CN115963857A - Pesticide spraying method based on unmanned aerial vehicle - Google Patents
Pesticide spraying method based on unmanned aerial vehicle Download PDFInfo
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Abstract
The invention belongs to the technical field of intelligent plant protection, and provides a pesticide spraying method based on an unmanned aerial vehicle, wherein a pest and disease damage area of a hyperspectral remote sensing image at a corresponding position in an unmanned aerial vehicle operation area is identified through an RX algorithm; and optimizing the route path through the pest and disease damage area to obtain an optimized path. The pesticide spraying range is stably covered according to the distance relation between the actual pest and disease damage area and the navigation path, and the path generation speed can be greatly improved. And can avoid the repeated spraying in part non-pest and disease damage region, the difference between the position in balanced judgement non-pest and disease damage region to can avoid the repeated spraying in part non-pest and disease damage region intelligently, very big reduction flight turning point has reduced the speed reduction or the variable speed in the unmanned aerial vehicle flight, has improved unmanned aerial vehicle's spraying operation speed.
Description
Technical Field
The invention belongs to the technical field of intelligent plant protection, and particularly relates to a pesticide spraying method based on an unmanned aerial vehicle.
Background
In the large-scale popularization and application of unmanned aerial vehicle plant protection operation, the existing operation path planning and spraying control of pesticide spraying of the unmanned aerial vehicle are manually planned according to flight path planning software and image control point layout software carried by the unmanned aerial vehicle, however, the problems of multiple spraying and missed spraying of pesticide are more, pesticide waste is serious, partial areas are possibly not covered by pesticide, and the covering surface cannot achieve the best effect.
In the existing pesticide spraying method of the unmanned aerial vehicle, china with the publication number of CN115145307A specially facilitates 10 months and 4 days in 2022, discloses a pesticide spraying control system and method based on the unmanned aerial vehicle, and by acquiring real-time liquid level change information during the working period of a pump, the relation between the real-time liquid level in a pesticide box and the sprayed pesticide amount is acquired according to the real-time liquid level change information; meanwhile, a spraying track of the unmanned aerial vehicle is obtained by monitoring GPS position information of the unmanned aerial vehicle in real time, the spraying track is superposed on a relation curve of liquid level and pesticide amount, and then a spraying pesticide amount distribution diagram of a sprayed target field is obtained by combining longitude and latitude and height information of the unmanned aerial vehicle, so that the pesticide amount distribution of the target field is obtained, real-time monitoring of pesticide spraying paths and spraying amount of the unmanned aerial vehicle is realized, and the phenomena of excessive spraying and missed spraying of the field are avoided; meanwhile, a corresponding medicine amount demand graph is obtained according to the multispectral satellite image of the target field, and the medicine amount distribution graph is compared with the medicine amount demand graph, so that the spraying effect of the target field is effectively evaluated. Although the above-mentioned patent application can evaluate the operation effect, can confirm whether the field piece that needs to spray the pesticide sprays enough medicine concentration, but because unmanned aerial vehicle is the operation in the air of "Z" style of calligraphy, unmanned aerial vehicle need real-time speed reduction or variable speed when spraying the operation according to the dose demand picture, and this scheme does not change the airline planning of manual setting to some extent, when the field piece that sprays the operation is not regular field piece (for example not circular, region such as rectangle), unmanned aerial vehicle can not guarantee can cover the operation region completely, and only in the change of the real-time dynamic adjustment dose of in-process of flying, consequently can not carry out the adaptability according to condition such as insect pest/weeds and adjust and spray the route.
Disclosure of Invention
The invention aims to provide a pesticide spraying method based on an unmanned aerial vehicle, which aims to solve one or more technical problems in the prior art and at least provide a beneficial choice or creation condition.
In order to achieve the purpose, the invention provides a pesticide spraying method based on an unmanned aerial vehicle, which specifically comprises the following steps:
s1: acquiring an electronic map of a plot to be sprayed as an unmanned aerial vehicle operation area, and acquiring a hyperspectral remote sensing image of the unmanned aerial vehicle operation area;
s2: setting a starting point, a terminal point and obstacle position information in an unmanned aerial vehicle operation area; setting a route path from a starting point to a terminal point in an unmanned aerial vehicle operation area;
s3: identifying a pest and disease damage area of the hyperspectral remote sensing image at a corresponding position in the unmanned aerial vehicle operation area through an RX algorithm;
s4: and optimizing the route path through the pest and disease damage area to obtain an optimized path.
Preferably, the method further comprises the step S5: the unmanned aerial vehicle flies according to the optimized path and simultaneously carries out pesticide spraying operation.
Further, the pesticide loaded by the unmanned aerial vehicle is 2% of thiacloprid microcapsule suspending agent, 3% of thiacloprid microcapsule suspending agent, 8% of beta-cypermethrin microcapsule suspending agent, 25% of chlorbenzuron suspending agent, 0.5% of emamectin benzoate emulsifiable concentrate, 30% of abamectin, 25% of emamectin benzoate suspending agent, bacillus thuringiensis, 3.2% of abamectin and the like.
Further, in S1, the land to be sprayed includes any one of a forest land, a vegetable land, a dry land, a water irrigation land, a strelitzia field or an irrigation water field (any one of pine, eucalyptus, bamboo, cedar, phoenix tree, agilawood, poplar, camellia oleifera, citrus, grapefruit, apple, pear, lychee, longan, pineapple, rice, sorghum, corn and wheat is planted in the land to be sprayed).
Further, in S1, the electronic map is discrete data of ground elements and phenomena having determined coordinates and attributes within a certain coordinate system acquired through a satellite, an unmanned aerial vehicle.
Further, in S1, the hyperspectral remote sensing image of the unmanned aerial vehicle operation area is acquired by an onboard full-spectrum multi-modal imaging spectrometer or acquired by a hyperspectral remote sensing satellite.
Further, in S2, the method for setting the route path from the starting point to the end point comprises any one of A-Algorithm, K-shortest path-based Algorithm, dijkstra Algorithm, APF Algorithm (APF: artificial Potential Field Algorithm) and SAA Algorithm (SAA: simulated Annealing Algorithm).
Further, in S2, unmanned aerial vehicle is agricultural unmanned aerial vehicle or plant protection unmanned aerial vehicle, sets up unmanned aerial vehicle' S initial parameter: the amount of the atomized particles is 1-3L/mu, preferably 1.65L/mu, the atomized particles are 50-100 mu m, preferably 80 mu m, the lane spacing is 8-15 m, the flying height is 8-15 m, the flying speed is 3-10 m/s, the boundary safety distance is 2-5 m, and the barrier safety distance is 2-5 m;
further, in S3, the method for identifying the pest and disease damage area of the hyperspectral remote sensing image at the corresponding position in the unmanned aerial vehicle operation area through the RX algorithm comprises the following steps: and inputting the spectral reflectivity of each pixel point in the hyperspectral remote sensing image into an RX (Reed-Xiaooli hyperspectral target detection algorithm) algorithm to identify a pest and disease damage area at a corresponding position in the unmanned aerial vehicle operation area.
The hyperspectral remote sensing monitoring of the plant diseases and insect pests is judged by measuring the change of the chlorophyll content in the plant, and the spectral reflectivity of the chlorophyll has obvious characteristics and can change along with the change of the wavelength. The spectral reflectance of chlorophyll of plants is very low at 0.5-0.7 μm, and is significantly increased at 0.7-0.9 μm in the near infrared band, because green plants can absorb the radiant energy of this band. As chlorophyll in plant bodies of plant diseases and insect pests is gradually reduced, the light absorption capacity is reduced, the reflectivity of visible light is obviously improved, and the reflectivity of an infrared region is obviously reduced, especially in a near-infrared band.
Preferably, the method for identifying the pest and disease damage area of the hyperspectral remote sensing image at the corresponding position in the unmanned aerial vehicle operation area through the RX algorithm comprises the following steps: a pest region at a corresponding position in a pest information unmanned aerial vehicle operation region is obtained through a method disclosed in a patent publication No. CN 107347849A.
The pest and disease damage area is an area formed by pest and disease damage pixel points, or an internal area of a margin line formed by the pest and disease damage pixel points; the pest and disease damage pixel points are corresponding pixel points in the hyperspectral remote sensing image, wherein the visible light reflectivity of the pest and disease damage pixel points is higher than the average value of the visible light reflectivity of each pixel point in the hyperspectral remote sensing image, or the pest and disease damage pixel points are corresponding pixel points in the hyperspectral remote sensing image, wherein the near-infrared band spectral reflectivity of the pest and disease damage pixel points is lower than the average value of the near-infrared band spectral reflectivity of each pixel point in the hyperspectral remote sensing image.
Further, in S4, the method for optimizing the route path through the pest and disease area to obtain the optimized path comprises the following steps:
setting the lane distance (8-15 m) as MG, taking a point on the lane path as an identification point at every interval MG from the starting point, and taking each identification point as the circle center and 0.5 multiplied by MG as the radius to obtain a plurality of circular identification subareas; sequentially taking a sequence consisting of identification partitions formed by the identification points as an identification partition sequence RLocal from a starting point to an end point, wherein RLocal = { RL (i) }, i is the serial number of the identification partitions, i belongs to [1, N1], N1 is the number of the identification partitions, and RL (i) is the ith identification partition in the sequence RLocal;
in the value range of i, calculating Euclidean distances between the geometric center of gravity of each pest and disease damage region in RL (i) and P1 (i) by taking the identification point corresponding to RL (i) as P1 (i), and taking the mean value of all the Euclidean distances in RL (i) as RLmean (i); selecting the geometric gravity center point with the maximum distance value between the geometric gravity center point of each pest and disease damage area in RL (i) and P1 (i) as a far center point P2 (i), selecting the geometric gravity center point with the minimum distance value between the geometric gravity center point of each pest and disease damage area in RL (i) and P1 (i) as a near center point P3 (i), and screening all recognition partitions in a recognition partition sequence RLocal to form an optimization candidate set RLSUB (i) with a set RL (i) of which the distances from the recognition points to P2 (i) are smaller than RLmean (i); taking N2 as the number of elements in RLSUB (i), wherein RLSUB (i, j) is the identification point of the jth identification partition in RLSUB (i), j is the serial number of the identification point of the identification partition in RLSUB (i), and j belongs to [1, N2];
if N2=0, then RL (i) is noted as the identified partition that does not need to be optimized; and if N2 is greater than 0, optimizing RL (i) of the route path in the value range of i.
Above scheme can discern the offset position of identification point on insect pest area and the unmanned aerial vehicle airline, can discern the accurate relative position in insect pest area, guarantees the relative precision of insect pest position coordinate.
Further, the method for optimizing RL (i) of the en-route path within the value range of i comprises the following steps:
in the value range of j, calculating the mean value of the distances between the far center point P2 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as the far center distance AD; calculating the mean value of the distances between the near center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as the near center distance BD; recording the recognition point RLSUB (i, j) with the shortest distance between the far-center point P2 (i) and each recognition point RLSUB (i, j) in RLSUB (i) as PF; recording the identification point RLSUB (i, j) with the shortest distance between the center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) as a PN;
when AD is larger than or equal to BD, taking the direction from the far center point P2 (i) to PF as an adjusting direction, and moving the identification point P1 (i) on the route path to the adjusting direction by a far center distance AD;
when AD < BD, the direction from the near center point P3 (i) to PN is used as the adjusting direction, and the identification point P1 (i) on the route path is moved to the adjusting direction by the near center distance BD.
Preferably, the navigation path, i.e. the optimized path, is retrieved by a curve fitting method or directly interconnected according to all the adjusted identified points.
Preferably, the navigation path connected to the recognition point P1 (i) before the movement is reconnected to the recognition point P1 (i) after the movement, i.e., the optimized path.
The optimized path generated by the technical scheme can stably cover the pesticide spraying range according to the distance relation between the actual pest and disease damage area and the navigation path under the condition that the unmanned aerial vehicle flies at a high speed, so that the repeated spraying coverage area of the pest and disease damage area on the navigation path coverage area is less in condition, the optimized path can fully utilize the mutual coverage of the close positions between the unmanned aerial vehicle paths on the premise of laminating the pest and disease damage area, the algorithm efficiency is higher, and the path generation speed can be greatly improved. However, the above technical solutions still cannot avoid repeated spraying in some non-pest areas, and the turning points are many, and require the unmanned aerial vehicle to decelerate or change speed, which affects the spraying efficiency, so the present invention proposes the following preferable scheme:
preferably, the method for optimizing RL (i) of the en-route path within the range of values of i comprises the following steps:
calculating the deviation index dev (i) of the plant diseases and insect pests of RL (i), wherein the specific method comprises the following steps:
wherein the function of deviation degree
The | P2 (i) -RLSUB (i, j) | is the distance from the far center point P2 (i) to the identification point of the jth identification partition in RLSUB (i);
l P3 (i) -RLSUB (i, j) | is the distance from the proximal point P3 (i) to the recognition point of the jth recognition partition in RLSUB (i);
calculating pest position deviation indexes dev (i) of all RL (i) in the value range of i, calculating the average value of all dev (i) as meandev, and marking all identification partitions RL (i) of dev (i) being more than or equal to meandev as the to-be-optimized identification partitions;
when RL (i) is a to-be-optimized identification partition, calculating the mean value of the distances between a far center point P2 (i) and each identification point RLSUB (i, j) in RLSUB (i) in the value range of j and recording as a telecentric distance AD; calculating the mean value of the distances between the near center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as the near center distance BD; recording the recognition point RLSUB (i, j) with the shortest distance between the far-center point P2 (i) and each recognition point RLSUB (i, j) in RLSUB (i) as PF; recording the identification point RLSUB (i, j) with the shortest distance between the approximate center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) as PN;
when AD is larger than or equal to BD, taking the direction from the far center point P2 (i) to PF as an adjusting direction, and moving the identification point P1 (i) on the route path to the adjusting direction by a far center distance AD;
when AD < BD, the direction from the near center point P3 (i) to PN is used as the adjusting direction, and the identification point P1 (i) on the route path is moved to the adjusting direction by the near center distance BD.
Preferably, the navigation path, i.e. the optimized path, is retrieved by a curve fitting method or directly interconnected according to all the adjusted identified points.
Preferably, the navigation path connected to the recognition point P1 (i) before the movement is reconnected to the recognition point P1 (i) after the movement, i.e., the optimized path.
According to the preferred scheme, the pest position deviation index is the deviation degree between the far center point and the near center point of the pest position and the identification point on the navigation path, so that the difference between the positions of the non-pest region and the pest region is judged in a balanced manner, repeated spraying of part of the non-pest region can be avoided intelligently, turning points of flying are greatly reduced, the deceleration or speed change of the unmanned aerial vehicle in flying is reduced, and the spraying operation speed of the unmanned aerial vehicle is improved.
The invention also provides a pesticide spraying system based on the unmanned aerial vehicle, which comprises the following components: the processor executes the computer program to realize steps in the unmanned aerial vehicle-based pesticide spraying method, the unmanned aerial vehicle-based pesticide spraying system can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud data center, the operable system can include, but is not limited to, the processor, the memory and a server cluster, and the processor executes the computer program to operate in the following units of the system:
the remote sensing area acquisition unit is used for acquiring an electronic map of a plot to be sprayed as an unmanned aerial vehicle operation area and acquiring a hyperspectral remote sensing image of the unmanned aerial vehicle operation area;
the unmanned aerial vehicle initialization unit is used for setting a starting point, an end point and obstacle position information in an unmanned aerial vehicle operation area; setting a course path from a starting point to an end point in an unmanned aerial vehicle operation area;
the pest and disease area identification unit is used for identifying a pest and disease area of the hyperspectral remote sensing image at a corresponding position in the unmanned aerial vehicle operation area through an RX algorithm;
and the path optimization unit is used for optimizing the route path through the pest and disease damage area to obtain an optimized path.
The invention has the beneficial effects that: according to the pesticide spraying method based on the unmanned aerial vehicle, the pesticide spraying range is stably covered according to the distance relation between the actual pest and disease area and the navigation path, so that the repeated spraying coverage area of the pest and disease area on the navigation path coverage area is less in occurrence, the coverage of the similar positions among the unmanned aerial vehicle paths can be fully utilized on the premise of attaching the pest and disease area by optimizing the path, the algorithm efficiency is high, and the path generation speed can be greatly improved. And can avoid the repeated spraying in part non-pest and disease damage region, the difference between the position in balanced judgement non-pest and disease damage region to can avoid the repeated spraying in part non-pest and disease damage region intelligently, very big reduction flight turning point has reduced the speed reduction or the variable speed in the unmanned aerial vehicle flight, has improved unmanned aerial vehicle's spraying operation speed.
Drawings
The above and other features of the invention will be more apparent from the detailed description of the embodiments shown in the accompanying drawings in which like reference characters designate the same or similar elements, and it will be apparent that the drawings in the following description are merely exemplary of the invention and that other drawings may be derived by those skilled in the art without inventive effort, wherein:
FIG. 1 is a flow chart of a pesticide spraying method based on an unmanned aerial vehicle;
fig. 2 is a block diagram of a pesticide spraying system based on unmanned aerial vehicles.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The same reference numbers will be used throughout the drawings to refer to the same or like parts.
Fig. 1 is a flow chart illustrating a pesticide spraying method based on an unmanned aerial vehicle according to the present invention, and fig. 1 is a flow chart illustrating a pesticide spraying method based on an unmanned aerial vehicle according to an embodiment of the present invention, and a preferred embodiment is described in detail. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
S1: acquiring an electronic map of a land parcel to be sprayed for operation as an unmanned aerial vehicle operation area, and acquiring a hyperspectral remote sensing image of the unmanned aerial vehicle operation area;
s2: setting starting point and end point and obstacle position information in an unmanned aerial vehicle operation area; setting a route path from a starting point to a terminal point in an unmanned aerial vehicle operation area;
s3: identifying a pest and disease area of the hyperspectral remote sensing image at a corresponding position in the unmanned aerial vehicle operation area through an RX algorithm;
s4: and optimizing the route path through the pest and disease damage area to obtain an optimized path.
Preferably, the method further comprises the step S5: the unmanned aerial vehicle flies in a land to be sprayed according to an optimized path on the electronic map and performs pesticide spraying operation according to set parameters (the using amount per mu (1-3L/mu, preferably 1.65L/mu), the atomizing particles (50-100 mu m, preferably 80 mu m), the route spacing (8-15 m), the flying height (8-15 m), the flying speed (3-10 m/s), the boundary safety distance (2-5 m) and the barrier safety distance (2-5 m)).
Further, in S1, the land to be sprayed includes any one of a forest land, a vegetable land, a dry land, a water-irrigated land, a strewn land, or an irrigation water land.
Further, in S1, the electronic map is discrete data of ground elements and phenomena having determined coordinates and attributes within a certain coordinate system acquired through a satellite, an unmanned aerial vehicle.
The starting point is the position (generally the current position) of the unmanned aerial vehicle taking off in the unmanned aerial vehicle operation area, the end points are the end point positions of the unmanned aerial vehicle flying in the unmanned aerial vehicle operation area respectively, the obstacle position information is the obstacle position in the unmanned aerial vehicle operation area, and the unmanned aerial vehicle needs to keep a preset obstacle safety distance (2-5 m) with the obstacle position.
The unmanned aerial vehicle carries out flying movement according to the position coordinate data corresponding to the route path in the unmanned aerial vehicle operation area on the electronic map of the route path.
Further, in S2, setting a route path from the starting point to the ending point is a result of path planning within a selected area of an electronic map of the plot to be sprayed with the job.
Further, in S2, the method for setting the route path from the starting point to the end point comprises any one of A-Algorithm, K-shortest path based Algorithm, dijkstra Algorithm, APF Algorithm (APF: artificial Potential Field Algorithm) and SAA Algorithm (SAA: simulated Annealing Algorithm).
Further, in S2, unmanned aerial vehicle is agricultural unmanned aerial vehicle or plant protection unmanned aerial vehicle, sets up unmanned aerial vehicle' S initial parameter: the amount of the atomized particles is 1-3L/mu, preferably 1.65L/mu, the atomized particles are 50-100 mu m, preferably 80 mu m, the lane spacing is 8-15 m, the flying height is 8-15 m, the flying speed is 3-10 m/s, the boundary safety distance is 2-5 m, and the barrier safety distance is 2-5 m;
further, in S3, the method for identifying the pest and disease damage area of the hyperspectral remote sensing image at the corresponding position in the unmanned aerial vehicle operation area through the RX algorithm comprises the following steps: and inputting the spectral reflectivity of each pixel point in the hyperspectral remote sensing image into an RX (Reed-Xiaooli hyperspectral target detection algorithm) algorithm to identify a pest and disease damage area at a corresponding position in the unmanned aerial vehicle operation area.
The hyperspectral remote sensing monitoring of the plant diseases and insect pests is judged by measuring the change of the chlorophyll content in the plant, and the spectral reflectivity of the chlorophyll has obvious characteristics and can change along with the change of the wavelength. The spectral reflectance of chlorophyll of plants is very low at 0.5-0.7 μm, and is significantly increased at 0.7-0.9 μm in the near infrared band, because green plants can absorb the radiant energy of this band. As chlorophyll in plant bodies of plant diseases and insect pests is gradually reduced, the light absorption capacity is reduced, the reflectivity of visible light is obviously improved, and the reflectivity of an infrared region is obviously reduced, especially in a near-infrared band.
Preferably, the method for identifying the pest and disease damage area of the hyperspectral remote sensing image at the corresponding position in the unmanned aerial vehicle operation area through the RX algorithm comprises the following steps: an insect disease area (RX abnormal detection method) at a corresponding position in an unmanned aerial vehicle operation area is obtained through the method disclosed in the patent publication No. CN 107347849A.
The pest and disease damage area is an area formed by pest and disease damage pixel points, or an internal area of a margin line formed by the pest and disease damage pixel points; the pest and disease damage pixel points are corresponding pixel points in the hyperspectral remote sensing image, wherein the visible light reflectivity of the pest and disease damage pixel points is higher than the average value of the visible light reflectivity of each pixel point in the hyperspectral remote sensing image, or the pest and disease damage pixel points are corresponding pixel points in the hyperspectral remote sensing image, wherein the near-infrared band spectral reflectivity of the pest and disease damage pixel points is lower than the average value of the near-infrared band spectral reflectivity of each pixel point in the hyperspectral remote sensing image.
Further, in S4, the method for optimizing the route path through the pest and disease area to obtain the optimized path comprises the following steps:
setting a flight path distance (8-15 m) as MG, taking a point on a flight path from a starting point at every MG as an identification point, and taking each identification point as a circle center and 0.5 multiplied by MG as a radius to obtain a plurality of circular identification subareas; sequentially taking a sequence consisting of identification partitions formed by the identification points as an identification partition sequence RLocal from a starting point to an end point, wherein RLocal = { RL (i) }, i is the serial number of the identification partitions, i belongs to [1, N1], N1 is the number of the identification partitions, and RL (i) is the ith identification partition in the sequence RLocal;
in the value range of i, calculating Euclidean distances between the geometric gravity center point of each pest and disease damage area in RL (i) and P1 (i) by taking the identification point corresponding to RL (i) as P1 (i), and then taking the mean value of all the Euclidean distances in RL (i) as RLmean (i); selecting the geometric gravity center point with the maximum distance value between the geometric gravity center point of each pest and disease damage area in RL (i) and P1 (i) as a far center point P2 (i), selecting the geometric gravity center point with the minimum distance value between the geometric gravity center point of each pest and disease damage area in RL (i) and P1 (i) as a near center point P3 (i), and screening all recognition partitions in a recognition partition sequence RLocal to form an optimization candidate set RLSUB (i) with a set RL (i) of which the distances from the recognition points to P2 (i) are smaller than RLmean (i); taking N2 as the number of elements in RLSUB (i), wherein RLSUB (i, j) is the identification point of the jth identification partition in RLSUB (i), j is the serial number of the identification point of the identification partition in RLSUB (i), and j belongs to [1, N2];
if N2=0, then RL (i) is noted as the identified partition that does not need to be optimized; and if N2 is more than 0, optimizing RL (i) of the route path in the value range of i.
Further, the method for optimizing RL (i) of the en-route path within the value range of i comprises the following steps:
in the value range of j, calculating the mean value of the distances between the far center point P2 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as a far center distance AD; calculating the mean value of the distances between the near center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as the near center distance BD; recording the recognition point RLSUB (i, j) with the shortest distance between the far-center point P2 (i) and each recognition point RLSUB (i, j) in RLSUB (i) as PF; recording the identification point RLSUB (i, j) with the shortest distance between the center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) as a PN;
when AD is larger than or equal to BD, taking the direction from the far center point P2 (i) to PF as an adjusting direction, and moving the identification point P1 (i) on the route path to the adjusting direction by a far center distance AD;
when AD < BD, the direction from the near center point P3 (i) to PN is used as the adjusting direction, and the identification point P1 (i) on the route path is moved to the adjusting direction by the near center distance BD.
Preferably, the navigation path, i.e. the optimized path, is retrieved by a curve fitting method or directly interconnected according to all the adjusted identified points.
Preferably, the navigation path connected to the recognition point P1 (i) before the movement is reconnected to the recognition point P1 (i) after the movement, i.e., the optimized path.
The optimized path generated by the technical scheme can stably cover the pesticide spraying range according to the distance relation between the actual pest and disease damage area and the navigation path under the condition that the unmanned aerial vehicle flies at a high speed, so that the repeated spraying coverage area of the pest and disease damage area on the navigation path coverage area is less in condition, the optimized path can fully utilize the mutual coverage of the close positions between the unmanned aerial vehicle paths on the premise of laminating the pest and disease damage area, the algorithm efficiency is higher, and the path generation speed can be greatly improved. However, the above technical solutions still cannot avoid repeated spraying in some non-pest areas, and the turning points are many, and require the unmanned aerial vehicle to decelerate or change speed, which affects the spraying efficiency, so the present invention proposes the following preferable scheme:
preferably, the method for optimizing RL (i) of the en-route path within the range of values of i comprises the following steps:
calculating the pest and disease damage position deviation index dev (i) of RL (i), wherein the specific method comprises the following steps:
wherein the deviation degree function
| P2 (i) -RLSUB (i, j) | is the distance from the remote center point P2 (i) to the identification point of the jth identification partition in RLSUB (i);
l P3 (i) -RLSUB (i, j) | is the distance from the proximal point P3 (i) to the recognition point of the jth recognition partition in RLSUB (i);
calculating pest position deviation indexes dev (i) of all RL (i) in the value range of i, calculating the average value of all dev (i) as meandev, and marking all identification partitions RL (i) of dev (i) being more than or equal to meandev as the to-be-optimized identification partitions;
when RL (i) is the identification partition to be optimized, calculating the mean value of the distances between the far center point P2 (i) and each identification point RLSUB (i, j) in RLSUB (i) in the value range of j and recording the mean value as the telecentric distance AD; calculating the mean value of the distances between the near center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as the near center distance BD; recording the recognition point RLSUB (i, j) with the shortest distance between the far-center point P2 (i) and each recognition point RLSUB (i, j) in RLSUB (i) as PF; recording the identification point RLSUB (i, j) with the shortest distance between the approximate center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) as PN;
when AD is larger than or equal to BD, taking the direction from the far center point P2 (i) to PF as an adjusting direction, and moving the identification point P1 (i) on the route path to the adjusting direction by a far center distance AD;
when AD < BD, the direction from the near center point P3 (i) to PN is used as the adjusting direction, and the identification point P1 (i) on the route path is moved to the adjusting direction by the near center distance BD.
Preferably, the navigation path, i.e. the optimized path, is retrieved (after the elimination of the en-route path) according to all the adjusted identified points by a curve fitting method or directly interconnected.
Preferably, the navigation path connected to the recognition point P1 (i) before the movement is reconnected to the recognition point P1 (i) after the movement, i.e., the optimized path.
According to the preferred scheme, the pest position deviation index is the deviation degree between the far center point and the near center point of the pest position and the identification point on the navigation path, so that the difference between the positions of the non-pest region and the pest region is judged in a balanced manner, repeated spraying of part of the non-pest region can be avoided intelligently, turning points of flying are greatly reduced, the deceleration or speed change of the unmanned aerial vehicle in flying is reduced, and the spraying operation speed of the unmanned aerial vehicle is improved.
As shown in fig. 2, the pesticide spraying system based on the unmanned aerial vehicle according to the embodiment of the present invention includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in one of the above-described drone-based pesticide spraying method embodiments when executing the computer program, the processor executing the computer program to operate in the elements of the following system:
the remote sensing area acquisition unit is used for acquiring an electronic map of a plot to be sprayed as an unmanned aerial vehicle operation area and acquiring a hyperspectral remote sensing image of the unmanned aerial vehicle operation area;
the unmanned aerial vehicle initialization unit is used for setting a starting point, an end point and obstacle position information in an unmanned aerial vehicle operation area; setting a route path from a starting point to a terminal point in an unmanned aerial vehicle operation area;
the pest and disease area identification unit is used for identifying a pest and disease area of the hyperspectral remote sensing image at a corresponding position in the unmanned aerial vehicle operation area through an RX algorithm;
and the path optimization unit is used for optimizing the route path through the pest and disease damage area to obtain an optimized path.
The pesticide spraying system based on the unmanned aerial vehicle comprises: the pesticide spraying system based on the unmanned aerial vehicle can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud data center and the like, and the operable systems can include, but are not limited to, a processor, a memory and a computer program which is stored in the memory and can be operated on the processor.
A pesticide sprinkling system based on unmanned aerial vehicle can operate in computing equipment such as desktop computer, notebook computer, palm computer and high in the clouds data center. The pesticide spraying system based on the unmanned aerial vehicle comprises, but is not limited to, a processor and a memory. It will be appreciated by those skilled in the art that the example is merely an example of a drone-based pesticide spraying method and does not constitute a limitation of a drone-based pesticide spraying method and may include more or less components than a proportion, or a combination of certain components, or different components, for example the drone-based pesticide spraying system may also include input and output devices, network access devices, buses, or the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete component Gate or transistor logic, discrete hardware components, etc. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the pesticide spraying system based on the unmanned aerial vehicle, and various interfaces and lines are utilized to connect various subareas of the whole pesticide spraying system based on the unmanned aerial vehicle.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the drone-based pesticide spraying method by running or executing the computer programs and/or modules stored in the memory, as well as invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Although the description of the present invention has been presented in considerable detail and with reference to a few illustrated embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively encompass the intended scope of the invention. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (8)
1. An unmanned aerial vehicle-based pesticide spraying method is characterized by comprising the following steps:
s1: acquiring an electronic map of a plot to be sprayed as an unmanned aerial vehicle operation area, and acquiring a hyperspectral remote sensing image of the unmanned aerial vehicle operation area;
s2: setting a starting point, a terminal point and obstacle position information in an unmanned aerial vehicle operation area; setting a course path from a starting point to an end point in an unmanned aerial vehicle operation area;
s3: identifying a pest and disease damage area of the hyperspectral remote sensing image at a corresponding position in the unmanned aerial vehicle operation area through an RX algorithm;
s4: and optimizing the route path through the pest and disease damage area to obtain an optimized path.
2. The unmanned aerial vehicle-based pesticide spraying method as claimed in claim 1, wherein in S2, the method for setting the route path from the starting point to the end point comprises any one of A-algorithm, K-shortest path-based algorithm, dijkstra algorithm, APF algorithm and SAA algorithm.
3. The pesticide spraying method based on the unmanned aerial vehicle as claimed in claim 1, wherein in S2, the unmanned aerial vehicle is an agricultural unmanned aerial vehicle or a plant protection unmanned aerial vehicle, and initial parameters of the unmanned aerial vehicle are set as follows: the dosage per mu is 1-3L/mu, the atomized particles are 50-100 mu m, the flight path spacing is 8-15 m, the flight height is 8-15 m, the flight speed is 3-10 m/s, the boundary safety distance is 2-5 m, and the barrier safety distance is 2-5 m.
4. The pesticide spraying method based on the unmanned aerial vehicle as claimed in claim 1, wherein in S3, the method for identifying the pest and disease damage area of the hyperspectral remote sensing image at the corresponding position in the unmanned aerial vehicle operation area through the RX algorithm comprises the following steps: inputting the spectral reflectivity of each pixel point in the hyperspectral remote sensing image into an RX algorithm to identify a pest and disease damage area at a corresponding position in an unmanned aerial vehicle operation area; the pest and disease damage area is an area formed by pest and disease damage pixel points or an internal area of an edge line formed by the pest and disease damage pixel points; the disease and pest pixel points are corresponding pixel points in the hyperspectral remote sensing images, wherein the visible light reflectivity of the disease and pest pixel points is higher than the average value of the visible light reflectivity of each pixel point in the hyperspectral remote sensing images, or the disease and pest pixel points are corresponding pixel points in the hyperspectral remote sensing images, wherein the near-infrared band spectral reflectivity of the hyperspectral remote sensing images is lower than the average value of the near-infrared band spectral reflectivity of each pixel point in the hyperspectral remote sensing images.
5. The unmanned aerial vehicle-based pesticide spraying method as claimed in claim 3, wherein in S4, the method for obtaining the optimized path by optimizing the route path through the pest and disease area comprises the following steps:
setting the route distance as MG, taking a point on the route path as an identification point at every MG interval from the starting point, and obtaining a plurality of circular identification subareas by taking each identification point as the circle center and taking 0.5 multiplied by MG as the radius; sequentially taking a sequence consisting of identification partitions formed by the identification points as an identification partition sequence RLocal from a starting point to an end point, wherein RLocal = { RL (i) }, i is the serial number of the identification partitions, i belongs to [1, N1], N1 is the number of the identification partitions, and RL (i) is the ith identification partition in the sequence RLocal;
in the value range of i, calculating Euclidean distances between the geometric gravity center point of each pest and disease damage area in RL (i) and P1 (i) by taking the identification point corresponding to RL (i) as P1 (i), and then taking the mean value of all the Euclidean distances in RL (i) as RLmean (i); selecting the geometric gravity center point with the maximum distance value between the geometric gravity center point of each pest and disease damage area in RL (i) and P1 (i) as a far center point P2 (i), selecting the geometric gravity center point with the minimum distance value between the geometric gravity center point of each pest and disease damage area in RL (i) and P1 (i) as a near center point P3 (i), and screening out all recognition points from a recognition partition sequence RLocal to form an optimization candidate set RLSUB (i) with a set RL (i) in which the distances from the recognition points to P2 (i) are smaller than RLmean (i); taking N2 as the number of elements in RLSUB (i), wherein RLSUB (i, j) is the identification point of the jth identification partition in RLSUB (i), j is the serial number of the identification point of the identification partition in RLSUB (i), and j belongs to [1, N2];
if N2=0, then recording RL (i) as the identified partition that does not need to be optimized; and if N2 is greater than 0, optimizing RL (i) of the route path in the value range of i.
6. The unmanned aerial vehicle-based pesticide spraying method as claimed in claim 5, wherein the method for optimizing RL (i) of the air route path within the value range of i comprises the following steps:
in the value range of j, calculating the mean value of the distances between the far center point P2 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as the far center distance AD; calculating the mean value of the distances between the near center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as the near center distance BD; recording the recognition point RLSUB (i, j) with the shortest distance between the far-center point P2 (i) and each recognition point RLSUB (i, j) in RLSUB (i) as PF; recording the identification point RLSUB (i, j) with the shortest distance between the approximate center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) as PN;
when AD is larger than or equal to BD, taking the direction from the far center point P2 (i) to PF as an adjusting direction, and moving the identification point P1 (i) on the route path to the adjusting direction by a far center distance AD;
when AD < BD, the identification point P1 (i) on the route path is moved to the adjustment direction by the near center distance BD by taking the direction from the near center point P3 (i) to PN as the adjustment direction.
7. The unmanned aerial vehicle-based pesticide spraying method as claimed in claim 5, wherein the method for optimizing RL (i) of the air route path within the value range of i comprises the following steps:
calculating the deviation index dev (i) of the plant diseases and insect pests of RL (i), wherein the specific method comprises the following steps:
wherein the deviation degree function
The | P2 (i) -RLSUB (i, j) | is the distance from the far center point P2 (i) to the identification point of the jth identification partition in RLSUB (i);
| P3 (i) -RLSUB (i, j) | is the distance from the centromere P3 (i) to the identification point of the jth identification partition in RLSUB (i);
in the value range of i, calculating pest position deviation indexes dev (i) of all RL (i), calculating the average value of all dev (i) as meandev, and marking all identification partitions RL (i) of dev (i) being more than or equal to meandev as the to-be-optimized identification partitions;
when RL (i) is the identification partition to be optimized, calculating the mean value of the distances between the far center point P2 (i) and each identification point RLSUB (i, j) in RLSUB (i) in the value range of j and recording the mean value as the telecentric distance AD; calculating the mean value of the distances between the near center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) and recording as the near center distance BD; recording the recognition point RLSUB (i, j) with the shortest distance between the far-center point P2 (i) and each recognition point RLSUB (i, j) in RLSUB (i) as PF; recording the identification point RLSUB (i, j) with the shortest distance between the approximate center point P3 (i) and each identification point RLSUB (i, j) in RLSUB (i) as PN;
when AD is larger than or equal to BD, taking the direction from the far center point P2 (i) to PF as an adjusting direction, and moving the identification point P1 (i) on the route path to the adjusting direction by a far center distance AD;
when AD < BD, the identification point P1 (i) on the route path is moved to the adjustment direction by the near center distance BD by taking the direction from the near center point P3 (i) to PN as the adjustment direction.
8. The utility model provides a pesticide sprinkling system based on unmanned aerial vehicle which characterized in that, a pesticide sprinkling system based on unmanned aerial vehicle includes: a processor, a memory, and a computer program stored in and running on the memory, the processor when executing the computer program implementing the steps in a method of unmanned aerial vehicle-based pesticide spraying according to any one of claims 1 to 7, the unmanned aerial vehicle-based pesticide spraying system running in a computing device of a desktop computer, a laptop computer, a palm computer, or a cloud data center.
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