Deep Learning Image Authentication for Agricultural Products
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Solution Overview
Problem
Current methods for authenticating agricultural products, such as tea, rice, and coffee beans, are vulnerable to falsification due to the lack of distinguishable features, making it difficult for consumers to verify product authenticity, leading to potential monetary losses for both producers and consumers.
Innovation Solution
An image authentication method using a deep learning model that segments images into sub-images and employs a voting strategy to identify global features, combined with a real-time product authentication system incorporating a blockchain for traceable history and quality parameter management, allowing for fast and secure product verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional traceable history labels (QR Codes) are used for product authentication, then consumers can access producer and supplier information, but the system is vulnerable to falsification as malicious actors can add fake producing information to the QR Code
Solution Approach 1:
The patent segments the product authentication into two independent parts: (1) a traceable history label containing producer/supplier information, and (2) a deep learning-based image authentication system that verifies actual product characteristics. This segmentation allows the system to maintain the informational value of QR codes while adding a separate verification mechanism that is resistant to falsification, as the image-based authentication cannot be easily altered like digital data.
Solution Approach 2:
The patent introduces a deep learning model as an intermediary between the product and the consumer verification process. This intermediary analyzes images of the actual product (such as tea leaves, rice grains, or coffee beans) and compares them against authentic reference data, providing an independent verification layer that mediates between the potentially falsifiable QR code and the consumer's need for trustworthy authentication.
2Adaptability or versatility
If high-quality agricultural products (tea, rice, coffee beans) are sold without distinctive visual features, then producers can maintain market flexibility, but consumers cannot distinguish authentic high-quality products from low-quality substitutes
Solution Approach 1:
The patent replaces manual visual inspection and expert knowledge (mechanical systems) with an automated deep learning-based image recognition system. The deep learning model has been trained to detect subtle visual features and patterns in agricultural products that are imperceptible to human consumers, automatically analyzing images of tea leaves, rice grains, or coffee beans to verify authenticity without requiring consumers to have specialized knowledge or skills.
3Loss of time
If consumers manually verify product authenticity through traceable history systems, then they can access production information, but they cannot directly verify the actual product quality at the moment of purchase
Solution Approach 1:
The patent performs preliminary action by pre-training the deep learning model with authentic reference images and data before deployment. The system prepares authentication criteria and visual特征 databases in advance, so that when consumers scan a product at the moment of purchase, the verification process can immediately compare the product image against pre-established authentic patterns, providing both speed and accuracy without requiring real-time expert analysis.
Data Source
AI summary
An image authentication method and a real-time product authentication system are provided. The method includes the following steps: inputting a training image in a training phase; segmenting the training image into a plurality of training sub-images; and training the deep learning model by using the plurality of training sub-images, wherein each of the plurality of training images includes a global feature property. The steps in the testing phase include: inputting a testing image and segmenting the testing image into a plurality of testing sub-images; performing a test on each of the plurality of testing sub-images and checking whether each of the plurality of testing sub-images is associated with one of the plurality of categories; and outputting an authentication result to identify whether the testing image is associated with one of the plurality of categories.