Smart Agriculture ›› 2021, Vol. 3 ›› Issue (1): 1-15.doi: 10.12133/j.smartag.2021.3.1.202102-SA033
• 专题--作物表型前沿技术与应用 • 下一篇
收稿日期:
2021-02-11
修回日期:
2021-03-10
出版日期:
2021-03-30
发布日期:
2021-06-01
基金资助:
作者简介:
张 建(1981-),男,博士,副教授,研究方向为低空无人机农业遥感。电话:027-87282137。E-mail:
ZHANG Jian1(), XIE Tianjin1, YANG Wanneng2, ZHOU Guangsheng3
Received:
2021-02-11
Revised:
2021-03-10
Online:
2021-03-30
Published:
2021-06-01
摘要:
株高是动态衡量作物健康和整体生长状况的关键指标,广泛用于估测作物的生物学产量和最终籽粒产量。传统的人工测量方式存在规模小、效率低以及耗时长等问题。近十年来,近地遥感技术在农业领域发展迅速,使得高精度、高频次、高效率的作物株高采集成为可能。本文首先回顾了国内外基于遥感手段获取株高研究的论文发表情况;其次对获取株高的不同平台以及传感器的基本原理、优势及其局限性进行了介绍和评述,重点论述了激光雷达和可见光相机两种传感器的测高流程与涉及的关键技术;在此基础上归纳了株高在作物生物量估算、倒伏监测、产量预测和辅助育种等方面的应用研究进展;最后对近地遥感技术在株高获取上存在的问题进行讨论分析,并从测高平台和传感器、裸土探测和插值算法、株高应用研究及农学与遥感测高差异四个方向进行了展望,可为今后近地遥感测高的研究与方法应用提供参考。
中图分类号:
张建, 谢田晋, 杨万能, 周广生. 近地遥感技术在大田作物株高测量中的研究现状与展望[J]. 智慧农业(中英文), 2021, 3(1): 1-15.
ZHANG Jian, XIE Tianjin, YANG Wanneng, ZHOU Guangsheng. Research Status and Prospect on Height Estimation of Field Crop Using Near-Field Remote Sensing Technology[J]. Smart Agriculture, 2021, 3(1): 1-15.
表1
基于近场遥感方式获取大田作物株高的应用研究
应用 | 传感器 | 平台/测量高度 | 作物 | 模型 | RMSE | R2 |
---|---|---|---|---|---|---|
生物量估算 | 激光雷达[ | 地面固定平台 | 小麦 | 幂函数回归模型 | 1.76 t/ha | 0.82 |
超声波[ | 地面固定平台 | 生菜 | 指数回归模型 | —— | 0.80 | |
可见光相机[ | 无人机/50 m | 小麦 | 偏最小二乘回归模型 | 0.96 t/ha | 0.74 | |
可见光相机[ | 无人机/25 m | 水稻 | 随机森林 | 2.10 t/ha | 0.90 | |
可见光相机[ | 无人机/44 m | 洋葱 | 作物体积模型 | 1.53 t/ha | 0.95 | |
倒伏监测 | 激光雷达[ | 无人机/15 m | 玉米 | 通过株高变化定量测定倒伏程度,株高测量精度R2=0.964,RMSE=0.127 m | ||
可见光相机[ | 无人机/20~50 m | 玉米 | 通过设定阈值量化作物倒伏率,与地面实测值相比R2=0.50,RMSE=0.09 | |||
可见光相机[ | 无人机/35 m | 大麦 | 通过设定阈值量化作物倒伏率,与地面实测值相比,其最佳精度R2=0.96,RMSE=0.08 | |||
产量预测 | 可见光相机[ | 无人机/50 m | 玉米 | 多元回归模型 | 0.13 t/ha | 0.74 |
可见光相机[ | 无人机/50 m | 甘蔗 | 作物模型 | 1.09 t/ha | 0.44 | |
可见光相机[ | 无人机/50 m | 棉花 | 多元回归模型 | 0.16 t/ha | 0.94 | |
可见光相机[ | 无人机/30 m | 大豆 | 偏最小二乘回归模型 | 0.42 t/ha | 0.81 | |
高光谱相机[ | 无人机/50 m | 小麦 | 偏最小二乘回归模型 | 0.65 t/ha | 0.77 | |
辅助育种 | 可见光相机[ | 无人机/30 m | 小麦 | 对株高性状进行全基因组和QTL标记,其预测的基因组值与实际值相关性在0.47~0.53之间 | ||
可见光相机[ | 无人机/40~60 m | 玉米 | 通过对7个与株高相关的性状进行全基因组关联研究,共鉴定出68个QTL,其中35%的QTL与已被报道的控制株高性状的QTL重合 |
表2
基于精度和成本角度总结不同数据完备性下的作物株高估算
类别 | 条件 | 精度 | 成本 | ||||||
---|---|---|---|---|---|---|---|---|---|
DTM | GCP | 地面数据 | 冠层密度 | R2 | RMSE | 人工成本 | 时间成本 | 操作成本 | |
1 | √ | √ | 稀疏/密集 | ★★★★★ | ★★★★ | ★★☆ | ★☆ | ★★ | |
√ | 稀疏/密集 | ★★★★★ | ★★★★★ | ☆ | ☆ | ☆ | |||
2 | √ | 稀疏 | ★★★★★ | ★★★★ | ★★★☆ | ★★★★ | ★★★ | ||
密集 | ★☆ | / | ★★★☆ | ★★★★ | ★★★ | ||||
√ | 稀疏 | ★★★★★ | ★★★★★ | ★☆ | ★★★ | ★☆ | |||
√ | 密集 | ★☆ | ★★ | ★☆ | ★★★ | ★☆ | |||
3 | √ | 稀疏 | ★★ | / | ★★★★ | ★★☆ | ★★★★ | ||
密集 | ★★ | / | ★ | ★★ | ★★ | ||||
√ | 稀疏 | ★★ | ★★★ | ★★★★ | ★★☆ | ★★★★ | |||
√ | 密集 | ★★ | ★★★ | ★ | ★★ | ★★ | |||
4 | 稀疏 | ★ | / | ★★★★★ | ★★★★★ | ★★★★★ | |||
密集 | ★ | / | ★★★★★ | ★★★★★ | ★★★★★ | ||||
√ | 稀疏 | ★ | ★☆ | ★★★ | ★★★★ | ★★★☆ | |||
√ | 密集 | ★ | ★☆ | ★★★ | ★★★★ | ★★★☆ |
表3
无人机搭载激光雷达系统的测高研究
传感器 | 观测 对象 | FOV /(°) | 测距精度/cm | 重量/kg | 飞行速度/(m·s-1) | 测量高度/m | 点云 密度/(pts·m-2) | 精度 |
---|---|---|---|---|---|---|---|---|
Livox MID 40 | 林木[ | 38.40 | 2.00 | 0.76 | 4.00 | 100.00 | 464.5 | R2=0.96,RMSE=0.59 m |
RIEGL VUX-1UAV | 玉米[ | 330 | 0.50 | 3.50 | 3.00 | 15.00 | 112.0~570.0 | R2=0.96,RMSE=0.13 m |
小麦[ | 5.85 | 41.84 | 997.0 | R2=0.78,RMSE=0.03 m | ||||
马铃薯[ | 5.85 | 41.84 | 833.0 | R2=0.50,RMSE=0.12 m | ||||
甜菜[ | 5.85 | 41.84 | 933.0 | R2=0.70,RMSE=0.07 m | ||||
玉米[ | — | 150.00 | 420.0 | R2=0.65,RMSE=0.24 m | ||||
大豆[ | — | 150.00 | 420.0 | R2=0.40,RMSE=0.09 m | ||||
Velodyne VLP-16 | 棉花[ | 360 | 3.00 | 0.83 | 2.00 | 9.00 | 1682.0 | RE=12.73%,RMSE=0.03 m |
大豆[ | 0.50 | 9.00 | 1600.0 | RE=5.14% |
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