使用深度特征提取和机器学习分类器集成的自动化果品种分类
Ibrar Ahmad1, Aftab Khaliq2, Bushra Siddique1
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Foods (Basel, Switzerland)
|February 13, 2026
概括
使用人工智能的自动果品种分类显著减少错误和收获后损失. 混合深度学习和机器学习模型实现100%的准确性,对实时应用程序进行更快的处理.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 人工智能的人工智能是人工智能.
背景情况:
- 手动果分类效率低下,导致发展中国家的收获后损失很大.
- 开发自动化系统对于提高效率和减少经济影响至关重要.
研究的目的:
- 开发一个计算高效和高度准确的人工智能框架,用于自动化果品种分类.
- 为了在水果分类系统中实现实时应用.
主要方法:
- 评估了八种深度转移学习模型作为特征提取器.
- 结合这些与十个经典的机器学习分类器.
- 使用精度,日志损失,内存使用,训练时间和推断延迟来评估性能.
主要成果:
- 混合模型EfficientNetB0-线性差异分析 (LDA) 和ResNet50-逻辑回归实现了100%的测试准确性.
- 与完全卷积神经网络 (CNN) 模型相比,推断时间减少了多达330倍.
- 证明了最先进的准确性,计算成本大大降低.
结论:
- 混合深度学习和机器学习架构为高效准确的自动化果分类提供了可行的解决方案.
- 开发的框架适用于实时应用和工业水果分类.
- 未来的工作包括现实世界的验证和嵌入式硬件部署.
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