Smart Agriculture ›› 2021, Vol. 3 ›› Issue (1): 1-15.doi: 10.12133/j.smartag.2021.3.1.202102-SA033
• Topic--Frontier Technology and Application of Agricultural Phenotype • Next Articles
ZHANG Jian1(), XIE Tianjin1, YANG Wanneng2, ZHOU Guangsheng3
Received:
2021-02-11
Revised:
2021-03-10
Online:
2021-03-30
Published:
2021-06-01
CLC Number:
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.
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URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.2021.3.1.202102-SA033
Table 1
Application of near-field remote sensing method to obtain plant height of field crops
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 |
Table 2
Evaluation summary of accuracy and cost for crop height estimation
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 | ★ | ★☆ | ★★★ | ★★★★ | ★★★☆ |
Table 3
Studies on the height measurement of UAVs equipped with LiDAR system
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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