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@article{196561,
        author = {Sumitha I and Pragadheesh Thirumal M and Akiladithya R and Dhivakar B S and Mahalakshmi J},
        title = {Biolens: AI-Driven Diatom Analysis For Environmental Monitoring And Risk Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3466-3473},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196561},
        abstract = {Aquatic ecosystems face increasing threats from eutrophication, pollution, and oxygen depletion, which affect biodiversity, water security, and public health. Traditional monitoring methods are often reactive, detecting environmental degradation only after damage occurs, while manual microscopic analysis of bioindicators is resource-intensive and struggles with highly clustered samples. This paper proposes BioLens, an advanced AI-driven environmental monitoring framework that integrates digital microscopy, a two-stage deep learning pipeline, and automated ecological scoring for early risk detection. To overcome the challenge of overlapping microorganisms in real-world samples, the system utilizes StarDist, an instance segmentation model, to precisely extract isolated diatoms. 
These extractions are subsequently processed by a YOLO (You Only Look Once) classification engine trained to identify 81 distinct diatom genera with a validation accuracy of 98.12%. The AI predictions are then mathematically mapped to a 1-to-5 biological tolerance scale to compute a quantitative Water Quality Index (WQI). BioLens provides scalable, real-time ecosystem assessment, shifting aquatic management from reactive response to proactive environmental risk mitigation.},
        keywords = {},
        month = {April},
        }