Year: 2025
Type: article
Abstract: The analysis of leaf information derived from digitized leaf images enables the efficient, noninvasive, and real-time estimation of chlorophyll content in a cost-effective manner, facilitating high-throughput assessment. In the present study, leaf color information was captured in various color spaces, such as RGB, HSI and L*a*b*. The entropy weighting method has been employed to estimate the chlorophyll content measured via Soil Plant Analysis Development (SPAD) chlorophyll meter values. The a*, R-B-G, R-G, (a*+b*)/L, a*/b*, (R-G)/(R + G + B), (R-B)/(R + B), H/S and (R-G)/(R + G) exhibited strong correlations (R2 = 0.745) with the SPAD values. Furthermore, the swarm intelligence algorithm, viz. the improved whale optimization algorithm (IMWOA), was applied to assess wheat leaf chlorophyll content by selected image color indices. The experimental results indicate that the IMWOA can achieve the most accurate estimation, obtaining an R2 of 0.77 and a root mean square error (RMSE) of 2.16.
Authors: Yufei Song, Zhiguo Liu, Xi Meng, Yi Zhou, Yan Li
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W4413681757