A smart agricultural planting monitoring system based on internet of things big data

Abstract

The application discloses an agricultural planting monitoring system based on Internet of Things big data in the field of agricultural planting monitoring, which comprises an environment monitoring module, a pest monitoring module, an image acquisition module and a pest control module, and the environment monitoring module, the pest monitoring module, the image acquisition module and the pest control module communicate with each other; the environment monitoring module comprises a soil information acquisition unit and an air information acquisition unit; the pest monitoring module comprises a crop information acquisition unit and a pest type acquisition unit; the image acquisition module comprises a picture acquisition unit and a video acquisition unit; and the pest control module comprises a natural enemy attracting unit, a processing unit and a pesticide spraying unit. The application can accurately and efficiently acquire the pest conditions suffered by crops in the system coverage area, timely and effectively prevent and control the pests, and can kill the pests through the natural enemies of the pests in the area where the pesticide is not completely killed.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of agricultural planting monitoring, and specifically relates to a smart agricultural planting monitoring system based on Internet of Things big data. BACKGROUND

[0002] With the continuous development of Internet of Things and big data technology, China, as a large agricultural country, has gradually applied Internet of Things and big data technology to agriculture; and smart agricultural related technical equipment and technical systems have gradually appeared, which has brought great improvement in planting efficiency, planting control and many other aspects for traditional planting.

[0003] For example, the smart agricultural pest remote detection system described in Chinese patent document CN114332461A includes a data extraction module for distinguishing the type of crops in the farmland to be detected from big data and extracting the planting information of the crops of the type, and extracting the accompanying pest type data of the crops of the type based on big data; a region division module for dividing the farmland to be detected into regions; an image segmentation and identification module for segmenting the crop leaf surface image and identifying the characteristic pest spots of the accompanying pest type in each region obtained after segmentation processing; a pest spot spreading region identification module for calculating the pest spot area and the penetration rate of the farmland region, and marking the calculation results; a pest grade detection and judgment module for detecting and judging the pest grade of the farmland to be detected; and a smart agricultural pest remote detection method is also proposed to better realize the functions of the above system.

[0004] The above technical solution aims to solve the problem that small pests are not easy to collect by detecting the pest spots on plants to detect plant diseases and insect pests; however, in actual use, the following problems exist: when the plant appears a pest spot, the plant has been damaged by the pest, affecting the normal growth of the plant; and some pests will damage the plant in a scattered manner, so that they may not cause a large area of easily collected pest spots on the plant surface. This makes the pest monitoring effect unsatisfactory, and the crops cannot be accurately prevented and treated from diseases and insect pests as soon as possible. SUMMARY

[0005] The purpose of the present application is to provide a smart agricultural planting monitoring system based on Internet of Things big data, which can accurately and efficiently obtain the pest situation suffered by crops in the system coverage area, and timely and effectively prevent and treat plant diseases and insect pests, thereby reducing the probability of crop diseases and insect pests.

[0006] In order to achieve the above purpose, the technical solution of the present application is as follows: a smart agricultural planting monitoring system based on Internet of Things big data includes an environment monitoring module, a pest monitoring module, an image acquisition module and a pest control module, and the environment monitoring module, the pest monitoring module, the image acquisition module and the pest control module communicate with each other;

[0007] The environmental monitoring module comprises a soil information acquisition unit and an air information acquisition unit; the soil information acquisition unit is configured to acquire the temperature, humidity, water content and soil pH of the soil in the system coverage area in real time; the air information acquisition unit is configured to acquire the temperature, humidity and light intensity of the air in the system coverage area in real time;

[0008] The pest monitoring module comprises a crop information acquisition unit and a pest type acquisition unit; the crop information acquisition unit is configured to acquire the type of the crop planted in the system coverage area based on image recognition technology, and acquire the related information of the crop based on big data; the pest type acquisition unit is configured to acquire the type of the pest to which the crop is susceptible based on big data according to the type of the crop, and acquire the habits of the pest and the natural enemies of the pest based on big data, to generate pest data and natural enemy data, respectively;

[0009] The image acquisition module comprises a picture acquisition unit and a video acquisition unit; the image acquisition unit is configured to acquire images of the crops in the system coverage area at a preset time interval and a preset unit area to generate detection pictures; the video acquisition unit is configured to acquire real-time videos of the system coverage area to generate detection videos;

[0010] The pest control module comprises a natural enemy attracting unit, a processing unit and a pesticide spraying unit; the processing unit is configured to determine whether the system coverage area is suitable for the survival of the pest based on the information monitored by the environmental monitoring module, and identify whether there is a pest in each detection image when the system coverage area is suitable for the survival of the pest; if a pest is identified in the detection image, the pesticide spraying unit sprays pesticide on the area with the pest; if no pest is identified in the detection image, the natural enemy attracting unit sprays a specified natural enemy attractant on the crops in the system coverage area at a preset time interval and a preset unit area according to the pest data and the natural enemy data; the processing unit is further configured to identify whether the natural enemy of the pest stays in each detection picture, and calculate the stay time of the natural enemy of the pest in the area to which the detection picture belongs when the natural enemy of the pest is detected to stay in the detection picture; when the stay time of the natural enemy of the pest exceeds a preset threshold, the pesticide spraying unit sprays pesticide on the area where the natural enemy of the pest stays.

[0011] The technical principle of the above scheme is as follows:

[0012] Firstly, the environmental monitoring module monitors various environmental information in the system coverage area, and determines whether the current area is a climate prone to pests and diseases based on the various environmental information. At the same time, the image acquisition module starts to acquire images of each unit area in the system coverage area according to the preset time and the preset unit area, obtains the types of crops planted in each unit area based on the detection pictures and the detection videos, determines the types of crops, and then obtains the pests that the planted crops are prone to through big data. In the environment prone to pests and diseases, on the one hand, larger pests are directly identified through image acquisition, and for the larger pests that can be identified, drug spraying is directly performed to kill the pests; on the other hand, the attractant of the natural enemy of the pests is sprayed at a preset time interval in each unit area of the system coverage area to attract the natural enemy of the pests into the system coverage area. Since the natural enemy of the pests feeds on the pests, it will stay around the crops. Subsequently, whether the natural enemy of the pests stays in the same unit area for a long time is detected, and when the natural enemy of the pests stays around the crops for a long time, the drug spraying unit sprays drugs on the crop area where the natural enemy of the pests stays for a long time, so as to kill smaller pests that cannot be directly identified through image recognition.

[0013] The above scheme has the following beneficial effects:

[0014] 1. Compared with the prior art, the present scheme identifies the types of crops, obtains the pests that the crops are prone to based on big data, and screens the types of pests that the crops may be subjected to in combination with real-time climate and environmental conditions, thereby reducing the monitoring range of pests and improving the monitoring efficiency and accuracy. In the case that the crops in the system coverage area may be subjected to pests, on the one hand, the pests that can be identified through image recognition are directly killed; on the other hand, the natural enemy of the pests that is larger than the pests is attracted, and the natural enemy of the pests is then identified through image recognition and the staying time of the natural enemy of the pests is determined. The pests that cannot be identified through image recognition in the area where the natural enemy of the pests stays are killed. Compared with smaller pests, the natural enemy of the pests is larger and easier to collect through image acquisition.

[0015] In addition, since the natural enemy of the pests is attracted, the natural enemy of the pests is introduced on the basis of pesticide killing, which realizes pest control by pests. For the areas where pesticide killing is not complete, the natural enemy of the pests can assist in killing the pests, so that whether the crops have pests and diseases can be accurately determined and the effect of controlling pests and diseases of the crops can be improved.

[0016] 2. Compared with the existing technology, this solution collects images of various areas of crops within the system coverage area in a regular cycle, and combines it with video collection of the entire crop area. It can quickly kill identifiable pests when they are found, and accurately collect the activities of the pests' natural enemies by combining images and videos, and promptly and efficiently disinfect areas where pests may exist, achieving the effect of prevention first and combining prevention and control, and reducing the chance of crops being affected by diseases and pests.

[0017] In addition, this system adopts regional drug spraying, spraying drugs only in areas with pests, which can effectively avoid drug waste.

[0018] In summary, the present invention can accurately and efficiently obtain the pest conditions suffered by crops in the area covered by the system, and promptly and effectively prevent and control pests and diseases, thereby reducing the chances of crops being affected by pests and diseases.

[0019] Furthermore, the pest species acquisition unit is also used to acquire the size and appearance of the natural enemies of pests based on big data, and to sort the natural enemies according to their ease of collection based on their size and appearance to generate a natural enemy sequence. The natural enemy attractant unit preferentially acquires the natural enemy attractants corresponding to the easy-to-collect natural enemies according to the natural enemy sequence for spraying.

[0020] Beneficial effect: By luring natural enemies of pests that are easier to collect, the image acquisition module can more accurately collect the activities of natural enemies in the crop area, facilitating more accurate acquisition of the pest and disease conditions suffered by the crops.

[0021] Furthermore, the environmental monitoring module also includes a geographic information collection unit and a climate information collection unit. The geographic information collection unit is used to collect geographic information of the crop planting area, and the climate information collection unit is used to collect climate information of the crop planting area in real time. The pest species acquisition unit is also used to delete natural enemies that are difficult to attract in the natural enemy sequence based on the geographic information and climate information.

[0022] Beneficial effects: By combining the geographical location and meteorological conditions of crops, some natural enemies that are unlikely to appear under the current geographical location and meteorological conditions are deleted from the natural enemy sequence, avoiding the attraction of natural enemies that are unlikely to appear, resulting in failure in natural enemy attraction and the subsequent inability to accurately judge the situation of crop diseases and pests.

[0023] Furthermore, it also includes a remote control module, which includes a system control unit and a data transmission unit. The system control unit is used to remotely control the pest control module based on the Internet of Things, and the data transmission unit is used to send the operating data of each module in the system to the mobile terminal based on the Internet of Things.

[0024] Beneficial effects: the user can remotely observe the operation of each module in the system through the mobile terminal, and can understand the growth of crops by viewing the images and videos of crops in the system coverage area, and can actively control the pest control module to prevent and control pests, so that the user can more conveniently understand the growth of crops and can actively intervene in the protection of pests.

[0025] Further, the remote control module further comprises a data storage unit for recording detection pictures and detection videos and the operation of the pesticide spraying unit.

[0026] Beneficial effects: through the detection pictures and detection videos, the user can accurately obtain the types of pests that are prone to occur in the current crop planting area, and adjust the planting of crops in combination with the frequency of pesticide spraying.

[0027] Further, the environmental monitoring module further comprises a weather monitoring unit for obtaining future weather information, and the pesticide spraying unit adjusts the amount of pesticide spraying according to the future weather information.

[0028] Beneficial effects: by obtaining the possible weather changes in the future according to the future weather information, the pesticide spraying unit adjusts the amount of pesticide spraying according to the weather changes to avoid waste of pesticides.

[0029] Further, the environmental monitoring module, the pest monitoring module, the image acquisition module, the pest control module and the remote control module communicate with each other through the Internet of Things.

[0030] Beneficial effects: communication between modules through the Internet of Things facilitates timely mutual mobilization between modules.

[0031] Further, the pest control module further comprises a natural enemy release unit for storing natural enemies of pests and releasing natural enemies of pests when there are no natural enemies in the natural enemy sequence.

[0032] Beneficial effects: by actively releasing natural enemies, it is convenient to obtain the area of pests with small size that cannot be identified when natural enemies cannot be attracted.

[0033] Further, it further comprises a detection rod, a solar power generation assembly is installed at the top of the detection rod, a device rod is fixedly connected below the solar power generation assembly, a spraying head of the image acquisition unit, a video acquisition unit, a pesticide spraying unit and a spraying head of the natural enemy attracting unit are installed on the device rod respectively, a rotating rod is rotatably connected at the top of the detection rod, a wind deflector is fixedly connected to the rotating rod, an elastic brush is installed at the bottom of the wind deflector, and the elastic brush abuts against the solar panel of the solar power generation assembly.

[0034] The beneficial effect is that each electrical equipment is powered by the solar power generation assembly, since the detection rod is installed in the crop planting area (outdoor), when the wind force received by the wind deflector reaches a certain amount, the wind deflector will rotate, the elastic brush is driven to rotate at the same time to clean the solar panel, so that the solar power generation assembly maintains a better power generation level.In addition, the natural enemy attractant is sprayed by the spray head of the natural enemy attractant unit when the wind deflector rotates, so that the natural enemy attractant can spread farther, thereby improving the attraction effect of the natural enemy of the pest, and when the wind is large enough, the crops are temporarily scattered by the wind, so that the sprayed attractant or medicine can reach the inside of the crop planting area, so that the attractant and the medicine can play the best effect.

[0035] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0036] Fig. 1 The system structure block diagram of the embodiment of the present application is based on the big data of Internet of Things intelligent agricultural planting monitoring system.

[0037] Fig. 2 The basic flowchart of the embodiment of the present application is based on the big data of Internet of Things intelligent agricultural planting monitoring system.

[0038] Fig. 3 The detection rod structure schematic diagram of the embodiment of the present application is based on the big data of Internet of Things intelligent agricultural planting monitoring system. DETAILED DESCRIPTION

[0039] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary, only for explaining the present application, and cannot be understood as limiting the present application.

[0040] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "longitudinal", "transverse", "vertical", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0041] In the description of the present application, unless otherwise specified and limited, it is necessary to explain that the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be mechanical connection or electrical connection, it can be the communication inside two elements, it can be direct connection or indirect connection through intermediate medium, and the specific meaning of the above terms can be understood by those skilled in the art according to the specific circumstances.

[0042] The specific embodiments are further described in detail below:

[0043] The reference signs in the drawings of the specification include: remote control module 1, environment monitoring module 2, pest monitoring module 3, image acquisition module 4, pest control module 5, solar power generation component 6, device rod 7, image acquisition unit 8, spray head of pesticide spraying unit 9, spray head of natural enemy attracting unit 10, video acquisition unit 11, wind deflector 12, elastic brush 13, rotating rod 14.

[0044] Example one: as shown in the accompanying Figs. 1-3 : A smart agricultural planting monitoring system based on Internet of Things big data, comprising an environment monitoring module 2, a pest monitoring module 3, an image acquisition module 4 and a pest control module 5, the environment monitoring module 2, the pest monitoring module 3, the image acquisition module 4 and the pest control module 5 communicate with each other through the Internet of Things, the system as a whole adopts the Internet of Things architecture, the environment monitoring module 2, the pest monitoring module 3 and the image acquisition module 4 are arranged in the sensing layer to collect information and images; the pest control module 5 is arranged in the application layer to control pests according to the collected information and images.

[0045] The environment monitoring module 2 comprises a soil information acquisition unit, an air information acquisition unit and a weather monitoring unit; the soil information acquisition unit adopts a soil pH sensor and a soil temperature and humidity conductivity three-in-one sensor, which cooperates to complete real-time acquisition of the temperature, humidity, moisture content and soil acidity and alkalinity of the soil in the system coverage area, and through monitoring the temperature, humidity, acidity and alkalinity and moisture content of the soil, it can effectively obtain whether the soil in the system coverage area is suitable for the growth and development of pests at different times, and provide favorable conditions for subsequent analysis of pest species.

[0046] The air information acquisition unit adopts a temperature, humidity and light intensity three-in-one sensor, which completes real-time acquisition of the temperature, humidity and light intensity of the air in the system coverage area, and through analysis of the environmental air information, it can effectively obtain whether the current environment is suitable for the survival of pests.

[0047] The weather monitoring unit directly obtains future weather information through the Internet of Things, which provides a basis for subsequent pesticide spraying amount.

[0048] The above-mentioned environment monitoring module 2 completes the collection of the soil condition, the air condition and the weather condition in the system coverage area.

[0049] The pest monitoring module 3 includes a crop information acquisition unit and a pest type acquisition unit; the crop information acquisition unit is used to acquire the type of the crop planted in the system coverage area based on image recognition technology, and acquire the easy growth environment, disease control measures and other related information of the crop from the network based on big data, so as to ensure the accuracy and practicality of the crop information acquisition. The user can actively input the related information according to the actual situation.

[0050] The pest type acquisition unit is used to acquire the pest type that the crop is prone to based on the type of the crop and based on big data, and acquire the habits of the pest and the natural enemies of the pest based on big data, to generate pest data and natural enemy data respectively.

[0051] Specifically, for example, when planting wheat, the crop information acquisition unit can acquire the diseases that the wheat is prone to, including wheat stripe rust, leaf rust, stem rust, scab, loose smut, yellow dwarf, red dwarf, full-etch disease, gibberella disease, leaf spot disease, etc. The optimal temperature for the germination and emergence of wheat seeds is 15-20℃; the optimal temperature for the growth of wheat roots is 16-20℃, and the minimum temperature is 2℃, which is inhibited when it exceeds 30℃. When the temperature is 2-4℃, the tillering growth begins, and the optimal temperature is 13-18℃, which slows down when it is higher than 18℃. The wheat stem generally starts to elongate at 10℃ or above, forms a short and stout stem at 12-16℃, and is prone to overgrowth when it is higher than 20℃, resulting in weak stems and easy lodging. The optimal temperature for the wheat filling period is 20-22℃. When there is a lot of dry and hot wind and the average daily temperature is higher than 25℃, the filling process is shortened due to excessive water loss, resulting in a decrease in grain weight. The pests that the wheat is prone to include wheat aphids, wheat seed flies, sap-sucking insects, red spiders, leafhoppers, grubs, golden needle insects, mole crickets, wheat leaf bees, wheat stem flies, etc. The natural enemies of various pests include ladybugs, hoverflies, parasitic bees, aphid-eating wasps, aphid-eating wasps, crab spiders and grasshoppers, etc.

[0052] The image acquisition module 4 includes a picture acquisition unit and a video acquisition unit 11; the image acquisition unit 8 and the video acquisition unit 11 adopt the device + solar component mode, and the specific structure is shown in FIG. 2. Fig. 3As shown, the detection rod of the system is installed in the planting area, the solar power generation assembly 6 is installed at the top of the detection rod, the solar power generation assembly 6 converts light energy into electrical energy and stores the electrical energy for use of the electrical equipment, the equipment rod 7 is fixedly connected below the solar power generation assembly 6, the image acquisition unit 8 and the video acquisition unit 11 are respectively installed on the equipment rod 7, the image acquisition unit 8 and the video acquisition unit 11 both adopt high-definition cameras, the image acquisition unit 8 acquires images of crops in the system coverage area according to a preset time interval and a preset unit area to generate detection pictures, and the video acquisition unit 11 acquires real-time videos of the system coverage area to generate detection videos. Specifically, the interval time and the unit area can be adjusted according to the requirements of crop disease and pest control in different periods of planting crops, for general wheat planting, the time interval of image acquisition can be set to one hour to complete image acquisition of all areas in the system coverage area once, and the unit area can be set to 1 m2.

[0053] The rotating rod 14 is rotatably connected to the top of the detection rod, the rotating rod 14 is fixedly connected with the wind deflector 12, the elastic brush 13 is installed at the bottom of the wind deflector 12, and the elastic brush 13 abuts against the solar panel of the solar power generation assembly 6. The wind deflector 12 rotates, the attractant is sprayed by the spraying head 10 of the natural enemy attracting unit, the attractant can be diffused farther, the attracting effect of the natural enemy of the pest is improved, and when the wind is large enough, the crops are temporarily scattered, so that the sprayed attractant or medicine can reach the inside of the crop planting area, and the attractant and the medicine can play the best effect.

[0054] The pest control module 5 includes a natural enemy attracting unit, a processing unit and a medicine spraying unit; the spraying head 9 of the medicine spraying unit and the spraying head 10 of the natural enemy attracting unit are both installed on the equipment rod 7, and when the image acquisition unit 8 adjusts the angle to acquire images, the spraying head 9 of the medicine spraying unit and the spraying head 10 of the natural enemy attracting unit both adjust the angle together with the image acquisition unit 8.

[0055] The processing unit determines whether the system coverage area is suitable for pests to live according to the information monitored by the environment monitoring module 2, identifies whether there are pests in each detection image when the system coverage area is suitable for pests to live, sprays pesticides on the area where pests are present through the pesticide spraying unit if pests are identified in the detection image, and sprays the specified natural enemy attractant through the natural enemy attracting unit according to the pest data and the natural enemy data if no pests are identified in the detection image. The natural enemy attractant is sprayed on the crops in the system coverage area at a preset time interval and a preset unit area. The processing unit is also used to identify whether there are natural enemies of pests in each detection image, calculates the residence time of the natural enemies of pests in the area where the detection image belongs when the natural enemies of pests are detected in the detection image, and sprays pesticides on the area where the natural enemies of pests reside through the pesticide spraying unit when the residence time of the natural enemies of pests exceeds a preset threshold.

[0056] In order to avoid the inability to quickly attract natural enemies of pests, a natural enemy releasing unit is added in the pest control module 5, which is used to store natural enemies of pests and release the natural enemies of pests when there are no natural enemies in the natural enemy sequence.

[0057] For example, when wheat seed flies are detected in the wheat planted in the system coverage area, the processing module can quickly obtain the wheat seed fly pest through image recognition technology because the wheat seed fly is large in size and easy to identify through images, and the area where the image belongs is obtained, and the pesticide spraying unit sprays pesticides through the spraying head to control the wheat seed fly pest.

[0058] However, for small wheat aphids that are not easy to identify through images, the natural enemy attractant is sprayed through the spraying head 10 of the natural enemy attracting unit, and the spraying time interval and unit area of the natural enemy attractant are the same as the image acquisition time interval and unit area of the image acquisition unit 8. Since the natural enemies of wheat aphids, such as ladybugs, aphid flies, and parasitic bees, are large in size and easy to identify through images, the natural enemy attracting unit first needs to obtain the natural enemy attractant. During the image acquisition process of the image acquisition unit 8, the natural enemies of wheat aphids can be detected in the wheat planting area. Since ladybugs, aphid flies, and parasitic bees need to prey, they will stay in the wheat planting area for a long time. When the time reaches the set time, the pesticide spraying unit sprays pesticides on the area where the natural enemies of pests reside, thereby completing the control of the wheat aphids. At the same time, combined with future weather information, the pesticide spraying can be appropriately reduced for weather that will soon rain, and the pesticide spraying can be performed after the rain to avoid waste of pesticides.

[0059] Embodiment two: as shown in the accompanying Fig. 1As shown: compared with example one, the difference lies in that the pest species acquisition unit is also used for acquiring the size and appearance of the natural enemy of the pest based on big data, and the degree of easy collection of the natural enemy is sorted to generate a natural enemy sequence, and the natural enemy attracting unit sprays the natural enemy attractant corresponding to the easy-to-collect natural enemy according to the natural enemy sequence. The environment monitoring module 2 further comprises a geographic information acquisition unit and a climate information acquisition unit, the geographic information acquisition unit is used for acquiring geographic information of the crop planting area, and the climate information acquisition unit is used for acquiring real-time climate information of the crop planting area, and the pest species acquisition unit is also used for deleting the natural enemy not easy to attract in the natural enemy sequence according to the geographic information and the climate information.

[0060] For example, the natural enemies of wheat aphids include ladybugs, aphid flies, and parasitic bees, and the size sequence of the three is parasitic bees, aphid flies, and ladybugs; and the parasitic bees are easy to identify because their appearance color is greatly different from the color of wheat, so the natural enemy attractant is used to attract the parasitic bees first.

[0061] Example three: as shown in the accompanying Fig. 1 As shown: compared with example two, the difference lies in that the remote control module 1 is added, the remote control module 1 comprises a system control unit and a data transmission unit, the system control unit is used for remotely controlling the pest control module 5 based on the Internet of Things, and the data transmission unit is used for sending the running data of each module in the system to a mobile terminal based on the Internet of Things.

[0062] The remote control module 1 further comprises a data storage unit, the data storage unit is used for recording detection pictures and detection videos and the running condition of the pesticide spraying unit to produce system running logs; and the data storage unit is also used for acquiring the information collected by the environment monitoring module 2.

[0063] Specifically, the spraying of pesticides and natural enemy attractants is completed through mobile terminals such as mobile phones and computers, the pest control is completed through active intervention, the planting effect is obtained by checking the system running logs, and whether the current environment is suitable for crops is judged in combination with the information collected by the environment monitoring module 2, so as to facilitate the adjustment of the growth environment of crops.

[0064] The above is only an embodiment of the present application, and the well-known specific structure and / or characteristics in the scheme are not described in detail. It should be pointed out that for those skilled in the art, without departing from the structure of the present application, a number of modifications and improvements can be made, which should also be regarded as the protection scope of the present application, which will not affect the effect and practicality of the patent. The protection scope claimed in this application should be subject to the content of its claims, and the specific implementation mode and the like in the description can be used to explain the content of the claims.

Claims

1. An Internet of Things (IoT) big data-based smart agricultural planting monitoring system, characterized in that: The system comprises an environment monitoring module, a pest monitoring module, an image acquisition module and a pest control module, and the environment monitoring module, the pest monitoring module, the image acquisition module and the pest control module communicate with each other. The environment monitoring module comprises a soil information acquisition unit and an air information acquisition unit; the soil information acquisition unit is used for collecting the temperature, humidity, water content and soil pH of the soil in the system coverage area in real time; The air information acquisition unit is used for collecting the temperature, humidity and light intensity of the air in the system coverage area in real time; The pest monitoring module comprises a crop information acquisition unit and a pest type acquisition unit; the crop information acquisition unit is used for acquiring the type of the crop planted in the system coverage area based on image recognition technology, and acquiring the related information of the crop based on big data; the pest type acquisition unit is used for acquiring the type of the pest that the crop is susceptible to based on the type of the crop and big data, and acquiring the habits of the pest and the natural enemies of the pest based on big data, to generate pest data and natural enemy data respectively; The image acquisition module comprises a picture acquisition unit and a video acquisition unit; the image acquisition unit is used for acquiring images of the crops in the system coverage area at a preset time interval and a preset unit area to generate detection pictures; and the video acquisition unit is used for acquiring real-time videos of the system coverage area to generate detection videos; The pest control module comprises a natural enemy attracting unit, a processing unit and a pesticide spraying unit; The processing unit is used for determining whether the system coverage area is suitable for the survival of the pest based on the information monitored by the environment monitoring module, identifying whether there is a pest in each detection image when the system coverage area is suitable for the survival of the pest, spraying pesticide on the area with the pest if the detection image is identified to have the pest, spraying a specified natural enemy attractant on the crops in the system coverage area at a preset time interval and a preset unit area through the natural enemy attracting unit based on the pest data and the natural enemy data if the detection image is identified to have no pest, identifying whether there is a natural enemy of the pest staying in each detection picture, calculating the staying time of the natural enemy of the pest in the area where the detection picture belongs through the detection video when the natural enemy of the pest is detected to stay in the detection picture, and spraying pesticide on the area where the natural enemy of the pest stays through the pesticide spraying unit when the staying time of the natural enemy of the pest exceeds a preset threshold; The pest type acquisition unit is also used for acquiring the size and appearance of the natural enemy of the pest based on big data, and sorting the easy-to-collect degree of the natural enemy of the pest based on the size and appearance of the natural enemy of the pest to generate a natural enemy sequence, and the natural enemy attracting unit sprays the natural enemy attractant corresponding to the easy-to-collect natural enemy based on the natural enemy sequence; The system further comprises a detection rod, a solar power generation assembly is installed at the top of the detection rod, an equipment rod is fixedly connected below the solar power generation assembly, a spraying head of the image acquisition unit, a spraying head of the video acquisition unit, a spraying head of the pesticide spraying unit and a spraying head of the natural enemy attracting unit are installed on the equipment rod respectively, a rotating rod is rotatably connected to the top of the detection rod, a wind deflector is fixedly connected to the rotating rod, and an elastic brush is installed at the bottom of the wind deflector and abuts against the solar panel of the solar power generation assembly. 2.The IoT big data-based smart agricultural planting monitoring system according to claim 1, characterized in that: The environment monitoring module further comprises a geographic information acquisition unit and a climate information acquisition unit, the geographic information acquisition unit is used for acquiring geographic information of the crop planting area, the climate information acquisition unit is used for acquiring climate information of the crop planting area in real time, and the pest species acquisition unit is further used for deleting enemies in the enemy sequence that are not easy to attract according to the geographic information and the climate information. 3.The IoT big data based smart agriculture planting monitoring system according to claim 1, characterized in that: The remote control module further comprises a data storage unit, the data storage unit is used for recording detection pictures and detection videos and operation conditions of the pesticide spraying unit to produce system operation logs.

4. The IoT big data based smart agriculture plantation monitoring system as claimed in claim 1, wherein: The environment monitoring module further comprises a weather monitoring unit, the weather monitoring unit is used for acquiring future weather information, and the pesticide spraying unit adjusts the spraying amount according to the future weather information. 5.The IoT big data based smart agriculture plantation monitoring system according to claim 1, wherein: The environment monitoring module, the pest monitoring module, the image acquisition module, the pest control module and the remote control module communicate with each other through the Internet of Things. 6.The IoT big data based smart agriculture plantation monitoring system according to claim 1, wherein: The pest control module further comprises an enemy releasing unit, the enemy releasing unit is used for storing pest enemies and releasing the pest enemies when there is no enemy in the enemy sequence. 7.The IoT big data based smart agriculture plantation monitoring system as claimed in claim 1, wherein: ​

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