farming conditions.

This project will also standardise and use data from published studies and past/current experiments conducted by Soil CRC participants to ensure that the data will be findable, accessible, interoperable, and reusable. 

Full project title: Knowledge-guided machine learning optimisation of soil constraint management