AustraliaAgriculture and food production2023 – ongoingCBH Group
Acova visual grain-quality assessment
CBH reported faster grain analysis after Acova trials at receival sites
Problem
Grain receival required checks for defects and contamination. Growers also needed visibility into the findings behind quality assessments.
Approach
CBH and Deimos Laboratory developed visual assessment technology using machine learning and custom hardware. It detected defects and unwanted material in grain samples. CBH piloted it at several receival sites over the 2 years before September 2025 and gave growers access to defect images through its CDF app.
Results
No published figures
Takeaway
The pilot combined faster assessment with evidence growers could inspect. CBH reported shorter analysis times without publishing a measured reduction.
Categories
- Industry
- Agriculture and food production
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Computer visionPredictive analytics and ML
- Adoption
- Partner co-development
- Evidence
- Company's own claim
Sources
Source summaries
CBH reported completed trials of machine-learning grain assessment at its receival sites. It described reduced analysis times and grower access to defect images, with further deployment planned.
Full details are in the original sources.
