CN116686814A - Pesticide application control method, system and medium for plant protection unmanned aerial vehicle - Google Patents

Pesticide application control method, system and medium for plant protection unmanned aerial vehicle Download PDF

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Publication number
CN116686814A
CN116686814A CN202310928867.2A CN202310928867A CN116686814A CN 116686814 A CN116686814 A CN 116686814A CN 202310928867 A CN202310928867 A CN 202310928867A CN 116686814 A CN116686814 A CN 116686814A
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China
Prior art keywords
pesticide application
pest
information
natural enemy
plant protection
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CN202310928867.2A
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Chinese (zh)
Inventor
张贵森
王勇
郭慧玲
苗种
张宝俊
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Shanxi Agricultural University
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Shanxi Agricultural University
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Priority to CN202310928867.2A priority Critical patent/CN116686814A/en
Publication of CN116686814A publication Critical patent/CN116686814A/en
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
    • A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
    • A01M7/0025—Mechanical sprayers
    • A01M7/0032—Pressure sprayers
    • A01M7/0042—Field sprayers, e.g. self-propelled, drawn or tractor-mounted
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
    • A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
    • A01M7/0089—Regulating or controlling systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
    • B64D1/00—Dropping, ejecting, releasing, or receiving articles, liquids, or the like, in flight
    • B64D1/16—Dropping or releasing powdered, liquid, or gaseous matter, e.g. for fire-fighting
    • B64D1/18—Dropping or releasing powdered, liquid, or gaseous matter, e.g. for fire-fighting by spraying, e.g. insecticides
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
    • Y02A50/30—Against vector-borne diseases, e.g. mosquito-borne, fly-borne, tick-borne or waterborne diseases whose impact is exacerbated by climate change

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  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Pest Control & Pesticides (AREA)
  • Insects & Arthropods (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Environmental Sciences (AREA)
  • Mechanical Engineering (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Catching Or Destruction (AREA)

Abstract

The invention discloses a pesticide application control method, a pesticide application control system and a pesticide application control medium for a plant protection unmanned aerial vehicle, and aims to provide a high-efficiency and accurate crop pesticide application scheme. The method comprises the following steps: first, basic information of a target farmland is acquired. Secondly, judging the enrichment condition of pests by using an image recognition technology, and generating a pest enrichment report. Pest control regimens are then generated based on the historical dosing and pest enrichment reports. According to the pest control scheme, an optimal pesticide application time period of the plant protection unmanned aerial vehicle is determined. Then, the crop is subjected to a pesticide application operation during the optimal pesticide application period. Finally, judging the effect of the crops after the pesticide application operation so as to evaluate the pesticide application effect. The invention realizes the integration of farmland information acquisition, insect pest monitoring, pesticide application time determination and pesticide application effect evaluation, and improves the pesticide application efficiency and accuracy of the plant protection unmanned aerial vehicle.

Description

Pesticide application control method, system and medium for plant protection unmanned aerial vehicle
Technical Field
The invention relates to the technical field of agricultural pesticide application, in particular to a pesticide application control method, a pesticide application control system and a pesticide application control medium of a plant protection unmanned aerial vehicle.
Background
At present, the damage problem of insect pests to crops exists in agricultural production generally, so that plant protection pesticide application is one of important means for guaranteeing healthy growth of crops. Traditional application modes mainly depend on manual operation or mechanical equipment, but the methods have the problems of low efficiency, uneven application, excessive application, environmental pollution and the like, and lack of accurate application capability. The plant protection unmanned aerial vehicle is used as an emerging agricultural pesticide application tool, has the advantages of flexibility, high efficiency, rapidness and the like, can realize accurate pesticide application, reduces the using amount of pesticides, reduces environmental pollution, and improves the yield and quality of crops. However, at present, most of pesticide application modes of plant protection unmanned aerial vehicles still adopt traditional timing and quantitative pesticide application, and personalized pesticide application cannot be performed according to actual conditions of specific farmlands. Lack of monitoring and analysis of pest information results in poor application. Therefore, a plant protection unmanned aerial vehicle pesticide application control method capable of integrating farmland information, monitoring insect pests, intelligently judging optimal pesticide application time and evaluating pesticide application fruits is urgently needed. The method is based on advanced image recognition technology and data analysis method, can realize individuation of crop application, improves application efficiency and accuracy, and achieves scientific, environment-friendly and sustainable agricultural production targets.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a pesticide application control method, a pesticide application control system and a pesticide application control medium of a plant protection unmanned aerial vehicle.
The first aspect of the invention provides a pesticide application control method of a plant protection unmanned aerial vehicle, which comprises the following steps:
acquiring basic information of a target farmland, wherein the basic information of the farmland comprises position information of the target farmland, crop type information and insect pest information;
judging pest enrichment conditions based on an image recognition technology, and generating a pest enrichment report;
acquiring historical pesticide application doses of all crop planting areas in a target area, and generating a pest control scheme according to the historical pesticide application doses and pest enrichment reports;
according to the pest control scheme, determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle;
performing a pesticide application operation on the crop based on the pest control scheme and the optimal pesticide application time period;
judging the pesticide application effect of the crops after the pesticide application operation.
In this scheme, obtain target farmland basic information, farmland basic information includes target farmland positional information, crops kind information, insect pest information, specifically does:
acquiring target farmland position information based on GPS positioning equipment, wherein the position information comprises longitude and latitude data, constructing a map model, importing the target farmland position information into the map model, generating a target farmland map model, and displaying the target farmland map model in a preset display;
Acquiring crop type information according to farmland planting information, wherein the crop type information comprises a crop name and a growth height;
and obtaining pest information in the farmland according to farmland investigation data, wherein the pest information comprises pest names and pest characteristics.
In this scheme, based on the image recognition technology judges pest enrichment condition, the pest enrichment report is generated, specifically:
acquiring video data in the process of enriching pests in a preset area, and extracting video frame images of the video data to obtain a video frame image set;
extracting pixel information of each frame image of the video frame image set;
calculating a pixel covariance matrix of the video frame image set according to the pixel information, and calculating a feature vector and a feature value of the video frame image set according to the pixel covariance matrix;
sequencing the characteristic values from large to small, selecting a first characteristic value as a main component of a characteristic vector, and projecting original pixel information onto the main component to obtain video frame image set data with one-time dimension reduction;
calculating a similarity matrix of the video frame image set data of one-time dimension reduction, and calculating the conditional probability between the data points by using the similarity matrix to obtain a probability distribution map;
Initializing the positions of data points of the secondary dimension reduction, and calculating the conditional probability among the data points of the secondary dimension reduction according to the probability distribution diagram;
calculating KL divergence value according to the conditional probability between the probability distribution diagram and the data points of the secondary dimension reduction, and circularly adjusting the positions of the data points of the secondary dimension reduction until the KL divergence value reaches a preset divergence value to obtain a secondary dimension reduction video frame image set;
constructing an image processing model based on an image recognition technology;
each frame of video frame image of the secondary dimension reduction video frame image set is imported into an image processing model, and the position of each pest is marked by a dot to obtain a marked image;
and judging the enrichment degree of the pests according to the marked image, and generating a pest enrichment report, wherein the pest enrichment report comprises the quantity of the pests in the target farmland.
In this scheme, obtain the historical dose of applying medicine of each crops planting area in target area, according to historical dose of applying medicine and pest enrichment report generation pest control scheme, specifically be:
acquiring historical pesticide application doses of all crop planting areas in a target area, judging whether the historical pesticide application doses reach pollution standards, and if the historical pesticide application doses do not reach the pollution standards, generating a pesticide application control scheme of the plant protection unmanned aerial vehicle;
If the pollution standard is met, acquiring phototactic information of natural enemy insects of the pests in the target farmland, and setting a quantity threshold value of the natural enemy insects;
initializing a preset number of natural enemy insect illumination induction devices in a preset time period, and inducing natural enemy insects into a target farmland;
observing the quantity change information of natural enemy insects of the pests in real time through an infrared detection device, and drawing the quantity change information into a quantity-time change chart in real time;
if the number of the natural enemy insects in the target farmland is not more than the number threshold after the natural enemy insects are induced, setting a natural enemy insect induction network according to the preset number of natural enemy insect illumination induction equipment;
if the natural enemy insects are induced, the number of the natural enemy insects in the target farmland is larger than a number threshold value, the natural enemy insect illumination induction equipment is uniformly reduced, and the natural enemy insects are drained until the number of the natural enemy insects in the target farmland after induction is not larger than the number threshold value.
In this scheme, according to pest control scheme, confirm plant protection unmanned aerial vehicle's best application time quantum, specifically do:
acquiring time stamp information of the video frame image;
drawing a quantity-time curve of pest quantity change based on time change according to the pest enrichment report and the time stamp information of the video frame image;
Determining a time period with the maximum pest enrichment amount according to the change information of the amount-time curve;
and determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle according to the time period.
In this scheme, based on pest enrichment report and best application time quantum, carry out the operation of applying medicine to crops, specifically do:
dividing a target farmland into N small areas in a grid mode, and displaying the divided small areas in the target farmland map model;
in the optimal pesticide application time period, acquiring image information of each small area based on a camera device of the plant protection unmanned aerial vehicle;
extracting features of the image according to the image information, comparing the extracted features with pest features to obtain pests in the image, and counting the pests to obtain pest number information of each small area;
if the number of the pests is greater than the preset number, marking the small area as an area to be applied with the pesticide, acquiring the position information of the area to be applied with the pesticide, and marking in a target farmland map model;
setting a start point and an end point of a pesticide application route of the plant protection unmanned aerial vehicle based on the position information of the region to be subjected to pesticide application;
searching the position information of the to-be-applied area based on Dijkstra algorithm to obtain the shortest path of the plant protection unmanned aerial vehicle for applying the pesticide, and forming a pesticide application route;
And regulating and controlling the pesticide application amount of the plant protection unmanned aerial vehicle at different positions in real time according to the number information of pests, the position information of the region to be pesticide applied and the crop type information.
In this scheme, judge the effect of dosing to the crops after the operation of dosing, specifically do:
selecting a preset percentage of post-application video data within a preset time of the post-application region, and extracting post-application video frame data;
performing optical flow vector calculation on the video frame data after the medicine application to obtain an optical flow field;
performing motion filtration on the optical flow field to obtain optical flow information of pest motion;
based on the optical flow information, thresholding is carried out on the optical flow field, and the connected pixel points form a pest motion track;
counting the movement tracks of the pests to obtain the number of the pests after the pesticide is applied;
comparing the number of the pests after the pesticide application with the enriched number of the pests, and judging the pesticide application effect.
The second aspect of the invention also provides a pesticide application control system of a plant protection unmanned aerial vehicle, which comprises: the device comprises a memory and a processor, wherein the memory comprises a pesticide application control method program of the plant protection unmanned aerial vehicle, and when the pesticide application control method program of the plant protection unmanned aerial vehicle is executed by the processor, the following steps are realized:
Acquiring basic information of a target farmland, wherein the basic information of the farmland comprises position information of the target farmland, crop type information and insect pest information;
judging pest enrichment conditions based on an image recognition technology, and generating a pest enrichment report;
acquiring historical pesticide application doses of all crop planting areas in a target area, and generating a pest control scheme according to the historical pesticide application doses and pest enrichment reports;
according to the pest control scheme, determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle;
performing a pesticide application operation on the crop based on the pest control scheme and the optimal pesticide application time period;
judging the pesticide application effect of the crops after the pesticide application operation.
In this scheme, obtain the historical dose of applying medicine of each crops planting area in target area, according to historical dose of applying medicine and pest enrichment report generation pest control scheme, specifically be:
acquiring historical pesticide application doses of all crop planting areas in a target area, judging whether the historical pesticide application doses reach pollution standards, and if the historical pesticide application doses do not reach the pollution standards, generating a pesticide application control scheme of the plant protection unmanned aerial vehicle;
if the pollution standard is met, acquiring phototactic information of natural enemy insects of the pests in the target farmland, and setting a quantity threshold value of the natural enemy insects;
Initializing a preset number of natural enemy insect illumination induction devices in a preset time period, and inducing natural enemy insects into a target farmland;
observing the quantity change information of natural enemy insects of the pests in real time through an infrared detection device, and drawing the quantity change information into a quantity-time change chart in real time;
if the number of the natural enemy insects in the target farmland is not more than the number threshold after the natural enemy insects are induced, setting a natural enemy insect induction network according to the preset number of natural enemy insect illumination induction equipment;
if the natural enemy insects are induced, the number of the natural enemy insects in the target farmland is larger than a number threshold value, the natural enemy insect illumination induction equipment is uniformly reduced, and the natural enemy insects are drained until the number of the natural enemy insects in the target farmland after induction is not larger than the number threshold value.
The third aspect of the present invention also provides a computer readable storage medium comprising a pesticide application control method program of a plant protection unmanned aerial vehicle, which when executed by a processor, implements the steps of the pesticide application control method of a plant protection unmanned aerial vehicle as described in any one of the above.
The invention discloses a pesticide application control method, a pesticide application control system and a pesticide application control medium for a plant protection unmanned aerial vehicle, and aims to provide a high-efficiency and accurate crop pesticide application scheme. The method comprises the following steps: first, basic information of a target farmland is acquired. Secondly, judging the enrichment condition of pests by using an image recognition technology, and generating a pest enrichment report. Pest control regimens are then generated based on the historical dosing and pest enrichment reports. According to the pest control scheme, an optimal pesticide application time period of the plant protection unmanned aerial vehicle is determined. Then, the crop is subjected to a pesticide application operation during the optimal pesticide application period. Finally, judging the effect of the crops after the pesticide application operation so as to evaluate the pesticide application effect. The invention realizes the integration of farmland information acquisition, insect pest monitoring, pesticide application time determination and pesticide application effect evaluation, and improves the pesticide application efficiency and accuracy of the plant protection unmanned aerial vehicle.
Drawings
FIG. 1 illustrates a flow chart of a method of controlling the dispensing of a plant protection drone of the present invention;
FIG. 2 illustrates a flow chart of the present invention for generating a pest control scheme;
FIG. 3 shows a flow chart of the present invention for applying a pesticide to a crop;
fig. 4 shows a block diagram of a pesticide delivery control system of a plant protection drone of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a method for controlling the application of pesticides by a plant protection unmanned aerial vehicle.
As shown in fig. 1, a first aspect of the present application provides a method for controlling pesticide application of a plant protection unmanned aerial vehicle, including:
s102, acquiring basic information of a target farmland, wherein the basic information of the farmland comprises position information of the target farmland, crop type information and insect pest information;
s104, judging pest enrichment conditions based on an image recognition technology, and generating a pest enrichment report;
s106, acquiring historical pesticide application doses of various crop planting areas in the target area, and generating a pest control scheme according to the historical pesticide application doses and pest enrichment reports;
S108, determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle according to a pest control scheme;
s110, performing pesticide application operation on crops based on a pest control scheme and an optimal pesticide application time period;
s112, judging the pesticide application effect on the crops after the pesticide application operation.
According to the embodiment of the invention, the basic farmland information comprises target farmland position information, crop type information and insect pest information, and specifically comprises the following steps:
acquiring target farmland position information based on GPS positioning equipment, wherein the position information comprises longitude and latitude data, constructing a map model, importing the target farmland position information into the map model, generating a target farmland map model, and displaying the target farmland map model in a preset display;
acquiring crop type information according to farmland planting information, wherein the crop type information comprises a crop name and a growth height;
and obtaining pest information in the farmland according to farmland investigation data, wherein the pest information comprises pest names and pest characteristics.
According to the embodiment of the invention, the pest enrichment condition is judged based on the image recognition technology, and a pest enrichment report is generated, specifically:
Acquiring video data in the process of enriching pests in a preset area, and extracting video frame images of the video data to obtain a video frame image set;
extracting pixel information of each frame image of the video frame image set;
calculating a pixel covariance matrix of the video frame image set according to the pixel information, and calculating a feature vector and a feature value of the video frame image set according to the pixel covariance matrix;
sequencing the characteristic values from large to small, selecting a first characteristic value as a main component of a characteristic vector, and projecting original pixel information onto the main component to obtain video frame image set data with one-time dimension reduction;
calculating a similarity matrix of the video frame image set data of one-time dimension reduction, and calculating the conditional probability between the data points by using the similarity matrix to obtain a probability distribution map;
initializing the positions of data points of the secondary dimension reduction, and calculating the conditional probability among the data points of the secondary dimension reduction according to the probability distribution diagram;
calculating KL divergence value according to the conditional probability between the probability distribution diagram and the data points of the secondary dimension reduction, and circularly adjusting the positions of the data points of the secondary dimension reduction until the KL divergence value reaches a preset divergence value to obtain a secondary dimension reduction video frame image set;
Constructing an image processing model based on an image recognition technology;
each frame of video frame image of the secondary dimension reduction video frame image set is imported into an image processing model, and the position of each pest is marked by a dot to obtain a marked image;
and judging the enrichment degree of the pests according to the marked image, and generating a pest enrichment report, wherein the pest enrichment report comprises the quantity of the pests in the target farmland.
In the embodiment of the invention, as the data volume of the video frame image set is huge, firstly, the video frame image set is subjected to primary dimension reduction processing through PCA, then the video frame image set is subjected to secondary dimension reduction through t-sne, and finally useful video frame data are obtained, redundant data in the video frame data are removed, the data processing efficiency of the system is improved, the data receiving delay phenomenon of the plant protection unmanned aerial vehicle caused by data processing is further improved, and the pesticide application accuracy of the plant protection unmanned aerial vehicle is improved.
Fig. 2 shows a flow chart of the present invention for generating a pest control scheme.
According to the embodiment of the invention, the historical pesticide application doses of all crop planting areas in the target area are obtained, and a pest control scheme is generated according to the historical pesticide application doses and a pest enrichment report, specifically:
S202, acquiring historical pesticide application doses of various crop planting areas in a target area, judging whether the historical pesticide application doses reach pollution standards, and if the historical pesticide application doses do not reach the pollution standards, generating a pesticide application control scheme of the plant protection unmanned aerial vehicle;
s204, if the pollution standard is met, acquiring phototactic information of natural enemy insects of pests in the target farmland, and setting a quantity threshold value of the natural enemy insects;
s206, initializing a preset number of natural enemy insect illumination induction devices in a preset time period, and inducing natural enemy insects into a target farmland;
s208, observing the quantity change information of natural enemy insects of the pests in real time through an infrared detection device, and drawing the quantity change information into a quantity-time change chart in real time;
s210, if the number of natural enemy insects in the target farmland is not more than a number threshold after the natural enemy insects are induced, setting a natural enemy insect induction network according to the preset number of natural enemy insect illumination induction equipment;
s212, if the number of the natural enemy insects in the target farmland is larger than the number threshold after the natural enemy insects are induced, uniformly reducing the natural enemy insect illumination induction equipment, and conducting drainage on the natural enemy insects until the number of the natural enemy insects in the target farmland after the induction is not larger than the number threshold.
In the embodiment of the invention, firstly, judging whether the historical application dosage reaches the pollution standard, if so, predating the pests by inducing natural enemy insects of the pests into the target farmland, avoiding further pollution to the environment caused by re-application, realizing the effects of environmental protection and harmless pest killing, monitoring the number of the natural enemy insects in real time, and avoiding the damage to crops due to excessive number of the natural enemy insects; the threshold value of the number of the natural enemy insects is the maximum natural enemy insects which cannot harm crops in a target farmland; the preset time period is the condition of the night, so that the influence of sun illumination on illumination equipment is avoided, and the natural enemy insect induction effect is poor.
According to the embodiment of the invention, the optimal pesticide application time period of the plant protection unmanned aerial vehicle is determined according to the pest enrichment report, and specifically comprises the following steps:
acquiring time stamp information of the video frame image;
drawing a quantity-time curve of pest quantity change based on time change according to the pest enrichment report and the time stamp information of the video frame image;
determining a time period with the maximum pest enrichment amount according to the change information of the amount-time curve;
And determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle according to the time period.
It should be noted that, by analyzing the time stamp information of the video frame image and combining the quantity information of the pests in the pest enrichment report in the target farmland, a quantity-time curve is drawn. The curve can clearly show the change condition of the pest quantity along with time, so that a farmland manager can intuitively know the dynamic change of pest enrichment condition; according to the change information of the quantity-time curve, the system can accurately determine the time period with the greatest pest enrichment quantity, so that the system is the optimal plant protection pesticide application time period, and in the time period, the pesticide application is performed by using a plant protection unmanned aerial vehicle, so that the pest population can be effectively aimed, the pesticide consumption is reduced, the plant protection effect is improved to the greatest extent, and the purposes of high efficiency, energy conservation and environmental protection are achieved.
Fig. 3 shows a flow chart of the application operation of the present invention to crops.
According to the embodiment of the invention, the pesticide application operation is carried out on crops based on the pest enrichment report and the optimal pesticide application time period, specifically:
s302, dividing a target farmland into N small areas in a grid mode, and displaying the divided small areas in the target farmland map model;
S304, acquiring image information of each small area based on a camera device of the plant protection unmanned aerial vehicle in the optimal pesticide application time period;
s306, extracting features of the image according to the image information, comparing the extracted features with pest features to obtain pests in the image, and counting the pests to obtain pest number information of each small area;
s308, if the number of pests is greater than the preset number, marking the small area as an area to be applied with the pesticide, acquiring the position information of the area to be applied with the pesticide, and marking in a target farmland map model;
s310, setting a start point and an end point of a pesticide application route of the plant protection unmanned aerial vehicle based on the position information of the region to be sprayed;
s312, searching the position information of the to-be-applied area based on Dijkstra algorithm to obtain the shortest path of the plant protection unmanned aerial vehicle for applying the pesticide, and forming a pesticide application route;
s314, regulating and controlling the pesticide application amount of the plant protection unmanned aerial vehicle at different positions in real time according to the number information of pests, the position information of the region to be pesticide applied and the crop type information.
The method is characterized in that the target farmland is divided into N small areas in a grid mode, so that the farmland is managed and applied more finely; the preset number is the maximum number of pests in crops and causing no harm to the crops, which is set by a farmland manager; the Dijkstra algorithm is an algorithm for solving the shortest path in the graph, and can help the plant protection unmanned aerial vehicle to plan an optimal application route, so that time and resources are saved; in the embodiment of the invention, the pesticide application amount of the plant protection unmanned aerial vehicle at different positions can be regulated in real time, so that the plant protection unmanned aerial vehicle can be flexibly regulated according to actual conditions, and the pesticide is used for the area with higher pest density, thereby reducing the pesticide waste and ensuring that crops are fully protected.
According to the embodiment of the invention, the judging effect of the pesticide application on the crops after the pesticide application operation is as follows:
selecting a preset percentage of post-application video data within a preset time of the post-application region, and extracting post-application video frame data;
performing optical flow vector calculation on the video frame data after the medicine application to obtain an optical flow field;
performing motion filtration on the optical flow field to obtain optical flow information of pest motion;
based on the optical flow information, thresholding is carried out on the optical flow field, and the connected pixel points form a pest motion track;
counting the movement tracks of the pests to obtain the number of the pests after the pesticide is applied;
comparing the number of the pests after the pesticide application with the enriched number of the pests, and judging the pesticide application effect.
The method is characterized in that a preset percentage of areas after the pesticide application operation are selected, video data are acquired to judge pesticide application effects, and data processing capacity can be reduced; the optical flow vector refers to the displacement of the pixel points in time in the continuous images, and the optical flow field is an image composed of the optical flow vectors of all the pixel points. The motion condition of each pixel point in the image can be known by calculating the optical flow field; comparing the number of the pests after the pesticide application with the pest enrichment number can objectively evaluate the effect of the pesticide application operation, if the number of the pests after the pesticide application is obviously reduced and is smaller than the pest enrichment number, the pesticide application operation is proved to obtain a better effect, and the number of the pests is successfully controlled, otherwise, if the number of the pests after the pesticide application is still larger, the pesticide application scheme may need to be further optimized to improve the pesticide application effect.
According to an embodiment of the present invention, further comprising:
acquiring multispectral training data of crops in different health states, wherein the health states comprise health, sub-health and unhealthy;
establishing a plant health evaluation model, and importing multispectral training data into the plant health evaluation model for training;
multispectral data of crops in a target farmland are obtained through a multispectral sensor;
preprocessing multispectral data, wherein the preprocessing comprises radiation correction and noise removal;
leading the pretreated multispectral data into a plant health evaluation model to evaluate the plant health, and judging the health state of the plant;
and controlling the starting time and the pesticide applying time of the plant protection unmanned aerial vehicle according to the health state of the plants.
In the embodiment of the invention, the health state of the plant is evaluated by establishing the plant health evaluation model, so that the monitoring of human resources on the health state of crops is reduced; and the management efficiency of crops is improved.
In addition, the method for acquiring the historical pesticide application doses of each crop planting area in the target area judges whether the historical pesticide application doses reach the pollution standard or not, and specifically comprises the following steps:
acquiring historical pesticide application amount, degradation period of pesticide application in soil and pesticide spraying soil residue of each crop planting area in a target area through farmland pesticide application data;
Determining pollution standard of pesticide components in soil according to environmental protection criteria;
calculating according to the historical pesticide application dosage, the degradation period of the pesticide in the soil and the pesticide spraying soil residue, and predicting the soil pesticide deposition;
and judging the deposition degree of the soil pesticide by the pollution standard, and judging that the historical pesticide application dosage reaches the pollution standard if the deposition degree of the pesticide exceeds the pollution standard.
It is to be noted that, through obtaining historical application dose and predicting soil pesticide deposit degree, judge whether the farmland reaches the pollution standard according to the deposit degree of soil pesticide, saved the detection to farmland soil, effectively saved the time, improved pollution judgement efficiency.
According to an embodiment of the present invention, further comprising:
acquiring growth cycle information, planting time information and historical environment data of crops in a target farmland;
predicting environmental data of crops in a preset growth period according to the growth period information, the planting time information and the historical environmental data of the crops, wherein the environmental data comprises air temperature, humidity, wind speed and rainfall;
acquiring pest species information appearing in different growth stages in a historical crop growth period;
Acquiring natural enemy insect species information of the insect according to the insect species information, and searching environment data of the natural enemy insect suitable for survival;
comparing the environmental data of the crops in a preset growth period with the environmental data suitable for survival of natural enemy insects to obtain natural enemy insect species suitable for survival in the preset growth period;
and acquiring phototactic information of the proper natural enemy insect species, and inducing the proper natural enemy insect species into a target farmland in a preset growth period of crops.
In the embodiment of the invention, the natural enemy insects which are suitable for living in the environment data can be induced according to the environment data of the predicted crops in different growth periods, so that the induction efficiency of the natural enemy insects is improved.
Fig. 4 shows a block diagram of a pesticide delivery control system of a plant protection drone of the present invention.
The second aspect of the present invention also provides a pesticide application control system 4 of a plant protection unmanned aerial vehicle, the system comprising: the memory 41 and the processor 42, wherein the memory includes a pesticide application control method program of the plant protection unmanned aerial vehicle, and when the pesticide application control method program of the plant protection unmanned aerial vehicle is executed by the processor, the following steps are realized:
Acquiring basic information of a target farmland, wherein the basic information of the farmland comprises position information of the target farmland, crop type information and insect pest information;
judging pest enrichment conditions based on an image recognition technology, and generating a pest enrichment report;
acquiring historical pesticide application doses of all crop planting areas in a target area, and generating a pest control scheme according to the historical pesticide application doses and pest enrichment reports;
according to the pest control scheme, determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle;
performing a pesticide application operation on the crop based on the pest control scheme and the optimal pesticide application time period;
judging the pesticide application effect of the crops after the pesticide application operation.
According to the embodiment of the invention, the basic farmland information comprises target farmland position information, crop type information and insect pest information, and specifically comprises the following steps:
acquiring target farmland position information based on GPS positioning equipment, wherein the position information comprises longitude and latitude data, constructing a map model, importing the target farmland position information into the map model, generating a target farmland map model, and displaying the target farmland map model in a preset display;
acquiring crop type information according to farmland planting information, wherein the crop type information comprises a crop name and a growth height;
And obtaining pest information in the farmland according to farmland investigation data, wherein the pest information comprises pest names and pest characteristics.
According to the embodiment of the invention, the pest enrichment condition is judged based on the image recognition technology, and a pest enrichment report is generated, specifically:
acquiring video data in the process of enriching pests in a preset area, and extracting video frame images of the video data to obtain a video frame image set;
extracting pixel information of each frame image of the video frame image set;
calculating a pixel covariance matrix of the video frame image set according to the pixel information, and calculating a feature vector and a feature value of the video frame image set according to the pixel covariance matrix;
sequencing the characteristic values from large to small, selecting a first characteristic value as a main component of a characteristic vector, and projecting original pixel information onto the main component to obtain video frame image set data with one-time dimension reduction;
calculating a similarity matrix of the video frame image set data of one-time dimension reduction, and calculating the conditional probability between the data points by using the similarity matrix to obtain a probability distribution map;
initializing the positions of data points of the secondary dimension reduction, and calculating the conditional probability among the data points of the secondary dimension reduction according to the probability distribution diagram;
Calculating KL divergence value according to the conditional probability between the probability distribution diagram and the data points of the secondary dimension reduction, and circularly adjusting the positions of the data points of the secondary dimension reduction until the KL divergence value reaches a preset divergence value to obtain a secondary dimension reduction video frame image set;
constructing an image processing model based on an image recognition technology;
each frame of video frame image of the secondary dimension reduction video frame image set is imported into an image processing model, and the position of each pest is marked by a dot to obtain a marked image;
and judging the enrichment degree of the pests according to the marked image, and generating a pest enrichment report, wherein the pest enrichment report comprises the quantity of the pests in the target farmland.
In the embodiment of the invention, as the data volume of the video frame image set is huge, firstly, the video frame image set is subjected to primary dimension reduction processing through PCA, then the video frame image set is subjected to secondary dimension reduction through t-sne, and finally useful video frame data are obtained, redundant data in the video frame data are removed, the data processing efficiency of the system is improved, the data receiving delay phenomenon of the plant protection unmanned aerial vehicle caused by data processing is further improved, and the pesticide application accuracy of the plant protection unmanned aerial vehicle is improved.
According to the embodiment of the invention, the historical pesticide application doses of all crop planting areas in the target area are obtained, and a pest control scheme is generated according to the historical pesticide application doses and a pest enrichment report, specifically:
acquiring historical pesticide application doses of all crop planting areas in a target area, judging whether the historical pesticide application doses reach pollution standards, and if the historical pesticide application doses do not reach the pollution standards, generating a pesticide application control scheme of the plant protection unmanned aerial vehicle;
if the pollution standard is met, acquiring phototactic information of natural enemy insects of the pests in the target farmland, and setting a quantity threshold value of the natural enemy insects;
initializing a preset number of natural enemy insect illumination induction devices in a preset time period, and inducing natural enemy insects into a target farmland;
observing the quantity change information of natural enemy insects of the pests in real time through an infrared detection device, and drawing the quantity change information into a quantity-time change chart in real time;
if the number of the natural enemy insects in the target farmland is not more than the number threshold after the natural enemy insects are induced, setting a natural enemy insect induction network according to the preset number of natural enemy insect illumination induction equipment;
if the natural enemy insects are induced, the number of the natural enemy insects in the target farmland is larger than a number threshold value, the natural enemy insect illumination induction equipment is uniformly reduced, and the natural enemy insects are drained until the number of the natural enemy insects in the target farmland after induction is not larger than the number threshold value.
In the embodiment of the invention, firstly, judging whether the historical application dosage reaches the pollution standard, if so, predating the pests by inducing natural enemy insects of the pests into the target farmland, avoiding further pollution to the environment caused by re-application, realizing the effects of environmental protection and harmless pest killing, monitoring the number of the natural enemy insects in real time, and avoiding the damage to crops due to excessive number of the natural enemy insects; the threshold value of the number of the natural enemy insects is the maximum natural enemy insects which cannot harm crops in a target farmland; the preset time period is the condition of the night, so that the influence of sun illumination on illumination equipment is avoided, and the natural enemy insect induction effect is poor.
According to the embodiment of the invention, the optimal pesticide application time period of the plant protection unmanned aerial vehicle is determined according to the pest enrichment report, and specifically comprises the following steps:
acquiring time stamp information of the video frame image;
drawing a quantity-time curve of pest quantity change based on time change according to the pest enrichment report and the time stamp information of the video frame image;
determining a time period with the maximum pest enrichment amount according to the change information of the amount-time curve;
And determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle according to the time period.
It should be noted that, by analyzing the time stamp information of the video frame image and combining the quantity information of the pests in the pest enrichment report in the target farmland, a quantity-time curve is drawn. The curve can clearly show the change condition of the pest quantity along with time, so that a farmland manager can intuitively know the dynamic change of pest enrichment condition; according to the change information of the quantity-time curve, the system can accurately determine the time period with the greatest pest enrichment quantity, so that the system is the optimal plant protection pesticide application time period, and in the time period, the pesticide application is performed by using a plant protection unmanned aerial vehicle, so that the pest population can be effectively aimed, the pesticide consumption is reduced, the plant protection effect is improved to the greatest extent, and the purposes of high efficiency, energy conservation and environmental protection are achieved.
According to the embodiment of the invention, the pesticide application operation is carried out on crops based on the pest enrichment report and the optimal pesticide application time period, specifically:
dividing a target farmland into N small areas in a grid mode, and displaying the divided small areas in the target farmland map model;
in the optimal pesticide application time period, acquiring image information of each small area based on a camera device of the plant protection unmanned aerial vehicle;
Extracting features of the image according to the image information, comparing the extracted features with pest features to obtain pests in the image, and counting the pests to obtain pest number information of each small area;
if the number of the pests is greater than the preset number, marking the small area as an area to be applied with the pesticide, acquiring the position information of the area to be applied with the pesticide, and marking in a target farmland map model;
setting a start point and an end point of a pesticide application route of the plant protection unmanned aerial vehicle based on the position information of the region to be subjected to pesticide application;
searching the position information of the to-be-applied area based on Dijkstra algorithm to obtain the shortest path of the plant protection unmanned aerial vehicle for applying the pesticide, and forming a pesticide application route;
and regulating and controlling the pesticide application amount of the plant protection unmanned aerial vehicle at different positions in real time according to the number information of pests, the position information of the region to be pesticide applied and the crop type information.
The method is characterized in that the target farmland is divided into N small areas in a grid mode, so that the farmland is managed and applied more finely; the preset number is the maximum number of pests in crops and causing no harm to the crops, which is set by a farmland manager; the Dijkstra algorithm is an algorithm for solving the shortest path in the graph, and can help the plant protection unmanned aerial vehicle to plan an optimal application route, so that time and resources are saved; in the embodiment of the invention, the pesticide application amount of the plant protection unmanned aerial vehicle at different positions can be regulated in real time, so that the plant protection unmanned aerial vehicle can be flexibly regulated according to actual conditions, and the pesticide is used for the area with higher pest density, thereby reducing the pesticide waste and ensuring that crops are fully protected.
According to the embodiment of the invention, the judging effect of the pesticide application on the crops after the pesticide application operation is as follows:
selecting a preset percentage of post-application video data within a preset time of the post-application region, and extracting post-application video frame data;
performing optical flow vector calculation on the video frame data after the medicine application to obtain an optical flow field;
performing motion filtration on the optical flow field to obtain optical flow information of pest motion;
based on the optical flow information, thresholding is carried out on the optical flow field, and the connected pixel points form a pest motion track;
counting the movement tracks of the pests to obtain the number of the pests after the pesticide is applied;
comparing the number of the pests after the pesticide application with the enriched number of the pests, and judging the pesticide application effect.
The method is characterized in that a preset percentage of areas after the pesticide application operation are selected, video data are acquired to judge pesticide application effects, and data processing capacity can be reduced; the optical flow vector refers to the displacement of the pixel points in time in the continuous images, and the optical flow field is an image composed of the optical flow vectors of all the pixel points. The motion condition of each pixel point in the image can be known by calculating the optical flow field; comparing the number of the pests after the pesticide application with the pest enrichment number can objectively evaluate the effect of the pesticide application operation, if the number of the pests after the pesticide application is obviously reduced and is smaller than the pest enrichment number, the pesticide application operation is proved to obtain a better effect, and the number of the pests is successfully controlled, otherwise, if the number of the pests after the pesticide application is still larger, the pesticide application scheme may need to be further optimized to improve the pesticide application effect.
According to an embodiment of the present invention, further comprising:
acquiring multispectral training data of crops in different health states, wherein the health states comprise health, sub-health and unhealthy;
establishing a plant health evaluation model, and importing multispectral training data into the plant health evaluation model for training;
multispectral data of crops in a target farmland are obtained through a multispectral sensor;
preprocessing multispectral data, wherein the preprocessing comprises radiation correction and noise removal;
leading the pretreated multispectral data into a plant health evaluation model to evaluate the plant health, and judging the health state of the plant;
and controlling the starting time and the pesticide applying time of the plant protection unmanned aerial vehicle according to the health state of the plants.
In the embodiment of the invention, the health state of the plant is evaluated by establishing the plant health evaluation model, so that the monitoring of human resources on the health state of crops is reduced; and the management efficiency of crops is improved.
In addition, the method for acquiring the historical pesticide application doses of each crop planting area in the target area judges whether the historical pesticide application doses reach the pollution standard or not, and specifically comprises the following steps:
acquiring historical pesticide application amount, degradation period of pesticide application in soil and pesticide spraying soil residue of each crop planting area in a target area through farmland pesticide application data;
Determining pollution standard of pesticide components in soil according to environmental protection criteria;
calculating according to the historical pesticide application dosage, the degradation period of the pesticide in the soil and the pesticide spraying soil residue, and predicting the soil pesticide deposition;
and judging the deposition degree of the soil pesticide by the pollution standard, and judging that the historical pesticide application dosage reaches the pollution standard if the deposition degree of the pesticide exceeds the pollution standard.
It is to be noted that, through obtaining historical application dose and predicting soil pesticide deposit degree, judge whether the farmland reaches the pollution standard according to the deposit degree of soil pesticide, saved the detection to farmland soil, effectively saved the time, improved pollution judgement efficiency.
According to an embodiment of the present invention, further comprising:
acquiring growth cycle information, planting time information and historical environment data of crops in a target farmland;
predicting environmental data of crops in a preset growth period according to the growth period information, the planting time information and the historical environmental data of the crops, wherein the environmental data comprises air temperature, humidity, wind speed and rainfall;
acquiring pest species information appearing in different growth stages in a historical crop growth period;
Acquiring natural enemy insect species information of the insect according to the insect species information, and searching environment data of the natural enemy insect suitable for survival;
comparing the environmental data of the crops in a preset growth period with the environmental data suitable for survival of natural enemy insects to obtain natural enemy insect species suitable for survival in the preset growth period;
and acquiring phototactic information of the proper natural enemy insect species, and inducing the proper natural enemy insect species into a target farmland in a preset growth period of crops.
In the embodiment of the invention, the natural enemy insects which are suitable for living in the environment data can be induced according to the environment data of the predicted crops in different growth periods, so that the induction efficiency of the natural enemy insects is improved.
The third aspect of the present invention also provides a computer readable storage medium, where the computer readable storage medium includes a pesticide application control method program of a plant protection unmanned aerial vehicle, where the pesticide application control method program of the plant protection unmanned aerial vehicle, when executed by a processor, implements the steps of the pesticide application control method of the plant protection unmanned aerial vehicle according to any one of the above.
The application discloses a pesticide application control method, a pesticide application control system and a pesticide application control medium for a plant protection unmanned aerial vehicle, and aims to provide a high-efficiency and accurate crop pesticide application scheme. The method comprises the following steps: first, basic information of a target farmland is acquired. Secondly, judging the enrichment condition of pests by using an image recognition technology, and generating a pest enrichment report. Pest control regimens are then generated based on the historical dosing and pest enrichment reports. According to the pest control scheme, an optimal pesticide application time period of the plant protection unmanned aerial vehicle is determined. Then, the crop is subjected to a pesticide application operation during the optimal pesticide application period. Finally, judging the effect of the crops after the pesticide application operation so as to evaluate the pesticide application effect. The application realizes the integration of farmland information acquisition, insect pest monitoring, pesticide application time determination and pesticide application effect evaluation, and improves the pesticide application efficiency and accuracy of the plant protection unmanned aerial vehicle.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. The pesticide application control method of the plant protection unmanned aerial vehicle is characterized by comprising the following steps of:
acquiring basic information of a target farmland, wherein the basic information of the farmland comprises position information of the target farmland, crop type information and insect pest information;
judging pest enrichment conditions based on an image recognition technology, and generating a pest enrichment report;
acquiring historical pesticide application doses of all crop planting areas in a target area, and generating a pest control scheme according to the historical pesticide application doses and pest enrichment reports;
according to the pest control scheme, determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle;
performing a pesticide application operation on the crop based on the pest control scheme and the optimal pesticide application time period;
judging the pesticide application effect of the crops after the pesticide application operation.
2. The method for controlling pesticide application of a plant protection unmanned aerial vehicle according to claim 1, wherein the basic farmland information acquisition includes the position information of the target farmland, the crop type information and the pest information, specifically:
acquiring target farmland position information based on GPS positioning equipment, wherein the position information comprises longitude and latitude data, constructing a map model, importing the target farmland position information into the map model, generating a target farmland map model, and displaying the target farmland map model in a preset display;
Acquiring crop type information according to farmland planting information, wherein the crop type information comprises a crop name and a growth height;
and obtaining pest information in the farmland according to farmland investigation data, wherein the pest information comprises pest names and pest characteristics.
3. The method for controlling pesticide application of a plant protection unmanned aerial vehicle according to claim 1, wherein the pest enrichment condition is judged based on an image recognition technology, and a pest enrichment report is generated, specifically:
acquiring video data in the process of enriching pests in a preset area, and extracting video frame images of the video data to obtain a video frame image set;
extracting pixel information of each frame image of the video frame image set;
calculating a pixel covariance matrix of the video frame image set according to the pixel information, and calculating a feature vector and a feature value of the video frame image set according to the pixel covariance matrix;
sequencing the characteristic values from large to small, selecting a first characteristic value as a main component of a characteristic vector, and projecting original pixel information onto the main component to obtain video frame image set data with one-time dimension reduction;
calculating a similarity matrix of the video frame image set data of one-time dimension reduction, and calculating the conditional probability between the data points by using the similarity matrix to obtain a probability distribution map;
Initializing the positions of data points of the secondary dimension reduction, and calculating the conditional probability among the data points of the secondary dimension reduction according to the probability distribution diagram;
calculating KL divergence value according to the conditional probability between the probability distribution diagram and the data points of the secondary dimension reduction, and circularly adjusting the positions of the data points of the secondary dimension reduction until the KL divergence value reaches a preset divergence value to obtain a secondary dimension reduction video frame image set;
constructing an image processing model based on an image recognition technology;
each frame of video frame image of the secondary dimension reduction video frame image set is imported into an image processing model, and the position of each pest is marked by a dot to obtain a marked image;
and judging the enrichment degree of the pests according to the marked image, and generating a pest enrichment report, wherein the pest enrichment report comprises the quantity of the pests in the target farmland.
4. The method for controlling pesticide application of a plant protection unmanned aerial vehicle according to claim 1, wherein the step of obtaining the historical pesticide application dose of each crop planting area in the target area and generating a pest control scheme according to the historical pesticide application dose and the pest enrichment report is specifically as follows:
acquiring historical pesticide application doses of all crop planting areas in a target area, judging whether the historical pesticide application doses reach pollution standards, and if the historical pesticide application doses do not reach the pollution standards, generating a pesticide application control scheme of the plant protection unmanned aerial vehicle;
If the pollution standard is met, acquiring phototactic information of natural enemy insects of the pests in the target farmland, and setting a quantity threshold value of the natural enemy insects;
initializing a preset number of natural enemy insect illumination induction devices in a preset time period, and inducing natural enemy insects into a target farmland;
observing the quantity change information of natural enemy insects of the pests in real time through an infrared detection device, and drawing the quantity change information into a quantity-time change chart in real time;
if the number of the natural enemy insects in the target farmland is not more than the number threshold after the natural enemy insects are induced, setting a natural enemy insect induction network according to the preset number of natural enemy insect illumination induction equipment;
if the natural enemy insects are induced, the number of the natural enemy insects in the target farmland is larger than a number threshold value, the natural enemy insect illumination induction equipment is uniformly reduced, and the natural enemy insects are drained until the number of the natural enemy insects in the target farmland after induction is not larger than the number threshold value.
5. A method for controlling the application of pesticides to a plant protection drone according to claim 3, wherein the determining the optimal period of time for the application of pesticides to the plant protection drone according to the pest control scheme is specifically:
Acquiring time stamp information of the video frame image;
drawing a quantity-time curve of pest quantity change based on time change according to the pest enrichment report and the time stamp information of the video frame image;
determining a time period with the maximum pest enrichment amount according to the change information of the amount-time curve;
and determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle according to the time period.
6. The method for controlling the pesticide application of a plant protection unmanned aerial vehicle according to claim 1, wherein the pesticide application operation is performed on crops based on a pest enrichment report and an optimal pesticide application time period, specifically:
dividing a target farmland into N small areas in a grid mode, and displaying the divided small areas in the target farmland map model;
in the optimal pesticide application time period, acquiring image information of each small area based on a camera device of the plant protection unmanned aerial vehicle;
extracting features of the image according to the image information, comparing the extracted features with pest features to obtain pests in the image, and counting the pests to obtain pest number information of each small area;
if the number of the pests is greater than the preset number, marking the small area as an area to be applied with the pesticide, acquiring the position information of the area to be applied with the pesticide, and marking in a target farmland map model;
Setting a start point and an end point of a pesticide application route of the plant protection unmanned aerial vehicle based on the position information of the region to be subjected to pesticide application;
searching the position information of the to-be-applied area based on Dijkstra algorithm to obtain the shortest path of the plant protection unmanned aerial vehicle for applying the pesticide, and forming a pesticide application route;
and regulating and controlling the pesticide application amount of the plant protection unmanned aerial vehicle at different positions in real time according to the number information of pests, the position information of the region to be pesticide applied and the crop type information.
7. The method for controlling the pesticide application of the plant protection unmanned aerial vehicle according to claim 1, wherein the method for judging the pesticide application effect on the crops after the pesticide application operation is specifically as follows:
selecting a preset percentage of post-application video data within a preset time of the post-application region, and extracting post-application video frame data;
performing optical flow vector calculation on the video frame data after the medicine application to obtain an optical flow field;
performing motion filtration on the optical flow field to obtain optical flow information of pest motion;
based on the optical flow information, thresholding is carried out on the optical flow field, and the connected pixel points form a pest motion track;
counting the movement tracks of the pests to obtain the number of the pests after the pesticide is applied;
comparing the number of the pests after the pesticide application with the enriched number of the pests, and judging the pesticide application effect.
8. The pesticide application control system of the plant protection unmanned aerial vehicle is characterized by comprising a storage and a processor, wherein the storage comprises a pesticide application control method program of the plant protection unmanned aerial vehicle, and when the pesticide application control method program of the plant protection unmanned aerial vehicle is executed by the processor, the following steps are realized:
acquiring basic information of a target farmland, wherein the basic information of the farmland comprises position information of the target farmland, crop type information and insect pest information;
judging pest enrichment conditions based on an image recognition technology, and generating a pest enrichment report;
acquiring historical pesticide application doses of all crop planting areas in a target area, and generating a pest control scheme according to the historical pesticide application doses and pest enrichment reports;
according to the pest control scheme, determining the optimal pesticide application time period of the plant protection unmanned aerial vehicle;
performing a pesticide application operation on the crop based on the pest control scheme and the optimal pesticide application time period;
judging the pesticide application effect of the crops after the pesticide application operation.
9. The system of claim 8, wherein the acquiring the historical pesticide application doses for each crop planting area in the target area generates a pest control scheme according to the historical pesticide application doses and the pest enrichment report, specifically:
Acquiring historical pesticide application doses of all crop planting areas in a target area, judging whether the historical pesticide application doses reach pollution standards, and if the historical pesticide application doses do not reach the pollution standards, generating a pesticide application control scheme of the plant protection unmanned aerial vehicle;
if the pollution standard is met, acquiring phototactic information of natural enemy insects of the pests in the target farmland, and setting a quantity threshold value of the natural enemy insects;
initializing a preset number of natural enemy insect illumination induction devices in a preset time period, and inducing natural enemy insects into a target farmland;
observing the quantity change information of natural enemy insects of the pests in real time through an infrared detection device, and drawing the quantity change information into a quantity-time change chart in real time;
if the number of the natural enemy insects in the target farmland is not more than the number threshold after the natural enemy insects are induced, setting a natural enemy insect induction network according to the preset number of natural enemy insect illumination induction equipment;
if the natural enemy insects are induced, the number of the natural enemy insects in the target farmland is larger than a number threshold value, the natural enemy insect illumination induction equipment is uniformly reduced, and the natural enemy insects are drained until the number of the natural enemy insects in the target farmland after induction is not larger than the number threshold value.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a pesticide application control method program of a plant protection unmanned aerial vehicle, which when executed by a processor, implements the pesticide application control method of a plant protection unmanned aerial vehicle according to any one of claims 1-7.
CN202310928867.2A 2023-07-27 2023-07-27 Pesticide application control method, system and medium for plant protection unmanned aerial vehicle Pending CN116686814A (en)

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CN117151353A (en) * 2023-11-01 2023-12-01 广东省农业科学院植物保护研究所 Intelligent litchi pest identification and ecological regulation method, system and medium
CN118124838A (en) * 2024-05-08 2024-06-04 杭州而墨农业技术有限公司 Seedling condition and pest and disease damage early warning patrol unmanned aerial vehicle and method
CN118278593A (en) * 2024-05-30 2024-07-02 安徽省农业科学院植物保护与农产品质量安全研究所 Pest control route planning method and system based on plant protection unmanned aerial vehicle
CN118378783A (en) * 2024-04-17 2024-07-23 深圳市佰乐弗电子科技有限公司 Insect capturing visual monitoring system based on Internet

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117151353A (en) * 2023-11-01 2023-12-01 广东省农业科学院植物保护研究所 Intelligent litchi pest identification and ecological regulation method, system and medium
CN117151353B (en) * 2023-11-01 2024-04-26 广东省农业科学院植物保护研究所 Intelligent litchi pest identification and ecological regulation method, system and medium
CN118378783A (en) * 2024-04-17 2024-07-23 深圳市佰乐弗电子科技有限公司 Insect capturing visual monitoring system based on Internet
CN118124838A (en) * 2024-05-08 2024-06-04 杭州而墨农业技术有限公司 Seedling condition and pest and disease damage early warning patrol unmanned aerial vehicle and method
CN118278593A (en) * 2024-05-30 2024-07-02 安徽省农业科学院植物保护与农产品质量安全研究所 Pest control route planning method and system based on plant protection unmanned aerial vehicle
CN118278593B (en) * 2024-05-30 2024-08-27 安徽省农业科学院植物保护与农产品质量安全研究所 Pest control route planning method and system based on plant protection unmanned aerial vehicle

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