反射光谱用于农业作物的颜料的分类和预测
Renan Falcioni1, Werner Camargos Antunes1, José Alexandre M Demattê2
1Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, PR, Brazil.
Plants (Basel, Switzerland)
|June 28, 2023
概括
超光谱反射与人工智能相结合,可以准确识别作物颜料和特征. 这种非破坏性方法为农业作物监测和分类提供了准确,快速的评估.
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:
- 准确的作物表型是农业生产率和管理的关键.
- 颜色分析的传统方法往往具有破坏性和耗时性.
- 集成先进的分析技术可以克服这些局限性.
研究的目的:
- 通过使用超光谱数据,开发一种可靠和精确的方法,用于在六种主要农业作物中同时进行色素评估.
- 评估机器学习和人工智能算法的准确性,用于色素分类和预测.
- 为了确定最佳的波长和植被指数用于非破坏性作物分析.
主要方法:
- 在紫外线可见 (UV-VIS),近红外线 (NIR) 和短波红外线 (SWIR) 波段中利用反射光谱学.
- 应用机器学习算法,包括主要组件分析 (PCA) 和部分最小平方回归 (PLSR).
- 集成的超谱数据与15个植被指数,以提高准确性.
主要成果:
- 通过使用PCA-linked集群和kappa系数分析,实现了高分类准确度 (92-100%) 和精度.
- PLSR模型显示了强大的预测能力 (R2:0.77-0.89,RPD>2.1) 对于叶绿素,胡卜素,素和黄素.
- 与植被指数的整合提高了精度至60-100%,并优化了波长选择.
结论:
- 超光谱反射光谱是一种快速,精确和准确的非破坏性工具,用于在农业作物中同时评估色素.
- 这种方法为各种农业系统中的作物监测,分类和表型化提供了一个有希望的替代方案.
- 该研究验证了人工智能和机器学习在分析农业应用的复杂光谱数据方面的有效性.
关键词:
C3和C4类植物.PLSR模型的模型PLSR模型数字技术数字技术的使用.高通量表型化 (high-throughput phenotyping) 是一种高通量表型化方法.多变量工具是多变量工具.颜色定量定量定量定量定量的颜色.远程传感是一种遥感技术.更多相关视频
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