Classification Of Pollen Grains Research Articles

Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study

Pollen identification is necessary for several subfields of geology, ecology, and evolutionary biology. However, the existing methods for pollen identification are laborious, time-consuming, and require highly skilled scientists. Therefore, there is a pressing need for an automated and accurate system for pollen identification, which can be beneficial for both basic research and applied issues such as identifying airborne allergens. In this study, we propose a deep learning (DL) approach to classify pollen grains in the Great Basin Desert, Nevada, USA. Our dataset consisted of 10,000 images of 40 pollen species. To mitigate the limitations imposed by the small volume of our training dataset, we conducted an in-depth comparative analysis of numerous pre-trained Convolutional Neural Network (CNN) architectures utilizing transfer learning methodologies. Simultaneously, we developed and incorporated an innovative CNN model, serving to augment our exploration and optimization of data modeling strategies. We applied different architectures of well-known pre-trained deep CNN models, including AlexNet, VGG-16, MobileNet-V2, ResNet (18, 34, and 50, 101), ResNeSt (50, 101), SE-ResNeXt, and Vision Transformer (ViT), to uncover the most promising modeling approach for the classification of pollen grains in the Great Basin. To evaluate the performance of the pre-trained deep CNN models, we measured accuracy, precision, F1-Score, and recall. Our results showed that the ResNeSt-110 model achieved the best performance, with an accuracy of 97.24%, precision of 97.89%, F1-Score of 96.86%, and recall of 97.13%. Our results also revealed that transfer learning models can deliver better and faster image classification results compared to traditional CNN models built from scratch. The proposed method can potentially benefit various fields that rely on efficient pollen identification. This study demonstrates that DL approaches can improve the accuracy and efficiency of pollen identification, and it provides a foundation for further research in the field.

Artificial intelligence-based classification of pollen grains using attention-guided pollen features aggregation network

Visual classification of pollen grains is crucial for various agricultural applications, particularly for the protection, monitoring, and tracking of flora to preserve the biome and maintain the quality of honey-based products. Traditionally, pollen grain classification has been performed by trained palynologists using a light microscope. Despite their wide range of applications, still tiresome and time-consuming methods are used. Artificial intelligence (AI) can be used to automate the pollen grain classification process. Recently, numerous AI-based techniques for classifying pollen grains have been proposed. However, there is still possibility for performance enhancement including processing time, memory size, and accuracy. In this study, an attention-guided pollen feature aggregation network (APFA-Net) based on deep feature aggregation and channel-wise attention is proposed. Three publicly available datasets, POLLEN73S, POLLEN23E, and Cretan pollen, having a total of 7362 images from 116 distinct pollen types are used for experiments. The proposed method shows F-measure values of 97.37 %, 97.66 %, and 98.39 % with POLLEN73S, POLLEN23E, and Cretan Pollen datasets, respectively. We confirm that our method outperforms existing state-of-the-art methods.

Electro-Optical Classification of Pollen Grains via Microfluidics and Machine Learning.

In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7% for the electrical classifier, 76.7% for the optical classifier and 84.2% for the multimodal classifier. The accuracy is 82.8% for the electrical classifier, 84.1% for the optical classifier and 88.3% for the multimodal classifier. The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.

Pollen Grains Classification with a Deep Learning System GPU-Trained

Traditional approaches to automatic classification of pollen grains consisted of classifiers working with feature extractors designed by experts, which modeled pollen grains aspects of special importance for biologists. Recently, a Deep Learning (DL) algorithm called Convolutional Neural Network (CNN) has shown a great improvement in performance in many computer vision tasks such as classification, due to this great performance the computational requirements have increased considerably; therefore, it is advisable to use new platforms such as the Graphics Processing Unit (GPU), which offer large computational resources for the development of new systems with CNN. This paper presents the GPU-Trained implementation of a DL system with the CNN algorithm, proposing a CNN model capable of running on a GPU in real-time for the automatic classification of 19 different pollen grains belonging to 14 different families, which are found in high concentrations in Mexico, and which are large interest in areas such as beekeeping, paleoecology, botany, allergology, agriculture among others. These areas seek to improve the collection of palynological data in terms of time and accuracy. In order to evaluate our model, evaluation tests were performed in the NVIDIA Jetson TX2 Developer Kit GPU. Experimental results achieves around 90% in CCR and Sensitivity in the proposed model. Additionally, the proposed model works at a processing speed of 6,826 Frames Per Second (FPS) and has approximately 50% fewer parameters than reported in related works.

Распознавание микроскопических изображений пыльцевых зерен с помощью сверточной нейронной сети VGG-16

The article presents the result of an experiment on the application of transfer learning using the Visual Geometry Group with 16 layers (VGG-16) convolutional neural network in relation to the problem of recognizing pollen grains in images. An analysis of the information-theoretical base on the application of machine learning algorithms to the problem of classifying pollen grains over the past few years has shown the need to develop (apply) a new method for recognizing images of pollen grains obtained using an optical microscope. Currently, automatic classification for pollen identification is becoming a very active area of research. The article substantiates the task of automating the classification of pollen grains. The aim of the study is to analyze the efficiency and accuracy of classifying microscopic images of pollen grains using transfer learning of the VGG-16 convolutional neural network. Transfer learning was performed using the VGG-16 neural network, which has 13 convolutional layers grouped into 5 blocks with pooling and 3 smoothing layers at the output. Since transfer learning is used, the number of training epochs can be chosen to be small. For this network, only the smoothing output layers change, and the feature extraction remains the same as in the classical model. Therefore, it was chosen to use 10 training epochs. Other hyperparameters are as follows: Drop Out regularization with a probability of 50%, optimization method is ADAM, activation function is sigmoid, loss function is cross-entropy, batch size is 32 images. As a result, by adjusting the hyperparameters of the model and using augmentation, it was possible to achieve a share of correct recognitions of about 80%. At the same time, due to the different number of training examples, the particular characteristics of the classes differ somewhat. Thus, the maximum precision and recall reach 94% and 83%, respectively, for the Dandelion type. In the future, studies are planned to use augmentation as a preprocessing to create a balanced sample. By applying the VGG-16 convolutional neural network to the problem of pollen grain image recognition, high accuracy and efficiency of the method were achieved.

Precise automatic classification of 46 different pollen types with convolutional neural networks.

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models. Our proposal manages to properly classify up to 98% of the examples from a dataset with 46 different classes of pollen grains, produced by the Classifynder classification system. This is an unprecedented result which surpasses all previous attempts both in accuracy and number and difficulty of taxa under consideration, which include types previously considered as indistinguishable.

Program for automatic clustering of numerous (over 1000) images of pollen grains obtained using an optical microscope in Python, for learning the LeNet convolutional neural network

Рассматривается способ сортировки изображений пыльцевых зерен на этапе предобработки для формирования выборки для обучения сверточной нейронной сети LeNet. На python разработана программа кадрирования изображения пыльцы, полученного с помощью оптического микроскопа, ImageSplitter, с помощью которой было кадрировано изображение полифлорного меда на 1131 изображение размером 128 ´ 128 пикселей при шаге скользящего окна 32 пикселя. На python разработана программа первичной классификации, позволяющая отсортировывать пустые изображения от непустых посредством задачи условий и интервалов степени заполненности черным. Проведено исследование на возможность сортировки непустых изображений на три класса по проценту заполненности черным. Вычисляется доля черных точек среди всех Perimeter total (т. е. критерий занятой зерном площади кадра), доля черных точек среди точек на границах – критерий занятого зерном периметра кадра – Perimeter black и в процентном соотношении Perimeter percentage. Осуществлялась инвертированная пороговая бинаризация для выделения объектов. Были получены следующие результаты: в промежутке от 0 до 30 программа не вывела никаких изображений, т. е. папка output_result_0-30 оказалась пустой; в промежутке от 31–60 программа классифицировала 4 изображения, т. е. в папке output_result_31-60 оказалось 4 изображения; в промежутке 61–99 программа классифицировала 130 изображений.

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

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.

Towards automation of palynology 3: pollen pattern recognition using Gabor transforms and digital moments

AbstractThe classification of pollen grains using texture information in combination with shape features is presented in this paper. The surface texture of pollen is characterised by using Gabor transforms, the geometric shape is described by using moment invariants, and the pollen grains are classified by an artificial neural network. In an experiment with five types of pollen grains, more than 97% of samples are correctly classified. Copyright © 2004 John Wiley & Sons, Ltd.

The seasonal variation of airborne pollen grains that cause sugipollinosis in japan in the last three years

Abstract In Japan, the problems of Sugi (Cryptomeria japonica)-pollinosis have been much discussed in recent papers and journals. The author made an investigation on the airborne pollen grains from a scientific standpoint in connection with the incidence of pollinosis. By using the Cascade Impactor the author collected 600 liters of air sample a day, at the roof of the Pharmaceutical Science Building, Toho University, Funabashi, Chiba, Japan. Each air sampling for 2 hrs was repeated three times a week since 1969. The airborne pollen grains in each sample were counted under the microscope to identify the pollen types. The pollen grains were classified into six types according to the classification of pollen grains in Japan established by lkuse. The accumulated number of pollen of each species was analysed statistically by circular plot, Weibull plot, Edwards plot and semi-logarithmic plot. In this report mainly 3B type of pollen grains (Cryptomeria, Chamaecyparis), collected in the last three years are described. The total number of pollen grains in 1988 (2820 grains) was doubled in quantity as compared with that in 1987 (1177 grains) and in 1989 (1121 grains). The 3B type of pollen grains in 1988 (1450 grains) was 3.4 times as many as in 1987 (397 grains), and 12.9 times as in 1989 (112 grains). The 3B type of pollen grains represented the major portion of total pollen, and influenced the annual amount and the dispersal period.

A rapid method for mounting pollen grains; with special regard to sterility studies.

A method is described for the simultaneous mounting and double staining of mature pollen grains. The medium consists of 50 ml. of glycerol jelly to which 2.5 ml. of methyl green and 2 ml. of phloxine, both in 50% alcoholic solutions, have been added. Prior to the application of the jelly-dye mixture, the pollen is washed with 70% ethanol to remove adhering oils and resins. The staining reaction is differential and permits the rapid classification of pollen grains. The “functional” pollen expands and stains with both dyes whereas the aborted grains remain shrunken and take only the methyl green wall stain. The same reaction functions with orchid seed.