POLLEN73S: An image dataset for pollen grains classification

Astolfi, Gilberto; Gonçalves, Ariadne Barbosa; Menezes, Geazy Vilharva; Borges, Felipe Silveira Brito; Astolfi, Angelica Christina Melo Nunes; Matsubara, Edson Takashi; Álvarez, Marco; Pistori, Hemerson

doi:10.1016/j.ecoinf.2020.101165

Cited by 32 publications

(28 citation statements)

References 14 publications

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“…In this article, we evaluate the effectiveness of some CNNs by comparing their accuracy in recognizing pollen grains from microscopic images collected in the ABCPollen database. In [ 13 , 14 ] the authors used only one deep learning model—AlexNet—while this study considers and compares several networks, similarly to [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

“…In [ 15 ]—similar to this article—fine-tuned CNNs, pre-trained on the ImageNet dataset, were used. In our research, we tried to provide greater similarity of the pre-training and the target datasets.…”

Section: Discussionmentioning

confidence: 99%

“…In the case of analyzing more taxa, the performance of the models can be further improved by augmentation techniques, which allow for more training samples. This solution was applied among others in [ 14 , 15 ], where these operations were necessary as some classes consisted of a small number of instances.…”

Section: Discussionmentioning

confidence: 99%

“…Deep learning methods have been applied to the recognition of pollen grains (see, among others, [ 12 , 13 , 14 , 15 ]). The AlexNet and SmallerVGGNet models were used in [ 12 ] for the Pollen13K database, resulting in average F-scores of 0.87 and 0.85, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Fine-tuning of the pre-trained CNNs (8 different architectures) was applied in [ 15 ] to the pollen grains of 73 savannah plants. They obtained the best results (almost 96% accuracy) using the DenseNet model in the 5-fold cross-validation procedure.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Deep Learning Methods for Improving Pollen Monitoring

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning Methods for Improving Pollen Monitoring

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Piotrowska-Weryszko

et al. 2021

Sensors

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Pollen Grain Classification Challenge 2020

Battiato

Guarnera

Ortis

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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Deep Learning Recognition of a Large Number of Pollen Grain Types

Monteiro

Pinto

Rufino

2021

Communications in Computer and Information Science

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POLLEN73S: An image dataset for pollen grains classification

Cited by 32 publications

References 14 publications

Deep Learning Methods for Improving Pollen Monitoring

Deep Learning Methods for Improving Pollen Monitoring

Pollen Grain Classification Challenge 2020

Deep Learning Recognition of a Large Number of Pollen Grain Types

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