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浙江大学学报（农业与生命科学版）
浙江大学学报（农业与生命科学版）  2023, Vol. 49 Issue (4): 472-483    DOI: 10.3785/j.issn.1008-9209.2023.04.201
综述     
低空无人机遥感在油料作物表型分析中的应用
孙永祺1(),陈梦媛2,黄倩1,张康妮1,王兵3,刘飞2,4,周伟军1,4()
1.浙江大学农业与生物技术学院, 浙江 杭州 310058
2.浙江大学生物系统工程与食品科学学院, 浙江 杭州 310058
3.浙江省农业农村大数据发展中心, 浙江 杭州 310020
4.农业农村部光谱检测重点实验室, 浙江 杭州 310058
Application of low-altitude unmanned aerial vehicle remote sensing in the phenotypic analysis of oil crops
Yongqi SUN1(),Mengyuan CHEN2,Qian HUANG1,Kangni ZHANG1,Bing WANG3,Fei LIU2,4,Weijun ZHOU1,4()
1.College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, Zhejiang, China
2.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, Zhejiang, China
3.Zhejiang Big Data Development Center for Agriculture and Rural Affairs, Hangzhou 310020, Zhejiang, China
4.Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, Zhejiang, China
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摘要：

传统的油料作物田间表型数据采集方法费时费力，工作效率低。低空无人机遥感具有快速便捷、成本低、易操控等优势，提高了在中、小尺度区域遥感观测油料作物的形态学参数和生理生化指标的精细化程度，初步实现了油料作物田间生长信息的快速采集、处理与分析应用。本文综述了近年来国内外低空无人机遥感在油菜、大豆、花生、向日葵、油棕等油料作物表型分析上的研究进展，介绍了当前主流的无人机飞行平台、机载传感器以及作业流程，重点梳理了无人机遥感在油料作物形态学分析、生理生化指标检测、产量估测以及逆境胁迫监测等多方面的应用情况，指出了低空无人机遥感在油料作物监测领域存在的不足和未来的发展趋势，以期为智慧农业的后续发展和精准应用提供理论依据。
关键词： 无人机;  低空遥感;  油料作物;  表型分析;  长势监测    
Abstract:

The conventional approach to gathering field phenotypic data for oil crops is characterized by its time-consuming and labor-intensive nature, resulting in low work efficiency. Conversely, low-altitude unmanned aerial vehicle (UAV) remote sensing offers numerous advantages, including rapidity, convenience, low cost, and ease of manipulation. This technology enhances the precision of morphological parameters and physiological and biochemical indicators of oil crops measured by remote sensing in small- and medium-scale areas, thereby enabling the initial attainment of field growth information for oil crops. Furthermore, it facilitates the swift acquisition, processing, and analysis of such data. This study provided a comprehensive overview of the advancements made in domestic and foreign low-altitude UAV remote sensing for oil crops, including rape, soybean, peanut, sunflower, and oil palm, and it introduced the prevailing UAV flight platforms, airborne sensors, and operating procedures, and it focused on combing the application of UAV remote sensing in morphological analysis, detection of physiological and biochemical indicators, yield estimation, and monitoring of adversity stress in recent years. Furthermore, it identified the limitations and future prospects of low-altitude UAV remote sensing in the domain of oil crop monitoring, aiming to provide a theoretical basis for the subsequent development and accurate implementation of smart agriculture.
Key words: unmanned aerial vehicle    low-altitude remote sensing    oil crops    phenotypic analysis    growth monitoring
收稿日期: 2023-04-20 出版日期: 2023-08-29
CLC:  S252  
基金资助: 浙江省“尖兵”研发攻关计划项目(2023C02002-3);现代作物生产省部共建协同创新中心项目（CIC-MCP）;浙江省“三农九方”科技协作计划项目(2022SNJF010)
通讯作者: 周伟军     E-mail: yq_sun@zju.edu.cn;wjzhou@zju.edu.cn
作者简介: 孙永祺（https://orcid.org/0000-0003-3759-1740），E-mail：yq_sun@zju.edu.cn
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引用本文:

孙永祺,陈梦媛,黄倩,张康妮,王兵,刘飞,周伟军. 低空无人机遥感在油料作物表型分析中的应用[J]. 浙江大学学报（农业与生命科学版）, 2023, 49(4): 472-483.

Yongqi SUN,Mengyuan CHEN,Qian HUANG,Kangni ZHANG,Bing WANG,Fei LIU,Weijun ZHOU. Application of low-altitude unmanned aerial vehicle remote sensing in the phenotypic analysis of oil crops. Journal of Zhejiang University (Agriculture and Life Sciences), 2023, 49(4): 472-483.

链接本文:

https://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2023.04.201    或    https://www.zjujournals.com/agr/CN/Y2023/V49/I4/472

图1  无人机遥感机载传感器
图2  无人机遥感数据获取流程图
图3  无人机遥感数据处理流程图

研究对象

Object

传感器 Sensor

飞行高度

Flight altitude/m

研究内容

Research content

最优效果

Best performance

类型 Type型号 Model

油菜

Rape

RGB大疆经纬M600+Nikon D80020幼苗识别计数[13]R2=0.87
RGB大疆精灵4 RTK2杂交油菜父本识别[19]准确率为98%
RGB+多光谱Sony NEX-7；XIMEA MQ022MG-CM25植被覆盖率[20]R2=0.79

大豆

Soybean

RGB+多光谱大疆精灵4 Pro200种植区域分割[21]Kappa系数为0.94
高光谱Leica Aibot X6+Headwall Nano-Hyperspec250种植区域分割[22]准确率为99.13%

向日葵

Sunflower

多光谱大疆S900+MicaSense RedEdge-MX100种植区域识别分割[23]准确率为96.57%
RGB

大疆经纬M600+Sony ILCE-α7RⅡ；

Sony ILCE-α5100L；大疆FC6310

20～38植株识别[24]R2=0.89

花生

Peanut

RGB大疆Air 2S3.0～3.5出苗检测[18]准确率为91.2%

油棕

Oil palm

多光谱大疆精灵3+Parrot Sequoia10～50植株识别[25]准确率为98.3%
表1  无人机遥感在油料作物空间分布调查中的应用

研究对象

Object

传感器 Sensor

特征

Feature

最优模型

Best model

最优效果

Best performance

类型 Type型号 Model

大豆

Soybean

RGB+多光谱senseFly eBee X+Parrot Sequoia冠层纹理信息+植被指数[57]Cubist和随机森林R2=0.89
RGB+多光谱

大疆经纬M600+GoPro；MicaSense

RedEdge

图像特征+植被指数[20]CNNR2=0.78

油菜

Rape

多光谱大疆经纬M600+MicaSense Altum植被指数[54]线性回归R2=0.95
多光谱大疆经纬S1000+Mini-MCA植被指数+地面高光谱[58]线性回归R2=0.83

橄榄

Olive

RGB大疆经纬Spark冠层半径[59]线性回归偏差小于18%
表2  无人机遥感在油料作物产量估测中的应用
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