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1. Scope_3_Microalgae_shape_texture_convolution_classification Scope_3_Microalgae_shape_texture_convolution_classification Public

   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 lear…

   Python 4 3

2. Scope_1_Digitalised-prediction-of-blue-pigment-content-from-Spirulina-platensis Scope_1_Digitalised-prediction-of-blue-pigment-content-from-Spirulina-platensis Public

   The findings in the present study will be a breakthrough for the estimation of CPC concentration from S. platensis solely based on the information provided in the image without the need to perform …

   Python 1

3. Scope_2_A-cutting-edge-digital-approach-for-rapid-C-phycocyanin-detection-in-Spirulina-platensis Scope_2_A-cutting-edge-digital-approach-for-rapid-C-phycocyanin-detection-in-Spirulina-platensis Public

   Evaluate the robustness and performance between ML and DL models in predicting the CPC concentration under various image capturing devices, types of input image datasets, and lighting conditions. T…

   Python 1

4. Scope_4_Microalgae-detection-and-instance-segmentation Scope_4_Microalgae-detection-and-instance-segmentation Public

   This research work introduced various aspects from dataset preparation techniques to image pre-processing, model comparison, and performance analysis on the detection & instance segmentation of thr…