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Gimenez B, Joannin S, Pasquet J, Beaufort L, Gally Y, de Garidel-Thoron T, Combourieu-Nebout N, Bouby L, Canal S, Ivorra S, Limier B, Terral JF, Devaux C, Peyron O. A user-friendly method to get automated pollen analysis from environmental samples. THE NEW PHYTOLOGIST 2024; 243:797-810. [PMID: 38807290 DOI: 10.1111/nph.19857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/05/2024] [Indexed: 05/30/2024]
Abstract
Automated pollen analysis is not yet efficient on environmental samples containing many pollen taxa and debris, which are typical in most pollen-based studies. Contrary to classification, detection remains overlooked although it is the first step from which errors can propagate. Here, we investigated a simple but efficient method to automate pollen detection for environmental samples, optimizing workload and performance. We applied the YOLOv5 algorithm on samples containing debris and c. 40 Mediterranean plant taxa, designed and tested several strategies for annotation, and analyzed variation in detection errors. About 5% of pollen grains were left undetected, while 5% of debris were falsely detected as pollen. Undetected pollen was mainly in poor-quality images, or of rare and irregular morphology. Pollen detection remained effective when applied to samples never seen by the algorithm, and was not improved by spending time to provide taxonomic details. Pollen detection of a single model taxon reduced annotation workload, but was only efficient for morphologically differentiated taxa. We offer guidelines to plant scientists to analyze automatically any pollen sample, providing sound criteria to apply for detection while using common and user-friendly tools. Our method contributes to enhance the efficiency and replicability of pollen-based studies.
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Affiliation(s)
- Betty Gimenez
- ISEM, Univ Montpellier, CNRS, IRD, 34090, Montpellier, France
| | - Sébastien Joannin
- ISEM, Univ Montpellier, CNRS, IRD, 34090, Montpellier, France
- School of Earth, Environment & Society, McMaster University, L8S 4K1, Hamilton, ON, Canada
| | - Jérôme Pasquet
- AMIS, Univ Paul-Valérie Montpellier 3, 34090, Montpellier, France
- TETIS, INRAE, AgroParisTech, Cirad, CNRS, Univ Montpellier, 34090, Montpellier, France
| | - Luc Beaufort
- CEREGE, Aix Marseille Université, CNRS, IRD, Coll. France, INRAE, 13545, Aix-en-Provence, France
| | - Yves Gally
- CEREGE, Aix Marseille Université, CNRS, IRD, Coll. France, INRAE, 13545, Aix-en-Provence, France
| | | | | | - Laurent Bouby
- ISEM, Univ Montpellier, CNRS, IRD, 34090, Montpellier, France
| | - Sandrine Canal
- ISEM, Univ Montpellier, CNRS, IRD, 34090, Montpellier, France
| | - Sarah Ivorra
- ISEM, Univ Montpellier, CNRS, IRD, 34090, Montpellier, France
| | - Bertrand Limier
- ISEM, Univ Montpellier, CNRS, IRD, 34090, Montpellier, France
- INRAE, Centre Occitanie-Montpellier, 34000, Montpellier, France
| | | | - Céline Devaux
- ISEM, Univ Montpellier, CNRS, IRD, 34090, Montpellier, France
- Institut de Recherche en Biologie Végétale, Département de Sciences Biologiques, Université de Montréal, H1X 2B2, Montreal, QC, Canada
| | - Odile Peyron
- ISEM, Univ Montpellier, CNRS, IRD, 34090, Montpellier, France
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2
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Garga B, Abboubakar H, Sourpele RS, Gwet DLL, Bitjoka L. Pollen Grain Classification Using Some Convolutional Neural Network Architectures. J Imaging 2024; 10:158. [PMID: 39057729 PMCID: PMC11277931 DOI: 10.3390/jimaging10070158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 06/24/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
The main objective of this work is to use convolutional neural networks (CNN) to improve the performance in previous works on their baseline for pollen grain classification, by improving the performance of the following eight popular architectures: InceptionV3, VGG16, VGG19, ResNet50, NASNet, Xception, DenseNet201 and InceptionResNetV2, which are benchmarks on several classification tasks, like on the ImageNet dataset. We use a well-known annotated public image dataset for the Brazilian savanna, called POLLEN73S, composed of 2523 images. Holdout cross-validation is the name of the method used in this work. The experiments carried out showed that DenseNet201 and ResNet50 outperform the other CNNs tested, achieving results of 97.217% and 94.257%, respectively, in terms of accuracy, higher than the existing results, with a difference of 1.517% and 0.257%, respectively. VGG19 is the architecture with the lowest performance, achieving a result of 89.463%.
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Affiliation(s)
- Benjamin Garga
- ENSAI, Laboratory of Energy, Signal, Imaging and Automation, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon; (B.G.); (R.S.S.); (D.L.L.G.); (L.B.)
| | - Hamadjam Abboubakar
- ENSAI, Laboratory of Energy, Signal, Imaging and Automation, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon; (B.G.); (R.S.S.); (D.L.L.G.); (L.B.)
- Departement of Computer Engineering, University Institute of Technology, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon
- Laboratory of Analysis, Simulations and Tests, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon
| | - Rodrigue Saoungoumi Sourpele
- ENSAI, Laboratory of Energy, Signal, Imaging and Automation, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon; (B.G.); (R.S.S.); (D.L.L.G.); (L.B.)
- ENSAI, Department of Mathematics and Computer Sciences, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon
| | - David Libouga Li Gwet
- ENSAI, Laboratory of Energy, Signal, Imaging and Automation, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon; (B.G.); (R.S.S.); (D.L.L.G.); (L.B.)
- EGCIM, Department of Fundamental Sciences and Engineering Techniques, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon
| | - Laurent Bitjoka
- ENSAI, Laboratory of Energy, Signal, Imaging and Automation, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon; (B.G.); (R.S.S.); (D.L.L.G.); (L.B.)
- ENSAI, Department of Electrical Engineering, Energetics and Automation, University of Ngaoundere, Ngaoundere P.O. Box 455, Cameroon
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3
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Adaïmé MÉ, Kong S, Punyasena SW. Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes. PNAS NEXUS 2024; 3:pgad419. [PMID: 38205029 PMCID: PMC10777098 DOI: 10.1093/pnasnexus/pgad419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/20/2023] [Indexed: 01/12/2024]
Abstract
The phylogenetic interpretation of pollen morphology is limited by our inability to recognize the evolutionary history embedded in pollen features. Deep learning offers tools for connecting morphology to phylogeny. Using neural networks, we developed an explicitly phylogenetic toolkit for analyzing the overall shape, internal structure, and texture of a pollen grain. Our analysis pipeline determines whether testing specimens are from known species based on uncertainty estimates. Features from specimens with uncertain taxonomy are passed to a multilayer perceptron network trained to transform these features into predicted phylogenetic distances from known taxa. We used these predicted distances to place specimens in a phylogeny using Bayesian inference. We trained and evaluated our models using optical superresolution micrographs of 30 extant Podocarpus species. We then used trained models to place nine fossil Podocarpidites specimens within the phylogeny. In doing so, we demonstrate that the phylogenetic history encoded in pollen morphology can be recognized by neural networks and that deep-learned features can be used in phylogenetic placement. Our approach makes extinction and speciation events that would otherwise be masked by the limited taxonomic resolution of the fossil pollen record visible to palynological analysis.
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Affiliation(s)
- Marc-Élie Adaïmé
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Shu Kong
- Faculty of Science and Technology, University of Macau, Macau 999078, China
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Surangi W Punyasena
- Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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Mahmood T, Choi J, Ryoung Park K. Artificial Intelligence-based Classification of Pollen Grains Using Attention-guided Pollen Features Aggregation Network. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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5
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Zhao LN, Li JQ, Cheng WX, Liu SQ, Gao ZK, Xu X, Ye CH, You HL. Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images. BIOLOGY 2022; 11:biology11121841. [PMID: 36552349 PMCID: PMC9775008 DOI: 10.3390/biology11121841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at "pollen localization problem") and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at "pollen classification problem"). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.
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Affiliation(s)
- Lin-Na Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jian-Qiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Wen-Xiu Cheng
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Su-Qin Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Zheng-Kai Gao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Xi Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
- Correspondence:
| | - Cai-Hua Ye
- Beijing Meteorological Service Center, Beijing 100089, China
| | - Huan-Ling You
- Beijing Meteorological Service Center, Beijing 100089, China
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Li C, Polling M, Cao L, Gravendeel B, Verbeek FJ. Analysis of Automatic Image Classification Methods for Urticaceae Pollen Classification. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.11.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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7
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Balmaki B, Rostami MA, Christensen T, Leger EA, Allen JM, Feldman CR, Forister ML, Dyer LA. Modern approaches for leveraging biodiversity collections to understand change in plant-insect interactions. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.924941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Research on plant-pollinator interactions requires a diversity of perspectives and approaches, and documenting changing pollinator-plant interactions due to declining insect diversity and climate change is especially challenging. Natural history collections are increasingly important for such research and can provide ecological information across broad spatial and temporal scales. Here, we describe novel approaches that integrate museum specimens from insect and plant collections with field observations to quantify pollen networks over large spatial and temporal gradients. We present methodological strategies for evaluating insect-pollen network parameters based on pollen collected from museum insect specimens. These methods provide insight into spatial and temporal variation in pollen-insect interactions and complement other approaches to studying pollination, such as pollinator observation networks and flower enclosure experiments. We present example data from butterfly pollen networks over the past century in the Great Basin Desert and Sierra Nevada Mountains, United States. Complementary to these approaches, we describe rapid pollen identification methods that can increase speed and accuracy of taxonomic determinations, using pollen grains collected from herbarium specimens. As an example, we describe a convolutional neural network (CNN) to automate identification of pollen. We extracted images of pollen grains from 21 common species from herbarium specimens at the University of Nevada Reno (RENO). The CNN model achieved exceptional accuracy of identification, with a correct classification rate of 98.8%. These and similar approaches can transform the way we estimate pollination network parameters and greatly change inferences from existing networks, which have exploded over the past few decades. These techniques also allow us to address critical ecological questions related to mutualistic networks, community ecology, and conservation biology. Museum collections remain a bountiful source of data for biodiversity science and understanding global change.
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Automatic Classification of Pollen Grain Microscope Images Using a Multi-Scale Classifier with SRGAN Deblurring. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Pollen allergies are seasonal epidemic diseases that are accompanied by high incidence rates, especially in Beijing, China. With the development of deep learning, key progress has been made in the task of automatic pollen grain classification, which could replace the time-consuming and laborious manual identification process using a microscope. In China, few pioneering works have made significant progress in automatic pollen grain classification. Therefore, we first constructed a multi-class and large-scale pollen grain dataset for the Beijing area in preparation for the task of pollen classification. Then, a deblurring pipeline was designed to enhance the quality of the pollen grain images selectively. Moreover, as pollen grains vary greatly in size and shape, we proposed an easy-to-implement and efficient multi-scale deep learning architecture. Our experimental results showed that our architecture achieved a 97.7% accuracy, based on the Resnet-50 backbone network, which proved that the proposed method could be applied successfully to the automatic identification of pollen grains in Beijing.
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9
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Vallez N, Bueno G, Deniz O, Blanco S. Diffeomorphic transforms for data augmentation of highly variable shape and texture objects. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106775. [PMID: 35397412 DOI: 10.1016/j.cmpb.2022.106775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/28/2022] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Training a deep convolutional neural network (CNN) for automatic image classification requires a large database with images of labeled samples. However, in some applications such as biology and medicine only a few experts can correctly categorize each sample. Experts are able to identify small changes in shape and texture which go unnoticed by untrained people, as well as distinguish between objects in the same class that present drastically different shapes and textures. This means that currently available databases are too small and not suitable to train deep learning models from scratch. To deal with this problem, data augmentation techniques are commonly used to increase the dataset size. However, typical data augmentation methods introduce artifacts or apply distortions to the original image, which instead of creating new realistic samples, obtain basic spatial variations of the original ones. METHODS We propose a novel data augmentation procedure which generates new realistic samples, by combining two samples that belong to the same class. Although the idea behind the method described in this paper is to mimic the variations that diatoms experience in different stages of their life cycle, it has also been demonstrated in glomeruli and pollen identification problems. This new data augmentation procedure is based on morphing and image registration methods that perform diffeomorphic transformations. RESULTS The proposed technique achieves an increase in accuracy over existing techniques of 0.47%, 1.47%, and 0.23% for diatom, glomeruli and pollen problems respectively. CONCLUSIONS For the Diatom dataset, the method is able to simulate the shape changes in different diatom life cycle stages, and thus, images generated resemble newly acquired samples with intermediate shapes. In fact, the other methods compared obtained worse results than those which were not using data augmentation. For the Glomeruli dataset, the method is able to add new samples with different shapes and degrees of sclerosis (through different textures). This is the case where our proposed DA method is more beneficial, when objects highly differ in both shape and texture. Finally, for the Pollen dataset, since there are only small variations between samples in a few classes and this dataset has other features such as noise which are likely to benefit other existing DA techniques, the method still shows an improvement of the results.
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Affiliation(s)
- Noelia Vallez
- VISILAB, University of Castilla-La Mancha, E.T.S. Ingenieria Industrial, Avda. Camilo Jose Cela s/n, Ciudad Real 13071, Spain.
| | - Gloria Bueno
- VISILAB, University of Castilla-La Mancha, E.T.S. Ingenieria Industrial, Avda. Camilo Jose Cela s/n, Ciudad Real 13071, Spain
| | - Oscar Deniz
- VISILAB, University of Castilla-La Mancha, E.T.S. Ingenieria Industrial, Avda. Camilo Jose Cela s/n, Ciudad Real 13071, Spain
| | - Saul Blanco
- Institute of the Environment, University of Leon, Leon E-24071, Spain
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Tsiknakis N, Savvidaki E, Manikis GC, Gotsiou P, Remoundou I, Marias K, Alissandrakis E, Vidakis N. Pollen Grain Classification Based on Ensemble Transfer Learning on the Cretan Pollen Dataset. PLANTS 2022; 11:plants11070919. [PMID: 35406899 PMCID: PMC9002917 DOI: 10.3390/plants11070919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/17/2022] [Accepted: 03/26/2022] [Indexed: 12/03/2022]
Abstract
Pollen identification is an important task for the botanical certification of honey. It is performed via thorough microscopic examination of the pollen present in honey; a process called melissopalynology. However, manual examination of the images is hard, time-consuming and subject to inter- and intra-observer variability. In this study, we investigated the applicability of deep learning models for the classification of pollen-grain images into 20 pollen types, based on the Cretan Pollen Dataset. In particular, we applied transfer and ensemble learning methods to achieve an accuracy of 97.5%, a sensitivity of 96.9%, a precision of 97%, an F1 score of 96.89% and an AUC of 0.9995. However, in a preliminary case study, when we applied the best-performing model on honey-based pollen-grain images, we found that it performed poorly; only 0.02 better than random guessing (i.e., an AUC of 0.52). This indicates that the model should be further fine-tuned on honey-based pollen-grain images to increase its effectiveness on such data.
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Affiliation(s)
- Nikos Tsiknakis
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas–FORTH, 70013 Heraklion, Greece; (G.C.M.); (K.M.)
- Correspondence:
| | - Elisavet Savvidaki
- Department of Agriculture, Hellenic Mediterranean University, 71004 Heraklion, Greece; (E.S.); (E.A.)
| | - Georgios C. Manikis
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas–FORTH, 70013 Heraklion, Greece; (G.C.M.); (K.M.)
| | - Panagiota Gotsiou
- Department of Food Quality and Chemistry of Natural Products, Mediterranean Agronomic Institute of Chania (M.A.I.Ch./CIHEAM), 73100 Chania, Greece; (P.G.); (I.R.)
| | - Ilektra Remoundou
- Department of Food Quality and Chemistry of Natural Products, Mediterranean Agronomic Institute of Chania (M.A.I.Ch./CIHEAM), 73100 Chania, Greece; (P.G.); (I.R.)
| | - Kostas Marias
- Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas–FORTH, 70013 Heraklion, Greece; (G.C.M.); (K.M.)
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Greece;
| | - Eleftherios Alissandrakis
- Department of Agriculture, Hellenic Mediterranean University, 71004 Heraklion, Greece; (E.S.); (E.A.)
| | - Nikolas Vidakis
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Greece;
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Khanzhina N, Filchenkov A, Minaeva N, Novoselova L, Petukhov M, Kharisova I, Pinaeva J, Zamorin G, Putin E, Zamyatina E, Shalyto A. Combating data incompetence in pollen images detection and classification for pollinosis prevention. Comput Biol Med 2022; 140:105064. [PMID: 34861642 DOI: 10.1016/j.compbiomed.2021.105064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/20/2021] [Accepted: 11/20/2021] [Indexed: 11/30/2022]
Abstract
Automatic pollen images recognition is crucial for pollinosis symptoms prevention and treatment. The problem of pollen recognition can be efficiently solved using deep learning, however neural networks require tens of thousands of images to generalize. At the same time, the existing open pollen images datasets are very small. In this paper, we present a novel open pollen dataset annotated for both detection and classification tasks. Based on our dataset we study learning from a small data using different state-of-the-art approaches. For the detection task we propose to use our new Bayesian RetinaNet network, which models aleatoric uncertainty. We compare it with the baseline RetinaNet and demonstrate that our model allows for higher detection precision. For the classification task we compare the impact of pre-training on the synthetic images from generative adversarial networks (GANs) and metric-based few-shot learning. Namely, we pre-trained our small convolutional neural network and Siamese neural network classifiers on synthetic pollen images generated by two GANs: StyleGAN and Self-attention GAN. The best classifier is the convolutional neural network pre-trained on StyleGAN images. Our best models achieved 96.3% of mean average precision for the detection task and 97.7% of F1 measure for the classification task on 13 pollen plant species. We have implemented the best models in our pollen recognition web service, which is available for palynologists on request.
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Affiliation(s)
- Natalia Khanzhina
- Machine Learning Lab, ITMO University, 49 Kronverksky Pr., Saint Petersburg, 197 101, Russia.
| | - Andrey Filchenkov
- Machine Learning Lab, ITMO University, 49 Kronverksky Pr., Saint Petersburg, 197 101, Russia
| | - Natalia Minaeva
- Perm State Medical University, 26 Petropavlovskaya St., Perm, 614 000, Russia
| | - Larisa Novoselova
- Perm State National Research University, 15 Bukireva St., Perm, 614 990, Russia
| | - Maxim Petukhov
- Machine Learning Lab, ITMO University, 49 Kronverksky Pr., Saint Petersburg, 197 101, Russia
| | - Irina Kharisova
- Perm State National Research University, 15 Bukireva St., Perm, 614 990, Russia
| | - Julia Pinaeva
- Perm State National Research University, 15 Bukireva St., Perm, 614 990, Russia
| | - Georgiy Zamorin
- Perm State National Research University, 15 Bukireva St., Perm, 614 990, Russia
| | - Evgeny Putin
- Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong
| | - Elena Zamyatina
- Perm State National Research University, 15 Bukireva St., Perm, 614 990, Russia; National Research University "Higher School of Economics", Faculty of Economics, Management, and Business Informatics, 38 Studencheskaya St., 614 070, Perm, Russia
| | - Anatoly Shalyto
- Machine Learning Lab, ITMO University, 49 Kronverksky Pr., Saint Petersburg, 197 101, Russia
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12
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Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1). APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11146657] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Pollen analysis and the classification of several pollen species is an important task in melissopalynology. The development of machine learning or deep learning based classification models depends on available datasets of pollen grains from various plant species from around the globe. In this paper, Cretan Pollen Dataset v1 (CPD-1) is presented, which is a novel dataset of grains from 20 pollen species from plants gathered in Crete, Greece. The pollen grains were prepared and stained with fuchsin, in order to be captured by a camera attached to a microscope under a ×400 magnification. In addition, a pollen grain segmentation method is presented, which segments and crops each unique pollen grain and achieved an overall detection accuracy of 92%. The final dataset comprises 4034 segmented pollen grains of 20 different pollen species, as well as the raw data and ground truth, as annotated by an expert. The developed dataset is publicly accessible, which we hope will accelerate research in melissopalynology.
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13
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Kubera E, Kubik-Komar A, Piotrowska-Weryszko K, Skrzypiec M. Deep Learning Methods for Improving Pollen Monitoring. SENSORS 2021; 21:s21103526. [PMID: 34069411 PMCID: PMC8159113 DOI: 10.3390/s21103526] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 11/16/2022]
Abstract
The risk of pollen-induced allergies can be determined and predicted based on data derived from pollen monitoring. Hirst-type samplers are sensors that allow airborne pollen grains to be detected and their number to be determined. Airborne pollen grains are deposited on adhesive-coated tape, and slides are then prepared, which require further analysis by specialized personnel. Deep learning can be used to recognize pollen taxa based on microscopic images. This paper presents a method for recognizing a taxon based on microscopic images of pollen grains, allowing the pollen monitoring process to be automated. In this research, a deep CNN (convolutional neural network) model was built from scratch. Publicly available deep neural network models, pre-trained on image data (not including microscopic pictures), were also used. The results show that even a simple deep learning model produces quite good results when the classification of pollen grain taxa is performed directly from the images. The best deep learning model achieved 97.88% accuracy in the difficult task of recognizing three types of pollen grains (birch, alder, and hazel) with similar structures. The derived models can be used to build a system to support pollen monitoring experts in their work.
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Affiliation(s)
- Elżbieta Kubera
- Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, ul. Głęboka 28, 20-950 Lublin, Poland
- Correspondence: (E.K.); (A.K.-K.)
| | - Agnieszka Kubik-Komar
- Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, ul. Głęboka 28, 20-950 Lublin, Poland
- Correspondence: (E.K.); (A.K.-K.)
| | - Krystyna Piotrowska-Weryszko
- Department of Botany and Plant Physiology, University of Life Sciences in Lublin, Akademicka 15, 20-950 Lublin, Poland;
| | - Magdalena Skrzypiec
- Institute of Mathematics, Maria Curie-Sklodowska University, pl. Marii Curie-Skłodowskiej 1, 20-031 Lublin, Poland;
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