减少在无人机图像中的土壤和叶子阴影干扰,用于棉花气监测
Caixia Yin1, Zhenyang Wang1, Xin Lv2
1College of Agriculture, Xinjiang Agricultural University, Urumqi, China.
Frontiers in plant science
|September 2, 2024
概括
这项研究提出了一种无人机图像分类方法,通过尽量减少土壤和叶子影子干扰来精确监测棉花的含量. 开发的技术改进了植被指数分析,以可靠地监测棉花.
科学领域:
- 农业遥感 农业遥感
- 植物生理学 植物生理学
- 图像处理 图像处理
背景情况:
- 土壤和叶子的阴影掩盖了棉花的光谱,阻碍了精确的含量监测.
- 无人机 (UAV) 图像对精准农业至关重要,但容易受到光谱干扰.
研究的目的:
- 开发一种无人机图像分类方法,以消除土壤和叶子影子干扰.
- 通过使用光谱数据,可靠地监测棉花中含量 (LNC).
主要方法:
- 使用绿光 (550nm) 谱数据和植被指数 (VI) 分析.
- 应用了高斯过器 (GF),SG光滑 (SG) 和组合的GF&SG预处理.
- 实现了序列图像像素分类,以尽量减少阴影和土壤效应.
- 采用机器学习方法,包括支持向量机器回归 (SVMR),来预测LNC.
主要成果:
- 光谱预处理显著改善了VI和LNC之间的关系,减少了数据标准偏差.
- 顺序图像分类有效地减少了土壤和叶子阴影对VI的干扰.
- 优化的VI (2-3, 2-4, 2-5) 与LNC的相关性更强 (r=0.5).
- 在LNC预测方面,SVMR模型实现了高性能 (R2=0.86,RMSE=1.01,MAE=0.71).
结论:
- 拟议的无人机图像分类技术有效地减轻了土壤和叶子阴影对棉花光谱的影响.
- 这种方法有助于有效及时预测棉花叶的含量.
- 该研究提高了农业监测遥感的可靠性.
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