An efficient oil content estimation technique using microscopic microalgae images

Abstract The world is substantially reliant on fossil fuels and the rapid depletion of these fuels raise the concern to find out alternative efficient energy resources. Microalgae biofuels emerged as one of the promising alternate renewable sources of energy that have the capability to substitute fossil fuels. This paper presents a new approach to explore the assessment of the oil content in microalgae using microscopic images. A novel method for the automatic estimation of oil content in the microalgae from the microscopic images has been developed in the present study using different imaging techniques. An automated computer vision based method is employed for the segmentation of the cells and oil content particles from the microscopic images of microalgae. The segmented cells were then analysed to compute the present oil particles as the main region of interest for the analysis of the lipid content. The proposed algorithm uses resolution invariant features such as the ratio of the area of the region of interests for the estimation of the oil content and that discriminatory information regarding the oil content remains unchanged under any resolution. Finally, the findings of the conventional approach were compared to the predicted values of the extracted oil content obtained from the proposed methodology where a low average error of 6.071% is obtained in all test microscopic image samples. The developed framework is computationally efficient and has a low time complexity, making it suitable for usage in real-world applications.