Pollen Grain Recognition Using Deep Learning

Daood, Amar; Ribeiro, Eraldo; Bush, Mark B.

doi:10.1007/978-3-319-50835-1_30

Cited by 43 publications

(38 citation statements)

References 13 publications

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“…In a similar take, in [ 18 ] an approach based on pre-trained CNN transfer learning is presented, obtaining results around 94% of CCR for data sets obtained by light-microscopy and scanning electron microscopy. However, they do not specify if the results are obtained over the training set or the test set, and hence they are not directly comparable with ours.…”

Section: Discussionmentioning

confidence: 99%

“…However, there is another way of tackling the problem of pollen image classification: instead of analytically extracting features from the images, one can rely on an automated system to do that job. An example of such alternative is presented, for example, in [ 18 ], where a model learns not only the features but also the classifier itself from training data under a deep learning framework. To further enhance the classification ability, the proposal makes use of transfer learning to leverage knowledge from networks that have been pre-trained on large datasets of images, to train a dataset of 30 pollen types, achieving a 94% correct classification rate.…”

Section: State Of the Artmentioning

confidence: 99%

“…For example, [ 10 – 13 , 15 , 20 , 21 ] work with sets of data containing less than 10 pollen types. The results presented in [ 6 – 9 , 14 , 18 ] work with larger sets of data. Particularly [ 18 ] presents results over 94% for a set of images containing 30 different pollen types.…”

Section: State Of the Artmentioning

confidence: 99%

“…The results presented in [ 6 – 9 , 14 , 18 ] work with larger sets of data. Particularly [ 18 ] presents results over 94% for a set of images containing 30 different pollen types. Of course, as the sets contain more classes, the classification process becomes more complicated, presenting lower performances as in [ 6 ].…”

Section: State Of the Artmentioning

confidence: 99%

See 3 more Smart Citations

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

Sevillano¹,

Aznarte²

2018

PLoS ONE

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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 improvements especially for allergy-related information systems, but also for other application fields as paleoclimate reconstruction, quality control of honey based products, collection of evidences in criminal investigations or fabric dating and tracking. This paper presents three state-of-the-art deep learning classification methods applied to the recently published POLEN23E image dataset. The three methods make use of convolutional neural networks: the first one is strictly based on the idea of transfer learning, the second one is based on feature extraction and the third one represents a hybrid approach, combining transfer learning and feature extraction. The results from the three methods are indeed very good, reaching over 97% correct classification rates in images not previously seen by the models, where other authors reported around 70.

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Section: Discussionmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

See 2 more Smart Citations

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

Sevillano¹,

Aznarte²

2018

PLoS ONE

View full text Add to dashboard Cite

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“…For paleoecology, the NNs are a promising approach for automated pollen grain recognition that would aid in the pollen identification process [82]. In addition, NNs have been used in paleoecological research for classification of indicator species [83], estimating paleo-salinity changes in sea surface water [84], and pollen-based quantitative climate reconstructions [85,86].…”

Section: Methodsmentioning

confidence: 99%

Predictive pollen-based biome modeling using machine learning

Sobol

Finkelstein

2018

PLoS ONE

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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 African pollen dataset and a global ecosystem classification scheme. To test the applicability of traditional and machine-learning based classification models for the task of biome prediction from high dimensional modern pollen data, we train a total of eight classification models, including Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Classification Decision Tree, Random Forest, Neural Network, and Support Vector Machine. The ability of each model to predict biomes from pollen data is statistically tested on an independent test set. The Random Forest classifier outperforms other models in its ability correctly classify biomes given pollen data. Out of the eight models, the Random Forest classifier scores highest on all of the metrics used for model evaluations and is able to predict four out of five biome classes to high degree of accuracy, including arid, montane, tropical and subtropical closed and open systems, e.g. forests and savanna/grassland. The model has the potential for accurate reconstructions of past biomes and awaits application to fossil pollen sequences. The Random Forest model may be used to investigate vegetation changes on both long and short time scales, e.g. during glacial and interglacial cycles, or more recent and abrupt climatic anomalies like the African Humid Period. Such applications may contribute to a better understanding of past shifts in vegetation cover and ultimately provide valuable information on drivers of climate change.

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Pollen wall patterns as a model for biological self‐assembly

Radja

2020

J Exp Zool Pt B

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We are still far from being able to predict organisms' shapes purely from their genetic codes. While it is imperative to identify which encoded macromolecules contribute to a phenotype, determining how macromolecules self-assemble independently of the genetic code may be equally crucial for understanding shape development. Pollen grains are typically single-celled microgametophytes that have decorated walls of various shapes and patterns. The accumulation of morphological data and a comprehensive understanding of the wall development makes this system ripe for mathematical and physical modeling. Therefore, pollen walls are an excellent system for identifying both the genetic products and the physical processes that result in a huge diversity of extracellular morphologies.In this piece, I highlight the current understanding of pollen wall biology relevant for quantification studies and enumerate the modellable aspects of pollen wall patterning and specific approaches that one may take to elucidate how pollen grains build their beautifully patterned walls.

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Pollen Grain Recognition Using Deep Learning

Cited by 43 publications

References 13 publications

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

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

Predictive pollen-based biome modeling using machine learning

Pollen wall patterns as a model for biological self‐assembly

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