根据新型光谱指数和机器学习模型估计冬季小麦叶中的度
Shihao Cui1, Zhijun Li1, Zijun Tang1
1Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China.
Plants (Basel, Switzerland)
|September 13, 2025
概括
准确的冬季小麦气状况评估对于可持续农业至关重要. 新的多维光谱指数与随机森林模型相结合,显著改善了叶子度估计,从而实现了非破坏性监测.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 植物生理学 植物生理学
背景情况:
- 优化作物施肥对于可持续农业至关重要.
- 超光谱数据有助于监测作物生理变化,但状态估计具有挑战性.
研究的目的:
- 开发准确的方法来估计冬季小麦叶子度 (LNC) 使用高光谱数据.
- 评估传统,2D和3D光谱指数与机器学习模型相结合的有效性.
主要方法:
- 实地实验收集了冬季小麦的超光谱反射率和LNC数据.
- 导出和分析传统的,2D和3D光谱指数与LNC的相关性.
- 开发并比较使用极端学习机器 (ELM),反向传播神经网络 (BPNN) 和随机森林 (RF) 算法的LNC估计模型.
主要成果:
- 不同的三维散射指数 (DTSI) 与LNC的相关性最高 (r = 0.721).
- 结合经验和3D光谱指数,提高了估计准确度.
- 使用综合指数的随机森林模型实现了最佳的验证性能 (R2 = 0.827).
结论:
- 新的多维光谱指数提高了冬季小麦LNC估计的准确性.
- 这种方法提供了一种可行且准确的实时,非破坏性的状态监测方法.
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