Produce Identification Using Multi-Wavelength Illumination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Produce items, due to their variability in size and shape, and susceptibility to damage, pose challenges for automated labeling, leading to increased costs and potential errors during checkout when not labeled, as they often require manual identification at POS terminals.
Innovation Solution
A POS terminal system equipped with a processor-controlled illumination device that captures images of produce items under different light wavelengths, processes these images to determine characteristics, and matches them against known items for identification, reducing the need for manual input and minimizing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated labeling equipment is designed for produce items, then labeling efficiency is improved, but device complexity and cost increase due to the need to handle variability in size, shape, and fragility
Solution Approach 1:
The patent replaces mechanical labeling systems with an optical identification system using multiple wavelength illumination devices and image capture. Instead of physically handling and labeling each produce item, the system uses light interaction to identify produce characteristics, eliminating complex mechanical labeling equipment while maintaining identification capability.
Solution Approach 2:
The system changes the parameter of light wavelength to interact with different produce characteristics. By using multiple wavelengths (e.g., visible, infrared, ultraviolet), the system can detect various properties of produce items without physical contact, avoiding the need for complex mechanical handling equipment.
2Ease of manufacture
If produce items are not labeled, then cost is reduced, but identification accuracy and speed decrease during checkout
Solution Approach 1:
The produce items identify themselves through their natural interaction with light at different wavelengths. The system captures images of unlabeled produce and uses mathematical processing to determine characteristics, allowing the produce to 'self-identify' without人工 intervention or additional labeling costs.
Solution Approach 2:
The system exploits changes in light interaction (analogous to color changes) at different wavelengths to identify produce characteristics. By analyzing how produce reflects, absorbs, or transmits light across multiple wavelengths, the system can distinguish between different produce types without physical labels.
3Adaptability or versatility
If manual identification is used at POS terminal, then flexibility is maintained, but checkout time increases and error potential increases
Solution Approach 1:
The patent replaces manual visual identification with an automated optical analysis system. The processor automatically captures images, processes them mathematically, and identifies produce characteristics, eliminating the time-consuming manual inspection process while maintaining the flexibility to handle various produce types.
Solution Approach 2:
The system creates digital copies (images) of the produce items and processes these copies to determine characteristics. This allows automated analysis without physically handling the produce, maintaining flexibility while dramatically reducing identification time and errors.
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 system enhances the speed and accuracy of identifying unlabeled produce items, reducing checkout time and error rates by automating the identification process, while maintaining cost-effectiveness.
Implementation Method 1
The illumination devices consist of multiple types of illumination devices where each type is designed to emit light energy at a different primary wavelength
Data Source
AI summary
An apparatus, method and system are presented for identifying produce. Multiple images of a produce item captured using five different types of illumination. The captured images are processed to determine parameters of the produce item and those parameters are compared to parameters of known produce to identify the produce item.