Shelf Life Prediction via Visual Quality Mapping
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Solution Overview
Problem
Conventional methods for predicting the shelf life of perishable food items are inadequate as they rely on visual features and discrete values, failing to accurately estimate quality in real-time and do not account for varying storage conditions throughout the supply chain.
Innovation Solution
A system and method that utilize visual data and storage data to determine current quality parameter values by mapping storage conditions to preconfigured weather zones, enabling accurate shelf life prediction using a quality parameter prediction module and shelf life prediction module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image-based models are used to detect defects and grade food, then visual feature detection is achieved, but shelf life prediction and continuous quality parameter estimation are not possible
Solution Approach 1:
The patent transforms the output of visual feature detection from discrete quality grades to continuous quality parameter values by introducing a quality parameter prediction module that maps visual features to continuous shelf life predictions, thereby enabling both accurate measurement and versatile prediction applications
2Measurement precision
If image-based models are trained at specific storage conditions, then quality assessment is accurate at those conditions, but the model cannot identify freshness when storage conditions vary
Solution Approach 1:
The patent creates a universal quality prediction model that functions across multiple storage conditions by integrating storage condition parameters into the prediction framework, allowing the same model to accurately assess food quality whether stored in refrigeration, ambient conditions, or during transport
Solution Approach 2:
The patent makes the quality prediction model dynamic by incorporating variable storage condition inputs, allowing the model to adapt its predictions based on actual storage temperature, humidity, and duration rather than relying on fixed-condition training data
3Ease of operation
If discrete quality values are used for grading food, then simple classification is achieved, but continuous shelf life prediction and real-time quality monitoring are not possible
Solution Approach 1:
The patent changes the parameter representation from discrete quality grades to continuous quality parameter values, enabling both the simplicity of automated grading and the precision of continuous shelf life prediction by using a continuous prediction scale that can be applied in real-time
Data Source
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AI summary
This disclosure relates generally to method and system for predicting shelf life of perishable food items. In supply chain management, current technology provides limited capability in providing relation between visual image of food item and a quality parameter value at different storage conditions. The system includes a quality parameter prediction module and a shelf life prediction module. The method obtains input data from user comprising a visual data and a storage data of each food item. The quality parameter prediction module determines a current quality parameter value of the food item from a look-up table. The shelf life prediction module predicts the shelf life of food item based on the current quality parameter value, a critical quality parameter value, and the storage data. The look-up table comprising a plurality of weather zones are generated based on relationship dynamics between the visual image of food item and the quality parameter value.