Least square and Gaussian process for image based microalgal density estimation

Efficiently monitoring microalgal density in real time is critical in closed systems of cultivating algae. In the monitoring methods proposed in the literature, image based techniques present practically potential since they are nondestructive and more biosecured. However, in the existing image analysis methods, parameters of the color-to-grayscale conversion formulae are predefined and only applicable to monitor some specific microalgae strains. Therefore, in this paper we propose a generic approach based on least square to optimize those parameters, which are data-driven and can be used to monitor any type of microalgae. More importantly, apart from the widely used linear regression paradigm, we propose a nonlinear regression model based on Gaussian process to better learn relationship between data representation of measured images and densities of microalgae. The nonlinear regression model is then utilized to efficiently estimate density of algal species. The proposed approach was evaluated in the real-world dataset of Chlorella vulgaris microalgae, where the obtained results as compared with those obtained by some existing techniques demonstrate its effectiveness.