VGG-MFO-色对林海普通的甜度预测
Chun Fang1, Runhong Shen2, Meiling Yuan1
1Zhejiang Provincial Key Laboratory for Cutting Tools, Research Institute of Motor and Intelligent Control Technology, Taizhou University, Jiaojiang, Taizhou, 318000, Zhejiang, China.
Scientific reports
|April 6, 2025
概括
一个新的VGG-MFO-Orange卷积神经网络 (CNN) 模型使用非破坏性图像分析准确地分类普通的甜味. 这种先进的水果分类技术提高了农业现代化和市场竞争力.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 食品科学 食品科学 食品科学
背景情况:
- 消费者对高品质水果的需求不断增加,需要先进的,非破坏性的方法来评估水果的属性,如甜味.
- 传统的水果分类方法往往是主观和劳动密集型的,限制了大规模农业操作的效率.
研究的目的:
- 开发和验证一种创新的卷积神经网络 (CNN) 模型,用于准确,非破坏性地分类普通的色甜味.
- 引入一个新的注意色 (AO) 机制和多尺度特征优化 (MFO) 模块,以提高 CNN 在水果质量评估中的性能.
主要方法:
- 创建了一个由5022张林海普通图像组成的数据集,其中包括糖检测用于甜度分类.
- 设计和训练了一种VGG-MFO-Orange CNN模型,将AO和MFO模块与VGG13集成在一起.
- 拟议模型的性能与六个已有的CNN架构 (DenseNet121,MobileNet_v2,ResNet50,ShuffleNet,VGG13,VGG13_bn) 进行了基准测试.
主要成果:
- VGG-MFO-Orange CNN模型实现了86.8%的验证准确度,超过了其他六种经典的CNN模型.
- 该模型在预测普通的色甜味方面表现出显著的准确性和强大的概括能力.
- 非破坏性方法在细粒度水果分类中被证明是有效的.
结论:
- 开发的VGG-MFO-Orange CNN模型提供了一种高效和准确的方法,用于非破坏性的水果分类,特别是对普通的甜味.
- 这项技术通过改善水果质量评估和提高农产品的市场竞争力来支持农业现代化.
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