CN107315381B - Monitoring method for crop diseases and insect pests - Google Patents
Monitoring method for crop diseases and insect pests Download PDFInfo
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- CN107315381B CN107315381B CN201710649474.2A CN201710649474A CN107315381B CN 107315381 B CN107315381 B CN 107315381B CN 201710649474 A CN201710649474 A CN 201710649474A CN 107315381 B CN107315381 B CN 107315381B
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Abstract
The invention relates to the technical field of agricultural production, in particular to a crop disease and insect pest monitoring method which is carried out by utilizing an unmanned aerial vehicle and cloud computing and comprises the steps of establishing a crop disease and insect pest database, utilizing the unmanned aerial vehicle to cruise and shoot field crop images, computing, early warning, accurately shooting, carrying out secondary computing, guiding disease and insect pest control and the like.
Description
Technical Field
The invention relates to the technical field of agricultural production, in particular to a method for monitoring crop diseases and insect pests.
Background
At present, in agricultural production, the disease and insect pest progress condition can be tracked and monitored by using a remote sensing monitoring technology, the method is used for accurate prevention and control work, timely discovery and timely treatment are achieved, and early prevention and control are facilitated. The principle is that the plant diseases and insect pests cause the change of the properties of pigments, moisture, nitrogen elements and the like of the leaf cell structure of crops, so that the change of a reflection spectrum is caused, and the reflection spectrum of the plant diseases and insect pests and the reflection spectrum of the visible light to thermal infrared band of normal crops have obvious difference.
Unmanned remote monitoring has been used in limited applications for agricultural production in the united states, australia, etc. For example, in the united states, a planting user has a wheat field rust condition monitored by an unmanned aerial vehicle, and it is obvious from the wheat field rust condition where a serious disaster area exists. People also use an unmanned aerial vehicle to check the dodder in the alfalfa field, so that early prevention can be achieved before a large-scale outbreak of a disaster. However, these limited monitoring techniques cannot be widely applied to the whole-process production management of other common field crops due to the limitations of basic databases, computing equipment and monitoring methods.
As an aerial monitoring technology, agricultural remote sensing is a favorable means for promoting the precision of agriculture. Agricultural remote sensing monitoring mainly takes crops and soil as objects. In the visible light-near infrared spectrum wave band, the reflectivity of the crops is mainly influenced by the pigments, cell structures and water content of the crops, particularly, the visible light red light wave band has a strong absorption wave band, and the near infrared wave band has a strong reflection characteristic, so that the method can be used for monitoring the growth vigor of the crops, the quality of the crops, the plant diseases and insect pests of the crops and the like. The total reflectivity of the visible-near infrared spectrum of the soil is relatively low, and the visible spectrum band is mainly influenced by coloring components such as soil organic matters, ferric oxide and the like. Therefore, the inherent reflectance spectrum characteristics of land features such as soil and crops are the basis of agricultural remote sensing.
The application of satellite remote sensing means in agricultural production has been developed for a long time, but the satellite is easily affected by weather environment and has a long orbit period. Comparatively speaking, unmanned aerial vehicle flexibility is stronger, easy deployment. With the further promotion of unmanned aerial vehicle platforms, sensors and software technologies, the unmanned aerial vehicle can be used as a supplementary means of other remote sensing platforms such as satellites, and a more complete monitoring network can be built in agriculture.
Disclosure of Invention
In order to timely and efficiently monitor the possible diseases and pests in the whole process of field crop production management, the invention provides a crop disease and pest monitoring method.
The invention is realized by the following technical scheme:
a monitoring method for crop diseases and insect pests is carried out by utilizing an unmanned aerial vehicle and cloud computing, and comprises the following steps:
(1) establishing a crop disease and pest database: the method comprises the steps of shooting specific photos of different plant diseases and insect pests of field crops in each growth period and local photos with remarkable characteristics, and storing the specific photos, the sample photos of the various plant diseases and insect pests and prevention measures in a plant disease database in a classified manner to serve as comparison basis of cloud computing; the specific photo is a characteristic photo in a uniform format which is different from other diseases and insect pests or normal growth and development expressions when the crops are influenced by specific diseases, insect pests or poor management to normally grow and develop in a specific growth period; the local photos are characteristic photos of the local part, the limited plant number or the single plant of the field crops subjected to the diseases and insect pests and are different from the other disease and insect pest expressions in a unified format; the disease and pest database can be a single disease and pest database aiming at the production management of specific crops or a disease and pest database aiming at specific diseases and pests, and can also be a comprehensive database aiming at limited varieties of crops and diseases and pests thereof in a specific area or a more comprehensive large-scale comprehensive database;
(2) the method comprises the following steps of (1) utilizing an unmanned aerial vehicle to cruise and shoot field crop images: setting an unmanned aerial vehicle cruising route according to the landform and the growth condition of a crop planting area, continuously shooting crop high-definition images with the format consistent with the specific photo format of the pest and disease database by the unmanned aerial vehicle, and transmitting the images back to an unmanned aerial vehicle ground control station in real time for storage;
(3) calculating and early warning: comparing the field crop image obtained in the step (2) with the pictures of the disease and pest database frame by using cloud computing, screening out photos with the similarity of more than 20%, listing specific areas, and providing corresponding disease and pest early warning;
(4) and (3) accurate shooting: accurately shooting the specific area which is early-warned in the step (3) in a close range by the unmanned aerial vehicle, wherein the optical technical parameters and the picture format which are accurately shot are consistent with the local picture format and the sample picture format in the pest and disease damage database, and transmitting the optical technical parameters and the picture format back to the ground control station of the unmanned aerial vehicle in real time for storage;
(5) and (3) secondary calculation: comparing the accurate image shot in the step (4) with local photos and sample photos in a pest and disease database frame by frame through cloud computing, comprehensively considering factors such as weather conditions, crop growth conditions, other pest and disease influences, field fertilizer and water management conditions, terrain and landform, shooting angles and the like when the unmanned aerial vehicle shoots, screening out photos with similarity of more than 50%, and determining pest and disease range, type, characteristics, harm degree and prevention measures;
(6) and (3) guiding pest control: and guiding agricultural production management personnel to timely prevent and treat the crop diseases and insect pests in the disease and insect pest area according to the disease and insect pest prevention and treatment measures.
In the method for monitoring the crop diseases and insect pests, the similarity of the step (2) and/or the step (4) is corrected according to the weather condition, the crop growth condition, the influence of other diseases and insect pests, the field fertilizer and water management condition, the corresponding difference between the terrain and the landform, the shooting angle and the shooting time of the specific picture or the local picture and the sample picture when the unmanned aerial vehicle shoots so as to reduce the misjudgment and improve the early warning accuracy,
according to the monitoring method for the crop diseases and insect pests, the specific photos, the local photos and the sample photos comprise image data which are displayed when 2 or more than 2 common diseases and insect pests of a specific crop in a specific growth period occur simultaneously, pertinence is enhanced, operation efficiency is improved, the crop diseases and insect pests are monitored more timely and accurately, and crop production management is facilitated.
The invention provides a method for monitoring crop diseases and insect pests, which comprises the following steps of adding a field investigation step between the step (5) and the step (6) before disease and insect pest control: and (4) arranging technicians to check the pest and disease damage plots determined in the step (5) on the spot to confirm the occurrence condition of the pest and disease damage, properly adjusting the prevention and treatment measures provided in the step (5), accurately mastering the agricultural condition, timely, accurately and effectively adopting the proper prevention and treatment measures, and improving the prevention and treatment effect.
The monitoring method for crop diseases and insect pests can be applied in a matched manner by combining with an agricultural satellite remote sensing monitoring technology, and when agricultural satellite remote sensing monitoring shows that certain diseases and insect pests may occur in field crops, the monitoring method is adopted to accurately monitor target diseases and insect pests, so that the method is more timely and effective.
The method for monitoring the crop diseases and insect pests can be used for bulk crops such as rice, wheat, corn, soybean, cotton, rape and the like, can also be used for special crops such as sesame, sorghum, oat, mung bean, peanut, sugarcane and the like, and economic crops such as fruit trees, vegetables, flowers, nursery stocks, traditional Chinese medicinal materials and the like which are planted in large areas or vegetation crops in forests, lawns and wetlands.
Drawings
FIG. 1: schematic view of crop pest monitoring process.
Detailed Description
The present invention is further illustrated by the following specific examples.
Example 1:
a method for monitoring plant diseases and insect pests of single-season hybrid rice in lake and lake regions is carried out by using an unmanned aerial vehicle and cloud computing, and comprises the following steps:
(1) establishing a single-season hybrid rice disease and insect pest database in the lake basin: specific photos of different plant diseases and insect pests in each growth period of the single-season hybrid rice in the lake and lake regions and local photos with remarkable characteristics are taken, and the specific photos, the sample photos of the various plant diseases and insect pests and prevention and treatment measures are classified and stored in a plant disease and insect pest database to serve as comparison basis of cloud computing; the specific photo is a characteristic photo in a uniform format, which is different from other diseases and insect pests or normal growth and development expressions when the single-season hybrid rice in the lake basin is influenced by specific diseases, insect pests or poor management to normally grow and develop in a specific growth period; the local photo is a characteristic photo of a uniform format of the part, limited plant number or the difference of a single plant from other plant diseases and insect pests of the single-season hybrid rice in the lake basin; (ii) a
(2) The unmanned aerial vehicle is used for cruising and shooting images of single-season hybrid rice in lake and river areas: setting an unmanned aerial vehicle cruising route according to the topography and the growth condition of the single-season hybrid rice paddy field in the lake-lake basin, continuously shooting a crop high-definition image with a format consistent with that of a specific photo in a pest and disease database by an unmanned aerial vehicle, transmitting the crop high-definition image back to an unmanned aerial vehicle ground control station in real time, and storing the crop high-definition image;
(3) calculating and early warning: comparing the image of the single-season hybrid rice in the lake-lake basin obtained in the step (2) with the image of the disease and pest database frame by using cloud computing, screening out photos with the similarity of more than 20%, listing specific areas, and providing corresponding disease and pest early warning;
(4) and (3) accurate shooting: accurately shooting the specific area which is early-warned in the step (3) in a close range by the unmanned aerial vehicle, wherein the optical technical parameters and the picture format which are accurately shot are consistent with the local picture format and the sample picture format in the pest and disease damage database, and transmitting the optical technical parameters and the picture format back to the ground control station of the unmanned aerial vehicle in real time for storage;
(5) and (3) secondary calculation: comparing the accurate image shot in the step (4) with local photos and sample photos in a pest and disease database frame by frame through cloud computing, comprehensively considering factors such as weather conditions, crop growth conditions, other pest and disease influences, field fertilizer and water management conditions, terrain and landform, shooting angles and the like when the unmanned aerial vehicle shoots, screening out photos with similarity of more than 50%, and determining pest and disease range, type, characteristics, harm degree and prevention measures;
(6) and (3) guiding pest control: and guiding agricultural production management personnel to timely prevent and treat the crop diseases and insect pests in the disease and insect pest area according to the disease and insect pest prevention and treatment measures.
Example 2:
a monitoring method for red spider hazards in citrus orchard in Gannan region is carried out by using an unmanned aerial vehicle and cloud computing, and comprises the following steps:
(1) establishing a red spider hazard database in a citrus orchard in the Jiangxian region: shooting specific photos of red spiders harming the citrus orchard in the Jiangnan region in each growth period and local photos with remarkable characteristics, and storing the specific photos and the local photos together with sample photos of the red spiders harming the citrus orchard in the Jiangnan region and prevention and treatment measures in a disease and pest database in a classified manner to serve as comparison bases of cloud computing; the specific photo is a characteristic photo with a unified format which is different from other pest and disease damage expressions when oranges in a citrus garden in the Gannan region are damaged by red spiders in different growth periods; the local photo is a characteristic photo of a uniform format which is different from the expressions of other plant diseases and insect pests of the local and limited plant numbers of red spiders in the citrus orchard in the Jiangxian region or a single plant;
(2) the unmanned aerial vehicle is used for cruising and shooting red spider hazard images in citrus orchard in Jiangxian region: setting a cruising route of the unmanned aerial vehicle according to the landform and the growth condition of the citrus orchard in the Jiangnan region, continuously shooting a high-definition image of crops with the format consistent with that of a specific photo in a pest and disease database by the unmanned aerial vehicle, and transmitting the high-definition image back to a ground control station of the unmanned aerial vehicle in real time for storage;
(3) calculating and early warning: comparing the red spider damage images obtained in the citrus orchard in the Jiangnan region in the step (2) with the pest and disease database pictures frame by using cloud computing, screening out photos with the similarity of more than 20%, listing specific areas, and providing red spider damage early warning;
(4) and (3) accurate shooting: accurately shooting the specific area which is early-warned in the step (3) in a close range by the unmanned aerial vehicle, wherein the optical technical parameters and the picture format which are accurately shot are consistent with the local picture format and the sample picture format in the pest and disease damage database, and transmitting the optical technical parameters and the picture format back to the ground control station of the unmanned aerial vehicle in real time for storage;
(5) and (3) secondary calculation: comparing the accurate image shot in the step (4) with local photos and sample photos in a pest and disease database frame by frame through cloud computing, comprehensively considering factors such as weather conditions, crop growth conditions, other pest and disease influences, field fertilizer and water management conditions, terrain and landform, shooting angles and the like when the image is shot by an unmanned aerial vehicle, screening out photos with similarity of more than 50%, and determining the range, the damage degree and the prevention and control measures of the red spiders;
(6) field investigation, arranging technicians to carry out field inspection on the red spider harmful land blocks determined in the step (5), confirming the occurrence condition of the red spiders, and properly adjusting the prevention and treatment measures proposed in the step (5);
(7) and (3) guiding pest control: and (4) guiding agricultural production management personnel to timely control red spiders in the red spider harmful areas according to pest control measures.
Example 3:
a method for monitoring pine moth damage of pine woods in Dabie mountain areas is characterized in that the pine moth damage of the pine woods in the Dabie mountain areas is monitored by using an unmanned aerial vehicle and cloud computing according to pine moth occurrence conditions monitored and early warned by an agricultural satellite, and the method comprises the following steps:
(1) establishing a pine moth hazard database of a pine forest in a Dabie mountain area: shooting specific photos of pine moth damage in each growing period of the pine forest in the Dabie mountain area and local photos with remarkable characteristics, and storing the specific photos, the sample photos of the pine moth damage in the pine forest in the Dabie mountain area and control measures in a disease and pest database in a classified manner to serve as comparison basis of cloud computing; the specific photo is a characteristic photo with a uniform format which is different from other pest and disease damage expressions when pine forest in Dabie mountain area is damaged by pine caterpillar in each growth period; the local photo is a characteristic photo of a uniform format, wherein the local photo is a part, a limited number of plants or a single plant of a pine forest in the Dabie mountain area damaged by pine caterpillars and is different from other pest and disease damage expressions;
(2) utilize unmanned aerial vehicle to cruise and shoot big mountain area pine forest pine moth harm image: setting an unmanned aerial vehicle cruising route according to the landform and the growth condition of the pine forest in the Dabie mountain area, continuously shooting a crop high-definition image with a format consistent with that of a specific photo in a pest and disease database by the unmanned aerial vehicle, and transmitting the image back to an unmanned aerial vehicle ground control station in real time for storage;
(3) calculating and early warning: comparing the pine moth damage image of the pine forest in the Dabie mountain area obtained in the step (2) with the pictures of the pest database frame by using cloud computing, screening out the pictures with the similarity of more than 20%, listing specific areas, and providing corresponding pine moth early warning;
(4) and (3) accurate shooting: accurately shooting the specific area which is early-warned in the step (3) in a close range by the unmanned aerial vehicle, wherein the optical technical parameters and the picture format which are accurately shot are consistent with the local picture format and the sample picture format in the pest and disease damage database, and transmitting the optical technical parameters and the picture format back to the ground control station of the unmanned aerial vehicle in real time for storage;
(5) and (3) secondary calculation: comparing the accurate image shot in the step (4) with local photos and sample photos in a pest and disease database frame by frame through cloud computing, comprehensively considering factors such as weather conditions, crop growth conditions, other pest and disease influences, terrain and topography, shooting angles and the like when the image is shot by an unmanned aerial vehicle, screening out photos with similarity of more than 50%, and determining pine moth damage range, characteristics, damage degree and prevention measures;
(6) and (3) guiding pest control: and guiding agricultural production management personnel to prevent and control the pine moth damage area in time according to the pine moth prevention and control measures.
Claims (2)
1. A monitoring method for crop diseases and insect pests is carried out by utilizing an unmanned aerial vehicle and cloud computing, and comprises the following steps:
(1) establishing a crop disease and pest database: the method comprises the steps of shooting specific photos of different plant diseases and insect pests of field crops in each growth period and local photos with remarkable characteristics, and storing the specific photos, the sample photos of the various plant diseases and insect pests and prevention measures in a plant disease database in a classified manner to serve as comparison basis of cloud computing; the specific photo is a characteristic photo in a uniform format which is different from other diseases and insect pests or normal growth and development expressions when the crops are influenced by specific diseases, insect pests or poor management to normally grow and develop in a specific growth period; the local photos are characteristic photos of the local part, the limited plant number or the single plant of the field crops subjected to the diseases and insect pests and are different from the other disease and insect pest expressions in a unified format;
(2) the method comprises the following steps of (1) utilizing an unmanned aerial vehicle to cruise and shoot field crop images: setting an unmanned aerial vehicle cruising route according to the landform and the growth condition of a crop planting area, continuously shooting crop high-definition images with the format consistent with the specific photo format of the pest and disease database by the unmanned aerial vehicle, and transmitting the images back to an unmanned aerial vehicle ground control station in real time for storage;
(3) calculating and early warning: comparing the field crop image obtained in the step (2) with the pictures of the disease and pest database frame by using cloud computing, screening out photos with the similarity of more than 20%, listing specific areas, and providing corresponding disease and pest early warning;
(4) and (3) accurate shooting: accurately shooting the specific area which is early-warned in the step (3) in a close range by the unmanned aerial vehicle, wherein the optical technical parameters and the picture format which are accurately shot are consistent with the local picture format and the sample picture format in the pest and disease damage database, and transmitting the optical technical parameters and the picture format back to the ground control station of the unmanned aerial vehicle in real time for storage;
(5) and (3) secondary calculation: comparing the accurate image shot in the step (4) with local photos and sample photos in a pest and disease database frame by frame through cloud computing, screening out photos with similarity of more than 50%, and determining pest and disease range, type, characteristics, harm degree and prevention measures;
(6) field investigation: arranging technicians to check the field of the pest and disease damage plots determined in the step (5), confirming the occurrence condition of the pest and disease damage, and properly adjusting the prevention and treatment measures proposed in the step (5);
(7) and (3) guiding pest control: performing crop pest control according to pest control measures;
and (3) correcting the similarity of the photos obtained in the step (2) and/or the step (4) according to the weather condition, the crop growth condition, the influence of other plant diseases and insect pests, the field fertilizer and water management condition, the terrain and landform, the shooting angle and the corresponding difference between the special photo or the local photo and the sample photo when the unmanned aerial vehicle shoots.
2. A method of monitoring a pest and disease damage of a crop as claimed in claim 1 wherein the specific photograph, the partial photograph and the sample photograph include image data of 2 or more species of pest and disease damage commonly observed in a specific crop during a specific growth period occurring simultaneously.
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