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Revolutionising Allergen Detection: Integrating AI and Bioinformatics for Automated Airborne Pollen and Fungal Spore Monitoring

Updated: May 13



Airborne allergens, such as pollen grains and fungal spores, pose significant health risks, especially with the increasing prevalence of seasonal allergies exacerbated by climate change. Traditional methods of detecting these allergens are laborious, time-consuming, and often require specialised knowledge. However, recent advancements in artificial intelligence (AI) and bioinformatics present a revolutionary approach to automate the detection process, leading to faster and more comprehensive insights.


In a recent study conducted by N. Irabanta Singh, a former Professor of Life Sciences at Manipur (Central) University, Imphal, India, and currently affiliated with Nibiaa Consultancy Pvt. Ltd. The integration of AI with bioinformatics has been explored to develop an innovative solution for detecting airborne pollen grains and fungal spores with the help of Nibiaa Devices.



Fig 1 Senscap Vision AI v2


Fig: Senscap W1110 Dev Board


The study proposes an IoT-based hardware setup that combines a LoRaWAN Datalogger with Senscap Vision AI v2 with a microscope to capture microscopic images of pollen grains and fungal spores. These images are then analysed using custom Convolutional Neural Network (CNN) architectures, such as YOLO (You Only Look Once) v5, to classify and identify different types of pollen grains and fungal spores with high accuracy rates ranging from 90% to 95%.


One of the key advantages of this approach is the use of edge computing enables real-time prediction and analysis without relying on cloud-based processing. This not only reduces processing time but also minimises costs associated with cloud services.


To enhance the training process of the machine learning models, the study leverages pre-collected bioinformatics data, including information on prevalent pollen types in the Manipur region of India. This localised approach allows for the customization of detection algorithms to better suit the local pollen landscape, leading to more precise identification and classification of allergens.



Fig: Result


The integration of AI and bioinformatics in allergen detection also addresses the limitations of traditional monitoring methods, such as Hirst-type volumetric sampling, which are often slow and provide temporally low-resolution data. By automating the detection process and providing real-time insights, this approach enables timely warnings for allergic diseases and facilitates proactive measures for allergy management.



Fig: data visualization using IoT platform


Moreover, the study emphasises the role of AI in revolutionising allergen detection by utilising computer vision techniques, such as Convolutional Neural Networks (CNNs), to extract useful insights from digital images and videos. These techniques, combined with edge AI computing, offer enhanced data security, real-time analytics, lower internet bandwidth usage, reduced power consumption, and improved responsiveness.


In conclusion, the integration of AI and bioinformatics for automated airborne allergen monitoring represents a significant advancement in allergology and environmental health research. This approach not only streamlines the detection process but also enhances scalability, accuracy, and accessibility, ultimately contributing to improved public health outcomes and better allergy management strategies.

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