Mobile Sensor Platform to Sample Complex and Spatially Variable Environments for Plant and Soil Attributes

Labour is a large contributor to agricultural input costs; the operations are repetitive, occur in harsh environments and are critical for cost-sensitive decision making. This project will develop the ‘Agronaut’, a scouting robotic platform to sense and monitor crop growth and quality, and field’s micro-climate conditions.

The Problem

Characterizing the crop growth and profiling the field sub-canopy environment are labour-intensive activities that agronomists, consultants, and farmers must do to assess crop phenology, quality and health, to decide on any intervention during the growing season to optimise yield. In addition, intra-field spatial variability can be as large as temporal variability in one field. Such information and the required spatial-temporal variability are not available from current airborne (drones, UAV) and spaceborne sensors (satellites). Despite a lot of research and effort to validate the technical feasibility of robotic and autonomous systems in agriculture, commercial applications of RAS in broadacre agriculture are not yet available.

Our Solution

We are going to build the Agronaut – a RAS that will traverse the field and collect crop information in the sub-canopy environment. This project will focus on sensing and data fusion across onboard sensors and other above-canopy sensors, and integration into on-farm management.

Collaborators

This project is lead by Roger Lawes and will involve collaboration effort between researchers in the A&F, Data61 and MR Business Units.