Assessment of wheat chlorophyll content based on an improved whale optimization algorithm

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.