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

Accurate and efficient estimation of chlorophyll content from digitized leaf images is essential for high-throughput, noninvasive plant monitoring. In this study, we propose a novel chlorophyll content estimation model based on an improved whale optimization algorithm (IMWOA). The IMWOA integrates three key innovations—a nonlinear control factor adjustment for balanced exploration and exploitation, an adaptive weight mechanism to regulate search dynamics, and a differential evolution strategy to enhance population diversity. These enhancements collectively improve the global search capability and convergence precision of the original WOA. The selected color indices from RGB, HSI, and L*a*b* (CIELAB) spaces, filtered via entropy weighting and stepwise regression, were used as inputs to a support vector regression (SVR) model optimized by the IMWOA. The experimental results indicate that the IMWOA can achieve the most accurate estimation, with an R 2 of 0.77 and a root mean square error (RMSE) of 2.16.