Article
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Preserved in Portico This version is not peer-reviewed
Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
Version 1
: Received: 2 November 2023 / Approved: 2 November 2023 / Online: 3 November 2023 (01:36:05 CET)
Version 2 : Received: 3 November 2023 / Approved: 3 November 2023 / Online: 6 November 2023 (01:22:18 CET)
Version 2 : Received: 3 November 2023 / Approved: 3 November 2023 / Online: 6 November 2023 (01:22:18 CET)
A peer-reviewed article of this Preprint also exists.
Kamiwaki, Y.; Fukuda, S. Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves. Algorithms 2024, 17, 16. Kamiwaki, Y.; Fukuda, S. Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves. Algorithms 2024, 17, 16.
Abstract
This study aims to clarify the influence of photographic environments under different light sources on image-based SPAD value prediction. Radish leaf patches of 1.5 cm diameter were photographed under halogen or LED light. The input variables for the SPAD value prediction using random forests were RGB values, HSL values, and HSV values. Additionally, the light color temperature (LCT) and illuminance (ILL) were used as the input variables. Model performance was assessed using Pearson’s correlation coefficient (COR), Nash-Sutcliffe efficiency (NSE), and root mean squared error (RMSE). SPAD value prediction resulted in high accuracy in a stable light environment; CORRGB+ILL+LCT and CORHSL+ILL+LCT were 0.929 and 0.922, respectively. Image-based SPAD value prediction was effective under halogen light with a similar color temperature at dusk; CORRGB+ILL and CORHSL+ILL were 0.895 and 0.876, respectively. The HSL value under LED could be used to predict the SPAD value with high accuracy; COR, NSE, and RMSE were 0.972, 0.944, and 2.07, respectively. The partial dependence plots of the H value indicate a change from blue to green with increasing SPAD values. Further studies are required to examine this method under outdoor conditions in spatiotemporally dynamic light environments.
Keywords
Lighting conditions; Leaf colors; Random Forests; Raphanus sativus L. var. sativus; RGB; SPAD value; Machine learning; Image processing; Color image processing
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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