Multimodal deep learning for real-time cocoa bean sorting
An advanced cocoa bean sorting solution leveraging multimodal deep learning, integrating microwave imaging and computer vision for rapid and accurate real-time identification and separation of cocoa bean fractions on production lines.
Overview
This innovative solution utilizes multimodal deep learning to enhance cocoa bean sorting processes by integrating microwave imaging with computer vision and language models. The approach allows for the rapid, real-time identification and separation of cocoa bean fractions, enhancing efficiency and accuracy in production lines.
Technical specifications
Key features:
- Microwave Imaging (MI): Utilizes safe, non-invasive microwave signals to detect internal structures and constituents of cocoa beans based on dielectric properties.
- Computer Vision (CV): Captures real-time images to identify surface characteristics such as color and shape.
- Multimodal Deep Learning (MDL): Combines MI and CV data with descriptive text processing to enhance understanding and decision-making.
- Integration with Language Models: Uses a vision transformer and Llama2-7B model to align visual features with text descriptions for rapid response.
- 3D Imaging and Beamforming: Employs ultra-wideband antennas and beamforming algorithms to produce detailed 3D images of cocoa bean components.
Technology readiness level
Currently at TRL 4, this solution has been validated in lab conditions using prototypes based on previous studies. Future validation will involve real-time testing on conveyor belts with cracked cocoa beans, integrating advanced imaging and machine learning techniques for practical deployment.
About University of Alabama
The University of Alabama is a large, comprehensive public research university in Tuscaloosa with a broad research portfolio and a strong pipeline of undergraduate and graduate talent. Industry engages through a centralized corporate partnership office that streamlines sponsored research and standard agreements. On‑campus prototyping and materials characterization facilities, shared cores, and project spaces support collaboration; a well‑established co‑op and internship program connects companies with students year‑round. Research is supported by competitive federal funding from agencies such as NSF, DOE, DoD, and NIH. A dedicated technology transfer office manages IP, licensing, and startup support, and coordinates with regional economic development partners near the state’s automotive manufacturing corridor.
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