使用VIS-NIR高光谱成像和机器学习,非破坏性检测Elasmopalpus lignosellus感染新鲜
André Rodríguez-León1, Jimy Oblitas1,2, Jhonsson Luis Quevedo-Olaya1
1Facultad de Ciencias Agrarias, Universidad Nacional de Cajamarca, Av. Atahualpa N° 1050, Cajamarca 06002, Peru.
Foods (Basel, Switzerland)
|January 28, 2026
概括
通过使用VIS-NIR高光谱成像和机器学习,可以提前检测较小的玉米茎 (Elasmopalpus lignosellus) 对的内部损伤. 这种非破坏性方法提高了农产品出口的质量控制.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 在中受到Elasmopalpus lignosellus的内部损害对农业出口行业构成了重大挑战.
- 传统的视觉检查方法缺乏早期检测这种害虫的灵敏度,影响质量控制.
研究的目的:
- 为了评估VIS-NIR高光谱成像与机器学习相结合,以区分受感染和健康的矛.
- 开发一种非破坏性方法,用于早期检测的内部损伤.
主要方法:
- 获取 900 个青样本的均衡数据集.
- 萨维茨基-戈莱和SNV用于光谱预处理的应用.
- 四个机器学习分类器的比较:SVM,MLP,弹性网和XGBoost.
- 使用LOBO和RFE技术减少频谱.
主要成果:
- 优化的支持矢量机 (SVM) 模型表现出卓越的性能,交叉验证精度为0.9889和AUC为0.9997.
- 在3000个样本的外部验证中,准确率为97.9%,AUC为0.9976.
- 将频谱减少到60个频段保持了模型的高性能.
结论:
- 与机器学习相结合的VIS-NIR高光谱成像是一种可行的非破坏性技术,用于的质量控制.
- 这种方法显著改善了检测由Elasmopalpus lignosellus引起的内部损伤.
- 这些发现支持实施这些系统,以提高的出口质量.
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