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RoyWeiiiii / Scope_3_Microalgae_shape_texture_convolution_classification

The goal of this study is to classify microalgae of different species such as Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis, using machine learning (ML) and deep learning (DL) methods
http://dx.doi.org/10.1016/j.algal.2024.103400
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adaptivethreshold azure-custom-vision bilateral-filtering canny-edge-detection chlamydomonas-reiinhardtii chlorella-vulgaris classification deep-learning gray-scale-images image-processing knn-classification machine-learning microalgae-recognition morphological-features real-time-monitoring sobel-edge-detector spirulina-platensis svm-classifier texture-features

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Graphical Abstract

Research pipeline_Experiment_1

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

By Jun Wei Roy Chong, Kuan Shiong Khoo, Kit Wayne Chew, Huong-Yong Ting, Iwamoto Koji, Roger Ruan, Zengling Ma, Pau Loke Show

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 optimized 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.

Keywords: Microalgae; Machine learning (ML); Deep learning (DL); Geometric feature; Texture feature; Image pre-processing

Image Dataset

File "Microalgae_image_dataset.zip" contains the image dataset used in the paper "Artificial intelligence-driven microalgae autotrophic batch cultivation: A comparative study of machine and deep learning-based image classification models". Three types of microalgae species namely Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis are available in the image dataset. The image dataset is publicly available for academic and research purposes. An alternative google drive link has been provided to access the microalgae image dataset: https://drive.google.com/drive/folders/1UazrsYfoejysAEgHafdloA0_QjuY-ey1?usp=drive_link

Referencing and citation

If you find Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis classification useful in your research, please consider citing: Based on the DOI: http://dx.doi.org/10.1016/j.algal.2024.103400