Non-invasive Produce Quality Assessment via Spectroscopy and Imaging
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Solution Overview
Problem
Existing methods for determining the quality of produce are challenging due to the inability to objectively and non-invasively assess quality metrics, leading to waste and inefficient pricing strategies.
Innovation Solution
The use of data models that correlate non-invasive produce assessments, such as image-based assessments and analysis of volatiles and compounds emitted by the produce, to dynamically assess and predict future quality metrics over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If destructive quality measurements are used to assess produce quality, then measurement precision is improved, but the produce quality and availability deteriorate
Solution Approach 1:
The patent replaces mechanical destructive measurement systems with optical and spectroscopic systems. Image processing captures visual characteristics without contact, while spectroscopy analyzes chemical composition through light interaction, both enabling quality assessment without physically damaging the produce.
Solution Approach 2:
The patent introduces an intermediary information processing system that mediates between the produce and the measurement process. By using image data and spectral signatures as intermediaries, the system infers quality characteristics without direct destructive interaction with the produce itself.
2Reliability
If non-invasive assessment methods are used, then produce quality is preserved, but measurement precision deteriorates
Solution Approach 1:
The patent creates a multi-functional assessment system that combines image processing for visual characteristics, spectroscopy for chemical composition, and machine learning algorithms for integrated quality prediction. This universal approach enables non-invasive measurement of multiple quality parameters simultaneously, achieving both preservation and precision.
Solution Approach 2:
The patent creates digital copies of produce characteristics through image capture and spectral signatures. These digital representations serve as permanent records of quality attributes, allowing repeated analysis without physical contact and enabling accurate quality assessment while preserving the original produce.
3Device complexity
If quality metrics are not dynamically updated, then system complexity is reduced, but pricing accuracy deteriorates
Solution Approach 1:
The patent implements continuous quality monitoring where the system periodically reassesses produce quality and dynamically updates pricing. This continuous action ensures pricing accuracy reflects current produce conditions, while the automated nature of the process manages system complexity through standardized procedures.
Solution Approach 2:
The patent establishes a feedback loop where quality assessment results directly influence pricing decisions, which are then monitored for consumer response. This feedback mechanism enables dynamic pricing adjustment based on actual produce quality, improving pricing accuracy while the algorithmic automation keeps complexity manageable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for real-time, non-destructive assessment of produce quality, reducing waste and enabling dynamic pricing strategies that incentivize the consumption of produce at peak ripeness.
Implementation Method 1
analysis of produce-emitted volatiles and compounds
Data Source
AI summary
Systems and methods described herein can non-destructively determine current produce quality. An evaluation system can, at a first time, receive image data of produce and identify a type of the produce using machine learning techniques. The produce can be sent to an end-consumer retail environment. A quality assessment system can, at a second time, receive, from a user device in the retail environment, a request for a current quality of a particular produce that includes scanned data of that produce. The quality assessment system can identify that produce, retrieve, from a data store, machine learning trained models for identifying quality features of that produce, determine, based on applying the models to the scanned data, a current quality of that produce, and transmit, to the user device, the current quality. The produce's price can also be dynamically adjusted based on the current quality of that produce.