CN112640870A - Plant protection unmanned aerial vehicle-based pest control system and method - Google Patents
Plant protection unmanned aerial vehicle-based pest control system and method Download PDFInfo
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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
- A01M7/0089—Regulating or controlling systems
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64C—AEROPLANES; HELICOPTERS
- B64C39/00—Aircraft not otherwise provided for
- B64C39/02—Aircraft not otherwise provided for characterised by special use
- B64C39/024—Aircraft not otherwise provided for characterised by special use of the remote controlled vehicle type, i.e. RPV
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
Abstract
The invention provides a plant protection unmanned aerial vehicle-based pest control system and method; based on the technology of machine vision and deep learning and carry on multiple sensor, to different crops, establish the field knowledge base of different crops growth condition and plant diseases and insect pests protection knowledge, discern the growth condition and the plant diseases and insect pests condition of crops through deep learning technology and multiple sensor, can real-time acquisition crops growth condition and the relevant information of plant diseases and insect pests, greatly reduced the work load that artifical observation detected, improve agricultural production efficiency. Aiming at the problem of pesticide proportioning, the invention constructs a knowledge base in the field of pesticide spraying proportioning according to the related knowledge of the pesticide dosage proportioning of crops, applies a deep learning technology, and calculates different pesticide spraying proportioning aiming at different crops, thereby saving the step of manual proportioning calculation and greatly improving the working efficiency of agricultural plant protection.
Description
Technical Field
The invention belongs to the technical field of intelligent agriculture, and relates to a plant protection unmanned aerial vehicle-based pest control system and method.
Background
China is a large country for agricultural production and also a large country for population. The total population is about 14 hundred million people, is the top of the world population, is still in the steady increase of the total population at present, and has very important significance on food safety and resident life quality of China if the yield of food products can be ensured. The data show that the total sowing area of Chinese grains is about 17.56 hundred million acres by 2018, wherein the area of rice is 3.02 hundred million acres, the area of wheat is 2.43 hundred million acres, and the area of corn is 4.21 hundred million acres.
While the crop yield in China is continuously increased, the crop yield is reduced along with the influence of diseases, worm grass and mouse damage, and the grain loss caused by the crop yield loss directly accounts for 17% of the total grain yield every year. The data show that the occurrence area of insect, grass and mouse damage is increased from about 19 hundred million acres in 1980 year to about 69 hundred million acres in 2017 year, and the occurrence area of rice, wheat and corn is 13.2, 9.0 and 10.8 hundred million acres respectively during 2018 year. Meanwhile, the total area of prevention and treatment per year is increased to about 80 hundred million acres. Although China strengthens prevention and control measures in the aspect of crop diseases, insects and rats to form more prevention and control technical systems, chemical pesticides are still used as main means for large-area prevention and control of the diseases, the insects and the rats in China at present, the total prevention and control accounts for about 80%, and green pollution-free prevention and control needs to be further strengthened. Because China lags behind the chemical pesticide spraying concept and the relatively developed countries of pesticide spraying machinery, the problems of excessive and blind use of chemical pesticides in farmlands in part of areas are caused. According to the statistics of 2018, the commodity quantity of pesticides in China is 85 ten thousand tons, the quantity of pesticides in China is reduced by 27 ten thousand tons, and the usage amount of chemical pesticides in unit area of China is 2.3 times of that in the United states. The blindly used chemical pesticide not only affects the quality of crops, but also pollutes the ecological environment of agricultural land. Therefore, research on agricultural intelligent plant protection equipment and an effective spraying technology suitable for the current agricultural situation of China is urgently needed, the use cost of chemical pesticides of crops is reduced, the unit yield of the crops is increased, the quality of the crops is improved, and the grain planting benefit is improved. Due to the characteristic of various landforms in planting areas in south and north China, the used pesticide instrument modes have certain difference, for example, in the planting areas in the plains in the north, large-scale plant protection machines are generally used for spraying pesticide, and special terraces such as terraces in hilly and mountainous areas in the south are still applied by using manual medicine carrying machine modes. The traditional pesticide spraying modes are mainly divided into three types: the large-scale plant protection machinery spouts the medicine, and artifical spout medicine and fixed wing aircraft spout the medicine mode. The labor intensity and the danger are high due to manual spraying operation; the ground machinery is difficult to advance in the field and easy to destroy cultivated land; the fixed wing aircraft has the defects of high plant protection operation cost, site restriction and the like. The traditional plant protection operation mode is not enough to realize the new proposition of 'implementing a green production mode and enhancing the sustainable development capability of agriculture'. With the development of random science and technology, multi-rotor unmanned aerial vehicles are favored by more and more people and the application field is more and more extensive. The unmanned aerial vehicle for agriculture and forestry plant protection also takes place at the same time. Plant protection unmanned aerial vehicle gives medicine to poor free of constraints in place with low costs, mobility are strong, can effectively reduce the harm to operating personnel in the process of giving medicine to poor free of constraints in place, and plant protection unmanned aerial vehicle has presented the trend of rapid development from 2014 so far. As a new high-efficiency plant protection mode, unmanned aerial vehicle plant protection in China has become an important research direction in the field of plant protection. For ground plant protection machinery, unmanned aerial vehicle plant protection not only does not receive the restriction of field geographical conditions such as hills, mountain region, inhomogeneous little plot, is favorable to the plant protection of crop later stage growth vigor such as maize, rice moreover. Compared with a piloted fixed wing aircraft, the unmanned aerial vehicle plant protection system does not need a special take-off and landing field and has the advantages of flexible field obstacle avoidance and the like, can fly to an accurate position quickly to accurately process a target area, and can be programmed in advance to carry out unmanned aerial vehicle operation in a navigation mode.
But current plant protection unmanned aerial vehicle's function is more single, generally only possesses the function that the pesticide sprayed, can only be observed and protect by the people to the growth situation of crops and the information of plant diseases and insect pests. Greatly increasing the workload of the agricultural plant protection. The proportion of the pesticide is an important link for agriculture and forestry plant protection. The varieties of crops are various, the pesticide proportioning conditions of different crops are also different greatly, and the manual calculation of the pesticide proportioning is time-consuming and labor-consuming.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the pest control system and method based on the unmanned aerial vehicle can acquire crop information states through the unmanned aerial vehicle, quickly judge pest types by combining an identification model in the system, quickly calculate different pesticide ratios based on a constructed pesticide ratio system, and greatly improve the working efficiency of agricultural value maintenance.
In order to solve the technical problems, the invention adopts the technical scheme that:
the invention provides a plant protection unmanned aerial vehicle-based pest control system, which comprises a plant protection unmanned aerial vehicle and a ground control station, wherein the plant protection unmanned aerial vehicle is connected with the ground control station through wireless transmission; the ground control station is provided with a multi-sensing information fusion system, a pest and disease identification system based on deep learning and a pesticide proportioning system based on deep learning.
Furthermore, the multi-sensing information acquisition box takes a microcomputer as a carrier, and the microcomputer is loaded with a temperature and humidity detection module, a gas detection module, a vision module, a 4G image transmission module and a data transmission image transmission module.
Further, the gas detection module comprises a gas detection module for detecting CO and CO2、N2、O2And a gas detection sensor for ethylene gas.
Further, the multi-sensor information fusion system performs information fusion based on a Bayesian estimation algorithm.
Further, the pest and disease identification system is constructed with a pest and disease field knowledge base, and a pest and disease identification model is constructed and trained through a deep learning technology.
Further, the pest and disease identification model is constructed based on an open-source deep learning frame tensoflow + kersa + YOLO model, and a YOLO identification model is adopted for target identification.
Further, the pesticide proportioning system is constructed with a pesticide proportioning field knowledge base, and a pesticide proportioning calculation model is constructed and trained through a deep learning technology.
A pest control method using the pest control system is characterized by comprising the following steps:
collecting information, wherein the plant protection unmanned aerial vehicle collects the temperature and humidity of crops, the gas information of a growth environment and the surface condition of the crops;
the information transmission module is used for transmitting the collected crop information to the ground control station, and workers monitor the information state of crops;
information fusion, namely performing information fusion on the collected crop information, eliminating useless and wrong information, reserving correct and useful components, and realizing information optimization;
and (5) information processing, namely judging the types of plant diseases and insect pests and calculating a pesticide proportioning scheme according to the fused optimization information.
Further, before the information processing, the method further comprises:
constructing a pest identification system, and constructing a pest field knowledge base of the crops by collecting pest information of various crops; constructing the crop disease and pest identification model and training the model by applying a deep learning technology, so that the crop disease and pest model has the capability of identifying crop diseases and pests;
a pesticide proportioning system is built, a pesticide proportioning field knowledge base is built by collecting data and calculation formulas of pesticide proportioning of various crops, a deep learning technology is applied, a pesticide proportioning calculation model is built, and the model is trained, so that the pesticide proportioning calculation model can calculate different pesticide proportioning according to different crops and different plant diseases and insect pests.
Further, plant protection unmanned aerial vehicle is to humiture, growing environment of cropsCollecting gas information and surface condition of crops comprises detecting temperature and humidity of crops by temperature and humidity sensor, and collecting gas information and surface condition of crops by CO and CO2、N2、O2And the gas detection sensor for ethylene gas collects the gas information of the growing environment, and the surface condition of crops is collected through the high-definition camera.
Compared with the prior art, the invention has the beneficial effects that:
(1) carry on the information acquisition of multiple sensor through the microcomputer and carry out the crops growth condition to reach unmanned aerial vehicle ground control station through the data transmission picture biography module with the information transmission who gathers, can be in real time effectual information of obtaining crops growth condition environment and plant diseases and insect pests.
(2) By the information fusion technology, useless and wrong information can be eliminated, correct and useful components are reserved, information optimization is achieved, optimized crop environment information is obtained, and subsequent identification processing is more accurate and convenient.
(3) By constructing a crop disease and pest field knowledge base, constructing and training a crop disease and pest identification model by applying deep learning, and identifying the type and degree of the disease and pest according to the information of an expert base.
(4) By constructing a knowledge base in the field of crop pesticide proportioning, applying a deep learning technology to construct and train a crop pesticide proportioning calculation model, and according to an expert base and information of crop diseases and insect pests identified in the previous step, different pesticide proportioning can be carried out according to different crops and different disease and insect pest conditions.
Drawings
The detailed structure of the invention is described in detail below with reference to the accompanying drawings
FIG. 1 is a diagram of the overall system of the present invention;
FIG. 2 is a functional block diagram of the plant protection unmanned aerial vehicle of the present invention;
FIG. 3 is a flow chart of the system of the present invention;
FIG. 4 is a flow chart of an embodiment of the present invention;
FIG. 5 is a Bayesian information fusion flow chart;
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and are intended to be illustrative of the invention and should not be construed as limiting the invention.
Example 1
Referring to fig. 1 and 2, the invention provides a plant protection unmanned aerial vehicle-based pest control system, which comprises a plant protection unmanned aerial vehicle and a ground control station, wherein the plant protection unmanned aerial vehicle is connected with the ground control station through wireless transmission, and the plant protection unmanned aerial vehicle comprises an unmanned aerial vehicle body, a multi-sensor information acquisition box and a pesticide spraying device; the ground control station is provided with a multi-sensing information fusion system, a pest and disease identification system based on deep learning and a pesticide proportioning system based on deep learning.
Specifically, the multi-sensor information acquisition box takes a microcomputer as a carrier, preferably a Raspberry Pi (Raspberry Pi), 1/2/4 USB interfaces and a 10/100 Ethernet interface (A type does not have a network port) are arranged around the Raspberry Pi, the multi-sensor information acquisition box can be connected with a keyboard, a mouse and a network cable, and meanwhile, the multi-sensor information acquisition box is provided with a television output interface of video analog signals and an HDMI high-definition video output interface. Raspberry group carries on the humiture detection module, gaseous detection module, vision module, 4G picture pass the module and pass the module with the biography picture, as shown in fig. 2 for plant protection unmanned aerial vehicle functional module diagram:
the temperature and humidity detection module is used for detecting the temperature and humidity of the growth of crops by a temperature and humidity sensor;
the gas detection module comprises a gas detection module for detecting CO and CO2、N2、O2And a gas detection sensor for gases such as ethylene and the like, which is used for detecting the gas condition of the crop growing environment;
the vision module is used for detecting the surface condition and the pest and disease damage condition of agricultural products by a Shimei shooting camera or a digital camera;
the 4G image transmission module is used for sending signals required by transmission by the 4G module;
the data transmission module transmits the collected crop information data and the image information,
and the pesticide spraying module is used for spraying pesticides to crops suffering from diseases and insect pests.
Above-mentioned crops information includes the humiture of crops, production environment gas and the crops surface condition, and the information transmission who will gather through the number biography picture passes the module and reaches unmanned aerial vehicle bottom surface control station, supplies the information state of staff real-time supervision crops.
Furthermore, the multi-sensor information fusion system performs information fusion on the collected crop information based on a Bayesian estimation algorithm.
Bayesian estimation provides a means for data fusion, and is a common method for fusing multi-sensor high-level information in a static environment. The method combines sensor information according to a probability principle, the measurement uncertainty is expressed by conditional probability, and when the observation coordinates of a sensor group are consistent, the data of the sensors can be directly fused, but in most cases, the data measured by the sensors are fused by adopting Bayesian estimation in an indirect mode.
The multi-bayesian estimation uses each sensor as a bayesian estimation, synthesizes the associated probability distribution of each individual object into a combined posterior probability distribution function, provides the final fusion value of the multi-sensor information by using the likelihood function of the combined distribution function as the minimum, and provides a feature description of the whole environment by fusing the information with a prior model of the environment, as shown in fig. 5, which is a bayesian information fusion flow chart.
Through information fusion, the optimization of information can be realized, and agricultural workers can obtain better real-time information of crops.
A crop pest and disease identification model is constructed on the basis of an open-source deep learning frame tensoflow + kersa + yolo model at a ground control station, and the model is trained to have the capability of identifying crop pests and diseases. The YOLO recognition model is adopted for target recognition, so that the types and characteristics of plant diseases and insect pests can be rapidly and accurately recognized.
Meanwhile, a set of knowledge base in the field of crop pesticide proportioning is constructed. A set of pesticide ratio calculation model is built by applying a deep learning technology, and the model is trained, so that the model can calculate different pesticide ratios according to different crops and different plant diseases and insect pests.
Example 2
The invention also provides a pest control method adopting the pest control system, and the pest control method comprises the following steps:
collecting information, wherein the plant protection unmanned aerial vehicle collects the temperature and humidity of crops, the gas information of a growth environment and the surface condition of the crops;
the information transmission module is used for transmitting the collected crop information to the ground control station, and workers monitor the information state of crops;
information fusion, namely performing information fusion on the collected crop information, eliminating useless and wrong information, reserving correct and useful components, and realizing information optimization;
and (5) information processing, namely judging the types of plant diseases and insect pests and calculating a pesticide proportioning scheme according to the fused optimization information.
It should be noted that before information processing, that is, before judging pest types and calculating pesticide ratios, a crop pest identification system and a pesticide distribution system need to be constructed, specifically:
establishing a crop disease and insect pest identification system, and establishing a crop disease and insect pest field knowledge base by collecting disease and insect pest data of various crops; constructing the crop disease and pest identification model and training the model by applying a deep learning technology, so that the crop disease and pest model has the capability of identifying crop diseases and pests;
a crop pesticide proportioning system is constructed, a pesticide proportioning field knowledge base is constructed by collecting data and calculation formulas of various crop pesticide proportioning, a pesticide proportioning calculation model is constructed by applying a deep learning technology, and the model is trained, so that the pesticide proportioning calculation model can calculate different pesticide proportioning according to different crops and different plant diseases and insect pests.
Further, plant protection unmanned aerial vehicle gathers the humiture of crops, growth environment gas information and the crop surface condition including, detects the crops humiture through temperature and humidity sensor, through CO, CO2、N2、O2And the gas detection sensor for ethylene gas collects the gas information of the growing environment, and the surface condition of crops is collected through the high-definition camera.
In conclusion, aiming at the problems of low pest protection efficiency and pesticide proportioning, the pest control system and method based on the plant protection unmanned aerial vehicle are designed, the machine vision and deep learning technology is applied, various sensors are carried, and a field knowledge base of different crop growth conditions and pest protection knowledge is established aiming at different crops. The growth condition and the pest condition of the crops are identified through the deep learning technology and various sensors, the related information of the growth condition and the pest condition of the crops can be obtained in real time, the workload of manual observation and detection is greatly reduced, and the agricultural production efficiency is improved. Aiming at the problem of pesticide proportioning, the invention constructs a pesticide spraying proportioning field knowledge base according to the related knowledge of the pesticide dosage proportioning of crops, applies a deep learning technology, and carries out different pesticide spraying proportioning calculations aiming at different crops, thereby saving the step of manual proportioning calculation and greatly improving the working efficiency of agricultural plant protection.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A plant protection unmanned aerial vehicle-based pest control system is characterized by comprising a plant protection unmanned aerial vehicle and a ground control station, wherein the plant protection unmanned aerial vehicle is connected with the ground control station through wireless transmission; the ground control station is provided with a multi-sensing information fusion system, a pest and disease identification system based on deep learning and a pesticide proportioning system based on deep learning.
2. A pest control system according to claim 1, wherein the multi-sensing information collection box uses a microcomputer as a carrier, and the microcomputer is loaded with a temperature and humidity detection module, a gas detection module, a vision module, a 4G diagram transmission module and a data transmission diagram transmission module.
3. A pest control system according to claim 2 wherein the gas detection module includes means for detecting CO, CO2、N2、O2And a gas detection sensor for ethylene gas.
4. The intelligent agricultural system of claim 1, wherein the multi-sensory information fusion system performs information fusion based on a bayesian estimation algorithm.
5. A pest control system according to claim 4 wherein the pest recognition system is configured to build a crop pest field knowledge base and to build and train a crop pest recognition model by deep learning techniques.
6. A pest control system according to claim 5 wherein the pest identification model is constructed based on an open source deep learning framework tensoflow + kersa + YOLO model, and the YOLO identification model is used for target identification.
7. A pest control system as claimed in claim 1 wherein the pesticide proportioning system is constructed with a knowledge base of the field of crop pesticide proportioning, and the model for identifying the pesticide proportioning of crops is constructed and trained by deep learning techniques.
8. A pest control method using a pest control system according to any one of claims 1 to 7, characterised in that the pest control method comprises:
collecting information, wherein the plant protection unmanned aerial vehicle collects the temperature and humidity of crops, the gas information of a growth environment and the surface condition of the crops;
the information transmission module is used for transmitting the collected crop information to the ground control station, and workers monitor the information state of crops;
information fusion, namely performing information fusion on the collected crop information, eliminating useless and wrong information, reserving correct and useful components, and realizing information optimization;
and (5) information processing, namely judging the types of plant diseases and insect pests and calculating a pesticide proportioning scheme according to the fused optimization information.
9. A pest control method according to claim 8, further including, prior to said information processing:
constructing a pest identification system, and constructing a crop pest field knowledge base by collecting pest information of various crops; constructing the crop disease and pest identification model and training the model by applying a deep learning technology, so that the model has the capability of identifying the crop disease and pest;
a pesticide proportioning system is built, a crop pesticide proportioning field knowledge base is built by collecting data and calculation formulas of various crop pesticide proportioning, a crop pesticide proportioning calculation model is built by applying a deep learning technology, and the model is trained so that the model can calculate different pesticide proportioning according to different crops and different plant diseases and insect pests.
10. A pest control method as claimed in claim 8, characterised in thatIn that, plant protection unmanned aerial vehicle gathers the humiture of crops, growth environment gas information and the crops surface condition including, detects the crops humiture through temperature and humidity sensor, through CO, CO2、N2、O2And the gas detection sensor for ethylene gas collects the gas information of the growing environment, and the surface condition of crops is collected through the high-definition camera.
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