Artificial intelligence-based pollen classification machine in apiculture: design, implementation and evaluation.

IF 3.5 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Journal of the Science of Food and Agriculture Pub Date : 2025-10-06 DOI:10.1002/jsfa.70238

Dilara Gerdan Koc, Caner Koc, Aytül Ucak Koc

{"title":"Artificial intelligence-based pollen classification machine in apiculture: design, implementation and evaluation.","authors":"Dilara Gerdan Koc, Caner Koc, Aytül Ucak Koc","doi":"10.1002/jsfa.70238","DOIUrl":null,"url":null,"abstract":"Background: Bee pollen is a bioactive substance valued for its nutritional and health-promoting properties. However, consistent quality control and classification are hampered by variability in its chemical and biological composition, which is largely dependent on botanical origin. Artificial intelligence offers an opportunity to overcome these limitations through automated and standardized classification approaches.Results: A deep learning-driven system was developed to classify pollen samples based on color properties, a key visual biomarker of floral origin. Convolutional neural networks including MobileNet, InceptionV3, Xception, NasNet Large, DenseNet201 and YOLOv8 were evaluated. DenseNet201 achieved the highest classification accuracy (98.5%), while YOLOv8, integrated for real-time performance, reached 91.4% accuracy with rapid processing speed. Laboratory-scale validation confirmed the reliability of the system in differentiating monofloral pollen types.Conclusion: The proposed AI-based classification system provides a robust solution for the standardization and traceability of pollen products. Its real-time classification capability offers beekeepers a practical tool for sustainable and hygienic pollen collection, with potential applications across the food, pharmaceutical, nutraceutical and cosmetic industries. © 2025 Society of Chemical Industry.","PeriodicalId":17725,"journal":{"name":"Journal of the Science of Food and Agriculture","volume":" ","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Science of Food and Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1002/jsfa.70238","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Bee pollen is a bioactive substance valued for its nutritional and health-promoting properties. However, consistent quality control and classification are hampered by variability in its chemical and biological composition, which is largely dependent on botanical origin. Artificial intelligence offers an opportunity to overcome these limitations through automated and standardized classification approaches.

Results: A deep learning-driven system was developed to classify pollen samples based on color properties, a key visual biomarker of floral origin. Convolutional neural networks including MobileNet, InceptionV3, Xception, NasNet Large, DenseNet201 and YOLOv8 were evaluated. DenseNet201 achieved the highest classification accuracy (98.5%), while YOLOv8, integrated for real-time performance, reached 91.4% accuracy with rapid processing speed. Laboratory-scale validation confirmed the reliability of the system in differentiating monofloral pollen types.

Conclusion: The proposed AI-based classification system provides a robust solution for the standardization and traceability of pollen products. Its real-time classification capability offers beekeepers a practical tool for sustainable and hygienic pollen collection, with potential applications across the food, pharmaceutical, nutraceutical and cosmetic industries. © 2025 Society of Chemical Industry.

查看原文本刊更多论文

基于人工智能的蜂业花粉分类机：设计、实现与评价。

背景：蜂花粉是一种生物活性物质，因其营养和促进健康的特性而受到重视。然而，其化学和生物成分的变化很大程度上取决于植物来源，这阻碍了一致的质量控制和分类。人工智能提供了一个机会，通过自动化和标准化的分类方法来克服这些限制。结果：开发了一个深度学习驱动的系统，根据花粉样本的颜色特性（花起源的关键视觉生物标志物）进行分类。对MobileNet、InceptionV3、Xception、NasNet Large、DenseNet201和YOLOv8等卷积神经网络进行评价。DenseNet201的分类准确率最高（98.5%），而集成实时性能的YOLOv8的分类准确率达到91.4%，处理速度快。实验室规模的验证证实了该系统在区分单花花粉类型方面的可靠性。结论：基于人工智能的分类系统为花粉产品的标准化和可追溯性提供了可靠的解决方案。它的实时分类能力为养蜂人提供了一个可持续和卫生的花粉收集实用工具，在食品，制药，营养保健和化妆品行业具有潜在的应用。©2025化学工业协会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of the Science of Food and Agriculture 工程技术-农业综合

CiteScore

8.10

自引率

4.90%

发文量

634

审稿时长

3.1 months

期刊介绍： The Journal of the Science of Food and Agriculture publishes peer-reviewed original research, reviews, mini-reviews, perspectives and spotlights in these areas, with particular emphasis on interdisciplinary studies at the agriculture/ food interface. Published for SCI by John Wiley & Sons Ltd. SCI (Society of Chemical Industry) is a unique international forum where science meets business on independent, impartial ground. Anyone can join and current Members include consumers, business people, environmentalists, industrialists, farmers, and researchers. The Society offers a chance to share information between sectors as diverse as food and agriculture, pharmaceuticals, biotechnology, materials, chemicals, environmental science and safety. As well as organising educational events, SCI awards a number of prestigious honours and scholarships each year, publishes peer-reviewed journals, and provides Members with news from their sectors in the respected magazine, Chemistry & Industry . Originally established in London in 1881 and in New York in 1894, SCI is a registered charity with Members in over 70 countries.