基于深度学习的宏观真菌分类:对准真菌识别先进模型的比较分析
Sifa Ozsari1, Eda Kumru2, Fatih Ekinci3
1Department of Computer Engineering, Faculty of Engineering, Ankara University, Ankara 06830, Türkiye.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究使用深度学习对六种宏观真菌物种进行了分类. DenseNet121模型实现了92%的准确性,显示了人工智能在生物多样性研究和真菌保护方面的前景.
科学领域:
- 菌类学 菌类学是指菌类学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 精确识别宏观真菌物种对于生态研究和保护至关重要.
- 传统的真菌分类方法可能耗时,需要专门的专业知识.
- 深度学习为自动化和高效的宏观真菌识别提供了潜在的解决方案.
研究的目的:
- 使用先进的深度学习模型对六种不同的宏观真菌物种进行分类.
- 评估各种机器学习和深度学习技术对真菌图像识别的性能.
- 确定最有效的深度学习模型,用于准确的宏观真菌物种分类.
主要方法:
- 使用了5种机器学习技术和12种深度学习模型,包括DenseNet121,MobileNetV2,ConvNeXt,EfficientNet和swin变压器.
- 在六种宏观真菌物种的图像上训练模型:Amanita pantherina,Boletus edulis,Cantharellus cibarius,Lactarius deliciosus,Pleurotus ostreatus和Tricholoma terreum. 这些物种包括:
- 基于准确性和曲线下的面积 (AUC) 评分来评估模型性能.
主要成果:
- DenseNet121模型实现了最高的分类准确性 (92%) 和AUC得分 (95%).
- 基于变压器的模型,特别是swin变压器,在这个分类任务中效率较低.
- 该研究强调了深度学习在基于视觉数据的基础上区分宏观真菌物种的潜力.
结论:
- 深度学习,特别是DenseNet121架构,对于宏观真菌物种分类非常有效.
- 通过扩大数据集,整合各种数据类型 (例如生物化学,遗传学) 和采用组合方法,可以进一步改进.
- 这项研究为推进生物多样性研究提供了宝贵的见解,并为宏观真菌的生态保护提供了信息.
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