Artificial intelligence-driven microalgae autotrophic batch cultivation: A comparative study of machine and deep learning-based image classification models

Abstract

The goal of this study is to classify microalgae of different species, using machine learning (ML) and deep learning (DL) methods. At present, we applied gray-scaling, bilateral filtering, adaptive thresholding, Sobel edge detection, and Canny edge detection, for the segmentation of microalgae. Morphological and texture descriptors, which are part of the important geometrical features, were used for feature extraction. Results indicates that the final combined features, with optimised image pre-processing techniques, produced high accuracy of 96.93 % and 97.63 % for k-nearest neighbours (k−NN) and support vector machine (SVM) classifiers, respectively. Overall, the Azure custom vision model performed the best with the highest accuracy of 97.67 % and 97.86 % at probability threshold of 50 % and 80 %, respectively. Our study aimed to bridge artificial intelligence technologies to microalgae based on understanding of shape, texture, and convolution features, which could accelerate the development of real-time monitoring, as well as rapid and precise microalgae classification.