AIMC Topic: Pollen

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Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks.

PloS one Sep 14, 2018
In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable im...
Humans Neural Networks, Computer Databases, Factual Image Processing, Computer-Assisted Machine Learning Pollen
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Predictive pollen-based biome modeling using machine learning.

PloS one Aug 23, 2018
This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published Af...
Statistics, Nonparametric Pollen Fossils Supervised Machine Learning Climate Change Bayes Theorem Discriminant Analysis Africa Logistic Models Models, Statistical Decision Trees Support Vector Machine Algorithms Ecosystem Neural Networks, Computer
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Computational intelligence applied to discriminate bee pollen quality and botanical origin.

Food chemistry Jun 3, 2017
The aim of this work was to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), and support vector machines (SVM) to predict physicochemical composition of bee pollen mixture given their botanical origin. To ob...
Bees Pollen Support Vector Machine Plants Neural Networks, Computer Animals
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Honey bee hairs and pollenkitt are essential for pollen capture and removal.

Bioinspiration & biomimetics Mar 23, 2017
While insect grooming has been observed and documented for over one hundred years, we present the first quantitative analysis of this highly dynamic process. Pollinating insects, like honey bees, purposely cover themselves with millions of pollen par...
Biomimetic Materials Hair Pollination Viscosity Pollen Grooming Adhesiveness Animals Robotics Video Recording Bees
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Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains.

PloS one Jun 8, 2016
The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types...
Grassland Pollen Machine Learning Brazil
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Data-Driven Detection of Nocturnal Pollen Fragmentation Triggered by High Humidity in an Urban Environment.

Environmental science & technology Jul 1, 2025
Biological particulate matter (BioPM) in the urban environment can affect human health and climate. Pollen, a key BioPM component, produces smaller particles when fragmented, significantly impacting public health. However, detecting pollen fragmentat...
Pollen Cities Particulate Matter Humidity Environmental Monitoring
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Digital image processing combined with machine learning: A novel approach for bee pollen classification.

Food research international (Ottawa, Ont.) Jul 1, 2025
The classification of bee pollen is crucial for ensuring product authenticity, quality control, and fraud prevention, particularly given the high commercial value of stingless bee pot-pollen. Although traditional pollen analysis methods are available...
Algorithms Animals Machine Learning Color Bees Pollen Image Processing, Computer-Assisted Discriminant Analysis
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Deep learning-based high-throughput detection of in vitro germination to assess pollen viability from microscopic images.

Journal of experimental botany Nov 21, 2023
In vitro pollen germination is considered the most efficient method to assess pollen viability. The pollen germination frequency and pollen tube length, which are key indicators of pollen viability, should be accurately measured during in vitro cultu...
Pollen Tube Germination Pollen Deep Learning
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Single-frame 3D lensless microscopic imaging via deep learning.

Optics express Aug 29, 2022
Since the pollen of different species varies in shape and size, visualizing the 3-dimensional structure of a pollen grain can aid in its characterization. Lensless sensing is useful for reducing both optics footprint and cost, while the capability to...
Deep Learning Imaging, Three-Dimensional Microscopy Neural Networks, Computer Pollen
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Automated Classification of Airborne Pollen using Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Pollen allergies are considered as a global epidemic nowadays, as they influence more than a quarter of the worldwide population, with this percentage expected to rapidly increase because of ongoing climate change. To date, alerts on high-risk allerg...
Allergens Germany Environmental Monitoring Seasons Pollen Neural Networks, Computer Forecasting
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