CN104535575A - Crop maturity identification platform based on unmanned aerial vehicle detection - Google Patents
Crop maturity identification platform based on unmanned aerial vehicle detection Download PDFInfo
- Publication number
- CN104535575A CN104535575A CN201510037940.2A CN201510037940A CN104535575A CN 104535575 A CN104535575 A CN 104535575A CN 201510037940 A CN201510037940 A CN 201510037940A CN 104535575 A CN104535575 A CN 104535575A
- Authority
- CN
- China
- Prior art keywords
- crops
- degree
- crop
- ripeness
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The invention relates to a crop maturity identification platform based on unmanned aerial vehicle detection. The crop maturity identification platform comprises a mobile hard disk, an image acquisition device, an image processing device and a digital signal processor; the digital signal processor is connected with the image acquisition device and the image processing device; after the unmanned aerial vehicle flies to a crop maturity acquisition region, the image acquisition device and the image processing device are turned on; the image acquisition device is used for shooting the acquisition region to obtain crop region images; the image processing device is used for performing image processing on the crop region images; the digital signal processor determines the maturity of crops in the acquisition region according to the image processing results; the mobile hard disk is connected with the digital signal processor to store the maturity of the crops in the acquisition region. With the crop maturity identification platform based on unmanned aerial vehicle detection, the current maturity of various crops within a large-area region is obtained flexibly and efficiently and important reference data is provided for local agricultural and forestry management departments to draw up plans.
Description
Technical field
The present invention relates to monitoring unmanned field, particularly relate to a kind of crop maturity degree Identification platform detected based on unmanned plane.
Background technology
Traditional proportion of crop planting mode is the distributing cropping pattern in units of peasant household, between the farmland of next-door neighbour, owing to adhering to different peasant households separately, the Planting Crops type of peasant household is different and planting habit is different, cause the crop maturity degree of plantation uneven, such cropping pattern is also difficult to use maximization machine tool to carry out unifying crops harvesting or plucking at one time, peasant household can only according to personal experience, judge the maturity of crops in oneself farmland, and when judging ripe, in units of family, organize staff to gather in separately.
Along with proportion of crop planting mode is by the development of distributing cropping pattern to intensive cropping pattern, the farmland of many next-door neighbours may belong to same agricultural management department, the crop type of plantation is identical, the time of plantation is also identical, cause the upgrowth situation of the crops in these farmlands to keep synchronous always, this makes described agricultural management department when determining crop maturity, employs maximization machine tool and carries out large-area unified crops harvesting or pluck becoming possibility.
But, in today of the intensive establishing in large scale of same crops, the judgement of degree of ripeness still relies on backward manual type, this mode is applied to Large Area of Crops degree of ripeness and judges to there will be following problem: (1) human eye touches and is limited in scope, may current crops be ripe, but the untouched most crops prematurity of human eye, at this moment gathers in, tremendous economic is caused to lose can to agricultural management department; (2) mode of artificial judgment too relies on personal experience, there is certain error between its judged result and standard results.
Thus, in order to solve the problem, need a kind of new crop maturity degree identifying schemes, artificial cognition mode can be substituted in the mode of the instrument identification of science, realize judging large area even the crop maturity degree of overlarge area, the situation avoiding degree of ripeness to judge by accident occurs.
Summary of the invention
In order to solve the problem, the invention provides a kind of crop maturity degree Identification platform detected based on unmanned plane, use the feature that unmanned plane is flexible, motor-driven, the electron device of degree of ripeness identification is mounted on unmanned plane, under the manipulation of far-end parametric controller, control unmanned plane and fly to regional, and in units of region, identify the degree of ripeness in each region one by one, thus large-area crop maturity degree situation can be obtained fast, for crops operating division harvesting or pluck the selection of period more valuable reference data be provided in time.
According to an aspect of the present invention, provide a kind of crop maturity degree Identification platform detected based on unmanned plane, described Identification platform comprises portable hard drive, image capture device, image processing equipment and digital signal processor, described digital signal processor is connected respectively with described image capture device and described image processing equipment, fly to the pickup area of crop maturity degree at described unmanned plane after, start described image capture device and described image processing equipment, described image capture device is used for taking described pickup area, to obtain crop area image, described image processing equipment is used for performing image procossing to described crop area image, described digital signal processor determines the degree of ripeness of crops in described pickup area according to processing result image, described portable hard drive connects described digital signal processor to store the degree of ripeness of crops in described pickup area.
More specifically, in the described crop maturity degree Identification platform based on unmanned plane detection, described Identification platform also comprises the Big Dipper positioning equipment, connects the Big Dipper Navsat, for the Big Dipper navigation data of real-time reception unmanned plane position, the precision of described the Big Dipper navigation data is 2 meters, radio altitude sensor, comprise radio transmitter, radio receiver and microcontroller, described microcontroller is connected respectively with described radio transmitter and described radio receiver, described radio transmitter launches radiowave earthward, described radio receiver receives the radiowave of ground return, described microcontroller calculates the present level of unmanned plane according to the launch time of described radio transmitter, the time of reception of described radio receiver and velocity of radio wave, and described velocity of radio wave is the light velocity, USB drives interface, for inserting outside USB flash disk, to receive the crops image template of each type stored in described outside USB flash disk, the crops image template of each type described is the benchmark binary image of the crops of each type, also for receiving the crops upper limit gray threshold and crops lower limit gray threshold that store in described outside USB flash disk, described crops upper limit gray threshold and described crops lower limit gray threshold are used for the crops in image and background separation, also for receiving the crop maturity degrees of data table stored in described outside USB flash disk, be index with crop type in described crop maturity degrees of data table, save the degree of ripeness threshold value that each crops is corresponding, judge amount threshold and each degree of ripeness grade, wireless transmitting-receiving equipments, for receiving the control signal that ground agricultural department monitor supervision platform sends, described control signal comprises the locator data of the pickup area that described agricultural department monitor supervision platform is specified, described locator data comprises corresponding collection the Big Dipper position, gathers height and regional location, described agricultural department monitor supervision platform sends multiple control signal in order one by one, and appointment pickup area corresponding in each control signal in described multiple control signal is different, unmanned plane power drive device, for the flight of described unmanned plane provides power, described portable hard drive and described USB drive interface to be connected, to receive and to store the crops image template of each type, crops upper limit gray threshold, crops lower limit gray threshold and crop maturity degrees of data table, described image capture device comprises aerial camera and secondary light source, described aerial camera is used for taking described pickup area, to obtain crop area image, the shooting that described secondary light source is described aerial camera provides illumination fill-in light, and the intensity of described illumination fill-in light and the light luminance of described secondary light source surrounding environment are inversely proportional to, described image processing equipment is connected respectively with described portable hard drive and described image capture device, comprise wavelet filtering device, gray processing processing apparatus, binary conversion treatment device, crop type recognition means and degree of ripeness grade recognition means, described wavelet filtering device is connected with described aerial camera, based on Haar wavelet transform wave filter, filtering process is performed to described crop area image, to obtain crops filtering image, described gray processing processing apparatus is connected respectively with described wavelet filtering device and described portable hard drive, the pixel identification of gray-scale value in described crops filtering image between described crops upper limit gray threshold and described crops lower limit gray threshold is formed multiple crops subimage, the corresponding strain crops of each crops subimage, described binary conversion treatment device is connected with described gray processing processing apparatus, based on default binary-state threshold by described multiple crops subimage binaryzation respectively, to generate multiple crops binaryzation subimage, described default binary-state threshold is the gray-scale value of a numerical value between 0-255, described crop type recognizer is connected respectively with described binary conversion treatment device and described portable hard drive, each crops binaryzation subimage is mated one by one with the crops image template of each type, to obtain crop type corresponding to each crops binaryzation subimage, for each different crops type that described multiple crops binaryzation subimage is corresponding, get the crop type that number percent that occurrence number occupies described multiple crops binaryzation subimage quantity is greater than preset percentage threshold value to export as pickup area crop type, described degree of ripeness grade recognition means and described gray processing processing apparatus, described crop type recognition means is connected respectively with described portable hard drive, the degree of ripeness threshold value corresponding with described pickup area crop type is found in described crop maturity degrees of data table, judge that amount threshold and each degree of ripeness grade are using as target degree of ripeness threshold value, target discrimination amount threshold and each target degree of ripeness grade, for each crops subimage, statistical pixel gray-scale value is less than or equal to the pixel quantity of described target degree of ripeness threshold value, when the pixel quantity added up is more than or equal to described target discrimination amount threshold, determine that the crops that described crops subimage is corresponding are maturation, when described pixel quantity is less than described target discrimination amount threshold, determine that the crops that described crops subimage is corresponding are prematurity, ripe determination result based on the crops of multiple crops subimage is added up and is exported the degree of ripeness of crops in described pickup area, described digital signal processor and described crop type recognition means, described degree of ripeness grade recognition means, described wireless transmitting-receiving equipments, described unmanned plane power drive device, described the Big Dipper positioning equipment is connected respectively with described radio altitude sensor, resolve to obtain described collection the Big Dipper position to described control signal, gather height and regional location, and control described unmanned plane power drive device and to fly to the pickup area corresponding with described control signal to drive described unmanned plane, described the Big Dipper navigation data with described collection the Big Dipper position consistency and described present level and described collection highly consistent time, start described image capture device and described image processing equipment, by regional location corresponding for current appointment pickup area, crop type and crop maturity degree are stored into described portable hard drive, send degree of ripeness information acquisition end signal subsequently, described wireless transmitting-receiving equipments, after receiving described degree of ripeness information acquisition end signal, forwards described degree of ripeness information acquisition end signal to described agricultural department monitor supervision platform, so that described agricultural department monitor supervision platform sends next control signal in order, wherein, each specifies pickup area corresponding regional location, crop type and crop maturity degree to form one to specify the degree of ripeness information simulation figure in units of pickup area by described portable hard drive.
More specifically, in the described crop maturity degree Identification platform based on unmanned plane detection, adopt pressure-altitude sensor to replace described radio altitude sensor, for according to the air pressure change near unmanned plane, detect the present level of unmanned plane.
More specifically, in the described crop maturity degree Identification platform based on unmanned plane detection, described wireless transmitting-receiving equipments is also for receiving the data request signal that described agricultural department monitor supervision platform sends, with under the control of described digital signal processor, the degree of ripeness information simulation figure in described portable hard drive is transmitted to described agricultural department monitor supervision platform by described wireless transmitting-receiving equipments.
More specifically, in the described crop maturity degree Identification platform based on unmanned plane detection, described digital signal processor is the digital processing unit DSP of TMS6000 series.
More specifically, in the described crop maturity degree Identification platform based on unmanned plane detection, described wavelet filtering device, described gray processing processing apparatus, described binary conversion treatment device, described crop type recognition means and described degree of ripeness grade recognition means adopt different fpga chips to realize respectively.
Accompanying drawing explanation
Below with reference to accompanying drawing, embodiment of the present invention are described, wherein:
Fig. 1 is the block diagram of the crop maturity degree Identification platform based on unmanned plane detection illustrated according to an embodiment of the present invention.
Fig. 2 is the block diagram of the radio altitude sensor of the crop maturity degree Identification platform based on unmanned plane detection illustrated according to an embodiment of the present invention.
Embodiment
Below with reference to accompanying drawings the embodiment of the crop maturity degree Identification platform based on unmanned plane detection of the present invention is described in detail.
Unmanned plane, i.e. unmanned spacecraft, its english abbreviation is " UAV ", is the not manned aircraft utilizing radio robot to handle with the presetting apparatus provided for oneself.
From technical standpoint definition, can unmanned plane be divided into Types Below: this several large class of depopulated helicopter, unmanned fixed-wing aircraft, unmanned multi-rotor aerocraft, unmanned airship, unmanned parasol.From the classification of purposes aspect, unmanned plane can be divided into military unmanned air vehicle and civilian unmanned plane.Military aspect, can be used for battle reconnaissance and supervision, positioning school are penetrated, injured assessment, electronic warfare, and civilian aspect, can be used for border patrol, nuclear radiation detection, aeroplane photography, mineral exploration aviation, the condition of a disaster supervision, traffic patrolling and security monitoring.
The large area data acquisition appearing as every field of unmanned plane provides conveniently, under the operation of remote control device, unmanned plane can be controlled and fly the top of each target location one by one to realize dissimilar data acquisition, especially for the region of large area agricultural cultivation, unmanned plane can be used for the collection of the various data that agricultural product upgrowth situation is correlated with, such as, the disease and pest situation of agricultural product, arid situation and degree of ripeness situation, data based on each strain crops collected are added up, obtain each reference data that agricultural management department formulates governing plan.
The crop maturity degree Identification platform detected based on unmanned plane of the present invention, take unmanned plane as the carrier of data acquisition equipment, for the feature of proportion of crop planting large area increasingly, based on wireless communication technology, work out the strategy of the degree of ripeness data acquisition of region one by one in units of region, completed the collection of the degree of ripeness data to target area, statistics and combination picture and make.
Fig. 1 is the block diagram of the crop maturity degree Identification platform based on unmanned plane detection illustrated according to an embodiment of the present invention, described Identification platform comprises image capture device 1, image processing equipment 2, digital signal processor 3, portable hard drive 4 and power-supply unit 5, described digital signal processor 3 is connected respectively with described image capture device 1, described image processing equipment 2, described portable hard drive 4 and described power-supply unit 5, and described image capture device 1 is connected with described image processing equipment 2.
Wherein, the pickup area of crop maturity degree flown to by described digital signal processor 3 at described unmanned plane after, start described image capture device 1 and described image processing equipment 2, described image capture device 1 is for taking described pickup area, to obtain crop area image, described image processing equipment 2 is for performing image procossing to described crop area image, the degree of ripeness of crops in described pickup area determined by described digital signal processor 3 according to processing result image, described portable hard drive 4 connects described digital signal processor 3 to store the degree of ripeness of crops in described pickup area, described power-supply unit 5 provides necessary electric power supply for described Identification platform.
Then, more specific description is carried out to the structure of field of the present invention damage caused by a drought harvester.
Described Identification platform also comprises: the Big Dipper positioning equipment, and connect the Big Dipper Navsat, for the Big Dipper navigation data of real-time reception unmanned plane position, the precision of described the Big Dipper navigation data is 2 meters.
As shown in Figure 2, described Identification platform also comprises radio altitude sensor, described radio altitude sensor comprises radio transmitter 6, radio receiver 7 and microcontroller 8, described microcontroller 8 is connected respectively with described radio transmitter 6 and described radio receiver 7, described radio transmitter 6 launches radiowave earthward, described radio receiver 7 receives the radiowave of ground return, described microcontroller 8 is according to the launch time of described radio transmitter 6, the time of reception of described radio receiver 7 and velocity of radio wave calculate the present level of unmanned plane, described velocity of radio wave is the light velocity.
Described Identification platform also comprises: USB drives interface, for inserting outside USB flash disk, to receive the crops image template of each type stored in described outside USB flash disk, the crops image template of each type described is the benchmark binary image of the crops of each type, also for receiving the crops upper limit gray threshold and crops lower limit gray threshold that store in described outside USB flash disk, described crops upper limit gray threshold and described crops lower limit gray threshold are used for the crops in image and background separation, also for receiving the crop maturity degrees of data table stored in described outside USB flash disk, be index with crop type in described crop maturity degrees of data table, save the degree of ripeness threshold value that each crops is corresponding, judge amount threshold and each degree of ripeness grade.
Described Identification platform also comprises: wireless transmitting-receiving equipments, for receiving the control signal that ground agricultural department monitor supervision platform sends, described control signal comprises the locator data of the pickup area that described agricultural department monitor supervision platform is specified, described locator data comprises corresponding collection the Big Dipper position, gathers height and regional location, described agricultural department monitor supervision platform sends multiple control signal in order one by one, and appointment pickup area corresponding in each control signal in described multiple control signal is different.
Described Identification platform also comprises: unmanned plane power drive device, for the flight of described unmanned plane provides power.
Described portable hard drive 4 and described USB drive interface to be connected, to receive and to store the crops image template of each type, crops upper limit gray threshold, crops lower limit gray threshold and crop maturity degrees of data table.
Described image capture device 1 comprises aerial camera and secondary light source, described aerial camera is used for taking described pickup area, to obtain crop area image, the shooting that described secondary light source is described aerial camera provides illumination fill-in light, and the intensity of described illumination fill-in light and the light luminance of described secondary light source surrounding environment are inversely proportional to.
Described image processing equipment 2 is connected respectively with described portable hard drive 4 and described image capture device 1, and described image processing equipment 2 comprises wavelet filtering device, gray processing processing apparatus, binary conversion treatment device, crop type recognition means and degree of ripeness grade recognition means.
Described wavelet filtering device is connected with described aerial camera, based on Haar wavelet transform wave filter, filtering process is performed to described crop area image, to obtain crops filtering image, described gray processing processing apparatus is connected respectively with described wavelet filtering device and described portable hard drive 4, the pixel identification of gray-scale value in described crops filtering image between described crops upper limit gray threshold and described crops lower limit gray threshold is formed multiple crops subimage, the corresponding strain crops of each crops subimage, described binary conversion treatment device is connected with described gray processing processing apparatus, based on default binary-state threshold by described multiple crops subimage binaryzation respectively, to generate multiple crops binaryzation subimage, described default binary-state threshold is the gray-scale value of a numerical value between 0-255.
Described crop type recognizer is connected respectively with described binary conversion treatment device and described portable hard drive 4, each crops binaryzation subimage is mated one by one with the crops image template of each type, to obtain crop type corresponding to each crops binaryzation subimage, for each different crops type that described multiple crops binaryzation subimage is corresponding, get the crop type that number percent that occurrence number occupies described multiple crops binaryzation subimage quantity is greater than preset percentage threshold value and export as pickup area crop type.
Described degree of ripeness grade recognition means and described gray processing processing apparatus, described crop type recognition means is connected respectively with described portable hard drive 4, the degree of ripeness threshold value corresponding with described pickup area crop type is found in described crop maturity degrees of data table, judge that amount threshold and each degree of ripeness grade are using as target degree of ripeness threshold value, target discrimination amount threshold and each target degree of ripeness grade, for each crops subimage, statistical pixel gray-scale value is less than or equal to the pixel quantity of described target degree of ripeness threshold value, when the pixel quantity added up is more than or equal to described target discrimination amount threshold, determine that the crops that described crops subimage is corresponding are maturation, when described pixel quantity is less than described target discrimination amount threshold, determine that the crops that described crops subimage is corresponding are prematurity, ripe determination result based on the crops of multiple crops subimage is added up and is exported the degree of ripeness of crops in described pickup area.
Described digital signal processor 3 and described crop type recognition means, described degree of ripeness grade recognition means, described wireless transmitting-receiving equipments, described unmanned plane power drive device, described the Big Dipper positioning equipment is connected respectively with described radio altitude sensor, resolve to obtain described collection the Big Dipper position to described control signal, gather height and regional location, and control described unmanned plane power drive device and to fly to the pickup area corresponding with described control signal to drive described unmanned plane, described the Big Dipper navigation data with described collection the Big Dipper position consistency and described present level and described collection highly consistent time, start described image capture device 1 and described image processing equipment 2, afterwards, by regional location corresponding for current appointment pickup area, crop type and crop maturity degree are stored into described portable hard drive 4, send degree of ripeness information acquisition end signal subsequently.
Described wireless transmitting-receiving equipments, after receiving described degree of ripeness information acquisition end signal, forwards described degree of ripeness information acquisition end signal to described agricultural department monitor supervision platform, so that described agricultural department monitor supervision platform sends next control signal in order.
Wherein, each specifies pickup area corresponding regional location, crop type and crop maturity degree to form one to specify the degree of ripeness information simulation figure in units of pickup area by described portable hard drive 4, described degree of ripeness information simulation figure is finally formed after the degree of ripeness data having gathered each appointment pickup area, calls when needed for regulatory authorities.
Wherein, in described Identification platform, pressure-altitude sensor can be adopted to replace described radio altitude sensor, for according to the air pressure change near unmanned plane, detect the present level of unmanned plane, described wireless transmitting-receiving equipments can also be used for the data request signal receiving the monitor supervision platform transmission of described agricultural department, with under the control of described digital signal processor 3, degree of ripeness information simulation figure in described portable hard drive 4 is transmitted to described agricultural department monitor supervision platform by described wireless transmitting-receiving equipments, described digital signal processor can type selecting be the digital processing unit DSP of TMS6000 series, alternatively, described wavelet filtering device, described gray processing processing apparatus, described binary conversion treatment device, described crop type recognition means and described degree of ripeness grade recognition means adopt different fpga chips to realize respectively.
In addition, Beidou satellite navigation system be China implementing independent development capability, independent operating GPS (Global Position System).System Construction target is: build up independent, open and compatible, advanced technology, reliable and stable Beidou satellite navigation system covering the whole world, promote that satellite navigation industrial chain is formed, form perfect national satellite navigation application industry to support, promote and security system, promote the widespread use of satellite navigation in national economy society every profession and trade, Beidou satellite navigation system is by space segment, ground segment and user segment three part composition, space segment comprises 5 satellites and 30 non-geo satellites, ground segment comprises master station, several land stations such as injection plant and monitoring station, user segment comprises Big Dipper user terminal and the terminal with other satellite navigation system compatibilities.
In addition, digital signal processor is not confined to audio frequency and video aspect, and he is widely used in many fields such as Communication and Information Systems, Signal and Information Processing, automatically control, radar, military affairs, Aero-Space, medical treatment, household electrical appliance.Be adopt general microprocessor to complete a large amount of digital signal processing computing, speed is comparatively slow, is difficult to meet actual needs in the past; And use bit slice microprocessor and quick paral-lel multiplier simultaneously, be once the effective way realizing digital signal processing, but the method device is more, logical design and program design complexity, power consumption is comparatively large, expensive.The appearance of digital signal processor DSP, well solves the problems referred to above.DSP can realize the process such as collection, conversion, filtering, valuation, enhancing, compression, identification to signal fast, to obtain the signal form meeting people's needs.
In addition, Haar wavelet transform is changed, and also known as Haar wavelet conversion, is proposed in 1909 by Haar, and being the simplest a kind of conversion in wavelet conversion (Wavelet transform), is also the wavelet conversion proposed the earliest.He be the western small echo of many shellfishes in the special case of N=2, can D2 be referred to as.In all orthogonalities (orthonormal) wavelet conversion China and Kazakhstan, your wavelet conversion (Haar wavelet) is the most simply one conversion, but he is not also suitable for comparatively level and smooth function, because he only has a vanishing moment (Vanishing Moment).
Adopt the crop maturity degree Identification platform detected based on unmanned plane of the present invention, for existing crop maturity degree detection means still based on manual detection, cannot follow Large Area of Crops plantation trend feature, use unmanned aerial vehicle platform, by acquisition technology, image recognition technology, wireless communication technology, location technology and height detection technique, complete the automatic identification to Large Area of Crops region-by-region degree of ripeness, improve efficiency and the accuracy of the identification of crop maturity degree.
Be understandable that, although the present invention with preferred embodiment disclose as above, but above-described embodiment and be not used to limit the present invention.For any those of ordinary skill in the art, do not departing under technical solution of the present invention ambit, the technology contents of above-mentioned announcement all can be utilized to make many possible variations and modification to technical solution of the present invention, or be revised as the Equivalent embodiments of equivalent variations.Therefore, every content not departing from technical solution of the present invention, according to technical spirit of the present invention to any simple modification made for any of the above embodiments, equivalent variations and modification, all still belongs in the scope of technical solution of the present invention protection.
Claims (6)
1. the crop maturity degree Identification platform detected based on unmanned plane, it is characterized in that, described Identification platform comprises portable hard drive, image capture device, image processing equipment and digital signal processor, described digital signal processor is connected respectively with described image capture device and described image processing equipment, fly to the pickup area of crop maturity degree at described unmanned plane after, start described image capture device and described image processing equipment, described image capture device is used for taking described pickup area, to obtain crop area image, described image processing equipment is used for performing image procossing to described crop area image, described digital signal processor determines the degree of ripeness of crops in described pickup area according to processing result image, described portable hard drive connects described digital signal processor to store the degree of ripeness of crops in described pickup area.
2., as claimed in claim 1 based on the crop maturity degree Identification platform that unmanned plane detects, it is characterized in that, described Identification platform also comprises:
The Big Dipper positioning equipment, connect the Big Dipper Navsat, for the Big Dipper navigation data of real-time reception unmanned plane position, the precision of described the Big Dipper navigation data is 2 meters;
Radio altitude sensor, comprise radio transmitter, radio receiver and microcontroller, described microcontroller is connected respectively with described radio transmitter and described radio receiver, described radio transmitter launches radiowave earthward, described radio receiver receives the radiowave of ground return, described microcontroller calculates the present level of unmanned plane according to the launch time of described radio transmitter, the time of reception of described radio receiver and velocity of radio wave, and described velocity of radio wave is the light velocity;
USB drives interface, for inserting outside USB flash disk, to receive the crops image template of each type stored in described outside USB flash disk, the crops image template of each type described is the benchmark binary image of the crops of each type, also for receiving the crops upper limit gray threshold and crops lower limit gray threshold that store in described outside USB flash disk, described crops upper limit gray threshold and described crops lower limit gray threshold are used for the crops in image and background separation, also for receiving the crop maturity degrees of data table stored in described outside USB flash disk, be index with crop type in described crop maturity degrees of data table, save the degree of ripeness threshold value that each crops is corresponding, judge amount threshold and each degree of ripeness grade,
Wireless transmitting-receiving equipments, for receiving the control signal that ground agricultural department monitor supervision platform sends, described control signal comprises the locator data of the pickup area that described agricultural department monitor supervision platform is specified, described locator data comprises corresponding collection the Big Dipper position, gathers height and regional location, described agricultural department monitor supervision platform sends multiple control signal in order one by one, and appointment pickup area corresponding in each control signal in described multiple control signal is different;
Unmanned plane power drive device, for the flight of described unmanned plane provides power;
Described portable hard drive and described USB drive interface to be connected, to receive and to store the crops image template of each type, crops upper limit gray threshold, crops lower limit gray threshold and crop maturity degrees of data table;
Described image capture device comprises aerial camera and secondary light source, described aerial camera is used for taking described pickup area, to obtain crop area image, the shooting that described secondary light source is described aerial camera provides illumination fill-in light, and the intensity of described illumination fill-in light and the light luminance of described secondary light source surrounding environment are inversely proportional to;
Described image processing equipment is connected respectively with described portable hard drive and described image capture device, comprise wavelet filtering device, gray processing processing apparatus, binary conversion treatment device, crop type recognition means and degree of ripeness grade recognition means, described wavelet filtering device is connected with described aerial camera, based on Haar wavelet transform wave filter, filtering process is performed to described crop area image, to obtain crops filtering image, described gray processing processing apparatus is connected respectively with described wavelet filtering device and described portable hard drive, the pixel identification of gray-scale value in described crops filtering image between described crops upper limit gray threshold and described crops lower limit gray threshold is formed multiple crops subimage, the corresponding strain crops of each crops subimage, described binary conversion treatment device is connected with described gray processing processing apparatus, based on default binary-state threshold by described multiple crops subimage binaryzation respectively, to generate multiple crops binaryzation subimage, described default binary-state threshold is the gray-scale value of a numerical value between 0-255, described crop type recognizer is connected respectively with described binary conversion treatment device and described portable hard drive, each crops binaryzation subimage is mated one by one with the crops image template of each type, to obtain crop type corresponding to each crops binaryzation subimage, for each different crops type that described multiple crops binaryzation subimage is corresponding, get the crop type that number percent that occurrence number occupies described multiple crops binaryzation subimage quantity is greater than preset percentage threshold value to export as pickup area crop type, described degree of ripeness grade recognition means and described gray processing processing apparatus, described crop type recognition means is connected respectively with described portable hard drive, the degree of ripeness threshold value corresponding with described pickup area crop type is found in described crop maturity degrees of data table, judge that amount threshold and each degree of ripeness grade are using as target degree of ripeness threshold value, target discrimination amount threshold and each target degree of ripeness grade, for each crops subimage, statistical pixel gray-scale value is less than or equal to the pixel quantity of described target degree of ripeness threshold value, when the pixel quantity added up is more than or equal to described target discrimination amount threshold, determine that the crops that described crops subimage is corresponding are maturation, when described pixel quantity is less than described target discrimination amount threshold, determine that the crops that described crops subimage is corresponding are prematurity, ripe determination result based on the crops of multiple crops subimage is added up and is exported the degree of ripeness of crops in described pickup area,
Described digital signal processor and described crop type recognition means, described degree of ripeness grade recognition means, described wireless transmitting-receiving equipments, described unmanned plane power drive device, described the Big Dipper positioning equipment is connected respectively with described radio altitude sensor, resolve to obtain described collection the Big Dipper position to described control signal, gather height and regional location, and control described unmanned plane power drive device and to fly to the pickup area corresponding with described control signal to drive described unmanned plane, described the Big Dipper navigation data with described collection the Big Dipper position consistency and described present level and described collection highly consistent time, start described image capture device and described image processing equipment, by regional location corresponding for current appointment pickup area, crop type and crop maturity degree are stored into described portable hard drive, send degree of ripeness information acquisition end signal subsequently,
Described wireless transmitting-receiving equipments, after receiving described degree of ripeness information acquisition end signal, forwards described degree of ripeness information acquisition end signal to described agricultural department monitor supervision platform, so that described agricultural department monitor supervision platform sends next control signal in order;
Wherein, each specifies pickup area corresponding regional location, crop type and crop maturity degree to form one to specify the degree of ripeness information simulation figure in units of pickup area by described portable hard drive.
3., as claimed in claim 2 based on the crop maturity degree Identification platform that unmanned plane detects, it is characterized in that:
Adopt pressure-altitude sensor to replace described radio altitude sensor, for according to the air pressure change near unmanned plane, detect the present level of unmanned plane.
4., as claimed in claim 2 based on the crop maturity degree Identification platform that unmanned plane detects, it is characterized in that:
Described wireless transmitting-receiving equipments is also for receiving the data request signal that described agricultural department monitor supervision platform sends, with under the control of described digital signal processor, the degree of ripeness information simulation figure in described portable hard drive is transmitted to described agricultural department monitor supervision platform by described wireless transmitting-receiving equipments.
5., as claimed in claim 2 based on the crop maturity degree Identification platform that unmanned plane detects, it is characterized in that:
Described digital signal processor is the digital processing unit DSP of TMS6000 series.
6., as claimed in claim 2 based on the crop maturity degree Identification platform that unmanned plane detects, it is characterized in that:
Described wavelet filtering device, described gray processing processing apparatus, described binary conversion treatment device, described crop type recognition means and described degree of ripeness grade recognition means adopt different fpga chips to realize respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510037940.2A CN104535575A (en) | 2015-01-25 | 2015-01-25 | Crop maturity identification platform based on unmanned aerial vehicle detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510037940.2A CN104535575A (en) | 2015-01-25 | 2015-01-25 | Crop maturity identification platform based on unmanned aerial vehicle detection |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104535575A true CN104535575A (en) | 2015-04-22 |
Family
ID=52851147
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510037940.2A Pending CN104535575A (en) | 2015-01-25 | 2015-01-25 | Crop maturity identification platform based on unmanned aerial vehicle detection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104535575A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105527657A (en) * | 2016-01-14 | 2016-04-27 | 河海大学 | Large-area farmland crop water status monitoring method and system based on unmanned aerial vehicle infrared thermal image acquisition |
| CN108901366A (en) * | 2018-06-19 | 2018-11-30 | 华中农业大学 | A kind of Incorporate citrus picking method |
| CN110412048A (en) * | 2019-07-25 | 2019-11-05 | 青岛大学 | A kind of electric power fault localization cruising device |
| CN110823187A (en) * | 2019-11-21 | 2020-02-21 | 中国太平洋财产保险股份有限公司 | Control method and device for measuring area to be measured based on aerial photography |
| CN111163628A (en) * | 2017-05-09 | 2020-05-15 | 蓝河技术有限公司 | Automatic plant detection using image data |
| CN111626148A (en) * | 2020-05-09 | 2020-09-04 | 杭州学联土地规划设计咨询有限公司 | Unmanned aerial vehicle farmland checking method, system, intelligent terminal and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6009186A (en) * | 1996-08-30 | 1999-12-28 | New Holland Braud S.A. | Method and apparatus for harvesting crop material |
| CN101226146A (en) * | 2008-01-25 | 2008-07-23 | 中国农业大学 | Prediction method for apple hardness based on multiple optical spectrum dispersion image |
| US7607338B1 (en) * | 2004-07-30 | 2009-10-27 | The United States Of America As Represented By The Secretary Of Agriculture | Handheld instrument for monitoring and/or identification of chemicals |
| US20110207231A1 (en) * | 2008-11-17 | 2011-08-25 | Cabot Security Materials Inc. | Melamine Assay Methods and Systems |
-
2015
- 2015-01-25 CN CN201510037940.2A patent/CN104535575A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6009186A (en) * | 1996-08-30 | 1999-12-28 | New Holland Braud S.A. | Method and apparatus for harvesting crop material |
| US7607338B1 (en) * | 2004-07-30 | 2009-10-27 | The United States Of America As Represented By The Secretary Of Agriculture | Handheld instrument for monitoring and/or identification of chemicals |
| CN101226146A (en) * | 2008-01-25 | 2008-07-23 | 中国农业大学 | Prediction method for apple hardness based on multiple optical spectrum dispersion image |
| US20110207231A1 (en) * | 2008-11-17 | 2011-08-25 | Cabot Security Materials Inc. | Melamine Assay Methods and Systems |
Non-Patent Citations (2)
| Title |
|---|
| 何勇 等: "农用无人机现状与发展趋势", 《现代农机》 * |
| 郭凝 等: "基于数字图像处理的番茄成熟度检测", 《中国科技论文在线》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105527657A (en) * | 2016-01-14 | 2016-04-27 | 河海大学 | Large-area farmland crop water status monitoring method and system based on unmanned aerial vehicle infrared thermal image acquisition |
| CN111163628A (en) * | 2017-05-09 | 2020-05-15 | 蓝河技术有限公司 | Automatic plant detection using image data |
| US11748976B2 (en) | 2017-05-09 | 2023-09-05 | Blue River Technology Inc. | Automated plant detection using image data |
| CN108901366A (en) * | 2018-06-19 | 2018-11-30 | 华中农业大学 | A kind of Incorporate citrus picking method |
| CN110412048A (en) * | 2019-07-25 | 2019-11-05 | 青岛大学 | A kind of electric power fault localization cruising device |
| CN110823187A (en) * | 2019-11-21 | 2020-02-21 | 中国太平洋财产保险股份有限公司 | Control method and device for measuring area to be measured based on aerial photography |
| CN111626148A (en) * | 2020-05-09 | 2020-09-04 | 杭州学联土地规划设计咨询有限公司 | Unmanned aerial vehicle farmland checking method, system, intelligent terminal and storage medium |
| CN111626148B (en) * | 2020-05-09 | 2023-07-11 | 浙江数治空间规划设计有限公司 | Unmanned aerial vehicle farmland checking method, unmanned aerial vehicle farmland checking system, intelligent terminal and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104535574A (en) | Crop ripeness identification method | |
| CN204346928U (en) | Based on the crop maturity degree Identification platform that unmanned plane detects | |
| CN104535575A (en) | Crop maturity identification platform based on unmanned aerial vehicle detection | |
| AU2019245339B2 (en) | Visual observer of unmanned aerial vehicle for monitoring horticultural grow operations | |
| CN204515530U (en) | Automatic running gear | |
| CN104730005A (en) | Ground-air integrated agricultural monitoring system and method | |
| CN104735423B (en) | The transmission facility identification platform being positioned on unmanned plane | |
| CN114020043A (en) | Unmanned aerial vehicle building project supervision system and method, electronic equipment and storage medium | |
| CN105208346B (en) | Transmission facility identification method based on unmanned plane | |
| Khuzaimah et al. | Application and potential of drone technology in oil palm plantation: Potential and limitations | |
| CN205175455U (en) | Tobacco field remote sensing monitoring unmanned aerial vehicle system | |
| CN110413002A (en) | A kind of unmanned aerial vehicle remote sensing information collecting method and device | |
| CN204514798U (en) | Air-ground integrated agricultural monitoring system | |
| CN205103173U (en) | Field crop information detection system based on multiclass image terminal - server framework | |
| CN205483037U (en) | Geographic information acquisition device | |
| Hung et al. | Using robotic aircraft and intelligent surveillance systems for orange hawkweed detection | |
| CN115649501A (en) | Night driving illumination system and method for unmanned aerial vehicle | |
| RU2648696C1 (en) | Agrotechnical complex with unmanned aerial vehicle | |
| CN114973006A (en) | Method, device and system for picking Chinese prickly ash and storage medium | |
| Wijesingha | Geometric quality assessment of multi-rotor unmanned aerial vehicle borne remote sensing products for precision agriculture | |
| US20220172306A1 (en) | Automated mobile field scouting sensor data and image classification devices | |
| CN208421639U (en) | A kind of precise positioning overlength continuation of the journey unmanned plane inspection disaster prevention system | |
| US20220046859A1 (en) | System and method for selective harvesting at night or under poor visibility conditions, night dilution and agriculture data collection | |
| CN211375429U (en) | A aircraft for geographical mapping for plateau area | |
| CN215953850U (en) | Laser radar point acquisition and transmission device, transmission device and real-time modeling device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150422 |