根据从无人驾驶飞行器获得的多光谱图像来估计土豆植物中的
YanPeng Ma1,2, ZhiChao Chen1, YiGuang Fan2
1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.
Frontiers in plant science
|October 9, 2023
概括
准确估计土豆含量 (PKC) 对作物健康至关重要. 将植被指数与来自无人机多谱数据的空间特征相结合,可以显著提高PKC估计的准确性.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 农业学是一种农业学.
背景情况:
- 植物含量 (PKC) 是作物营养状况的关键指标,对于有效的施肥策略至关重要.
- 准确的PKC估计有助于优化农业管理并确保作物健康.
研究的目的:
- 提高在关键生长阶段估计土豆含量 (PKC) 的准确性.
- 评估植被指数 (VIs) 和空间结构特征在PKC评估中的有效性.
- 调查光谱和空间信息的联合实用性,以精确监测营养.
主要方法:
- 利用基于无人机的多谱传感器,获取关键土豆生长阶段的数据.
- 提取的植被指数 (VIs),植被覆盖率 (FVC) 分数和灰色水平共发生矩阵 (GLCM) 纹理特征.
- 采用线性回归和逐步多重线性回归分析来建模PKC.
主要成果:
- 单独使用多光谱VI可以提供可行但可改进的PKC估计.
- 与VI集成FVC或纹理特征,与VI单独相比,提高了PKC估计的准确性.
- 将VI与FVC和纹理特征相结合,获得了最高的精度 (R2 = 0.630.84).
结论:
- 基于无人机的多光谱数据,包括光谱和空间特征,提供了一种可靠的方法来估计土豆PKC.
- 整合VI,FVC和纹理信息显著提高了营养状况评估的精度.
- 这种方法在优化农业管理和作物施肥策略方面具有巨大的潜力.
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