Commodity Authenticity Verification via Image Data Analysis
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Solution Overview
Problem
The issue of counterfeit commodities in the agricultural products market, where consumers face difficulties in verifying the authenticity and origin of products, leading to health risks and unfair competition for genuine producers, and there is a lack of reliable methods to ensure the quality and handling conditions of commodities.
Innovation Solution
A method involving a requesting and providing system where product codes are generated and verified using image data and related information, collected by producers, traders, and consumers, to authenticate the origin and integrity of commodities through a server-based system, utilizing blockchain technology and AI for image analysis to ensure the authenticity and quality of products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional marking methods (flags, logotypes, generic labels) are used to indicate origin, then the marking process is simple and inexpensive, but the reliability and accuracy of origin verification is poor
Solution Approach 1:
The patent uses image data (photographs) of the production structure as a copy or representation of the actual production environment. These images are stored and later compared against new images to verify that the production structure has not changed, providing a reliable but simple method for origin verification without requiring complex physical verification systems.
Solution Approach 2:
The system performs preliminary actions by collecting and storing image data of the production structure, harvest data, and generating the product code before the commodity reaches the consumer. This advance preparation creates a baseline for future verification, allowing quick and reliable origin checking without complex real-time verification systems.
2Reliability
If detailed information about growing conditions and handling is collected and verified, then product quality and authenticity are improved, but the complexity of data collection and verification increases
Solution Approach 1:
The system enables self-service verification where consumers can independently verify product authenticity using the provided product code and image comparison technology. The producer captures and stores the initial images, and the system automatically compares new images against the stored baseline, eliminating the need for complex manual verification processes and allowing consumers to perform their own authentication.
Solution Approach 2:
The system implements feedback mechanisms by comparing current images of the production structure against stored baseline images and analyzing discrepancies. This feedback loop automatically verifies whether the production environment has remained consistent, providing reliable authenticity verification without requiring complex manual inspection of all handling and growing conditions.
3Measurement precision
If image data and harvest data are collected and analyzed to verify production conditions, then measurement precision of origin verification is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system applies partial action by focusing verification efforts on key discriminative features in the images rather than analyzing every pixel or detail. The image comparison algorithm identifies and compares significant structural elements of the production environment, achieving high measurement precision while consuming fewer computational resources than a complete exhaustive analysis would require.
Data Source
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AI summary
A product code requesting method (400) of a requesting part, for obtaining a product code (A, B, C) from a server (130), in order to mark and/ or verify a mark of a container (145) comprising a commodity. The method (400) comprises: collecting (401) image data of the commodity; determining (402) information data related to the collected (401) image data; providing (403) a request for the product code (A, B, C) and/ or content verification, comprising ID of the requesting part; the collected (401) image data, and the determined (402) information data, to the central data processing system (130); and obtaining (404) either the product code (A, B, C), or the content verification of the product code (A, B, C), from the server (130). A verification and product code generative method (500) of a server (130) is also disclosed.