Development of a novel methodology for the measurement of Red19 kiwifruit colour quality : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics, Massey University, New Zealand
Loading...
Date
2021
DOI
Open Access Location
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Massey University
Rights
The Author
Abstract
Consumers love visually appealing fruit. Colour is a key aspect of this appeal and is determined by a multitude of physical and psychophysical phenomena. Compared to its green and gold counterparts, the colour of red kiwifruit is not easily measured using existing techniques due to the spatially varying nature of its flesh colour. Red19 kiwifruit’s flesh colour often presents as a mixture of green, yellow, and red to varying degrees. The current method used to measure kiwifruit redness is by a subjective observer, but this produces a noisy and unreliable dataset which makes quality control difficult.
The objective of this thesis is to create a colour measurement system for Red19 kiwifruit that transforms the current qualitative system into a quantitative system that can be utilised by the kiwifruit industry to produce
standard and reliable measures of kiwifruit redness.
Considering this goal, this thesis aims to explore what constitutes an objective grading scale of Red19 kiwifruit, and how a colour measurement system might be created that is invariant to changes in illuminant and spectral sensitivity (observers) and how this system could be deployed on a typical consumer smartphone.
This thesis presents a comprehensive exploration of colour constancy in relation to red kiwifruit. A new scale of redness is proposed, and a small sensory trial was undertaken to validate this scale. A dataset of hyperspectral and raw images was collected and utilised to create a model that estimates the average reflectance of a single red kiwifruit. Another model was then created to regress from the average reflectance of a kiwifruit to a final redness score where fruit were graded based on the outcome of the sensory trial.
The new redness scale proposed therein spans a range of 0 to 10,000 and is calculated by taking the difference of a kiwifruit’s average red and green RGB channel in the AdobeRGB colour space. This range was also divided by five to produce alternative class-based scale containing five classes each spanning sections of 2000 units over the new redness scale. This discrete scale is similar to the current qualitative scale used by Zespri to assess the colour of red kiwifruit.
Through the application of a convolutional machine learning model, the average reflectance spectra of a kiwifruit can be estimated producing an RMSE of 0.0109 and 0.0096 over the visible and visible NIR spectral ranges. A general regression model is then used to regress from a kiwifruit’s average reflectance spectra to a final redness score and this produces a mean absolute error of 213.82 with a standard deviation of 213.82 which is equivalent to an average error of 2.2% and standard deviation of 2.13% when considering the full range of scores on the redness scale. These models are then combined to produce a final model, named KiwiNet, that produces a correct kiwifruit classification rate of 91.06%. This model has been demonstrated to run on a typical consumer smartphone and can produce a reflectance estimate and redness score for each kiwifruit. This model has been demonstrated to be invariant to five different illuminants and twelve different spectral sensitivities.
Future work should look to carry out a larger sensory trial and explicitly corelate the proposed redness scale to the existing one. Likewise, this study highlighted that a kiwifruit image taken at 583nm appears to be used
by the model to estimate kiwifruit redness score and work to reconstruct this wavelength from RGB/RAW images could provide a single measure of redness in the future.
Description
Figure captions indicate permissions have been obtained.
Keywords
kiwifruit, colour constancy, CNN, GRNN, spectral super-resolution