一个微调的EfficientNet-B0卷积神经网络,用于准确有效地对果叶病进行分类
Hassan Ali1,2, Noora Shifa3, Rachid Benlamri4
1Centre of Excellence for Sustainability and Food Security, University of Doha for Science and Technology, Doha, 24449, Qatar. hassan.ali@udst.edu.qa.
Scientific reports
|July 16, 2025
概括
这项研究介绍了一种微调的EfficientNet-B0卷积神经网络 (CNN),用于准确的果叶疾病分类. 该模型在优化资源使用的情况下实现了高精度,使其成为实际作物管理的理想选择.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 准确的果疾病分类对于有效的作物管理和产量优化至关重要.
- 现有的方法需要提高准确性和效率,以适用于现实世界的应用.
研究的目的:
- 开发和评估一个微调的EfficientNet-B0卷积神经网络 (CNN) 用于自动化果叶疾病分类.
- 将微调模型的性能与其他已建立的CNN架构进行比较.
主要方法:
- 使用了预先训练的EfficientNet-B0模型,并进行了包括全局max聚合和dropout在内的架构修改.
- 实施了一个整体的培训策略,包括数据增强,分层分割,类权重和转移学习.
- 在PlantVillage (PV) 和Apple PV (APV) 数据集上评估模型,与EfficientNet-B0,EfficientNet-B3,Inception-v3,ResNet50和VGG16进行比较.
主要成果:
- 实现了高测试准确率的99.69% (APV) 和99.78% (PV).
- 在评估的数据集中表现优于EfficientNet-B0,EfficientNet-B3,VGG16,Inception-v3和ResNet50.
- 证明了具有竞争力的内存消耗和FLOP,与基础EfficientNet-B0模型相比,准确性得到了显著改进.
结论:
- 微调的CNN模型是植物叶病分类的有效解决方案,提供高精度和优化资源效率.
- 该方法适用于资源有限的环境,证明了结合转移学习,数据预处理和架构优化的力量.
- 这项研究强调了先进的深度学习技术在加强农业监测和疾病检测系统方面的潜力.
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