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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. The findings in our current study can overcome the bottleneck by eliminating the need for laborious manual extraction processes and reducing the time and

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RoyWeiiiii/Scope_2_A-cutting-edge-digital-approach-for-rapid-C-phycocyanin-detection-in-Spirulina-platensis

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Revolutionising biotechnology: A cutting-edge digital approach for rapid C-phycocyanin detection in Spirulina platensis

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Folder and files description

AI_models_Final => Contains the model configuration of SVM regressor, XGBoost regressor, and CNN

Combined_All_Batch_Days_Camera => Contains the combined data for all batches (3) and days (2, 4, 6, 8, 10, & 12) when using digital camera capturing device under various type of variables (colour models such as RGB, HSL, & CMYK), and lightning conditions (covered [not exposed to light]/ light disturbed [non_covered])

Combined_All_Batch_Days_Smartphone => Contains the combined data for all batches (3) and days (2, 4, 6, 8, 10, & 12) when using smartphone capturing device under various type of colour variables (colour models such as RGB, HSL, & CMYK), and lightning conditions (covered [not exposed to light]/ light disturbed [non_covered])

Data_Abs_Day_Colour_index_Normalised => Contains the combined data for all batches (3) and days (2, 4, 6, 8, 10, & 12) when using various image capturing devices (digital camera/ smartphone), type of colour variables (colour models such as RGB, HSL, & CMYK) with additonal 'Day' (period), 'Abs' (absorbance), and lightning conditions (covered [not exposed to light]/ light disturbed [non_covered])

Data_Colour_index_Normalised => Contains the combined data for all batches (3) and days (2, 4, 6, 8, 10, & 12) when using various image capturing devices (digital camera/ smartphone), type of colour variables (colour models such as RGB, HSL, & CMYK), and lightning conditions (covered [not exposed to light]/ light disturbed [non_covered])

Data_Day_Colour_index_Normalised => Contains the combined data for all batches (3) and days (2, 4, 6, 8, 10, & 12) when using various image capturing devices (digital camera/ smartphone), type of colour variables (colour models such as RGB, HSL, & CMYK) with additonal 'Day' (period), and lightning conditions (covered [not exposed to light]/ light disturbed [non_covered])

Exp_3_Overall_Microalgae_Tabulated_results.xlsx => Contains all the results tabulated in excel format

Spirulina platensis biomass & extracted CPC image dataset

The google drive [https://drive.google.com/drive/folders/1dXDUHCD9nTJaF0CFyxqUkHKZH8Ko1XjF?usp=drive_link] contains the cropped image dataset of Spirulina platensis biomass & extracted CPC grown under BG-11 medium in the period of 12 days. Subsequently, each day will contain a subfolder of both image capturing devices such as smartphone [Model => Iphone_13_Pro_Max] and digital camera [Model => Nikon_Z50]. Each image capturing device will contain a subfolder of covered [images taken without any light disturbances] and light_disturbed [images taken under light disturbed condition]. The experiment is conducted for 3 batches. The image dataset is publicly available for academic and research purposes.

Referencing and citation

If you find the prediction and analysis of C-phycocyanin (CPC) concentration as well as the image dataset useful in your research, please consider citing: Based on the DOI: Not published yet**

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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. The findings in our current study can overcome the bottleneck by eliminating the need for laborious manual extraction processes and reducing the time and

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