Abstract
The classification of honey pollen grains is performed in order to classify honey according to its botanical origin, which is of great importance in terms of marketing. This visual work is currently done by human specialists counting and classifying the pollen grains in microscopic images. This is a hard, time-consuming, and subject to observer variability task. Thus, automated methods are required to overcome the limitations of the conventional procedure. This paper deals with the automatic classification of honey pollens using five representative Neural Networks coming from the ImageNet Challenge: VGG16, VGG19, ResNet50, InceptionV3 and Xception. The ground truth is composed of 9983 samples of 16 different types of pollens corresponding to citrus and rosemary pollens and its companions. The best result was obtained with the InceptionV3 network, achieving an accuracy of 98.15%, that outperforms the results obtained in previous works.
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Acknowledgment
This work is part of the project PID2019-106800RB-I00 (2019) of the Ministry of Science and Innovation (MCIN), State Research Agency MCIN/AEI/https://doi.org/10.13039/501100011033/. It is also part of the AGROALNEXT/2022/043 project, financed by the Generalitat Valenciana, the Next Generation European Union and the Recovery, Transformation and Resilience Plan of the Government of Spain.
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López-García, F. et al. (2023). Classification of Honey Pollens with ImageNet Neural Networks. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_19
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DOI: https://doi.org/10.1007/978-3-031-44240-7_19
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