AIMC Topic: Pollen
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Harnessing artificial intelligence for analysing the impacts of nectar and pollen feeding in conservation biological control.
Current opinion in insect science
Feb 2, 2024
Plant-derived foods, such as nectar and pollen, have garnered substantial research attention due to their potential to support natural enemies of pests. This review is a pioneering exploration of the potential for artificial intelligence approaches t...
A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study.
Computers in biology and medicine
Nov 16, 2023
Airborne pollen can trigger allergic rhinitis and other respiratory diseases in the synthesised population, which makes it one of the most relevant biological contaminants. Therefore, implementing accurate forecast systems is a priority for public he...
Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.
The New phytologist
Sep 7, 2023
Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling...
Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy.
Scientific reports
Feb 24, 2023
Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classification task. With...
Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning.
ACS sensors
Nov 22, 2022
Exposure to bio-aerosols such as pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various types of pollen. To address this need, we present a mobile and...
Alternaria spore exposure in Bavaria, Germany, measured using artificial intelligence algorithms in a network of BAA500 automatic pollen monitors.
The Science of the total environment
Nov 17, 2022
Although Alternaria spores are well-known allergenic fungal spores, automatic bioaerosol recognition systems have not been trained to recognize these particles until now. Here we report the development of a new algorithm able to classify Alternaria s...
PollenDetect: An Open-Source Pollen Viability Status Recognition System Based on Deep Learning Neural Networks.
International journal of molecular sciences
Nov 3, 2022
Pollen grains, the male gametophytes for reproduction in higher plants, are vulnerable to various stresses that lead to loss of viability and eventually crop yield. A conventional method for assessing pollen viability is manual counting after stainin...
Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations.
The Science of the total environment
Aug 23, 2022
Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is e...
Detection and Recognition of Pollen Grains in Multilabel Microscopic Images.
Sensors (Basel, Switzerland)
Mar 31, 2022
Analysis of pollen material obtained from the Hirst-type apparatus, which is a tedious and labor-intensive process, is usually performed by hand under a microscope by specialists in palynology. This research evaluated the automatic analysis of pollen...
Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning.
IEEE transactions on bio-medical engineering
Jan 20, 2022
OBJECTIVE: In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches.
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