使用卷积神经网络和变压器进行超光谱成像,用于预测桃番茄的可溶性固体含量和pH值
Hengnian Qi1, Hongyang Li1, Liping Chen2
1School of Information Engineering, Huzhou University, Huzhou 313000, China.
Foods (Basel, Switzerland)
|January 23, 2024
概括
超光谱成像与CNN-Transformer模型相结合,可以准确预测桃番茄的质量. 这种非破坏性方法精确测量可溶性固体含量 (SSC) 和pH值,有助于生产和消费.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 溶性固体含量 (SSC) 和pH值是桃番茄的关键质量指标.
- 准确的,非破坏性的测量方法对于桃番茄的生产和消费至关重要.
- 现有的质量评估方法可能耗时或破坏性.
研究的目的:
- 开发一种快速而非破坏性的方法来确定桃番茄的SSC和pH值.
- 评估CNN-变压器模型对预测这些质量属性的性能.
- 为了确定与SSC和pH相关的特征光谱波段.
主要方法:
- 采用超光谱成像技术进行数据采集.
- 开发并比较传统的机器学习和深度学习模型,包括CNN-Transformer.
- 分析了光谱数据以确定SSC和pH,并可视化了模型识别的特征波长.
主要成果:
- CNN-变压器模型实现了SSC的高预测精度 (R2P=0.83).
- 该模型显示pH的预测准确度中等 (R2P=0.60).
- 确定了关键的光谱范围:SSC的1380-1650nm和pH的945-1280nm.
结论:
- 将光谱信息与CNN-Transformer模型集成显著提高了预测桃番茄SSC和pH的准确性.
- 开发的方法为实时质量评估提供了一个有希望的非破坏性方法.
- 这项技术可以支持改进桃番茄种植和质量控制.
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