Produce Bagging Detection via Machine Learning at Checkout
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Solution Overview
Problem
Self-checkout devices lack the capability to determine whether produce is in a bag or not, leading to decreased transaction throughput, poor user experience, and revenue loss due to incorrect bag tare weight removals.
Innovation Solution
A machine learning model is trained to classify produce items based on images captured during transactions, distinguishing between items in a bag and those not in a bag, and identifying organic produce through predefined markers, allowing for accurate tare weight adjustments and pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bag tare weights are removed from all recorded produce weights, then compliance with regulations is improved, but revenue is lost due to underestimation of unbagged produce weight
Solution Approach 1:
The self-checkout system automatically detects whether produce is bagged or unbagged using image recognition and makes the tare weight adjustment decision autonomously, without requiring customer intervention or manual input from the operator
Solution Approach 2:
The manual process of asking customers about bagging status is replaced with an automated image recognition system that visually detects whether produce is in a bag, using computational vision to determine the correct pricing
2Measurement precision
If customers are asked whether produce is in a bag through the user interface, then accuracy of bag detection is improved, but transaction throughput decreases and user experience worsens
Solution Approach 1:
The system performs self-detection of bagging status using image capture and machine learning models, eliminating the need for customer response and automated decision-making without human intervention
Solution Approach 2:
The interactive user interface process is replaced with an automated visual detection system that captures images and uses trained models to instantly determine bagging status without customer participation
3Measurement precision
If manual detection of bagged produce is implemented, then accuracy is improved, but device complexity increases and operation becomes more cumbersome
Solution Approach 1:
The image capture device serves multiple functions: capturing images of produce for weight measurement, detecting bagging status through image recognition, and providing data for automated pricing decisions, consolidating multiple functions into a single system component
Solution Approach 2:
Manual detection processes are replaced with automated machine learning models that process images to determine bagging status, using computational algorithms instead of human judgment
Data Source
AI summary
At least one image of a produce item on a scale of a terminal is captured during a transaction at the terminal. A machine learning model provides a classification for the item based on the image. The classification indicates whether the item is bagged or unbagged. When the item is in a bag, a tare weight for the bag is subtracted from the weight recorded by the scale to calculate a price for the item. When the item is unbagged, the weight recorded by the scale is used to calculate the price. In an embodiment, the model provides a classification that indicates whether the item is organic or non-organic. When the item is organic, a transaction interface is automatically populated with an organic produce selection and presented to an operator of the terminal for confirmation.