Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks
Categorising monoflorality of honey by human experts is labour intensive and time-consuming, resulting in the urgent need for automated techniques to overcome the constraints of traditional method which involves manually sorting of pollen grains. This study constructs a comprehensive dataset of poll...
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003498 |
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| author | José Miguel Valiente Juan José Martín-Osuna Ana María Peral Isabel Escriche |
| author_facet | José Miguel Valiente Juan José Martín-Osuna Ana María Peral Isabel Escriche |
| author_sort | José Miguel Valiente |
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| description | Categorising monoflorality of honey by human experts is labour intensive and time-consuming, resulting in the urgent need for automated techniques to overcome the constraints of traditional method which involves manually sorting of pollen grains. This study constructs a comprehensive dataset of pollen images, labelling them with an enhanced version of HoneyApp application. This Ground Truth termed POLLEN24_SP, comprises 32,285 pollen/particle images (captured by an expert using optical microscopy), covering the 24 most prevalent types of pollen grains found in Spanish honeys. Twelve different pre-existing Convolutional Neural Networks (CNN) were evaluated, achieving an accuracy rate of up to 98.03 % with EfficientNetV2M. The new version of in-house networks (PolleNetV2, PolleNetV2.mobile) was also introduced. The pre-existing CNN models' results were satisfactory in discerning the relative frequencies of different pollen types; nevertheless, our new proposals are promising due to their straightforward architecture as well as their scalability for integration into the HoneyApp ecosystem. Studying the classification errors made by the CNN models confirmed their strong generalization ability. To minimise errors in types such as Leguminoseae, segmenting this family is recommended. In addition, it is vital to continue expanding POLLEN24_SP dataset focusing on the underrepresented types to balance them and include more pollen types to better represent Spanish honeys. Finally, the results of this work will enable the creation of a public dataset to be diffused among the scientific community, following open science principles. The development of a computer application incorporating CNN models, makes these methodologies suitable for efficient classification of monofloral honeys. |
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| institution | Matheson Library |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
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| series | Ecological Informatics |
| spelling | doaj-art-d4a101267e7f44f3bb0d9f8c5f9180e72025-07-26T05:22:57ZengElsevierEcological Informatics1574-95412025-12-0190103340Assisting monofloral honey classification by automated pollen identification based on convolutional neural networksJosé Miguel Valiente0Juan José Martín-Osuna1Ana María Peral2Isabel Escriche3Institute of Control Systems and Industrial Computing (AI2), Universitat Politècnica de València, Camino de Vera. s/n. 46022, Valencia, Spain; Corresponding author.Institute of Control Systems and Industrial Computing (AI2), Universitat Politècnica de València, Camino de Vera. s/n. 46022, Valencia, SpainInstituto Universitario de Ingeniería de Alimentos (FoodUPV), Universitat Politècnica de València, Camino de Vera. s/n, 46022, Valencia, SpainInstituto Universitario de Ingeniería de Alimentos (FoodUPV), Universitat Politècnica de València, Camino de Vera. s/n, 46022, Valencia, Spain; Food Technology Department, Universitat Politècnica de València, Camino de Vera. s/n, 46022, Valencia, Spain; Corresponding author at: Instituto Universitario de Ingeniería de Alimentos (FoodUPV), Universitat Politècnica de València, Camino de Vera. s/n, 46022, Valencia, Spain.Categorising monoflorality of honey by human experts is labour intensive and time-consuming, resulting in the urgent need for automated techniques to overcome the constraints of traditional method which involves manually sorting of pollen grains. This study constructs a comprehensive dataset of pollen images, labelling them with an enhanced version of HoneyApp application. This Ground Truth termed POLLEN24_SP, comprises 32,285 pollen/particle images (captured by an expert using optical microscopy), covering the 24 most prevalent types of pollen grains found in Spanish honeys. Twelve different pre-existing Convolutional Neural Networks (CNN) were evaluated, achieving an accuracy rate of up to 98.03 % with EfficientNetV2M. The new version of in-house networks (PolleNetV2, PolleNetV2.mobile) was also introduced. The pre-existing CNN models' results were satisfactory in discerning the relative frequencies of different pollen types; nevertheless, our new proposals are promising due to their straightforward architecture as well as their scalability for integration into the HoneyApp ecosystem. Studying the classification errors made by the CNN models confirmed their strong generalization ability. To minimise errors in types such as Leguminoseae, segmenting this family is recommended. In addition, it is vital to continue expanding POLLEN24_SP dataset focusing on the underrepresented types to balance them and include more pollen types to better represent Spanish honeys. Finally, the results of this work will enable the creation of a public dataset to be diffused among the scientific community, following open science principles. The development of a computer application incorporating CNN models, makes these methodologies suitable for efficient classification of monofloral honeys.http://www.sciencedirect.com/science/article/pii/S1574954125003498Monofloral honey classificationPollen classificationPollen datasetLabelling and annotating applicationHoneyAppConvolutional neural networks |
| spellingShingle | José Miguel Valiente Juan José Martín-Osuna Ana María Peral Isabel Escriche Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks Ecological Informatics Monofloral honey classification Pollen classification Pollen dataset Labelling and annotating application HoneyApp Convolutional neural networks |
| title | Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks |
| title_full | Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks |
| title_fullStr | Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks |
| title_full_unstemmed | Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks |
| title_short | Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks |
| title_sort | assisting monofloral honey classification by automated pollen identification based on convolutional neural networks |
| topic | Monofloral honey classification Pollen classification Pollen dataset Labelling and annotating application HoneyApp Convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003498 |
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