A Conceptual Framework to Monitor Cyanobacteria Using Drones, Sensors, and Python Analytics

Abstract

This thesis is research-based and aims to present a conceptual framework for monitoring cyanobacterial blooms using an integrated approach that combines remote sensing and in-situ sensing to create a real-time bloom detection. The framework proposed in this project is designed and justified based on similar projects and articles related to this problem. The framework addresses the increasing need for scalable and cost-efficient monitoring systems capable of supporting early detection and confirmation of harmful cyanobacteria in aquatic environments. The framework operates in two complementary phases: aerial mapping and in-situ confirmation. The first phase employs drones equipped with RGB and multispectral cameras to capture wide-area imagery and identify potential bloom regions. The second phase uses water quality sensors such as pH, turbidity, dissolved organic carbon, pigment, and nutrient probes to validate and quantify cyanobacterial presence. Analytical processing is supported by machine learning and deep learning models developed within a Python-based environment. The framework’s modular design allows it to evolve from concept to field-ready implementation at early stages and in near future, a full automated systems as datasets grow. By integrating remote sensing with ground-truth validation, the proposed system enhances detection accuracy, spatial coverage, and ecological insight into cyanobacteria distribution and pollution sources.