Use Case 1: Prevention & Remediation of Soil Compaction and Acidity in Flemish Soils
Overview:
In Flanders, Belgium, the extensive use of heavy agricultural machinery has caused widespread soil compaction, which reduces soil quality and limits crop growth. This use case focuses on addressing these challenges through targeted and efficient solutions.
Challenges:
- Traditional subsoiling methods treat entire fields uniformly, leading to higher fuel costs and wasted resources.
- Mapping soil properties can help, but current methods are either too inaccurate or labour-intensive for precise soil management. There is a lack of affordable, accurate solutions for soil mapping.
- High variability in soil compaction across fields.
Smart Farming Applications involved:
- Variable-rate liming
- Variable-rate macronutrient fertilization
- Variable-depth tillage
- Carbon farming MRV solutions
Expected Outcomes:
- Cost-effective soil mapping, allowing for targeted soil compaction remediation.
- Reduced costs from more efficient use of fuel and labour.
- Improved crop yields and improved overall soil health.
Updates
From controlled setup to real conditions
A semi-field experimental setup was developed at the ILVO test site, enabling controlled yet field-relevant testing conditions.
This approach allows:
- repeated testing under consistent conditions
- validation of technologies before deployment in real farming environments
The setup was created by excavating and reconstructing soil layers to simulate compaction. While very high compaction levels (5 MPa) could not be achieved, the experiments provided valuable insights into soil behaviour and testing limitations.
Testing robotic solutions
The first iteration of the SQAT heavy-weight robot was tested both in controlled conditions and in the field.
The goal was to explore automated alternatives to traditional soil compaction measurements, which are currently time-consuming and labour-intensive.
Initial tests focused on using hammer frequency as a proxy for soil resistance. However, results showed that measurements were influenced by additional factors such as mantle friction, limiting their reliability.
Key learning and next steps
Based on these findings, the team is shifting towards automated cone resistance measurements, which provide more accurate and robust results.
The next phase includes:
- development of a multi-sensor robotic platform
- integration of penetrometers and additional sensors
- testing of both heavy and lightweight robotic systems in real field conditions
This iterative development process is central to SQAT’s approach, ensuring that solutions are tested, validated, and adapted before wider deployment.