CN105046909A - Agricultural loss assessment assisting method based on small-sized unmanned aerial vehicle - Google Patents
Agricultural loss assessment assisting method based on small-sized unmanned aerial vehicle Download PDFInfo
- Publication number
- CN105046909A CN105046909A CN201510338361.1A CN201510338361A CN105046909A CN 105046909 A CN105046909 A CN 105046909A CN 201510338361 A CN201510338361 A CN 201510338361A CN 105046909 A CN105046909 A CN 105046909A
- Authority
- CN
- China
- Prior art keywords
- image
- aerial vehicle
- unmanned aerial
- farmland
- suav
- 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 discloses an agricultural loss assessment assisting method based on a small-sized unmanned aerial vehicle. The method comprises the following steps: carrying out aerial photography by the small-sized unmanned aerial vehicle with a camera in an automatic cruise mode or a pilot's hand-controlled mode, collecting images and videos of farmland requiring loss assessment, and uploading collected images and videos to a PC namely an upper computer during collecting; after the PC namely the upper computer receives all images and videos, carrying out image mosaic and three-dimensional reconstruction on the images and videos, so as to obtain two-dimensional images and three-dimensional images of the farmland requiring loss assessment; further carrying out image processing on the obtained two-dimensional images and analyzing data of the three-dimensional images to obtain farmland information data, so as to assist agricultural loss assessment. The loss assessment result obtained through the method provided by the invention is more objective in comparison with conventional loss assessment result, the loss assessment process designed by the invention is more efficient in comparison with the conventional loss assessment process and transparency of the loss assessment process is guaranteed.
Description
Technical field
The present invention relates to a kind of agriculture setting loss backup system, particularly relate to a kind of agricultural based on SUAV (small unmanned aerial vehicle) and assist damage identification method.
Background technology
The locality such as the etesian wind of China, hail, flood, multiple, be scattered in countless compared with the disaster of large regions, therefore the loss caused also is great, the setting loss mode of the traditional sampling exploration taked at present mainly adopts satellite system to combine with artificial prospecting, the manpower and materials of this mode not only at substantial are with high costs but also there are the too fuzzy problems such as accurate not of the long exploration that expends time in, according to incompletely statistics, every year because statistics mapping problem causes soil setting loss smudgy, the property of loss is more than 10,000,000,000 yuan.Therefore, present urgent need wants a set of system can carrying out agriculture setting loss rapidly and accurately to reduce the loss as far as possible, this loss is for agricultural production worker, portion agriculture setting loss accurately can instruct them to launch remedial measures in time, loss is dropped to minimum point, in visible prior art, lacks the auxiliary setting loss detection mode that reaction velocity is fast, processing accuracy is high, efficiency is high.
The agriculture setting loss of prior art mainly relies on satellite information to combine with artificial setting loss to carry out, the agriculture setting loss carried out in this way not only needs to expend huge human and material resources and time, and objectivity is lower, the present invention is a brand-new direction, and the image information that can obtain according to image procossing is rapid, objective, carry out agriculture setting loss efficiently.
The present invention can when people enter farmland, gather image by SUAV (small unmanned aerial vehicle), host computer carries out image procossing and finally realize agriculture setting loss, whole process embodies the intelligentized feature of agriculture setting loss, the efficiency that improve agriculture setting loss also reduces the cost of agriculture setting loss greatly, in addition, carry out transparence and objectivity that agriculture setting loss improves agriculture setting loss in this way, decrease loss.
Summary of the invention
In order to solve Problems existing in background technology, a kind of agricultural based on SUAV (small unmanned aerial vehicle) has been the object of the present invention is to provide to assist damage identification method, SUAV (small unmanned aerial vehicle) is applied to agriculture setting loss field, the present invention makes full use of the reliability of the flight stability of SUAV (small unmanned aerial vehicle), the real-time of wireless video transmission and digitized image process, thus completes auxiliary agriculture setting loss.
Problem to be solved by this invention comprises the steps:
1) adopt the SUAV (small unmanned aerial vehicle) with camera by automatic cruise mode or fly hand steer mode and take photo by plane, gathering the image and the video that need setting loss farmland, while collection, collected image and video are uploaded to PC host computer in real time;
Before taking photo by plane, section arrives needs the place of carrying out agriculture setting loss first to understand concrete condition, mainly comprises the disaster-stricken main Types in the crops of planting in the size in farmland, farmland and farmland; Concrete condition for farmland is formulated and to be taken photo by plane scheme and image acquisition scheme, and scheme of taking photo by plane mainly comprises height of taking photo by plane, obtain the time interval of image, the route and select automatic cruising still to be taken photo by plane by manipulation SUAV (small unmanned aerial vehicle) of taking photo by plane, then enforcement is taken photo by plane.
2) after PC host computer receives all images and video, utilize image processing method to splice and three-dimensional reconstruction image and video, obtain the two dimensional image and the 3-D view that need setting loss farmland;
3) by carrying out image procossing further to gained two dimensional image and obtaining agricultural land information data to 3 d image data analysis, with auxiliary agriculture setting loss.
Described step 1) in, the scope of angle of taking photo by plane is 0 ° to 45 °, chooses suitable angle of taking photo by plane according to actual conditions, for two dimensional image, angle of taking photo by plane is 0 °, in 3-D view, angle of taking photo by plane not is 0 °, needs the take photo by plane acquisition image of farmland by different angles; Height H of taking photo by plane is obtained by following formulae discovery:
H/F
R＝D
W/S
W
Wherein, F
rrepresent the real focal length of taking photo by plane with camera lens, S
wrepresent the width of camera used of taking photo by plane, D
wrepresent the ground width that camera photographs; The ground width D that camera photographs
wobtained by following formula
D
W＝(IMW*GSD)/100
Wherein, IMW represents the width of captured image, and unit is pixel, and GSD represents the pixel that every cm width is representative in the picture.
Described two dimensional image interval gathers and obtains, and its time interval T adopts following formula:
T=((IMH*GSD)/100) * (1-Duplication)/V
Wherein, Duplication represents the Duplication between the image of image mosaic and three-dimensional reconstruction and image, and V represents that the speed that unmanned plane is taken photo by plane, unit are meter per second.
Described step 3) agricultural land information data mainly comprise the main disaster-stricken type in farmland, the complete area in farmland, disaster area, farmland and account for the ratio of farmland area, crops elevation information and farmland vegetation color distribution.
The complete area in described farmland is calculated by the proportionate relationship of PC host computer according to gained farmland two dimensional image and actual farmland, and crops elevation information is obtained in conjunction with its image high layer information by gained farmland 3-D view.
The main disaster-stricken type in farmland is observed by people's naked eyes and is obtained from the two dimensional image of farmland.The disaster-stricken main Types in its farmland comprise caused by temperature factor have heat evil, freeze injury, frost, tropical crops cold damage and chilling injury; What caused by the moisture factor has drought, flood, snow injury and damage by hail; Windburn is had by wind-induced; What caused by meteorological factor combined action has hot dry wind, cold rain and flood freezing injury; The plague of insects.
Elevation information according to gained 3-D view and crops obtains the particular location of crops lodging and the area of corresponding lodging, the area of withering is obtained by image procossing, withered area adds that the area of lodging obtains disaster area, farmland, obtains disaster area, farmland and account for the ratio of farmland area by disaster area divided by farmland area.
Farmland vegetation color distribution is carried out further image procossing by farmland two dimensional image and is obtained, crops elevation information and farmland vegetation color distribution can form pie chart and the bar graph form withered situation for the lodging and crops that judge crops respectively, simultaneously for calculating the disaster area in farmland.
Described method, further according to the two dimensional image and the 3-D view that need setting loss farmland, is chosen segmented areas and is carried out examine on the spot verification, obtains accurate agriculture setting loss information data.
Agriculture setting loss information data of the present invention not only comprises the agriculture setting loss information of taking photo by plane and obtaining, and also comprises the every terms of information carrying out field exploring and obtain, and can be used for carrying out loss appraisal and file.
Described step 2) exist between the adjacent image of each image that collects more than 70% redundant image region, for successive image splicing and three-dimensional reconstruction provide convenient.
Described step 2) image mosaic mainly carries out following three key steps successively: Image semantic classification, image registration, image mosaic.
Image semantic classification: suppression image being carried out to noise spot, carries out image mosaic when picture quality is undesirable, if without Image semantic classification, is easy to cause some error hiding.
Image registration: the sift feature treated in stitching image is extracted, finds best coupling, completes the alignment between image in the information extracted.
Image mosaic: after image registration, image is sewed up, and to the smoothing process in border sewed up, allow and sew up nature transition; Smoothing processing adopts bilinear interpolation method, uses the pixel value of following formulae discovery overlapping region:
Wherein, d1, d2 are points in overlapping region to the distance of overlapping region left margin and right margin respectively, and img1 (x, y) and img2 (x, y) represent the pixel of two images to be spliced respectively.
Described step 2) three-dimensional reconstruction mainly comprises four parts that the Image semantic classification process, feature extraction coupling, the camera that carry out successively estimate sparse reconstruction and dense three-dimensional reconstruction.
Image semantic classification: from video of taking photo by plane to the extraction of rebuilding key frame, namely extracts a frame as key frame according to the time interval of setting from video;
Feature extraction is mated: the SIFT feature adopting GPU to accelerate is extracted and matching process;
Sparse reconstruction estimated by camera: the method utilizing triangulation and tie up adjustment iteration carries out the estimation of camera parameter and the reconstruction of sparse 3 D point cloud, and the estimation of camera parameter adopts SBA to increase income function library, and the reconstruction of sparse 3 D point cloud adopts Bunder to increase income storehouse;
Dense three-dimensional reconstruction: utilize CMVS and PMVS further spread sparse some cloud and optimize.
The present invention is directed to the problems referred to above adopts SUAV (small unmanned aerial vehicle) exploration to carry out auxiliary setting loss, SUAV (small unmanned aerial vehicle) is surveyed agriculture setting loss and is arisen at the historic moment, SUAV (small unmanned aerial vehicle) is an emerging industry, also be in the introduction period in a market at home, present stage is also in exploration and trial stage in the application of industry-by-industry, some industry batches starting to apply are not very large yet, but in fact the use of SUAV (small unmanned aerial vehicle) defines the relation substituted mutually with other traditional products.Along with the continuous maturation of miniature self-service machine technology, the reduction of cost and the raising of efficiency, SUAV (small unmanned aerial vehicle) replaces other products to be irreversible trend.In airborne survey field, among a small circle, large scale, ageingly require high mapping occasion, SUAV (small unmanned aerial vehicle) than manned aircraft and satellite photo cost only have its 2/5 to 1/10.
The inventive method has the advantages such as reaction velocity is fast, processing accuracy is high, with low cost, and has higher efficiency, objectivity, the transparency through the result that unmanned plane carries out agriculture setting loss, can provide stronger evidence for agriculture setting loss.Basic process is SUAV (small unmanned aerial vehicle) is transported to the farmland need carrying out agriculture setting loss, taken photo by plane in disaster area, then based on information handling system of taking photo by plane, with reference to ground block message of accepting insurance, to taking photo by plane, result carries out Objects recognition and classification, obtain total disaster area of devastated, space distribution and the extent of damage roughly; Finally according to the Treatment Analysis result of impact of taking photo by plane, representative region prospecting verification can be chosen.
Compared with prior art, beneficial effect of the present invention is:
Accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention.
Fig. 2 is the connected mode schematic diagram of each parts of SUAV (small unmanned aerial vehicle) of the present invention.
Fig. 3 be the image acquisition point of the embodiment of the present invention and route of taking photo by plane schematic diagram is set.
Fig. 4 is the topography of the stitching image in the farmland of embodiment of the present invention gained.
Fig. 5 is the side image of the three-dimensional reconstruction image in the farmland of embodiment of the present invention gained.
Embodiment
Hereinafter with reference to accompanying drawing, preferred embodiment of the present invention is described in detail.Should be appreciated that preferred embodiment only in order to the present invention is described, instead of in order to limit the scope of the invention.
The device that the inventive method adopts comprises SUAV (small unmanned aerial vehicle), the telepilot supporting with SUAV (small unmanned aerial vehicle) and PC host computer, and PC host computer is connected by wireless transport module with between SUAV (small unmanned aerial vehicle); SUAV (small unmanned aerial vehicle) is mounted with and flies to control plate, GPS locating module, barometer, gyroscope and the cradle head mechanism with camera.
The connected mode of each parts of SUAV (small unmanned aerial vehicle) as shown in Figure 2.SUAV (small unmanned aerial vehicle) of the present invention is equipped with SUAV (small unmanned aerial vehicle) cradle head mechanism, electricity tune, ultrasonic detector, lithium battery, SUAV (small unmanned aerial vehicle) flight control units, PMU Power Management Unit, barometer, gyroscope and PCMS receive and dispatch the equipments such as module, and with supporting telepilot.With the supporting telepilot of SUAV (small unmanned aerial vehicle) for manipulating the flight of SUAV (small unmanned aerial vehicle); Host computer is used for the data, the information that get positional information and the reception SUAV (small unmanned aerial vehicle) of SUAV (small unmanned aerial vehicle) transmission sampled point.PCMS receives and dispatches module for receiving host computer and remote information and sending the data that SUAV (small unmanned aerial vehicle) flight control units receives to host computer; SUAV (small unmanned aerial vehicle) is used for PCMS to receive and dispatch instruction that module receives and sends all parts to and collect the data that all parts surveys; The SUAV (small unmanned aerial vehicle) real time position data that GPS locating module transmits for receiving gps satellite; Barometer is for measuring the real-time altitude information of SUAV (small unmanned aerial vehicle); Gyroscope is for measuring the real-time attitude angle of SUAV (small unmanned aerial vehicle); PMU Power Management Unit is for measuring power supply real time data; Cradle head mechanism is for taking the real-time video information around SUAV (small unmanned aerial vehicle).
In SUAV (small unmanned aerial vehicle), telepilot is received and dispatched module to the flight control signal that SUAV (small unmanned aerial vehicle) sends through PCMS and is sent to SUAV (small unmanned aerial vehicle) flight control units, SUAV (small unmanned aerial vehicle) survey every data and send by SUAV (small unmanned aerial vehicle) flight control units the wireless receiving module receiving and dispatching module and ground through PCMS and send host computer, the picture signal of video camera is sent to PC host computer through the image receiver module on image sending module and ground successively.
SUAV (small unmanned aerial vehicle) cradle head mechanism comprises SUAV (small unmanned aerial vehicle) multiaxis The Cloud Terrace, video camera, image sending module and is positioned at the image receiver module on ground, video camera is arranged on SUAV (small unmanned aerial vehicle) multiaxis The Cloud Terrace, and SUAV (small unmanned aerial vehicle) multiaxis The Cloud Terrace is connected to aircraft flight control module.
As shown in Figure 1, first the present invention, formulates the scheme of taking photo by plane for farmland after understanding the concrete condition in farmland; Secondly, SUAV (small unmanned aerial vehicle) carries out according to take photo by plane route and the photographic schemes preset work of taking photo by plane, and the image photographed and video are uploaded to PC host computer; Again, host computer carries out image procossing after receiving all images and complete video on PC host computer, obtains two dimensional image and the 3-D view in farmland; Finally, agricultural land information data are obtained by analysis of two-dimensional images and 3-D view.
Present system can be reconnoitred scene, farmland before Image Acquisition, reconnoitres content and mainly comprises the length and width in farmland and the barrier of periphery.According to the surrounding enviroment in farmland, have to adopt during large numbers of items and fly hand manipulation SUAV (small unmanned aerial vehicle) when periphery kind is implanted with a large amount of trees and heap and perform scheme of taking photo by plane, adopt when surrounding enviroment clear SUAV (small unmanned aerial vehicle) automatic cruising to perform scheme of taking photo by plane.
If fly hand manipulation SUAV (small unmanned aerial vehicle) perform take photo by plane scheme time, fly hand manipulation SUAV (small unmanned aerial vehicle) take off and start to carry out work of taking photo by plane according to predetermined route of taking photo by plane, in the process of taking photo by plane, fly hand by the SUAV (small unmanned aerial vehicle) height observing host computer and receive, constantly to adjust the angle of taking photo by plane of SUAV (small unmanned aerial vehicle), height thus guarantee carrying out smoothly of the work of taking photo by plane with the distance of barrier, the attitude angle of SUAV (small unmanned aerial vehicle) and video of taking photo by plane; If when SUAV (small unmanned aerial vehicle) automatic cruising performs and takes photo by plane scheme, fly hand by the coordinate position of image acquisition point, sequentially, height of taking photo by plane, angle of taking photo by plane is sent to SUAV (small unmanned aerial vehicle), carry out automatic cruising by SUAV (small unmanned aerial vehicle) and complete image acquisition under each collection point.
Specific embodiment of the invention process is as follows:
1) the place's disaster affected farmland choosing certain city carries out the object of agriculture setting loss as the specific embodiment of the invention, it is known that concrete condition is understood by warp-wise peasant household, this farmland is wide about 320 meters, be about 500 meters, about be combined into 240 mu, the main disaster-stricken type in this farmland is that the crop large area lodging that caused by strong wind, cooling and hail shooting and part crop wither, in addition, these farmland surrounding enviroment are comparatively simple, and barrier is less;
2) to take photo by plane equipment according to existing SUAV (small unmanned aerial vehicle), take photo by plane with the real focal length F of camera lens
rbe 10 millimeters, the width S of camera of taking photo by plane camera lens used
wit is 18 millimeters, the width IMW of captured image is 6400 pixels, unit is pixel, the representative in the picture pixel GSD of every cm width is 5 centimetres/pixel, Duplication between image and image is 75%, and the flying speed of taking photo by plane of SUAV (small unmanned aerial vehicle) is 45 kilometers/hour=12.5 meter per seconds, and obtaining taking photo by plane is highly 178 meters, the time interval obtaining image is 6.4 seconds, takes photo by plane route map as shown in Figure 3;
3) according to the scheme of taking photo by plane formulated, SUAV (small unmanned aerial vehicle) automatic cruising is taken photo by plane, the video of captured in real-time is sent to PC host computer by SUAV (small unmanned aerial vehicle) in automatic cruising process, the every image acquisition point of SUAV (small unmanned aerial vehicle) all can carry out image acquisition and by image uploading to PC host computer, PC host computer carries out process to image and obtains setting loss information data, carries out auxiliary setting loss to facilitate the personnel of needs;
4) to take photo by plane end, after PC host computer receives all images in collection point shooting and complete video of taking photo by plane, PIX4D software is adopted to carry out image procossing to the image got and video, obtain the two dimensional image in farmland and the 3-D view in farmland, Fig. 4 be the topography of the stitching image in farmland as shown in Figure 4, the side image of the three-dimensional reconstruction image in farmland is as shown in Figure 5.
5) by carrying out further image procossing to the two dimensional image in farmland, analysis carried out to 3-D view and its corresponding high layer information obtain corresponding agricultural land information data, as following table:
6) according to agricultural land information data, choose wheat and lodged and withered representative region, examine on the spot setting loss, verifies above-mentioned result of taking photo by plane, empirical tests, and examine on the spot result is substantially identical with result of taking photo by plane.
As can be seen here, the present invention carries out the image processing techniques of image and video acquisition, communication between SUAV (small unmanned aerial vehicle) and PC host computer and PC host computer to farmland by SUAV (small unmanned aerial vehicle), achieve the agriculture setting loss to disaster affected farmland, meanwhile, for setting loss provides objective evidence.
The present embodiment is preferred embodiment, and the present invention all can carry out agriculture setting loss to the various agricultural damages caused by temperature factor, the moisture factor, wind, meteorological factor combined action, insect pest.
By the present invention, agriculture setting loss is carried out to disaster affected farmland and can not only provide objective evidence strong reliably for agriculture setting loss, more greatly can improve speed and the correctness of agriculture setting loss and reduce setting loss cost, in addition, peasant household can carry out corresponding remedial measures according to result of the present invention and reduce the loss as far as possible, realizes agricultural production control effects.
Claims (10)
1. the agricultural based on SUAV (small unmanned aerial vehicle) assists a damage identification method, it is characterized in that comprising the steps:
1) adopt the SUAV (small unmanned aerial vehicle) with camera by automatic cruise mode or fly hand steer mode and take photo by plane, gathering the image and the video that need setting loss farmland, while collection, collected image and video are uploaded to PC host computer in real time;
2) after PC host computer receives all images and video, image mosaic and three-dimensional reconstruction are carried out to image and video, obtain the two dimensional image and the 3-D view that need setting loss farmland;
3) by carrying out image procossing further to gained two dimensional image and obtaining agricultural land information data to 3 d image data analysis, with auxiliary agriculture setting loss.
2. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 1 assists damage identification method, it is characterized in that:
Described step 1) in, the scope of angle of taking photo by plane is 0 ° to 45 °; Height H of taking photo by plane is obtained by following formulae discovery:
H/F
R＝D
W/S
W
Wherein, F
rrepresent the real focal length of taking photo by plane with camera lens, S
wrepresent the width of camera used of taking photo by plane, D
wrepresent the ground width that camera photographs; The ground width D that camera photographs
wobtained by following formula
D
W＝(IMW*GSD)/100
Wherein, IMW represents the width of captured image, and unit is pixel, and GSD represents the pixel that every cm width is representative in the picture.
3. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 1 assists damage identification method, it is characterized in that:
Described two dimensional image interval gathers and obtains, and its time interval T adopts following formula:
T=((IMH*GSD)/100) * (1-Duplication)/V
Wherein, Duplication represents the Duplication between the image of image mosaic and three-dimensional reconstruction and image, and V represents that the speed that unmanned plane is taken photo by plane, unit are meter per second.
4. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 1 assists damage identification method, it is characterized in that: described step 3) agricultural land information data mainly comprise the main disaster-stricken type in farmland, the complete area in farmland, disaster area, farmland and account for the ratio of farmland area, crops elevation information and farmland vegetation color distribution.
5. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 1 assists damage identification method, it is characterized in that: the complete area in described farmland is calculated by the proportionate relationship in farmland two dimensional image and actual farmland, and crops elevation information is obtained in conjunction with its image high layer information by gained farmland 3-D view.
6. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 1 assists damage identification method, it is characterized in that: described method is further according to the two dimensional image and the 3-D view that need setting loss farmland, choose segmented areas and carry out examine on the spot verification, obtain accurate agriculture setting loss information data.
7. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 1 assists damage identification method, it is characterized in that: described step 2) exist between the adjacent image of each image that collects more than 70% redundant image region.
8. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 1 assists damage identification method, it is characterized in that: described step 2) image mosaic mainly carries out following three key steps successively: Image semantic classification, image registration, image mosaic.
9. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 8 assists damage identification method, it is characterized in that: described image mosaic: sew up image after image registration, and to the smoothing process in border sewed up, allows and sew up nature transition; Smoothing processing adopts bilinear interpolation method, uses the pixel value of following formulae discovery overlapping region:
Wherein, d1, d2 are points in overlapping region to the distance of overlapping region left margin and right margin respectively, and img1 (x, y) and img2 (x, y) represent the pixel of two images to be spliced respectively.
10. a kind of agricultural based on SUAV (small unmanned aerial vehicle) according to claim 1 assists damage identification method, it is characterized in that: described step 2) three-dimensional reconstruction mainly comprises four parts that the Image semantic classification process, feature extraction coupling, the camera that carry out successively estimate sparse reconstruction and dense three-dimensional reconstruction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510338361.1A CN105046909A (en) | 2015-06-17 | 2015-06-17 | Agricultural loss assessment assisting method based on small-sized unmanned aerial vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510338361.1A CN105046909A (en) | 2015-06-17 | 2015-06-17 | Agricultural loss assessment assisting method based on small-sized unmanned aerial vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105046909A true CN105046909A (en) | 2015-11-11 |
Family
ID=54453415
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510338361.1A Pending CN105046909A (en) | 2015-06-17 | 2015-06-17 | Agricultural loss assessment assisting method based on small-sized unmanned aerial vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105046909A (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105842246A (en) * | 2016-05-27 | 2016-08-10 | 中国农业科学院农业信息研究所 | Farmland monitoring system and farmland monitoring method |
| CN106296821A (en) * | 2016-08-19 | 2017-01-04 | 刘建国 | Multi-view angle three-dimensional method for reconstructing based on unmanned plane and system |
| CN106407885A (en) * | 2016-08-22 | 2017-02-15 | 苏州华兴源创电子科技有限公司 | Small sized unmanned aerial vehicle based affected area estimating method |
| CN106528914A (en) * | 2016-09-21 | 2017-03-22 | 中国葛洲坝集团电力有限责任公司 | Montanic photovoltaic power station layout platform and layout method |
| CN106845360A (en) * | 2016-12-27 | 2017-06-13 | 郑州大学 | High-resolution crop surface model construction method based on unmanned aerial vehicle remote sensing |
| CN108088395A (en) * | 2016-11-23 | 2018-05-29 | 北京地林远景信息技术有限公司 | A kind of unmanned plane forest cruise camera work method |
| CN108492206A (en) * | 2018-03-27 | 2018-09-04 | 贵州省烟草公司毕节市公司 | A kind of tobacco leaf production management method and step based on unmanned plane |
| CN108537164A (en) * | 2018-04-08 | 2018-09-14 | 北京农业智能装备技术研究中心 | Attack a vital point sowing bud ratio monitoring method and device based on unmanned aerial vehicle remote sensing |
| CN108692709A (en) * | 2018-04-26 | 2018-10-23 | 济南浪潮高新科技投资发展有限公司 | A kind of farmland the condition of a disaster detection method, system, unmanned plane and cloud server |
| CN108780568A (en) * | 2017-10-31 | 2018-11-09 | 深圳市大疆创新科技有限公司 | A kind of image processing method, device and aircraft |
| CN109242201A (en) * | 2018-09-29 | 2019-01-18 | 上海中信信息发展股份有限公司 | A kind of method, apparatus and computer readable storage medium for predicting crop yield |
| CN109813286A (en) * | 2019-01-28 | 2019-05-28 | 中科光启空间信息技术有限公司 | A kind of lodging disaster remote sensing damage identification method based on unmanned plane |
| CN110084120A (en) * | 2019-03-26 | 2019-08-02 | 南方电网调峰调频发电有限公司 | Vegetation investigation method, apparatus, system, equipment and medium under the environment of island |
| CN110210375A (en) * | 2019-05-29 | 2019-09-06 | 南京信息工程大学 | Adaptive farm crops fertilizer deficiency region detection unmanned plane and detection method |
| CN110914780A (en) * | 2017-04-28 | 2020-03-24 | 株式会社OPTiM | System, method, and program for creating action plan of unmanned aerial vehicle |
| CN111307719A (en) * | 2020-03-11 | 2020-06-19 | 黄河水利委员会黄河水利科学研究院 | Method and device for analyzing erosion characteristics of sandstone areas by using aerial data images |
| CN112702565A (en) * | 2020-12-03 | 2021-04-23 | 浙江大学 | System and method for acquiring field plant phenotype information |
| CN117689481B (en) * | 2024-02-04 | 2024-04-19 | 国任财产保险股份有限公司 | Natural disaster insurance processing method and system based on unmanned aerial vehicle video data |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080266408A1 (en) * | 2007-04-26 | 2008-10-30 | Core Logic, Inc. | Apparatus and method for generating panorama image and computer readable medium stored thereon computer executable instructions for performing the method |
| CN101979961A (en) * | 2010-05-18 | 2011-02-23 | 中国地震局地球物理研究所 | Disaster condition acquisition system |
| CN102088569A (en) * | 2010-10-13 | 2011-06-08 | 首都师范大学 | Sequence image splicing method and system of low-altitude unmanned vehicle |
| CN102201115A (en) * | 2011-04-07 | 2011-09-28 | 湖南天幕智能科技有限公司 | Real-time panoramic image stitching method of aerial videos shot by unmanned plane |
| CN103679674A (en) * | 2013-11-29 | 2014-03-26 | 航天恒星科技有限公司 | Method and system for splicing images of unmanned aircrafts in real time |
| CN104035412A (en) * | 2014-06-12 | 2014-09-10 | 江苏恒创软件有限公司 | Crop diseases and pest monitoring system and method based on unmanned plane |
| CN203893849U (en) * | 2014-03-10 | 2014-10-22 | 中国农业科学院农业资源与农业区划研究所 | Unmanned aerial vehicle-mounted automatic acquiring system for plot space information |
| CN104199368A (en) * | 2014-09-20 | 2014-12-10 | 无锡北斗星通信息科技有限公司 | Field drought severity acquiring device positioned on unmanned plane |
| CN104301630A (en) * | 2014-09-10 | 2015-01-21 | 天津航天中为数据系统科技有限公司 | Video image splicing method and device |
-
2015
- 2015-06-17 CN CN201510338361.1A patent/CN105046909A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080266408A1 (en) * | 2007-04-26 | 2008-10-30 | Core Logic, Inc. | Apparatus and method for generating panorama image and computer readable medium stored thereon computer executable instructions for performing the method |
| CN101979961A (en) * | 2010-05-18 | 2011-02-23 | 中国地震局地球物理研究所 | Disaster condition acquisition system |
| CN102088569A (en) * | 2010-10-13 | 2011-06-08 | 首都师范大学 | Sequence image splicing method and system of low-altitude unmanned vehicle |
| CN102201115A (en) * | 2011-04-07 | 2011-09-28 | 湖南天幕智能科技有限公司 | Real-time panoramic image stitching method of aerial videos shot by unmanned plane |
| CN103679674A (en) * | 2013-11-29 | 2014-03-26 | 航天恒星科技有限公司 | Method and system for splicing images of unmanned aircrafts in real time |
| CN203893849U (en) * | 2014-03-10 | 2014-10-22 | 中国农业科学院农业资源与农业区划研究所 | Unmanned aerial vehicle-mounted automatic acquiring system for plot space information |
| CN104035412A (en) * | 2014-06-12 | 2014-09-10 | 江苏恒创软件有限公司 | Crop diseases and pest monitoring system and method based on unmanned plane |
| CN104301630A (en) * | 2014-09-10 | 2015-01-21 | 天津航天中为数据系统科技有限公司 | Video image splicing method and device |
| CN104199368A (en) * | 2014-09-20 | 2014-12-10 | 无锡北斗星通信息科技有限公司 | Field drought severity acquiring device positioned on unmanned plane |
Non-Patent Citations (3)
| Title |
|---|
| 张桦: "场景图像拼接关键技术研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
| 章国锋: "视频场景的重建与增强处理", 《中国博士学位论文全文数据库 信息科技辑》 * |
| 赵小川: "机器人创新设计", 《机器人技术创新设计》 * |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105842246B (en) * | 2016-05-27 | 2019-01-22 | 中国农业科学院农业信息研究所 | The method that farmland monitors system and monitors farmland |
| CN105842246A (en) * | 2016-05-27 | 2016-08-10 | 中国农业科学院农业信息研究所 | Farmland monitoring system and farmland monitoring method |
| CN106296821A (en) * | 2016-08-19 | 2017-01-04 | 刘建国 | Multi-view angle three-dimensional method for reconstructing based on unmanned plane and system |
| CN106407885A (en) * | 2016-08-22 | 2017-02-15 | 苏州华兴源创电子科技有限公司 | Small sized unmanned aerial vehicle based affected area estimating method |
| CN106528914A (en) * | 2016-09-21 | 2017-03-22 | 中国葛洲坝集团电力有限责任公司 | Montanic photovoltaic power station layout platform and layout method |
| CN108088395A (en) * | 2016-11-23 | 2018-05-29 | 北京地林远景信息技术有限公司 | A kind of unmanned plane forest cruise camera work method |
| CN106845360A (en) * | 2016-12-27 | 2017-06-13 | 郑州大学 | High-resolution crop surface model construction method based on unmanned aerial vehicle remote sensing |
| CN110914780A (en) * | 2017-04-28 | 2020-03-24 | 株式会社OPTiM | System, method, and program for creating action plan of unmanned aerial vehicle |
| CN110914780B (en) * | 2017-04-28 | 2023-08-08 | 株式会社OPTiM | Unmanned aerial vehicle operation plan creation system, method, and program |
| US11543836B2 (en) | 2017-04-28 | 2023-01-03 | Optim Corporation | Unmanned aerial vehicle action plan creation system, method and program |
| CN108780568A (en) * | 2017-10-31 | 2018-11-09 | 深圳市大疆创新科技有限公司 | A kind of image processing method, device and aircraft |
| CN108492206A (en) * | 2018-03-27 | 2018-09-04 | 贵州省烟草公司毕节市公司 | A kind of tobacco leaf production management method and step based on unmanned plane |
| CN108537164B (en) * | 2018-04-08 | 2020-08-28 | 北京农业智能装备技术研究中心 | Method and device for monitoring germination rate of dibbling and sowing based on unmanned aerial vehicle remote sensing |
| CN108537164A (en) * | 2018-04-08 | 2018-09-14 | 北京农业智能装备技术研究中心 | Attack a vital point sowing bud ratio monitoring method and device based on unmanned aerial vehicle remote sensing |
| CN108692709A (en) * | 2018-04-26 | 2018-10-23 | 济南浪潮高新科技投资发展有限公司 | A kind of farmland the condition of a disaster detection method, system, unmanned plane and cloud server |
| CN109242201A (en) * | 2018-09-29 | 2019-01-18 | 上海中信信息发展股份有限公司 | A kind of method, apparatus and computer readable storage medium for predicting crop yield |
| CN109813286A (en) * | 2019-01-28 | 2019-05-28 | 中科光启空间信息技术有限公司 | A kind of lodging disaster remote sensing damage identification method based on unmanned plane |
| CN110084120A (en) * | 2019-03-26 | 2019-08-02 | 南方电网调峰调频发电有限公司 | Vegetation investigation method, apparatus, system, equipment and medium under the environment of island |
| CN110210375A (en) * | 2019-05-29 | 2019-09-06 | 南京信息工程大学 | Adaptive farm crops fertilizer deficiency region detection unmanned plane and detection method |
| CN111307719A (en) * | 2020-03-11 | 2020-06-19 | 黄河水利委员会黄河水利科学研究院 | Method and device for analyzing erosion characteristics of sandstone areas by using aerial data images |
| CN112702565A (en) * | 2020-12-03 | 2021-04-23 | 浙江大学 | System and method for acquiring field plant phenotype information |
| CN117689481B (en) * | 2024-02-04 | 2024-04-19 | 国任财产保险股份有限公司 | Natural disaster insurance processing method and system based on unmanned aerial vehicle video data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105046909A (en) | Agricultural loss assessment assisting method based on small-sized unmanned aerial vehicle | |
| US11651478B2 (en) | Methods for agronomic and agricultural monitoring using unmanned aerial systems | |
| Matese et al. | Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging | |
| KR101793509B1 (en) | Remote observation method and system by calculating automatic route of unmanned aerial vehicle for monitoring crops | |
| Pajares | Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs) | |
| WO2020072702A1 (en) | Unmanned aerial vehicle system and methods | |
| CN103438869B (en) | A kind of large scale vegetation coverage aviation Dynamic Acquisition system | |
| CN104849274A (en) | Real-time detection method for drought status in detected area based on miniature unmanned plane | |
| CN107832655A (en) | A kind of take photo by plane system and output of cotton estimating and measuring method based on unmanned plane imaging near the ground | |
| US20180348760A1 (en) | Automatic Change Detection System | |
| Hill et al. | Mapping with aerial photographs: recording the past, the present, and the invisible at Marj Rabba, Israel | |
| CN207835662U (en) | One kind being based on Embedded unmanned aerial vehicle remote sensing image capturing system | |
| US20220330468A1 (en) | System and methods for gathering data relating to crops and for managing crop growing operations | |
| CN110413002A (en) | A kind of unmanned aerial vehicle remote sensing information collecting method and device | |
| Seyyedhasani et al. | Utility of a commercial unmanned aerial vehicle for in-field localization of biomass bales | |
| CN203385417U (en) | Large-scale aviation dynamic acquisition system for vegetation coverage | |
| Jensen et al. | Assessing grain crop attributes using digital imagery acquired from a low-altitude remote controlled aircraft | |
| Yusof et al. | Land clearing, preparation and drone monitoring using Red-Green-Blue (RGB) and thermal imagery for Smart Durian Orchard Management project | |
| CN113099169A (en) | Unmanned aerial vehicle, low-altitude remote sensing enabling platform and construction and data transmission method thereof | |
| Waskitho | Unmanned aerial vehicle technology in irrigation monitoring | |
| Kugler et al. | Supporting microclimate modelling with 3D UAS data acquisition | |
| Melo et al. | A system embedded in small unmanned aerial vehicle for vigor analysis of vegetation. | |
| Delparte et al. | sUAS for Wildlife Conservation–Assessing Habitat Quality of the Endangered Black-Footed Ferret | |
| Lee et al. | Improvement of field phenotyping from synchronized multi-camera image collection based on multiple UAVs collaborative operation systems | |
| Iuga et al. | Monitoring the health and vegetation of crops through multispectral cameras. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| 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: 20151111 |