Key messages

• Accurate Paddock Boundaries including internal boundaries are fundamental for autonomous systems and necessary to get the most value out of path planning and machinery investment tools like Launch Pad (www.vergeag.com).
• Path Planning to optimise the direction of the machinery guidance run lines (AB lines) can reduce time of operations and save inputs, the end goal is route planning that gives the machine or operator directions to work the paddock reducing inefficiencies with unskilled staff or crop damage.
• Equipment Explorer can be used to make machinery investment decisions (increasing machinery width, wheel traffic, overlap, section control) with relatable information based on growers’ specific farm/paddock shapes and machinery configurations.

Aims

Understand farm operational challenges for Australian grain growers and how tools, such as the Verge Ag Launch Pad, can be used to optimise efficiency. Findings will inform product development.

Introduction

Advancements in autonomous machinery and computer systems are revolutionising farm operations, enabling growers to tackle logistical challenges such as coordinating multiple machines, training inexperienced operators, and adapting to varying soil and climate conditions. These technologies enhance operational efficiency, potentially reducing input costs on fertiliser, herbicide, fuel, and labour. One such innovation, GPS machine guidance, enables growers to adopt Controlled Traffic Farming (CTF), where machinery wheel traffic is confined to designated tracks, minimising soil compaction. Implementing CTF requires developing a master set of run lines, also known as AB lines and boundaries to guide machinery, but operators still have flexibility in navigation, which can lead to inefficiencies—particularly in complex paddocks with internal obstacles. By optimising route planning within a paddock, growers can improve guidance for machinery operators, including casual staff unfamiliar with the terrain, ultimately enhancing efficiency and productivity.

GRDC’s Grain Automate project ‘On-farm operations optimisation’ led by Verge Technologies Australia Pty Ltd in collaboration with The Australian Controlled Traffic Farming Association is developing a new generation of farm operation optimisation tools. The three-year project is working together with consultants and growers across Australia, as well as the Verge development team based in Canada.

Using the Verge online platform (www.vergeag.com) the project focuses on improving efficiencies by allowing growers to upload their paddock boundaries and current machinery fleet data, building their digital farm, and from there allowing some of the following analyses:

1. Equipment Explorer for Fleet and Farm optimisation: Enhance machinery investment planning by designing the right sized equipment fleet based on paddock shape, terrain and other spatial attributes.
2. Growing season Pathway Planning: Determine optimal AB lines to maximise machinery efficiency, reduce idle time and fuel consumption, plan pathways to minimise crop damage and soil compaction, and assess the benefits of controlled traffic farming.
3. In-Paddock Operations Logistics Planning: Evaluate optimal refill points or grain bin placement (e.g. for grain, fertiliser, fuel) based on unit capacities of equipment operating in the paddock.
4. Analytical Tools for Cost- Benefit Analysis.

Method

Verge has engaged in a two-way information flow, demonstrating how the Path Planner and Equipment Explorer tools within the Launch Pad platform can be used while receiving feedback from growers and consultants on product development. Using a case study approach, Path Planner has been tested across 16 Australian grain farms in 2024 to explore how changes like equipment size, direction of travel, and section control can improve efficiency. There are six grower case studies in the GRDC Western and Northern regions and four in the Southern region. Growers were selected to represent a range of farm sizes, farming systems and climates across Australia. As well as testing the capabilities of Launch Pad, opportunities for optimisation were recorded for each farm. Growers have guided the development of more tailored solutions to address the specific challenges they face. The goal is to equip each grower with the tools to optimise their operation, leveraging their existing investments in GPS, autosteer, and section control, and creating optimised paths that fit their unique equipment and paddock characteristics.

Results

The most common opportunity for improving operational efficiency was optimising path planning for the longest run, identified by 51% of growers (9 out of 16). This was followed by route planning to manage internal obstacles and unskilled labour (44%, 7 growers). Other key areas included layout planning, such as the placement of fill-up stations (25%, 4 growers), machinery investment (15%, 4 growers), merging paddocks (2 growers), minimising wheel traffic areas (2 growers), and addressing wind and water management. Additionally, 31% of the case study growers had fully matched Controlled Traffic Farming (CTF) systems, 44% were semi-matching, and 25% did not use CTF.

Examples of the opportunities for farm optimisation are:

1. Path planning - optimised run line

Initial exploration of path planning with case study growers highlighted the need for accurate boundaries, including internal ones, to maximize tool value—such as AB Line placement and overlap calculations. For some CTF farms, it confirmed their optimal AB Line direction. Verge added the ability to input existing AB Line coordinates for seasonal path planning, while other farms found that merging paddocks and system changes made adjusting run lines more efficient.

A South Australian case study farm used Path Planner to optimize AB Lines for 2024 seeding. In one paddock, eliminating 44 run lines saved 2.5 hours of sowing time. With 52 similar paddocks, the potential impact is significant. However, severe wind erosion post-seeding highlighted the need to balance efficiency with erosion risk in low-rainfall dune/swale landscapes. Launch Pad can adjust for water erosion risk based on slope but not for wind erosion linked to wind direction.

2. Path planning - Route planning - staff management

Internal obstacles can increase overlap, wasting inputs and extending operation time, especially for casual staff unfamiliar with the paddocks. An Esperance grower, dealing with numerous salt lakes, often records video guides to train staff to work the paddock the most efficient way The Verge platform offers an alternative as once the most efficient route is determined the route can be sent to a mobile device and the user can see the path, overlayed on satellite imagery, including direction of travel and turning points (Figure 1.)

New 2024 updates let growers set start and end points, and from there the software determines the quickest route. While the initial route differed from the grower’s usual approach, they found the visual path and boundary intersections helpful. Future feature requests include a colour scale for efficiency, refill station inputs for casual staff, and manual route adjustments for better efficiency in narrow sections.

3. Equipment Explorer - Machinery investment – 12m versus 18m seeder – productivity

Equipment Explorer analysed a WA grower’s potential productivity gains from upgrading seeding machines from 12m to 18m. Their current two 12m machines cover 5,972ha, aligning with their CTF system. Using default speeds (8.85 km/h working, 5.63 km/h turning), estimated seeding times were:

• Two 12m machines: 303 hours (20 ha/hr)
• One 12m + one 18m: 248 hours (24 ha/hr)
• Two 18m machines: 209 hours (29 ha/hr)
• Two 24m machines: 162 hours (37 ha/hr)
• Larger machines significantly reduced seeding time, improving efficiency.

4. Equipment Explorer - Machinery investment – 12m versus 18m seeder section control

Wider seeders increase productivity but can also lead to more overlap, especially in smaller, obstacle-filled paddocks. Estimated overlap was 2.5% for 12m, 3.8% for 18m, and 5.0% for 24m machines, compared to just 0.7% with 3m section control. With seeding costs at $245/ha, annual overlap costs were $36,940 (12m), $55,011 (18m), and $73,538 (24m), versus $9,956 with section control (Figure 2.).

Using a $55,000 upgrade cost per machine, section control payback periods were:

• ● 12m: 3 years (one machine), 5 years (two)
• ● 18m: 2 years (one), 3 years (two)
• ● 24m: Just over 1 year (one), 2 years (two)

After five years, avoiding overlap on two 12m machines saves $24,917 compared to not upgrading.

5. Machinery investment - CTF wheel traffic area - 12m:12m:36m versus 12m:18m:36m

Upgrading to an 18m seeder boosts productivity but also increases wheel traffic for this grower’s long-standing 12m:12m:36m CTF system. Using a 3m track width and varying tire widths (600mm for 12m seeder/harvester, 710mm for 18m seeder, 480mm for 36m sprayer), wheel traffic impact was analysed. In a 234ha complex-shaped paddock, wheel traffic rose from 10.3% (24.2ha) with a 12m seeder to 16% (37.6ha) with an 18m seeder. In a 203ha rectangular paddock, it increased from 10% (20.3ha) to 14% (29.4ha), slightly less than in the complex paddock.

Conclusion

After engaging with 16 Australian grain growers in 2024, Verge's Launch Pad software was used to optimise seeder sizes (12m/18m/24m), direction of travel, and percent overlap, as well as understand sprayer and harvest efficiencies. Feedback led to development of a route planning tool for machinery and a wheel traffic area calculator for planning controlled traffic farming systems, with future focus on farm layout and autonomous systems. Features that have been developed so far include routes, the ability to input the grower’s AB lines, and wheel traffic area.

Routes will help to improve staff management and operation efficiency, especially for inexperienced casual staff. Determining the most efficient route from run lines is a work in progress and further parameters for machine turning, when to return to a refill station, avoiding areas where a machine will get stuck and understanding productivity or paddock efficiency will help define the most efficient route.

As the size of the machine increases, so too does the overlap percentage. These examples look at the most efficient operation of the paddock and it is likely under time pressure and with casual staff the overlap percentages could be higher. Overlap percentages may also be higher if machinery does not have auto-shutoff, as the Launch Pad software assumes all machines have this.

Acknowledgments

Project VER2304-001RTX Through the Grain Automate initiative, Grains Research and Development Corporation (GRDC) has partnered with Verge Ag and the Australian Controlled Traffic Farming Association to support the acceleration and adoption of machine automation, autonomy and digital technologies in the Australian grains industry. The research undertaken as part of this project is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC, the author would like to thank them for their continued support.

Contact details

Bindi Isbister, Agrarian Management, bindi@agrarian.com.au

Alice Butler, Farmanco, abulter@farmanco.com.au