CODE-AI: Cocoa Origin Detection Enabled by AI

Food fraud is a significant issue, providing a pressing need for low cost, simple to use authentication methods. Currently, consumers of agri-food products are calling for greater food-system transparency to inform their purchase decisions and reduce the risk of buying unsafe/illegal/unethical/counterfeit products. Traceability can meet this demand through in-depth tracking of supply-chain data. However, track-and-trace systems currently in-use are susceptible to fraud (as they are only tracking packaging) or are prohibitively expensive (when lab-analysis is required).

High value crops such as coffee/tea/cocoa grown in Africa/South-America/Asia are distributed worldwide and their supply-chain suffers from fraud issues causing damage in these global markets. Better traceability could realise ethics and sustainability claims, hold companies and governments accountable to their commitments (like Section 54 of the UK Modern Slavery Act 2015) and more accurately measure the social/environmental footprint of production in real-time, and at a lower cost.

This feasibility project aims to develop CODE-AI, an **AI-driven solution** for the identification of cocoa origin by 'scanning' the crop, which then will be part of a larger crop origin prediction tool, 'CropOrigin'.

**Our focus in this project is to combine the power of cutting-edge machine learning approaches, with the measurement of the intrinsic properties of cocoa beans, to allow the instant determination of geographical origin.**

The ultimate application ('CropOrigin') will involve an affordable hand-held spectral diagnostic device to scan cocoa in the form of bean/powder/food product. The scanned biological fingerprint is fed into an AI-enabled prediction tool, (CODE-AI for cocoa) via network connection and user interface for an instant identification of the geographical origin of crops. It will be supported by a database of unique geospatial and spectral signatures of agri-food products; an AI algorithm and software for several supply-chains.

The advances made in this project will benefit the consumer directly through the development of a traceability system that can be used by relevant controlling bodies to identify fraud, and by retailers, importers and manufacturers to authenticate product labelling, confirming its safety and compliance.

The Consortium who will realise this project include: Tiwakiki Consulting (Lead), an SME with significant software, R&D and project management capabilities; Rothamsted Research, a leading academic institution focused on crop research, and Crop Health and Protection (CHAP) via a subcontract.

While the current project will focus on cocoa, the approach has potential to be applied to many types of agricultural produce such as tea, coffee, leafy veg, nuts, etc.