Who
A Murdoch University–based research team developing a biodegradable “smart material” designed to be sprayed onto soil to influence water behavior at the surface.
Case Study
Executive Summary
Problem
They needed a reliable, teachable way to quantify soil wettability (water contact angle) on treated vs untreated soil. Their first attempts using Dropometer on real soil surfaces ran into consistent obstacles: rough, non-uniform baselines, cluttered backgrounds, rapid droplet penetration, and software-mode confusion often resulting in calculation failures even when the droplet looked “obvious” to the human eye.
Solution
An expert-guided workflow using the same Dropometer hardware, with practical changes that made soil measurements consistently solvable:
1. Use a Tilted Angle workflow to handle non-straight baselines
2. Use Extended mode when the background is cluttered
3. Apply a clear rule for Young–Laplace vs Polynomial fitting
4. Crop/tightly frame images around the droplet to reduce pixel-scale errors
5. Add usability upgrades for repeatable multi-user execution (e.g., optional mouse control via phone dongle, lighting discipline, enclosure concept)
Time to Value
Once the team applied cropping/tight framing and the correct baseline/fit modes during the expert troubleshooting deep dive, measurements that were previously error-prone became workable. The team is now preparing to ramp measurement throughput after training additional users.
Results
1. Current capture constraint identified: ~10–15 fps can miss fast droplet events on porous/rough soil
2. Upgrade path discussed: ~100 fps via scientific camera + desktop processing (for fast dynamics)
3. Sample format constraints documented: current work on tubes (~5–10 cm); planned work includes columns up to ~50 cm
4. Readiness milestone achieved: workflow changes converted “fails” into working calculations, enabling a team-wide SOP rollout
Client Snapshot
Industry
University research (soil / materials / applied sustainability)
Products / applications
Biodegradable smart material sprayed onto soil to influence water behavior (supporting goals such as improved water-use efficiency, reduced irrigation needs, and yield resilience)
QC stage (incoming / in-process / final)
R&D validation / formulation screening (quantifying treated vs untreated soil wettability)
Users (roles/shifts)
PhD researcher + broader research team (multi-user training underway)
Materials / surfaces tested
1. Treated vs untreated soils
2. Soil surfaces in tubes (~5–10 cm)
3. Planned measurements on columns (~50 cm)
4. Occasional tests on sprayed material deposited on paper (still challenging due to baseline and droplet asymmetry)
Key constraints
1. Soil roughness/porosity → baseline ambiguity and non-ideal droplet shapes
2. 10–15 fps phone capture → may miss fast dynamics
3. Height/clearance challenges for tall samples
4. Lighting/backlight battery constraints for field-like workflows
5. Need for a simple, teachable SOP for consistent results across users
The Challenge
Initial measurement workflow (first attempts on Dropometer)
Because contact angle measurement was new to this project, the team began by attempting to capture and analyze soil droplets directly on Dropometer. In early trials, the instrument setup was manageable but turning images into trustworthy contact angles was inconsistent on real soil.
Pain Points
1. Non-uniform soil surface makes baseline selection and droplet edge detection unreliable
2. Calculation failures triggered by small (pixel-scale) errors in contact-point placement
3. Fast droplet behavior (penetration/spreading) can be missed at 10–15 fps
4. Sample height constraints (tubes and columns require more stand/holder flexibility)
5. Software learning curve: uncertainty around which fit/mode to use for non-ideal droplets
6. Automation edge cases: desire for robust fail-safes for the automatic dropper
Why it mattered
Contact angle is the key quantitative signal for confirming soil surface wettability changes due to treatment. Without a reliable workflow, formulation comparisons slow down, confidence in conclusions drops, and scaling decisions (pot/field trials) become harder to justify—especially under deadline pressure.
Success criteria (what “better” meant)
- A repeatable method that produces trustworthy angles on soil
- A workflow simple enough to train multiple team members quickly
- Sufficient reliability and throughput to support upcoming trial milestones (lab → pot → field)
The Solution
What Was Deployed
Dropometer’s smartphone-based contact angle measurement workflow, used to capture and analyze droplets on treated and untreated soil samples, with optional use of an automated dropper.
Before vs After
Implementation
Timeline (high level)
Week 0
- 1. System assembled and first soil images captured
- 2. Early friction: rough baseline, inconsistent edge detection, sample positioning constraints
Week 1-2
- 1. Self-guided iteration on capture and software settings
- 2. Identified needs for better standardization (needle identification/calibration, consistent framing)
Weeks 3–4
- 1. Documented persistent blockers (frame rate limits, baseline ambiguity, mode selection confusion)
- 2. Scheduled deep-dive troubleshooting to produce a teachable “soil SOP”
Expert troubleshooting deep dive outcome
- 1. Converted error-prone analysis into a consistent workflow using Tilted baseline + Extended mode + cropping + fit selection rules
- 2. Prepared the team to train additional users and ramp up testing volume
Proof / Validation
Test method
Water contact angle measurements on treated vs untreated soil to quantify wettability changes and compare formulations.
Sample size and operators
To be reported after the first full test batch (multi-user rollout underway)
Repeatability / reproducibility
To be reported after first batch; expected to improve with standardized framing/cropping, baseline handling, and consistent soil prep.
Notes / assumptions
Soil roughness and porosity can drive droplet asymmetry and baseline ambiguity. Higher-speed capture may be required when droplet penetration occurs quickly.
Results
Measured Outcomes
Current capture constraint documented
~10–15 fps can miss rapid droplet behavior on porous/rough soil
Upgrade path defined
~100 fps scientific camera + desktop processing discussed for dynamic events
Sample geometry constraints identified
current tubes (~5–10 cm) vs planned columns (~50 cm) require stand/holder improvements
Operational Outcomes
The biggest unlock was workflow, not hardware changes: tight cropping/framing and the right baseline/mode selection turned prior “calculation errors” into working measurements
Clear decision rules reduced user confusion: when to use Young–Laplace vs Polynomial; when to use Extended mode
Multi-user readiness improved: practical controls (cropping SOP + optional mouse input) make it easier to train others and scale measurement throughput
Client Quote
“Once I cropped it… it’s working.”Samantha Viljoen - PhD researcher
What's Next
Delivered
Soil-ready workflow with Tilted baseline + Extended mode + cropping SOP + fit selection rules
Training-ready troubleshooting playbook for non-standard surfaces
In Pilot
Improve stand/holder adjustability for tall samples and field-like workflows
Lighting/battery optimization and consistency improvements
Automated dropper reliability and volume-aware fail-safe concept
Planned
Ramp testing after training additional users
Evaluate higher-frame-rate capture path for fast droplet dynamics
Translate lab measurement workflow toward pot/field trials once stand + power constraints are resolved