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WO2017114387A1 - 一种基于多旋翼无人机平台的作物生长监测方法及装置 - Google Patents

一种基于多旋翼无人机平台的作物生长监测方法及装置 Download PDF

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Publication number
WO2017114387A1
WO2017114387A1 PCT/CN2016/112369 CN2016112369W WO2017114387A1 WO 2017114387 A1 WO2017114387 A1 WO 2017114387A1 CN 2016112369 W CN2016112369 W CN 2016112369W WO 2017114387 A1 WO2017114387 A1 WO 2017114387A1
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WIPO (PCT)
Prior art keywords
module
crop growth
rotor
button
sensor
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PCT/CN2016/112369
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English (en)
French (fr)
Inventor
倪军
姚立立
朱艳
曹卫星
刘芳
庞方荣
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南京农业大学
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Priority to US16/066,675 priority Critical patent/US10845301B2/en
Publication of WO2017114387A1 publication Critical patent/WO2017114387A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0098—Plants or trees
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00—Type of UAV
    • B64U10/10—Rotorcrafts
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/30—UAVs specially adapted for particular uses or applications for imaging, photography or videography
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/40—UAVs specially adapted for particular uses or applications for agriculture or forestry operations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
    • G01N2021/317—Special constructive features
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
    • G01N2021/3181—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths using LEDs
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/02—Mechanical
    • G01N2201/021—Special mounting in general
    • G01N2201/0214—Airborne

Definitions

  • the invention relates to a crop growth monitoring method and device based on a multi-rotor UAV platform, relating to the field of precision agriculture, in particular to crop growth monitoring and diagnosis, in particular to a large area of crop growth information based on a drone platform, high throughput, Continuous, fast, real-time monitoring and diagnosis.
  • Patent ZL201210214137.8 has invented a multi-spectral crop growth sensor based on the theory of crop growth spectrum monitoring, and discloses a portable growth monitoring diagnostic instrument that can obtain crop growth information in real time without loss.
  • the equipment needs to be used in a clear weather, no wind and no cloud, and the crop canopy is relatively static and stable.
  • the test height is 1-1.2 meters from the canopy. It is convenient and flexible to use and has high precision.
  • the single-point measurement technology has a small monitoring range. It has high labor intensity and high labor cost, and it is artificially destructive to crops in field operations.
  • the multi-rotor UAV has the characteristics of simple operation, high efficiency, flexible hovering and strong applicability of terrain. It plays an increasingly prominent role in crop seedling monitoring, artificial pollination and disease plant protection, and its application is more and more extensive.
  • the crop growth monitoring based on the multi-rotor UAV platform is to perform multi-spectrometer or hyperspectral gyro on the UAV pan/tilt for aerial measurement, and offline to obtain the corrected image by special remote sensing analysis software. Processing such as splicing to interpret crop growth information. The process is cumbersome and requires the operation of remote sensing professionals. It is mostly used in scientific research. Interpreted crop growth information has hysteresis and cannot be directly applied to agricultural production. The various types of imaging spectrometers are expensive to load and cannot be promoted in agricultural production. popular. When the UAV is equipped with multi-spectral sensor test, it is limited by the effective test height of the sensor.
  • the rotor When the rotor is hovering at low altitude, it produces a high-intensity, high-density air flow field, which directly acts on the crop canopy, causing the canopy to be in a “non-stationary” state.
  • the sensor In the stochastic dynamic change, the sensor can not effectively capture the canopy reflectance spectrum; on the other hand, the canopy of the canopy under the action of the drone under the washing field, the specular reflection of the blade is more significant, and the crop growth spectrum monitoring theory
  • the premise is that the crop canopy is Lambertian, so there is still a big problem in simply applying the crop growth spectrum sensor and growth monitoring model to the UAV platform.
  • the technical problem to be solved by the present invention is to provide a method and device for monitoring crop growth applied to a multi-rotor UAV platform for the deficiencies in the background art.
  • the device overcomes the influence of the dredging field on the measurement, and can transmit the measurement data to the ground receiver online analysis and processing in real time, realizing continuous, real-time, high-throughput and wide-area acquisition of crop growth information.
  • a crop growth monitoring method based on a multi-rotor UAV platform includes the following steps:
  • Step 1 Fix the multi-spectral crop growth sensor on the gimbal support
  • Step 2 Operate the flight control so that the drone can hover at the h height of the crop canopy, and the multi-spectral crop growth sensor collects the crop canopy reflectance spectrum in real time;
  • Step 3 Operate the ground receiver "on” button, the ground receiver is initialized, the communication LED module starts the wireless connection with the load, and the red LED flashes at 1KHz frequency. After the connection is successful, the red LED is lit;
  • Step 4 Operate the “Measure” button on the ground receiver, the blue LED flashes at 1KHz frequency, and the data enters the data processing module through the wireless receiving module.
  • the LCD screen displays the canopy NDVI value and RVI value in real time, and operates the “Measure” button again.
  • the screen locks the NDVI value and the RVI value;
  • Step 5 operating the ground receiver "monitoring" button, calling the crop growth monitoring model, and the liquid crystal screen displays leaf layer nitrogen content, leaf layer nitrogen accumulation amount, leaf area index and leaf dry weight index;
  • Step 6 Operate the ground receiver "diagnosis" button, call the crop growth diagnosis model, and the LCD screen shows the degree of nitrogen deficiency and the amount of regulation.
  • the multi-rotor UAV platform-based crop growth monitoring method further comprises the step 7: operating the ground receiver "reset" button, and the ground receiver returns to an initialization state.
  • the data enters the data processing module through the wireless receiving module.
  • the crop canopy reflection spectrum data enters the data processing module through the wireless receiving module
  • the sunlight incident spectral data enters the data processing module through the analog I/O interface.
  • the call crop growth monitoring model is configurable.
  • the multi-spectral crop growth sensor is fixed on the gimbal support, and the position thereof is determined as follows:
  • the rotor and fuselage entities are digitized by means of 3D scanning, and the spatial coordinate data of the rotor and fuselage surface are obtained. Then reverse-rotating the rotor and the fuselage respectively, and finally assembling the rotor and the fuselage according to the physical diagram;
  • the fluid motion control equations are established, and the initial conditions and boundary conditions are determined; the stationary and rotating regions are divided, the nodes are determined, and the region is discretized; the discrete regions are meshed.
  • the numerical calculation of the flow field generated when the UAV is hovering is performed to obtain the basic shape of the rotor induced velocity field and the velocity field and pressure field distribution of different height planes;
  • the length of the gimbal support is determined to be greater than the length of the canopy surface airflow velocity field, and the multi-spectral crop growth sensor is mounted on the bracket At one end, the other end of the bracket is mounted with the same weight of the sensor, and the multispectral sensor measures the canopy target outside the air velocity field.
  • the length of the pan/tilt support is determined to be greater than the sum of the lengths of the canopy surface airflow velocity field diameters.
  • a crop growth monitoring device based on a multi-rotor UAV platform comprising a multi-rotor UAV, a load and a ground receiver;
  • the load component includes a multi-spectral crop growth sensor module 106, a signal amplification module 105, a controller module 103, a wireless data transmission module 104, and a power module 101 and a power control module 102 for power supply;
  • the power module 101 is powered by the power control module 102;
  • the power control module 102 is connected to the multi-spectrum crop growth sensor module 106, the signal amplification module 105, the controller module 103, and the wireless data transmission module 104, respectively;
  • the cloud platform includes a pan/tilt bracket 108, a fixed buckle 109, and a sensor weight 107; the multi-spectral crop growth sensor module 106, the signal amplification module 105, the controller module 103, the wireless data transmission module 104, the power module 101, and the power supply
  • the control module 102 is integrally fixed to one end of the pan-tilt bracket 108;
  • the sensor counterweight 107 is fixed to the other end of the pan-tilt bracket 108; the pan
  • the ground receiver component includes a wireless data receiving module 214, a signal amplifying module 205, a data processing module 212, a communication LED module 211, a button control module 213, a liquid crystal display module 210, and a power module for power supply.
  • the power module 201 and power control module 202 further comprising a ground receiver housing; wherein the power module 201 is powered to the power control module 202; the power control module 202 is connected to the wireless data receiving module 214, the signal amplification module 205, and the data processing module 212, a communication LED module 211, a button control module 213, a liquid crystal display module 210;
  • the power module 201, the power control module 202, the wireless data receiving module 214, the signal amplifying module 205, the data processing module 212, the communication LED module 211, the button control module 213, and the liquid crystal display module 210 are encapsulated in a ground receiver housing.
  • a further optimization scheme of a crop growth monitoring device based on a multi-rotor UAV platform is the position of the multi-spectral crop growth sensor installed on the gimbal support is the horizontal distribution and multi-spectral growth of the lower wash flow field when working with the multi-rotor drone.
  • the range of field of view of the sensor is determined.
  • the communication LED module includes two types of blue LEDs and red LEDs.
  • the button module includes “on”, “off”, “measurement”, “monitoring”, “diagnosis”, and further
  • the ground can also include a "reset” control button.
  • the button module adopts a double button circuit and a button anti-shake circuit.
  • the button anti-shake circuit uses an RC integration circuit to achieve clutter filtering and waveform correction.
  • the frequency band of the wireless data transmitting module and the wireless data receiving module is 780 MHz.
  • the power control module includes a flip-flop, a buck circuit, a voltage stabilizing circuit, and a decoupling circuit; wherein the trigger is sequentially Connect the buck circuit, the voltage regulator circuit, and the decoupling circuit.
  • a crop growth monitoring device based on a multi-rotor UAV platform of the present invention reduces the effective cost of the UAV spectrum monitoring device.
  • a crop growth monitoring device based on a multi-rotor UAV platform of the present invention overcomes the influence of the dredging field of the drone on the measurement.
  • the crop growth monitoring device based on the multi-rotor UAV platform of the present invention can transmit measurement data to the ground receiver online analysis and processing in real time, realizing continuous, real-time, high-throughput and wide-ranging crop growth information. Obtain.
  • a crop growth monitoring device based on a multi-rotor UAV platform capable of simultaneously coupling multiple crop growth diagnostic models to invert leaf nitrogen content, leaf layer nitrogen accumulation, leaf area index and leaf dry weight Indicators and other agricultural students' long parameters.
  • Figure 1 is a schematic view of the structure of the load member
  • FIG. 2 is a schematic diagram of the structure of the ground receiver component
  • Figure 3a is a schematic view of the planar structure of the rotor and fuselage of the quadrotor.
  • Figure 3b is a schematic view of the three-rotor UAV rotor and the three-dimensional structure of the fuselage.
  • Figure 4a is a schematic diagram of grid division of a stationary area of a quadrotor UAV.
  • Figure 4b is a schematic diagram of the mesh division of the rotating area of the quadrotor UAV.
  • Figure 5 is a velocity diagram of the 1.3m axis section below the rotor of a four-rotor UAV
  • Figure 6 is a 1.3m cross-section x-y velocity cloud diagram below the rotor of a quadrotor UAV
  • Figure 7a is a top plan view of a four-rotor UAV pan/tilt bracket.
  • Figure 7b is a side view of the four-rotor UAV pan/tilt support.
  • a crop growth monitoring device based on a multi-rotor UAV platform
  • the load component includes a multi-spectral crop growth sensor module 106, a signal amplification module 105, a controller module 103, and a wireless data transmission module 104 connected in sequence. And a power module 101 and a power control module 102 for power supply; and a cloud platform.
  • the power module, 101 is powered to the power control module 102; the power control module 102 is connected to the multi-spectrum crop growth sensor module, the signal amplification module 105, the controller module 103, and the wireless data transmission module 104, respectively;
  • the integration is fixed to one end of the pan-tilt bracket 108; the sensor weight 107 is fixed to the other end of the pan-tilt bracket 108; the pan-tilt is fastened to the aircraft by the fixed buckle 109.
  • the ground receiver component includes a wireless data receiving module 214, a signal amplifying module 205, a data processing module 212, a communication LED module 211, and a button connected in sequence.
  • the power module 201 is connected to the power control module 202.
  • the power control module 202 is respectively connected to the wireless data receiving module 214, the signal amplifying module 205, the data processing module 212, the communication LED module 211, the button control module 213, and the liquid crystal.
  • the screen displays the module 210.
  • the power module 201, the power control module 202, The wireless data receiving module 214, the signal amplifying module 205, the data processing module 212, the communication LED module 211, the button control module 213, and the liquid crystal display module 210 are packaged in the ground receiver housing.
  • a crop growth monitoring method based on a multi-rotor UAV platform is selected by using a phantom quadrotor UAV of Dajiang Innovation Technology Co., Ltd. as an example, and the rotor and the fuselage entity are digitized by means of three-dimensional scanning.
  • Rotor and fuselage surface coordinate data find the positioning line and surface in reverse engineering, make the section and section line, and align with the x-axis, y-axis, z-axis, respectively, complete the rotor and fuselage materialized shape, and Assembly.
  • a crop growth monitoring method based on a multi-rotor UAV platform according to the critical dimensions of the phantom quadrotor UAV rotor: rotor radius 103.5mm, shaft length 390mm, rotor pitch 250mm; rotor rated working speed 960r/min
  • the drone operation distance is 1300mm from the canopy height, and the mass conservation equation, momentum conservation equation and energy conservation equation of the lower wash airflow are established.
  • the boundary conditions of the object adopt the thermal insulation wall and the non-penetration boundary.
  • the far-field boundary conditions adopt the pressure far-field boundary, and the calculation area is divided into a rotation area including 4 rotors and a static area including a fuselage and an air flow field.
  • the static area is 1200 mm in diameter.
  • the height is 1850mm; the rotating area is 275mm in diameter and 18mm in height, wherein the rotor is 1500mm from the bottom surface.
  • the fixed area and the rotating area are unstructured meshed by the body-fitted grid.
  • the number of grids in the static area is 875695, and the number of grids in the rotating area is 603564.
  • the rotor grid and the fuselage grid are connected through the interface.
  • a crop growth monitoring method based on a multi-rotor UAV platform according to the set parameters described above, numerically calculating a flow field generated when a phantom quadrotor drone is hovering, and using CFX's own post-processing module is used for visualization.
  • the airflow is ejected on the one hand by the high-speed rotating rotor, and on the other hand by the rotor, so that a high-speed flow zone is formed near the rotor, the speed value is large, and the axial direction is strong. Component. From the cross-sectional velocity cloud diagram, the flow velocity in the center of the flow field is fast, and the flow velocity is decreasing in turn.
  • the velocity field induced under the rotor is symmetrically distributed about the central axis, farther from the central axis, and the velocity gradient and velocity are smaller. Due to the influence of the rotation of the rotating shaft, the lower airflow tends to both sides, and as the height decreases, the velocity in the z direction gradually decreases, and the airflow area becomes larger and larger.
  • the phantom quadrotor UAV operates at a height of 1300 mm from the canopy, and selects the distance of the canopy measurement point according to the distribution range of the airflow velocity field on the surface of the canopy.
  • the length of the gimbal bracket is determined to be 1600mm.
  • the multi-spectral crop growth sensor is installed at one end of the bracket, and the other end of the bracket is installed with the same weight of the sensor.

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Abstract

一种基于多旋翼无人机平台的作物生长监测装置，包括多旋翼无人机、载荷和地面接收器；载荷部件包括依次相连接的多光谱作物生长传感器模块（106）、信号放大模块（105）、控制器模块（103）、无线数据发送模块（104），以及用于供电的电源模块（101）和电源控制模块（102）；还包括云台。该基于多旋翼无人机平台的作物生长监测装置克服了无人机下洗流场对测量的影响，能够将测量数据实时传输至地面接收器在线分析处理，实现了作物生长信息连续、实时、便捷、大范围地获取。

Description

一种基于多旋翼无人机平台的作物生长监测方法及装置 技术领域
本发明一种基于多旋翼无人机平台的作物生长监测方法及装置，涉及精准农业领域，具体涉及作物生长监测、诊断，尤其是基于无人机平台的作物生长信息大区域、高通量、连续、快速、实时监测与诊断。
背景技术
作物生长信息实时、无损、高通量获取是作物生产精确管理的首要条件。在传统作物生产管理过程中，对作物生长状况往往缺乏准确量化认识；或虽对作物生长指标进行定量分析，但依赖于破坏性取样与化学分析，时效性差导致生产中普遍过量施肥(特别是氮肥)或肥料施用不足(如部分微量元素)，易造成生产成本上升、环境污染和土地可持续生产能力下降。近年来，基于反射光谱识别物体特征的无损监测技术获得了迅猛发展，使得实时、快速、精确、无损获取植物生长状况及植株生化组分成为可能，从而为作物生长的无损监测与诊断提供了新的途径和方法。专利ZL201210214137.8依据作物生长光谱监测理论，发明了一种多光谱作物生长传感器，公开了一种可以实时无损获取作物生长信息的便携式生长监测诊断仪。该设备需要在天气晴朗、无风无云、作物冠层相对静止稳定的环境下使用，测试高度距离冠层1-1.2米，使用方便灵活，精度高；但是单点的测量技术监测范围小，劳动强度大，人工成本高，而且田间作业时人为对作物的破坏性大。多旋翼无人机具有操作简单、作业高效、悬停灵活以及地形适用性强等特点，在作物苗情监测、人工授粉、病害植保中的作用越来越突出，应用越来越广泛。
现有的技术中，基于多旋翼无人机平台的作物生长监测都是在无人机云台上悬挂多光谱仪或者高光谱仪进行空中测量，并离线通过专用遥感分析软件对获取的影像进行校正、拼接等处理，解译出作物生长信息。过程繁琐，需要遥感专业人员操作，多应用于科学研究中；解译的作物生长信息具有滞后性，无法直接应用于农业生产，而且搭载的各类成像光谱仪载荷价格昂贵，无法在农业生产中推广普及。无人机搭载多光谱传感器测试时，受限于传感器有效测试高度，旋翼低空悬停时产生高强度、高密度空气流场，直接作用于作物冠层，导致冠层处于一种“非静止”的随机动态变化中，传感器无法有效地捕捉冠层反射光谱；另一方面，冠层在无人机下洗流场的作用下，叶片的镜面反射更为显著，而作物生长光谱监测理论 的前提是假设作物冠层呈朗伯体特性，因此，将作物生长光谱传感器及生长监测模型简单地套用到无人机平台上还存在着很大的问题。
发明内容
本发明所要解决的技术问题是针对背景技术中存在的不足，提供了一种应用于多旋翼无人机平台的作物生长监测方法及装置。该装置克服了无人机下洗流场对测量的影响，能够将测量数据实时传输至地面接收器在线分析处理，实现了作物生长信息连续、实时、高通量、大范围地获取。
一种基于多旋翼无人机平台的作物生长监测方法，包括如下步骤：
步骤1、将多光谱作物生长传感器固定于云台支架上；
步骤2、操作飞控，使无人机悬停于作物冠层h高度处，多光谱作物生长传感器实时采集作物冠层反射光谱；
步骤3、操作地面接收器“开启”按键，地面接收器初始化，通信LED模块启动与载荷的无线连接，红光LED以1KHz频率闪烁，连接成功后，红光LED点亮；
步骤4、操作地面接收器“测量”按键，蓝光LED以1KHz频率闪烁，数据通过无线接收模块进入数据处理模块中，液晶屏实时显示冠层NDVI值、RVI值，再次操作“测量”按键，液晶屏锁定NDVI值和RVI值；
步骤5、操作地面接收器“监测”按键，调用作物生长监测模型，液晶屏显示叶层氮含量、叶层氮积累量、叶面积指数和叶干重指标；
步骤6、操作地面接收器“诊断”按键，调用作物生长诊断模型，液晶屏显示氮肥匮缺程度及调控量。
优选地，所述的基于多旋翼无人机平台的作物生长监测方法，还包括步骤7：操作地面接收器“复位”按键，地面接收器返回初始化状态。优选地，所述数据通过无线接收模块进入数据处理模块具体为，作物冠层反射光谱数据通过无线接收模块进入数据处理模块，太阳光入射光谱数据通过模拟I/O接口进入数据处理模块。
优选地，所述调用作物生长监测模型可进行耦合。
优选地，所述步骤1的将多光谱作物生长传感器固定于云台支架上，其位置按照如下方法确定：
1)无人机旋翼及机身曲面三维造型：
对于不同类型多旋翼无人机，借助三维扫描对旋翼及机身实体进行数字化，得出旋翼及机身曲面空间坐标数据。然后分别对旋翼及机身进行逆向造型，最后按照实体图组装旋翼和机身；
2)无人机实体网格划分及数据求解：
根据无人机工作状态及下洗气流流动状态，建立流体运动控制方程组，并确定初始条件与边界条件；划分静止与转动区域，确定结点，进行区域离散化；对离散区域进行网格的划分；
3)流场数值计算及分析：
对无人机悬停时产生的流场进行数值计算，获取旋翼诱导速度场的基本形态和不同高度面的速度场和压力场分布；
4)多光谱作物生长传感器固定位置确定：
测量无人机距离作物冠层悬停高度h，依据气流速度场在冠层表面的分布范围，云台支架长度确定为大于冠层表面气流速度场直径长度，将多光谱作物生长传感器安装于支架一端，支架另一端安装与传感器同重量配重，多光谱传感器测量冠层目标物在气流速度场以外。
进一步地，所述的云台支架长度确定为大于冠层表面气流速度场直径长度之和。
一种基于多旋翼无人机平台的作物生长监测装置，包括多旋翼无人机、载荷和地面接收器；
所述载荷部件，包括依次相连接的多光谱作物生长传感器模块106、信号放大模块105、控制器模块103、无线数据发送模块104，以及用于供电的电源模块101和电源控制模块102；还包括云台；其中：所述电源模块101供电给电源控制模块102；所述电源控制模块102分别连接多光谱作物生长传感器模块106、信号放大模块105、控制器模块103、无线数据发送模块104；所述云台包括云台支架108、固定卡扣109以及传感器配重107；所述多光谱作物生长传感器模块106、信号放大模块105、控制器模块103、无线数据发送模块104、电源模块101和电源控制模块102集成固定于云台支架108一端；所述传感器配重107固定于云台支架108另一端；所述云台通过固定卡扣109紧固于飞行器上；
所述地面接收器部件，包括依次相连接的无线数据接收模块214、信号放大模块205、数据处理模块212、通信LED模块211、按键控制模块213、液晶屏显示模块210以及用于供电的电源模块201和电源控制模块202；还包括地面接收器外壳；其中，所述电源模块201供电给电源控制模块202；所述电源控制模块202分别连接无线数据接收模块214、信号放大模块205、数据处理模块212、通信LED模块211、按键控制模块213、液晶屏显示模块210； 所述电源模块201、电源控制模块202、无线数据接收模块214、信号放大模块205、数据处理模块212、通信LED模块211、按键控制模块213、液晶屏显示模块210封装于地面接收器外壳中。
一种基于多旋翼无人机平台的作物生长监测装置的进一步优化方案，多光谱作物生长传感器安装于云台支架的位置是通过多旋翼无人机工作时下洗流场的水平分布与多光谱生长传感器视场角范围确定。
作为本发明的一种基于多旋翼无人机平台的作物生长监测装置的进一步优化方案，如权利要求5所述的一种基于多旋翼无人机平台的作物生长监测装置，其特征是所述通信LED模块包括蓝光LED和红光LED两种。
作为本发明的一种基于多旋翼无人机平台的作物生长监测装置的进一步优化方案，所述按键模块包括“开启”、“关断”、“测量”、“监测”、“诊断”，进一步地还可以包括“复位”控制按键。按键模块采用双按键电路以及按键防抖电路，为了达到良好的去抖动效果，所述按键防抖电路利用RC积分电路来达成杂波的滤除与波形的修正。
作为本发明的一种基于多旋翼无人机平台的作物生长监测装置的进一步优化方案，所述无线数据发送模块和无线数据接收模块的频段为780MHz。
作为本发明的一种基于多旋翼无人机平台的作物生长监测装置的进一步优化方案，所述电源控制模块包括触发器、降压电路、稳压电路、去耦电路；其中所述触发器依次连接降压电路、稳压电路、去耦电路。
本发明采用以上技术方案，与现有技术相比的有益效果是：
1、本发明的一种基于多旋翼无人机平台的作物生长监测装置，降低了无人机光谱监测设备的有效成本。
2、本发明的一种基于多旋翼无人机平台的作物生长监测装置，克服了无人机下洗流场对测量的影响。
3、本发明的一种基于多旋翼无人机平台的作物生长监测装置，能够将测量数据实时传输至地面接收器在线分析处理，实现了作物生长信息连续、实时、高通量、大范围地获取。
4、本发明的一种基于多旋翼无人机平台的作物生长监测装置，能够同时耦合多种作物生长诊断模型，反演叶层氮含量、叶层氮积累量、叶面积指数和叶干重指标等多种农学生长参数。
附图说明
图1是载荷部件结构示意图
图2是地面接收器部件结构示意图
图3a是四旋翼无人机旋翼及机身平面结构示意图。
图3b是四旋翼无人机旋翼及机身立体结构示意图。
图4a是四旋翼无人机静止区域网格划分示意图。
图4b是四旋翼无人机转动区域网格划分示意图。
图5是四旋翼无人机旋翼下方1.3m轴截面速度云图
图6是四旋翼无人机旋翼下方1.3m横截面x-y速度云图
图7a是四旋翼无人机云台支架俯视结构示意图。
图7b是四旋翼无人机云台支架侧视结构示意图。
具体实施方式
下面结合附图和实施例，对本发明的具体实施方式作进一步详细描述。
以下实施例用于说明本发明，但不用来限制本发明的范围。
参照图1，一种基于多旋翼无人机平台的作物生长监测装置，载荷部件包括依次相连接的多光谱作物生长传感器模块106、信号放大模块105、控制器模块103、无线数据发送模块104，以及用于供电的电源模块101和电源控制模块102；还包括云台。其中，所述电源模块,101供电给电源控制模块102；所述电源控制模块102分别连接多光谱作物生长传感器模块、信号放大模块105、控制器模块103、无线数据发送模块104；所述云台包括云台支架108、固定卡扣109以及传感器配重107；所述多光谱作物生长传感器模块106、信号放大模块105、控制器模块103、无线数据发送模块104、电源模块101和电源控制模块102集成固定于云台支架108一端；所述传感器配重107固定于云台支架108另一端；所述云台通过固定卡扣109紧固于飞行器上。
参照图2，一种基于多旋翼无人机平台的作物生长监测装置，地面接收器部件包括依次相连接的无线数据接收模块214、信号放大模块205、数据处理模块212、通信LED模块211、按键控制模块213、液晶屏显示模块210以及用于供电的电源模块201和电源控制模块202；还包括地面接收器外壳。其中，所述电源模块,201供电给电源控制模块202；所述电源控制模块202分别连接无线数据接收模块214、信号放大模块205、数据处理模块212、通信LED模块211、按键控制模块213、液晶屏显示模块210。所述电源模块201、电源控制模块202、 无线数据接收模块214、信号放大模块205、数据处理模块212、通信LED模块211、按键控制模块213、液晶屏显示模块210封装于地面接收器外壳中。
参照图3，一种基于多旋翼无人机平台的作物生长监测方法，选用大疆创新科技有限公司的phantom四旋翼无人机为例，借助三维扫描对旋翼及机身实体进行数字化，得出旋翼及机身曲面空间坐标数据，在逆向工程中找出定位线和面，做出截面和截面线，并与x轴、y轴、z轴对齐，分别完成旋翼和机身实体化造型，并组装。
参照图4，一种基于多旋翼无人机平台的作物生长监测方法，根据phantom四旋翼无人机旋翼关键尺寸：旋翼半径103.5mm，轴长390mm，旋翼间距250mm；旋翼额定工作转速960r/min以及无人机作业距离冠层高度1300mm，建立下洗气流运动质量守恒方程、动量守恒方程和能量守恒方程。物面边界条件采用绝热壁和无穿透边界，远场边界条件采用压力远场边界，将计算区域划分为包含4旋翼的旋转区域和包括机身和气流场的静止区域，静止区域直径1200mm，高度1850mm；旋转区域直径275mm，高度18mm，其中旋翼距离底面1500mm。采用贴体网格对静止区域和转动区域进行非结构化网格划分，其中静止区域的网格数为875695，旋转区域的网格数603564，旋翼网格与机身网格通过interface衔接。
参照图5，图6，一种基于多旋翼无人机平台的作物生长监测方法，按照上述设定好的参数，对phantom四旋翼无人机悬停时产生的流场进行了数值计算，并用CFX自带的后处理模块进行可视化。从轴截面速度云图中看，气流一方面被高速旋转的旋翼甩出，另一方面受到旋翼的挤压，因此在旋翼附近形成高速流动区，速度值较大，而且带有较强的轴向分量。从横截面速度云图中看，流场中心流速快，四周流速依次递减；旋翼下方诱导的速度场是关于中心轴对称分布，距离中心轴较远，速度梯度和速度数值越小。由于旋转轴旋向的影响，下方气流趋于两侧，而且随着高度的不断下降，z方向的速度逐渐减小，气流作用面积也越来越大。
参照图7，一种基于多旋翼无人机平台的作物生长监测方法，phantom四旋翼无人机作业距离冠层高度1300mm，依据气流速度场在冠层表面的分布范围，选择冠层测量点距离速度场中心800mm处，云台支架长度确定为1600mm，将多光谱作物生长传感器安装于支架一端，支架另一端安装与传感器同重量配重。
上面所述的实施例仅仅对本发明的优选实施方式进行描述，并非对本发明的构思和范围进行限定，在不脱离本发明设计构思前提下，本领域中普通工程技术人员对本发明的技术方案做出的各种变型和改进，均应落入本发明的保护范围，本发明请求保护的技术内容已经全部记载在权利要求书中。

Claims (12)

  1. 一种基于多旋翼无人机平台的作物生长监测方法，其特征在于，包括如下步骤：
    步骤1、将多光谱作物生长传感器固定于云台支架上；
    步骤2、操作飞控，使无人机悬停于作物冠层h高度处，多光谱作物生长传感器实时采集作物冠层反射光谱；
    步骤3、操作地面接收器“开启”按键，地面接收器初始化，通信LED模块启动与载荷的无线连接，红光LED以1KHz频率闪烁，连接成功后，红光LED点亮；
    步骤4、操作地面接收器“测量”按键，蓝光LED以1KHz频率闪烁，数据通过无线接收模块进入数据处理模块中，液晶屏实时显示冠层NDVI值、RVI值，再次操作“测量”按键，液晶屏锁定NDVI值和RVI值；
    步骤5、操作地面接收器“监测”按键，调用作物生长监测模型，液晶屏显示叶层氮含量、叶层氮积累量、叶面积指数和叶干重指标；
    步骤6、操作地面接收器“诊断”按键，调用作物生长诊断模型，液晶屏显示氮肥匮缺程度及调控量。
  2. 根据权利要求1所述的基于多旋翼无人机平台的作物生长监测方法，其特征在于，还包括步骤7：操作地面接收器“复位”按键，地面接收器返回初始化状态。
  3. 根据权利要求1至2所述的基于多旋翼无人机平台的作物生长监测方法，其特征在于，所述数据通过无线接收模块进入数据处理模块为，作物冠层反射光谱数据通过无线接收模块进入数据处理模块，太阳光入射光谱数据通过模拟I/O接口进入数据处理模块。
  4. 根据权利要求1至3所述的基于多旋翼无人机平台的作物生长监测方法，其特征在于，所述调用作物生长监测模型可进行耦合。
  5. 根据权利要求1至4所述的基于多旋翼无人机平台的作物生长监测方法，其特征是所述步骤1的将多光谱作物生长传感器固定于云台支架上，其位置按照如下方法确定：
    1)无人机旋翼及机身曲面三维造型：
    对于不同类型多旋翼无人机，借助三维扫描对旋翼及机身实体进行数字化，得出旋翼及机身曲面空间坐标数据。然后分别对旋翼及机身进行逆向造型，最后按照实体图组装旋翼和机身；
    2)无人机实体网格划分及数据求解：
    根据无人机工作状态及下洗气流流动状态，建立流体运动控制方程组，并确定初始条件与边界条件；划分静止与转动区域，确定结点，进行区域离散化；对离散区域进行网格的划分；
    3)流场数值计算及分析：
    对无人机悬停时产生的流场进行数值计算，获取旋翼诱导速度场的基本形态和不同高度面的速度场和压力场分布；
    4)多光谱作物生长传感器固定位置确定：
    测量无人机距离作物冠层悬停高度h，依据气流速度场在冠层表面的分布范围，云台支架长度确定为大于冠层表面气流速度场直径长度，将多光谱作物生长传感器安装于支架一端，支架另一端安装与传感器同重量配重，多光谱传感器测量冠层目标物在气流速度场以外。
  6. 根据权利要求5所述的基于多旋翼无人机平台的作物生长监测方法，其特征在于，所述云台支架长度确定为大于冠层表面气流速度场直径长度与多光谱作物生长传感器视场直径长度之和。
  7. 一种基于多旋翼无人机平台的作物生长监测装置，其特征是包括多旋翼无人机、载荷和地面接收器；
    所述载荷部件，包括依次相连接的多光谱作物生长传感器模块(106)、信号放大模块(105)、控制器模块(103)、无线数据发送模块(104)，以及用于供电的电源模块(101)和电源控制模块(102)；还包括云台；其中：所述电源模块(101)供电给电源控制模块(102)；所述电源控制模块(102)分别连接多光谱作物生长传感器模块(106)、信号放大模块(105)、控制器模块(103)、无线数据发送模块(104)；所述云台包括云台支架(108)、固定卡扣(109)以及传感器配重(107)；所述多光谱作物生长传感器模块(106)、信号放大模块(105)、控制器模块(103)、无线数据发送模块(104)、电源模块(101)和电源控制模块(102)集成固定于云台支架(108)一端；所述传感器配重(107)固定于云台支架(108)另一端；所述云台通过固定卡扣(109)紧固于飞行器上；
    所述地面接收器部件，包括依次相连接的无线数据接收模块(214)、信号放大模块(205)、数据处理模块(212)、通信LED模块(211)、按键控制模块(213)、液晶屏显示模块(210)以及用于供电的电源模块(201)和电源控制模块(202)；还包括地面接收器外壳；其中，所述电源模块(201)供电给电源控制模块(202)；所述电源控制模块(202)分别连接无线数据接收模块(214)、信号放大模块(205)、数据处理模块(212)、通信LED模块(211)、按键控制模块(213)、液晶屏显示模块(210)；所述电源模块(201)、电源控制模块(202)、无线数据接收模块(214)、信号放大模块(205)、数据处理模块(212)、通信LED模块(211)、按键控制模块(213)、液晶屏显示模块(210)封装于地面接收器外壳中。
  8. 如权利要求7所述的一种基于多旋翼无人机平台的作物生长监测装置，其特征是所述 多光谱作物生长传感器安装于云台支架的位置是通过多旋翼无人机工作时下洗流场的水平分布与多光谱生长传感器视场角范围确定。
  9. 如权利要求7所述的一种基于多旋翼无人机平台的作物生长监测装置，其特征是所述通信LED模块包括蓝光LED和红光LED两种。
  10. 如权利要求7所述的一种基于多旋翼无人机平台的作物生长监测装置，所述按键模块包括“开启”、“关断”、“测量”、“监测”、“诊断”和“复位”控制按键,按键模块采用双按键电路以及按键防抖电路，为了达到良好的去抖动效果，所述按键防抖电路利用RC积分电路来达成杂波的滤除与波形的修正。
  11. 如权利要求7所述的一种基于多旋翼无人机平台的作物生长监测装置，所述无线数据发送模块和无线数据接收模块的频段为780MHz。
  12. 如权利要求7所述的一种基于多旋翼无人机平台的作物生长监测装置，所述电源控制模块包括触发器、降压电路、稳压电路、去耦电路；其中所述触发器依次连接降压电路、稳压电路、去耦电路。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110991562A (zh) * 2019-12-23 2020-04-10 南京大学 一种基于物种构成特征的动物类群地理区划方法
CN118150498A (zh) * 2024-05-13 2024-06-07 江苏省南京环境监测中心 水质光谱图像自动采集装置
CN118261057A (zh) * 2024-04-15 2024-06-28 广东技术师范大学 基于深度学习的无人机下洗风场预测方法及装置

Families Citing this family (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105510242B (zh) 2015-12-28 2019-06-04 南京农业大学 一种基于多旋翼无人机平台的作物生长监测方法及装置
CN110418572A (zh) * 2017-03-12 2019-11-05 株式会社尼罗沃克 作物拍摄用无人机
CN107018382A (zh) * 2017-04-27 2017-08-04 广东容祺智能科技有限公司 一种无人机作物农情图像采集与分析系统
CN106970632B (zh) * 2017-04-28 2019-05-07 华南农业大学 一种基于冠层涡旋稳态运动的旋翼无人机精准作业方法
CN108037081B (zh) * 2017-10-20 2020-07-28 中国农业大学 一种酿酒葡萄成熟度监测方法和系统
US11797000B2 (en) * 2018-03-27 2023-10-24 Nileworks Inc. Unmanned aerial vehicle, control system thereof and control program
CN108693122A (zh) * 2018-04-09 2018-10-23 南京农业大学 基于固定翼无人机平台的作物生长监测装置及方法
CN109187417B (zh) * 2018-09-18 2022-05-17 深圳市奥谱太赫兹技术研究院 一种无人机载的太赫兹波及高光谱遥感农作物监测系统
US20200217830A1 (en) * 2019-01-08 2020-07-09 AgroScout Ltd. Autonomous crop monitoring system and method
CN110181513B (zh) * 2019-05-30 2024-10-25 北京农业信息技术研究中心 作物冠层表型与同化同步测量机器人
CN110334683A (zh) * 2019-07-12 2019-10-15 四川长虹电器股份有限公司 基于无人机遥感系统智能识别植物的方法
CN112329125B (zh) * 2019-07-31 2024-01-09 比亚迪股份有限公司 Cae模型之中添加配重的方法、系统和存储介质
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CN111175784A (zh) * 2019-12-31 2020-05-19 塔里木大学 一种棉花冠层水分含量的卫星遥感监测方法
CN111523425A (zh) * 2020-04-15 2020-08-11 河北冀云气象技术服务有限责任公司 用于农作物的气象自动化观测方法和系统
CN112014330B (zh) * 2020-07-30 2024-09-10 北京农业智能装备技术研究中心 作物近地光谱数据采集方法及装置
CN112129757A (zh) * 2020-10-15 2020-12-25 安阳工学院 一种植物病虫害自适应检测系统和方法
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CN113110574B (zh) * 2021-04-13 2022-04-12 中国科学院生态环境研究中心 一种野外地面生态环境监测数据捕获方法及系统
CN113175956A (zh) * 2021-05-13 2021-07-27 杭州电子科技大学 一种作物长势监测的多光谱及光学相机传感器装置
CN114049051B (zh) * 2021-12-27 2022-05-10 西安迈远科技有限公司 基于Mavlink协议的植保无人机地面监测终端
CN115520377A (zh) * 2022-10-12 2022-12-27 安徽科技学院 一种六旋翼多光谱无人机变量追肥装置及其使用方法

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1873574A2 (en) * 2006-06-30 2008-01-02 Northrop Grumman Corporation Multi-spectral sensor system and methods
CN102102988A (zh) * 2009-12-22 2011-06-22 中国农业科学院农业环境与可持续发展研究所 农作物产量信息实时测量方法、系统及装置
CN102768186A (zh) * 2012-06-27 2012-11-07 南京农业大学 一种田间作物生长信息无损快速检测装置及检测方法
CN203512034U (zh) * 2013-09-10 2014-04-02 潘海洋 农林植保多旋翼无人机
CN103885454A (zh) * 2014-03-07 2014-06-25 华南农业大学 农用飞行器跟随冠层特征参数飞行的作业方法及装置
CN104035412A (zh) * 2014-06-12 2014-09-10 江苏恒创软件有限公司 一种基于无人机的农作物病虫害监测系统和方法
CN204514798U (zh) * 2015-03-27 2015-07-29 中国农业科学院农业信息研究所 地空一体化农业监测系统
CN105025262A (zh) * 2015-07-08 2015-11-04 常州华奥航空科技有限公司 大面积农业作业无人机
CN105510242A (zh) * 2015-12-28 2016-04-20 南京农业大学 一种基于多旋翼无人机平台的作物生长监测方法及装置

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103175789B (zh) * 2013-03-05 2016-07-06 中国科学院南海海洋研究所 基于多旋翼无人机的水质高光谱航空遥感系统
CN103425102A (zh) * 2013-07-30 2013-12-04 上海丰凸通讯科技有限公司 基于低空遥感、多光谱精准识别的智能施肥系统
CN103488179A (zh) * 2013-09-18 2014-01-01 航天科工深圳（集团）有限公司 无人机飞行模拟系统及模拟方法
CN104050649A (zh) * 2014-06-13 2014-09-17 北京农业信息技术研究中心 农业遥感系统
CN204154389U (zh) * 2014-09-30 2015-02-11 青岛市光电工程技术研究院 一种低空微型无人机载多光谱成像系统
CN104494838B (zh) * 2014-12-11 2016-06-22 北京欧普特科技有限公司 高光谱分辨率航空影像遥感测量用的无人机系统及其方法
CN104536467B (zh) * 2014-12-26 2017-08-08 国家电网公司 一种超视距飞行器巡视系统
CN104506818A (zh) * 2015-01-05 2015-04-08 南京采薇且歌信息科技有限公司 一种应用于北斗旋翼无人机的数据综合采集控制发送系统
CN204536909U (zh) * 2015-03-10 2015-08-05 深圳市科比特航空科技有限公司 一种基于植保无人机超声波可控定高作业系统
CN104881042B (zh) * 2015-06-09 2016-08-24 北京航空航天大学 一种多尺度航空遥感测试平台
CN204790354U (zh) * 2015-06-30 2015-11-18 江苏首控制造技术有限公司 分布式液晶屏多功能无人机手持遥控器
CN105182282B (zh) * 2015-10-23 2018-04-13 成都九华圆通科技发展有限公司 一种基于飞行器载升空干涉仪的监测测向系统

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1873574A2 (en) * 2006-06-30 2008-01-02 Northrop Grumman Corporation Multi-spectral sensor system and methods
CN102102988A (zh) * 2009-12-22 2011-06-22 中国农业科学院农业环境与可持续发展研究所 农作物产量信息实时测量方法、系统及装置
CN102768186A (zh) * 2012-06-27 2012-11-07 南京农业大学 一种田间作物生长信息无损快速检测装置及检测方法
CN203512034U (zh) * 2013-09-10 2014-04-02 潘海洋 农林植保多旋翼无人机
CN103885454A (zh) * 2014-03-07 2014-06-25 华南农业大学 农用飞行器跟随冠层特征参数飞行的作业方法及装置
CN104035412A (zh) * 2014-06-12 2014-09-10 江苏恒创软件有限公司 一种基于无人机的农作物病虫害监测系统和方法
CN204514798U (zh) * 2015-03-27 2015-07-29 中国农业科学院农业信息研究所 地空一体化农业监测系统
CN105025262A (zh) * 2015-07-08 2015-11-04 常州华奥航空科技有限公司 大面积农业作业无人机
CN105510242A (zh) * 2015-12-28 2016-04-20 南京农业大学 一种基于多旋翼无人机平台的作物生长监测方法及装置

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110991562A (zh) * 2019-12-23 2020-04-10 南京大学 一种基于物种构成特征的动物类群地理区划方法
CN110991562B (zh) * 2019-12-23 2023-11-24 南京大学 一种基于物种构成特征的动物类群地理区划方法
CN118261057A (zh) * 2024-04-15 2024-06-28 广东技术师范大学 基于深度学习的无人机下洗风场预测方法及装置
CN118150498A (zh) * 2024-05-13 2024-06-07 江苏省南京环境监测中心 水质光谱图像自动采集装置

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