Sensor-Based Food Classification for Commercial Kitchen Waste
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Solution Overview
Problem
Existing methods for classifying food items in commercial kitchens are inefficient, unsustainable, and prone to errors, particularly in environments without item-level point of sale data, leading to operational inefficiencies and poor waste management.
Innovation Solution
A system utilizing sensors and machine learning models to automatically classify food items based on sensor data, including image and weight data, with optional user input for validation, trained on historical data from local and global sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual classification methods are used by food consultants, then classification can be performed, but the solution is not scalable and can only be used infrequently
Solution Approach 1:
The system enables self-service classification where the food waste bin automatically performs classification through integrated sensors (camera, weight sensor, temperature sensor) and a trained machine learning model, eliminating the need for manual intervention by food consultants while maintaining high classification accuracy and enabling continuous operation
Solution Approach 2:
The manual mechanical classification process is replaced by an automated system using optical sensors (camera), weight sensors, temperature sensors, and a machine learning model that processes sensor data to automatically classify food items, achieving both high reliability and scalability
2Loss of information
If point of sale data triangulation is used, then some classification can be achieved, but operational inefficiencies are not captured including waste, over/under portioning, and errors in data input
Solution Approach 1:
The classification system segments the food waste identification process into multiple independent sensor measurements (visual characteristics via camera, weight via weight sensor, temperature via temperature sensor) that are independently captured and then integrated by the machine learning model, ensuring comprehensive data collection without relying on external point of sale systems
Solution Approach 2:
The system implements feedback by continuously monitoring food waste through sensors and using the trained machine learning model to provide real-time classification results, which can be fed back to the kitchen operation to identify waste patterns, portioning issues, and improve future food management decisions
3Productivity
If automated sensor-based classification is implemented, then classification speed increases, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating multiple sensor types (camera, weight sensor, temperature sensor) into a single unified classification platform that can handle various food waste classification tasks simultaneously, improving productivity while managing complexity through functional integration
Solution Approach 2:
The system uses a trained machine learning model that creates a digital copy or representation of food item characteristics from sensor data, enabling rapid classification by comparing new inputs against the trained model rather than requiring complex real-time analysis of all sensor parameters
Data Source
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AI summary
The present invention relates to a method for classifying food items. The method includes the steps of: capturing one or more sensor data relating to a food item event; and classifying the food item, at least in part, automatically using a model trained on sensor data. A system and software are also disclosed.