快速分类的甘节点和内部节点使用近红外光谱和机器学习技术
Siramet Veerasakulwat1, Agustami Sitorus2, Vasu Udompetaikul1
1Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
可见短波近红外 (Vis-SWNIR) 光谱和机器学习准确地分类了甘节点和内部节点. 这项技术可实现自动化种植过程,改善种植材料质量并减少芽损伤.
科学领域:
- 农业工程 农业工程
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 自动化甘种植需要精确区分节点和内部节点,以保护芽并优化种植材料.
- 目前的方法可能缺乏有效的大规模自动化所需的速度和准确性.
研究的目的:
- 评估可见短波近红外 (Vis-SWNIR) 光谱学与机器学习相结合的有效性,用于分类甘节点和内部节点.
- 为此分类任务确定最佳的机器学习模型和预处理技术.
主要方法:
- 从甘种子Khon Kaen 3.收集了光谱数据 (400-1000 nm).
- 应用了各种光谱预处理技术来增强特征.
- 线性差异分析 (LDA),K-最近邻居 (KNN) 和人工神经网络 (ANN) 用于分类.
主要成果:
- 所有评估的机器学习模型都实现了高分类准确性.
- 人工神经网络 (ANN) 与衍生预处理相结合,产生了最佳的性能.
- 在校准和验证套件中,F1得分为0.93,在独立测试套件中达到0.92.
结论:
- 视SWNIR光谱和机器学习为快速准确的甘节点/内部节点分类提供了可行的解决方案.
- 这种方法支持甘小豆制备的自动化以及精准农业的进步.
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