在大米中的NPK压力的表征和识别使用陆地超谱图像
Jinfeng Wang1, Yuhang Chu1, Guoqing Chen1
1College of Engineering, Northeast Agricultural University, Harbin 150000, China.
Plant phenomics (Washington, D.C.)
|July 25, 2024
概括
准确识别米的营养压力对农业至关重要. 一个新的深度学习模型,SHCFTT,通过使用高光谱图像在检测,和缺乏方面达到93.92-100%的准确性.
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:
- 营养压力显著限制了全球农业生产率.
- 及时进行植物健康评估对于可持续农业至关重要.
- 超光谱成像为植被监测和模式识别提供了先进的功能.
研究的目的:
- 建立14个NPK米中的营养应激条件的超谱图书馆.
- 开发和评估一个深度学习模型,以准确识别米营养压力模式.
- 将拟议模型的性能与传统机器学习和深度学习方法进行比较.
主要方法:
- 在14个NPK压力条件下使用SPECIM-IQ摄像头收集了420张米的高光谱图像.
- 分析了光谱反射率曲线,树冠光谱配置文件,植被指数和主要成分分析.
- 开发了一个基于变压器的深度学习网络 (SHCFTT),并将其与支持矢量机器,1D-CNN和3D-CNN进行了比较.
主要成果:
- 在各种营养应激下,在水植物中观察到明显的光谱特征和差异.
- SHCFTT模型在识别营养压力模式方面表现出卓越的表现.
- 在不同的建模策略和年份中,实现了高总精度,从93.92%到100%不等.
结论:
- 超光谱成像与深度学习相结合,有效地识别了米的营养压力.
- SHCFTT模型显著提高了在大米中检测营养应激的准确性.
- 这种方法有望增强精准农业和减轻由于营养缺乏而导致的作物产量损失.
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