Ripeness Detection Using Hue Histogram Peak Finding
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Solution Overview
Problem
Current agricultural robotic systems lack the capability to accurately determine the ripeness of produce, relying on human intervention for harvesting decisions.
Innovation Solution
A method and system that utilize light reflection measurements and image processing to estimate the ripeness of produce by illuminating the produce with specific wavelengths of light, measuring the reflected light intensities, and analyzing the hue histograms of the produce images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robotic systems are equipped with ripeness detection capability, then automation level and productivity are improved, but device complexity increases
Solution Approach 1:
The system uses a single image sensor to perform multiple functions: capturing images of produce, extracting color information, and determining ripeness status. This multi-functional approach increases automation capability while minimizing the addition of separate specialized devices, thereby controlling overall system complexity.
Solution Approach 2:
The patent replaces manual visual inspection by humans with an automated optical measurement system. By using light reflection properties and color space analysis (hue, saturation, value) instead of mechanical or manual methods, the system achieves automation while keeping the detection mechanism relatively simple and non-intrusive.
2Measurement precision
If multiple light wavelengths are used for ripeness measurement, then measurement precision is improved, but use of energy increases
Solution Approach 1:
Instead of using multiple separate light sources simultaneously, the system segments the spectral analysis into discrete wavelength measurements taken sequentially or in separate channels. This allows precise ripeness measurement through multi-wavelength analysis while reducing the total energy consumption compared to continuous multi-wavelength illumination.
Solution Approach 2:
The system measures reflectance at multiple specific wavelengths (e.g., red, green, blue regions) and analyzes the changes in these parameters to determine ripeness. By focusing on specific wavelength regions where carotenoid absorption changes occur, the system achieves high measurement precision without requiring full-spectrum illumination, thus controlling energy usage.
3Measurement precision
If image processing and hue histogram analysis are performed, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system extracts only the critical information needed for ripeness determination from the full image data - specifically the hue, saturation, and value color space parameters and their histograms. By focusing the processing on these key color features rather than analyzing every pixel in detail, the system achieves high measurement precision while minimizing processing time.
Solution Approach 2:
The patent applies partial action by analyzing only the most informative portions of the color data - specifically the hue histogram peaks and saturation values - rather than performing complete image processing on all pixels. This selective analysis approach provides sufficient accuracy for ripeness determination while significantly reducing computational processing time.
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 approach allows for accurate and efficient determination of produce ripeness, enabling robotic systems to autonomously decide when to harvest, thus increasing operational efficiency and reducing human intervention.
Implementation Method 1
measuring intensities of the light reflected from the produce at different frequencies
Implementation Method 2
converting the cropped region of the image to a hue, saturation, and value (HSV) color representation
Implementation Method 3
computing a histogram of pixel population of the cropped region of the image by hue and saturation
Implementation Method 4
identifying a peak in the hue histogram
Implementation Method 5
determining the degree of ripeness of the individual target item of produce from a location of the peak in the hue histogram
Data Source
AI summary
A method for estimating ripeness of produce includes illuminating the produce with light, measuring intensities of the light reflected from the produce at different frequencies, and determining a degree of ripeness of the produce from the relative intensities of the light reflected from the produce at the different frequencies.