Autonomous ai-based microscopy verification for cocoa bean fraction separation

Overview

An AI-driven microscopy verification system for cocoa bean fraction separation. The technology is designed to ensure correct separation by detecting specific contaminants in the sample. Traditional verification approaches can be labor-intensive and susceptible to human error, while an autonomous AI workflow can provide more precise, efficient, and cost-effective screening. By leveraging AI, the system can process hundreds of images without human aid, supporting reliable quality assurance for various screening technologies.

Technical specifications

The microscopy device is engineered for affordability and accuracy. It includes an AI module that can be trained to recognize contaminants specific to cocoa bean fractions. The solution comprises hardware, a platform, an app, and cloud support, forming a plug-and-play system. The device measures contaminants’ size, color, and load swiftly and accurately. Initial dataset creation and model training—based on non-conformity samples and defined contaminant classes—can be completed within 1 to 3 months depending on complexity.

Technology readiness level

The technology readiness level for this solution is currently at TRL 4, indicating validation in a lab environment and readiness for further development and testing. Future validation plans include dataset creation, model training, and iterative accuracy testing, achievable within a few months.