Multimodal Perishable Monitoring for Quantitative Freshness Tracking
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Solution Overview
Problem
Existing monitoring systems for perishable commodities rely on single parameters, providing qualitative analysis without quantitative measurement, and fail to account for varying significance of these parameters across different commodities.
Innovation Solution
A multimodal sensing apparatus using a suite of sensors, including audio/video/image, temperature, humidity, pH, gas, and microspectrophotometer sensors, coupled with an environment controller and machine learning models like CNN and SVR, to determine the health of perishable items by correlating environmental and sensory data with time series images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single parameter sensing is used, then device complexity is reduced, but measurement precision and reliability of quality assessment deteriorate
Solution Approach 1:
The patent combines multiple sensing modalities (visual, olfactory, tactile, temperature, humidity) into a unified monitoring system. The sensor suite integrates camera, gas sensors, humidity sensor, and temperature sensor to simultaneously capture different aspects of perishable commodity quality, resolving the contradiction by merging simple individual sensors into a complex but coordinated multi-parameter system that achieves high measurement precision.
Solution Approach 2:
The monitoring system is designed to be universally applicable to different types of perishable commodities (fruits, vegetables, meat, dairy) by incorporating a comprehensive sensor suite that can detect various degradation indicators across different commodity types. This multi-functional approach allows a single system to perform multiple quality assessment functions simultaneously.
2Device complexity
If single parameter sensing is used, then device complexity is reduced, but reliability of quality indication deteriorates
Solution Approach 1:
The system merges multiple independent sensing modalities (visual appearance, gas emissions, humidity, temperature) to provide redundant and complementary quality indicators. This combination increases reliability by cross-validating quality assessments across different parameters, ensuring that quality degradation is detected through multiple independent channels rather than relying on a single potentially unreliable parameter.
3Device complexity
If qualitative analysis only is provided, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The patent replaces subjective qualitative assessment with objective quantitative measurement systems. Instead of relying on human sensory evaluation (mechanical/biological system), the system uses electronic sensors and machine learning algorithms to automatically quantify quality parameters such as firmness, color, gas composition, and temperature, thereby capturing precise numerical data about freshness degree without information loss.
Solution Approach 2:
The system transforms qualitative quality attributes into quantitative parameters by measuring physical and chemical properties (gas composition, humidity, temperature, visual characteristics) that change as the commodity deteriorates. This parameter transformation enables precise tracking of freshness degree through measurable physical quantities rather than subjective qualitative descriptions.
4Ease of operation
If single parameter monitoring is used, then ease of operation is improved, but adaptability to different commodities deteriorates
Solution Approach 1:
The monitoring system incorporates a universal sensor suite that can adapt to different commodity types (fruits, vegetables, meat, dairy) through multi-functional sensing capabilities. The same hardware platform (camera, gas sensors, humidity and temperature sensors) serves multiple commodity types by detecting different quality indicators relevant to each type, maintaining ease of operation while achieving broad adaptability.
Solution Approach 2:
The system employs dynamic parameter selection and machine learning models that adapt to different commodity types. The health model is trained on commodity-specific data and can dynamically adjust which parameters are most relevant for each commodity type, allowing the system to maintain simple operation interfaces while adapting its monitoring focus to the specific characteristics of different perishable goods.
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
Provides quantitative freshness indices and ripening predictions, enabling precise monitoring and management of perishable goods by correlating sensory data with environmental conditions, enhancing freshness estimation and ripening tracking.
Implementation Method 1
a microspectrophotometer sensor to determine color and other features
Implementation Method 2
a gas sensor to determine emitted gasses
Implementation Method 3
a temperature sensor to determine temperature
Implementation Method 4
a humidity sensor to determine humidity
Data Source
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AI summary
Health of perishable commodities such as eatables deteriorate over time. State of art systems for health monitoring of perishable commodities rely on measurement of limited parameters and also fail to consider effect of environment on the health of the perishable commodities. Disclosed herein is an apparatus and method for multimodal sensing and monitoring of perishable commodities. The apparatus allows to change environment within a closed chamber in which the perishable commodity being monitored is kept, and in turn allows to generate health data in different environment settings. This data is used to generate a health model. Data collected in real-time are processed with the health model to establish a correlation with at least one image, wherein each of such images in the health model represents certain health state. Based on the established correlation, health of the perishable commodity is determined.