Food Quality Scoring Using Hyperspectral Imaging and CNNs
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Solution Overview
Problem
Existing methods struggle to objectively and quantitatively assess the quality of food items, particularly in distinguishing subtle color and feature differences that are challenging for the human eye, impacting the efficiency and accuracy of supply chain management.
Innovation Solution
Utilizing machine learning techniques, specifically convolutional neural networks, to analyze image data from hyperspectral cameras, enabling the detection and scoring of food item quality features such as color, ripeness, rot, and desiccation, with models trained to identify and quantify these qualities non-invasively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human eye observation is used to assess food item quality, then the method is simple and requires no complex equipment, but the ability to distinguish subtle color differences and quality metrics is limited
Solution Approach 1:
The patent replaces the mechanical/physiological human eye observation system with an automated imaging system comprising hyperspectral cameras, RGB cameras, and thermal cameras coupled with machine learning models. This substitution enables objective, quantitative assessment of food quality metrics including subtle color differences, ripeness, rot, and desiccation that are imperceptible to human observers.
Solution Approach 2:
The patent transforms the assessment from qualitative human perception to quantitative measurement by capturing image data across multiple spectral ranges (visible, near-infrared, thermal) and processing them through trained machine learning models to generate numerical quality scores. This parameter transformation enables detection of subtle changes in color, texture, and composition.
2Adaptability or versatility
If multiple quality metrics are assessed simultaneously, then the comprehensiveness of quality evaluation improves, but the complexity of the assessment system increases
Solution Approach 1:
The patent creates a universal quality assessment system that can evaluate multiple food quality metrics (color, ripeness, rot, desiccation, firmness) simultaneously using a single integrated platform. The system employs multiple trained machine learning models that can be executed in series or parallel to assess different quality aspects, making the system adaptable to various food types and quality assessment requirements.
Solution Approach 2:
The patent divides the complex quality assessment task into separate specialized machine learning models, each trained to detect specific quality metrics. These segmented models can be independently developed, trained, and executed, allowing for modular and flexible assessment of different quality attributes without requiring a single monolithic complex model.
3Measurement precision
If machine learning models are executed in series to enhance accuracy, then the quality assessment accuracy improves, but the processing time increases
Solution Approach 1:
The patent implements a dynamic model execution strategy where models can be executed in series when high accuracy is required or in parallel when processing speed is prioritized. The system adapts the execution mode based on specific assessment requirements, allowing flexible trade-off between accuracy and processing time for different operational scenarios.
Data Source
AI summary
Described herein are systems and methods for determining quality levels for food items using image data, such as time lapse RGB, hyperspectral, thermal, and/or multispectral images. The method can include receiving, from imaging devices, image data of food items, performing object detection on the image data to identify a bounding box around each food item, and identifying a quality level of each food item by applying trained models to the bounding boxes. The models were trained using image training data of other food items that was annotated based on previous identifications of a first portion of the other food items as having poor quality features and a second portion as having good quality features. The other food items and the food items are a same type. The method also includes determining, for each food item, a quality level score based on the identified quality level of the food item.