ArticleSensors (Basel, Switzerland)2022
Detection and Recognition of Pollen Grains in Multilabel Microscopic Images.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Preliminary Research on the Possibility of Automating the Identification of Pollen Grains in Melissopalynology Using AI, with Particular Emphasis on Computer Image Analysis Methods.Sensors (Basel, Switzerland) · 2026Article
- Efficient and scalable training set generation for automated pollen monitoring with Hirst-type samplers.Scientific reports · 2025Article
- Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.PLoS neglected tropical diseases · 2024Article
- Pollen Grain Classification Using Some Convolutional Neural Network Architectures.Journal of imaging · 2024Article
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- Deep learning-based high-throughput detection of in vitro germination to assess pollen viability from microscopic images.Journal of experimental botany · 2023Article
- High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons.Journal of experimental botany · 2023Article
- Analytics and Applications of Audio and Image Sensing Techniques.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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Abstract
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 material performed based on digital microscopic photos. A deep neural network called YOLO was used to analyze microscopic images containing the reference grains of three taxa typical of Central and Eastern Europe. YOLO networks perform recognition and detection; hence, there is no need to segment the image before classification. The obtained results were compared to other deep learning object detection methods, i.e., Faster R-CNN and RetinaNet. YOLO outperformed the other methods, as it gave the mean average precision (mAP@.5:.95) between 86.8% and 92.4% for the test sets included in the study. Among the difficulties related to the correct classification of the research material, the following should be noted: significant similarities of the grains of the analyzed taxa, the possibility of their simultaneous occurrence in one image, and mutual overlapping of objects.
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