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Lookup NU author(s): Dr Avinash Agarwal, Filipe De Jesus Colwell, Professor Thomas Hill, Professor Neil Boonham, Dr Ankush Prashar
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 The Author(s). Estimating pigment content of leafy vegetables via digital image analysis is a reliable method for high-throughput assessment of their nutritional value. However, the current leaf color analysis models developed using green-leaved plants fail to perform reliably while analyzing images of anthocyanin (Anth)-rich red-leaved varieties due to misleading or "red herring" trends. Hence, the present study explores the potential for machine learning (ML)-based estimation of nutritional pigment content for green and red leafy vegetables simultaneously using digital color features. For this, images of n = 320 samples from six types of leafy vegetables with varying pigment profiles were acquired using a smartphone camera, followed by extract-based estimation of chlorophyll (Chl), carotenoid (Car), and Anth. Subsequently, three ML methods, namely, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were tested for predicting pigment contents using RGB (Red, Green, Blue), HSV (Hue, Saturation, Value), and L∗a∗b∗ (Lightness, Redness-greenness, Yellowness-blueness) datasets individually and in combination. Chl and Car contents were predicted most accurately using the combined colorimetric dataset via SVR (R2 = 0.738) and RFR (R2 = 0.573), respectively. Conversely, Anth content was predicted most accurately using SVR with HSV data (R2 = 0.818). While Chl and Car could be predicted reliably for green-leaved and Anth-rich samples, Anth could be estimated accurately only for Anth-rich samples due to Anth masking by Chl in green-leaved samples. Thus, the present findings demonstrate the scope of implementing ML-based leaf color analysis for assessing the nutritional pigment content of red and green leafy vegetables in tandem.
Author(s): Agarwal A, De Jesus Colwell F, Correa Galvis VA, Hill TR, Boonham N, Prashar A
Publication type: Article
Publication status: Published
Journal: Biology Methods and Protocols
Year: 2025
Volume: 10
Issue: 1
Online publication date: 09/04/2025
Acceptance date: 07/04/2025
Date deposited: 19/05/2025
ISSN (electronic): 2396-8923
Publisher: Oxford University Press
URL: https://doi.org/10.1093/biomethods/bpaf027
DOI: 10.1093/biomethods/bpaf027
Data Access Statement: Data are available from the corresponding authors upon reasonable request.
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