Perishable Freshness Scoring Using Sensor Data and ML
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Solution Overview
Problem
The existing food supply chain lacks effective tracking and monitoring of perishable goods, leading to excessive waste, labor inefficiencies, and potential health risks due to spoilage and foodborne illnesses, as temperature and humidity conditions are often not closely monitored during transit.
Innovation Solution
A computing system that determines and utilizes scores, such as ProofScore and FreshScore, to assess the transparency and freshness of perishables by integrating sensor data and machine learning models, providing real-time monitoring and incentivizing stakeholders to improve handling and storage conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional paper-based tracking and disconnected data silos are used in the supply chain, then implementation simplicity is maintained, but monitoring precision and reliability of temperature/humidity conditions deteriorate, leading to undetected spoilage
Solution Approach 1:
The patent combines multiple previously disconnected data silos (temperature monitoring, humidity monitoring, location tracking, inventory management) into a single integrated supply chain platform. This merging enables comprehensive monitoring precision while managing system complexity through unified architecture and standardized data protocols.
Solution Approach 2:
The supply chain tracking platform is designed with multi-functional capabilities that can monitor various parameters (temperature, humidity, location, inventory status) across different food products and supply chain segments. This universal system replaces multiple separate tracking systems, improving overall monitoring precision without proportionally increasing complexity.
2Reliability
If real-time sensor data collection and machine learning analysis are implemented, then food safety and freshness assessment improve, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing sensor data during transit, rather than analyzing all data only at destination. Machine learning models are trained in advance on historical data to enable rapid real-time assessment. This preliminary preparation reduces data processing time while maintaining high food safety reliability.
Solution Approach 2:
The platform implements feedback mechanisms where machine learning models continuously analyze sensor data and provide real-time alerts when anomalies are detected. This feedback loop enables rapid response to potential spoilage events, maintaining high food safety standards while optimizing data processing efficiency through adaptive learning.
3Loss of information
If comprehensive sensor data from multiple supply chain hand-offs is integrated, then transparency of information improves, but data management complexity and storage requirements increase
Solution Approach 1:
The patent segments the supply chain data into distinct modules corresponding to different hand-offs and stakeholders (farmers, distributors, retailers, consumers). Each segment manages its own data locally while the platform provides integrated visibility. This segmentation improves transparency by making data accessible at each level while reducing overall data management complexity through distributed architecture.
4Loss of substance
If continuous monitoring throughout the supply chain is implemented, then food waste is reduced through early detection of spoilage, but energy consumption and operational costs increase
Solution Approach 1:
The system uses feedback from sensor data and machine learning analysis to trigger monitoring actions only when needed. Rather than continuous high-energy monitoring, the platform adjusts monitoring intensity based on detected conditions, alerting stakeholders only when potential spoilage is detected. This reduces energy consumption while maintaining effectiveness in preventing food waste.
Data Source
AI summary
Provided herein are techniques, devices, and systems for reducing waste by determining and utilizing scores associated with perishables that have been, or that are in the process of being, transported along a supply chain. A computing system may receive sensor data from a sensor within a threshold distance of the perishable, may provide the sensor data as input to a trained machine learning model, which generates, as output a score relating to a freshness of the perishable. This score and related information can be made accessible to users by associating an identifier with the score and with the information.