Herbaceous plant planting monitoring system and use method thereof

Abstract

The invention discloses a herbaceous plant planting monitoring system and a use method thereof. The herbaceous plant planting monitoring system comprises an environment monitoring module, a plant growth monitoring unmanned aerial vehicle, a pest control module, a local meteorological website, a system server and a remote mobile terminal. The environment monitoring module, the plant growth monitoring unmanned aerial vehicle, the pest control module and the local meteorological website collect environment data, plant growth data, pest control data and local meteorological website data respectively. And using an optimal germination period deviation index formula, an optimal growth period deviation index formula and a pest index formula to reversely deduce the difference between the actual growth factor and the theoretical growth factor, and performing real-time feedback control to realize an optimal planting result. According to the method, the environment, the plant growth state, the pest and disease damage state and weather forecast are combined, data of the optimal germination period and the optimal growth period of the herbaceous plants are compared and analyzed one by one, whole-process supervision and closed-loop feedback control of herbaceous plant planting are achieved, and the optimal planting effect is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of plant planting, and in particular to a herbaceous plant planting monitoring system and a use method thereof. Background Art

[0002] Herbs are precious resources given by nature, such as ginseng, angelica, bupleurum, spinach, rice, soybean, chrysanthemum, lavender, etc. They are not only of great value in the fields of medicine, food, and horticultural appreciation, but also can prevent soil erosion, increase soil fertility, improve soil structure, reduce greenhouse effect and purify the air. However, traditional herb planting management in my country is mainly based on manual experience management, which is time-consuming and labor-intensive, and the planting quality is underestimated, lacking accuracy, effectiveness and reliability.

[0003] With the development of science and technology, the herbaceous plant planting industry has also seen the emergence of a supervision method based on agricultural information technology. For example, Chinese patent CN118918397A discloses a system and method for interpreting paddy field planting areas using remote sensing image technology to track and manage changes in paddy fields, the direction of water flow, the state of terrain and changes in topography, which promotes the scientific management of agricultural production, the effective use of land and the protection of the ecological environment, but the agricultural production and planting process is not effectively supervised. Therefore, how to effectively use agricultural information technology to achieve quality monitoring of the optimal germination period and optimal growth period of herbaceous plants is a technical problem that urgently needs to be solved. Summary of the invention

[0004] In view of this, the present invention proposes a herbaceous plant planting monitoring system and a method of using the same to solve the existing quality monitoring problem of the optimal germination period and the optimal growth period of herbaceous plants.

[0005] The technical solution of the present invention is implemented as follows: On the one hand, the present invention provides a herbaceous plant planting monitoring system and a method of using the same, which includes an environmental monitoring module, a plant growth monitoring drone, a pest control module, a local meteorological website, a system server, and a remote mobile terminal;

[0006] The environmental monitoring module is connected to the 4G / 5G network of the system server, and is used to measure the data information of the herbaceous plant growth environment and send it to the system server, and receive the control command of the system server to perform heating, humidification, supplementary lighting and fertilization;

[0007] The plant growth monitoring drone is connected to the system server 4G / 5G network to monitor the whole process data of herbaceous plants from germination, seedling growth, flowering and fruiting and send it to the system server;

[0008] The pest control module is connected to the 4G / 5G network of the system server, and is used to monitor the data of herbaceous plant pests and diseases and send it to the system server, and receive the control command of the system server to kill insects;

[0009] The local weather website is connected to the system server through the Internet, so that the local weather website provides local weather information to the system server;

[0010] The system server is connected to the remote mobile terminal 4G / 5G network, and is used to receive data from the environmental monitoring module, the plant growth monitoring drone, the pest and disease control module and the local meteorological website. According to the optimal germination period and optimal growth period of the herbaceous plant, the optimal germination period deviation index formula, the optimal growth period deviation index formula and the pest and disease index formula are used to reversely deduce the difference between actual growth factors and theoretical growth factors, and real-time feedback control is performed to achieve the optimal planting effect, and specific information is sent to the remote mobile terminal to facilitate real-time query and management by the administrator.

[0011] Preferably, the environmental monitoring module includes an air temperature and humidity sensor, an air wind speed sensor, a CO2 concentration sensor, a light intensity sensor, a soil temperature and humidity sensor, a soil pH sensor, a nitrogen, phosphorus and potassium sensor, an RTU, an air heater, an air humidifier, a light fill light and a water and fertilizer control valve;

[0012] The air temperature and humidity sensor, the air wind speed sensor, the CO2 concentration sensor, the light intensity sensor, the soil temperature and humidity sensor, the soil pH value sensor, and the nitrogen, phosphorus and potassium sensor are connected to the RTU 0-5V or 4-20mA signal to collect air temperature and humidity, air wind speed, CO2 concentration, light intensity, soil temperature and humidity, soil pH value, nitrogen, phosphorus and potassium data and send them to the system server through RTU with TCP Modbus protocol 4G / 5G signal;

[0013] The RTU is electrically connected to the air heater, the air humidifier, the lighting fill light, and the water and fertilizer control valve TTL, and is used to receive the TCP Modbus protocol commands sent by the system server to complete the control of the air heater, the air humidifier, the lighting fill light, and the water and fertilizer control valve.

[0014] Preferably, the plant growth monitoring drone is equipped with a long-focal-length optical 4G / 5G camera, which can collect video images of herbaceous plant growth and send them to the system server to identify seed germination rate, plant height, crown size, leaf number, leaf morphology, leaf color, flowering rate and fruiting rate.

[0015] Preferably, the pest control module includes multiple close-focus optical WiFi cameras, RTUs and insect traps. The close-focus optical cameras can collect video images of herbaceous plant leaves and send them to the system server via the RTU via 4G / 5G signals using the FTP protocol. The system server identifies mosaics, leaf spots and insect holes on the leaves and sends TCPModbus protocol commands to the RTU via 4G / 5G signals to control the operation of the insect trap.

[0016] Preferably, the local meteorological website can provide the system server with local 14-day forecast weather conditions, air temperature, humidity and wind forecast data.

[0017] Preferably, the RTU uses the video telemetry terminal F9164 produced by Four-Faith Digital Technology Group, and the system server is compiled using Visual Studio Code.

[0018] In another aspect, the present invention provides a method for using a herbaceous plant planting monitoring system, comprising the following steps:

[0019] S1. The environmental monitoring module receives the Modbus query command from the system server and sends the air temperature X0, air humidity X1, air speed X2, CO2 concentration X3, light intensity X4, soil temperature X5, soil humidity X6, soil pH value X7, nitrogen, phosphorus and potassium value X8 to the system server;

[0020] S2. The system server averages the information X0-X8 in a unit time and calculates the actual average air temperature, the actual average air humidity, the actual average air speed, the actual average CO2 concentration, the actual average light intensity, the actual average soil temperature, the actual average soil humidity, the actual average soil pH value, and the actual average nitrogen, phosphorus and potassium value;

[0021] S3. The system server obtains the weather conditions, air temperature, humidity and wind forecast data for the past 14 days from the local meteorological website, and calculates the expected 14-day average air temperature, expected average air humidity, expected average air wind speed, expected average CO2 concentration, expected average light intensity, expected average soil temperature, expected average soil moisture, expected average soil pH value, and expected average nitrogen, phosphorus and potassium values;

[0022] S4. The system server substitutes the actual data and the estimated average data into the optimal germination period deviation index formula to calculate the optimal germination period deviation index. If the deviation index exceeds the threshold, the deviations from to, to, and to are analyzed item by item, and corresponding measures are taken to control the air heater, air humidifier, light supplementary lamp, and water and fertilizer control valve until the deviation index is controlled within the threshold range;

[0023] S5. The plant growth monitoring drone sends video images of herbaceous plants to the system server in real time, and uses image recognition to identify seed germination rate, plant height, canopy size, leaf number, leaf morphology, leaf color, flowering rate and fruiting rate;

[0024] S6. The system server compares the seed germination rate, plant height, canopy size, leaf number, leaf shape, leaf color, flowering rate and fruiting rate with the optimal growth period deviation index formula in real time. If any of the actual data is lower than the optimal growth period deviation index threshold value in the same period in history, the differences in historical meteorological conditions and water and fertilizer conditions between the actual growth period and the optimal growth period are analyzed, and corresponding measures such as air heating, air humidification, light supplementation and water and fertilizer addition are taken until the actual data is controlled within the optimal growth period deviation index threshold value;

[0025] S7. The pest and disease control module sends video images of some leaves of herbaceous plants to the system server in real time, uses image recognition method to identify the number of mosaics, leaf spots, insect holes, aphids, wilting, and root rot in the video images, and uses quantity estimation method to estimate the actual number of mosaics, leaf spots, insect holes, aphids, wilting, and root rot. According to the pest and disease index formula, the pest and disease risk index is calculated. If the risk index threshold is exceeded, the system server sends a command to the pest and disease control module to control the insect attractant lamp to turn on, and at the same time sends real-time data to the remote mobile terminal to remind the administrator to manually use sticky insect boards or drugs to disinfect.

[0026] The herbaceous plant planting monitoring system and the use method thereof of the present invention have the following beneficial effects compared with the prior art:

[0027] (1) Use drones equipped with long-focal-length optical 4G / 5G cameras to collect video images of herbaceous plant growth to identify seed germination rate, plant height, canopy size, leaf number, leaf morphology, leaf color, flowering rate, and fruiting rate;

[0028] (2) The 14-day weather forecast data was introduced and the optimal germination period deviation index formula was constructed to predict the growth environment of herbaceous plants in advance and improve it, thereby increasing the seed germination rate;

[0029] (3) The optimal growth period deviation index formula and the pest index formula were constructed to reversely calculate the difference between the actual growth factors and the theoretical growth factors during the growth period of herbaceous plants, and perform real-time feedback control to achieve the optimal planting effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A structural diagram of a herbaceous plant planting monitoring system of the present invention;

[0032] Figure 2 It is a structural diagram of the environment monitoring module of the present invention;

[0033] Figure 3 This is a structural diagram of the pest control module of the present invention;

[0034] Figure 4 The present invention is a workflow diagram of a herbaceous plant planting monitoring system and a method of using the same. DETAILED DESCRIPTION

[0035] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Embodiment 1

[0037] like Figure 1-3 As shown, a herbaceous plant planting monitoring system of the present invention includes an environmental monitoring module, a plant growth monitoring drone, a pest control module, a local meteorological website, a system server and a remote mobile terminal;

[0038] The environmental monitoring module is connected to the 4G / 5G network of the system server, and is used to measure the data information of the herbaceous plant growth environment and send it to the system server, and receive the control command of the system server to perform heating, humidification, supplementary lighting and fertilization;

[0039] The plant growth monitoring drone is connected to the system server 4G / 5G network to monitor the whole process data of herbaceous plants from germination, seedling growth, flowering and fruiting and send it to the system server;

[0040] The pest control module is connected to the 4G / 5G network of the system server, and is used to monitor the data of herbaceous plant pests and diseases and send it to the system server, and receive the control command of the system server to kill insects;

[0041] The local weather website is connected to the system server through the Internet, so that the local weather website provides local weather information to the system server;

[0042] The system server is connected to the remote mobile terminal 4G / 5G network, and is used to receive data from the environmental monitoring module, the plant growth monitoring drone, the pest and disease control module and the local meteorological website. According to the optimal germination period and optimal growth period of the herbaceous plant, the optimal germination period deviation index formula, the optimal growth period deviation index formula and the pest and disease index formula are used to reversely deduce the difference between actual growth factors and theoretical growth factors, and real-time feedback control is performed to achieve the optimal planting effect, and specific information is sent to the remote mobile terminal to facilitate real-time query and management by the administrator.

[0043] This system uses an environmental monitoring module, a plant growth monitoring drone, a pest control module, a local meteorological website, a system server, and a remote mobile terminal to implement an herbaceous plant planting monitoring system. The system combines the environment, plant growth status, pest and disease status, and weather forecasts, and uses the data of the optimal germination period and optimal growth period of herbaceous plants for comparative analysis one by one, thereby realizing full-process supervision and closed-loop feedback control of herbaceous plant planting to achieve the best planting effect. Through design, simulation, and verification, a modular product is formed, which can achieve rapid transplantation between different platforms and accelerate the product development process.

[0044] The environmental monitoring module includes an air temperature and humidity sensor, an air wind speed sensor, a CO2 concentration sensor, a light intensity sensor, a soil temperature and humidity sensor, a soil pH sensor, a nitrogen, phosphorus and potassium sensor, an RTU, an air heater, an air humidifier, a light fill light and a water and fertilizer control valve;

[0045] The air temperature and humidity sensor, the air wind speed sensor, the CO2 concentration sensor, the light intensity sensor, the soil temperature and humidity sensor, the soil pH value sensor, and the nitrogen, phosphorus and potassium sensor are connected to the RTU 0-5V or 4-20mA signal to collect air temperature and humidity, air wind speed, CO2 concentration, light intensity, soil temperature and humidity, soil pH value, nitrogen, phosphorus and potassium data and send them to the system server through RTU with TCP Modbus protocol 4G / 5G signal;

[0046] The RTU is electrically connected to the air heater, the air humidifier, the lighting fill light, and the water and fertilizer control valve TTL, and is used to receive the TCP Modbus protocol commands sent by the system server to complete the control of the air heater, the air humidifier, the lighting fill light, and the water and fertilizer control valve.

[0047] Among them, the RTU is implemented using the video telemetry terminal F9164 produced by Four-Faith Digital Technology Group, and the air temperature and humidity sensor, air wind speed sensor, CO2 concentration sensor, light intensity sensor, soil temperature and humidity sensor, soil pH value sensor, nitrogen, phosphorus and potassium sensor all use 0-5V or 4-20mA output.

[0048] The plant growth monitoring drone is equipped with a long-focus optical 4G / 5G camera, which can collect video images of herbaceous plant growth and send them to the system server to identify seed germination rate, plant height, canopy size, leaf number, leaf morphology, leaf color, flowering rate and fruiting rate;

[0049] The pest control module includes multiple close-focus optical WiFi cameras, RTUs and insect traps. The close-focus optical cameras can collect video images of herbaceous plant leaves and send them to the system server through the RTU via 4G / 5G signals using the FTP protocol. The system server identifies the mosaic, leaf spots and insect holes on the leaves and sends TCP Modbus protocol commands to the RTU via 4G / 5G signals to control the insect trap.

[0050] The local meteorological website can provide the system server with local 14-day forecast weather conditions, air temperature, humidity and wind force forecast data.

[0051] An herbaceous plant planting monitoring system in this embodiment adopts an environmental monitoring module, a plant growth monitoring drone, a pest and disease control module, a local meteorological website, a system server, and a remote mobile terminal to realize an herbaceous plant planting monitoring system. The system combines the environment, plant growth status, pest and disease status, and weather forecast, and uses the data of the optimal germination period and the optimal growth period of the herbaceous plant to compare and analyze one by one, thereby realizing the supervision of the entire process of herbaceous plant planting and closed-loop feedback control to achieve the optimal planting effect; through design, simulation, and verification, a modular product is formed, which can realize rapid transplantation between different platforms and accelerate the product development process.

[0052] Embodiment 2

[0053] A method for using a herbaceous plant planting monitoring system is provided, which uses the herbaceous plant planting monitoring system as described in Example 1, and includes the following steps:

[0054] S1. The environmental monitoring module receives the Modbus query command from the system server and sends the air temperature X0, air humidity X1, air speed X2, CO2 concentration X3, light intensity X4, soil temperature X5, soil humidity X6, soil pH value X7, nitrogen, phosphorus and potassium value X8 to the system server;

[0055] S2. The system server averages the information X0-X8 in the unit time and calculates the actual average air temperature Actual average air humidity Actual average air speed Actual average CO2 concentration Actual average light intensity Actual average soil temperature Actual average soil moisture Actual Average Soil pH Actual average nitrogen, phosphorus and potassium values

[0056] S3. The system server obtains the weather conditions, air temperature, humidity and wind forecast data for the past 14 days from the local meteorological website and calculates the expected average air temperature for the next 14 days Expected average air humidity Estimated average air speed Estimated average CO2 concentration Estimated average light intensity Estimated average soil temperature Estimated Average Soil Moisture Estimated Average Soil pH Estimated average NPK values

[0057] S4. The system server substitutes the actual data and the estimated average data into the optimal germination period deviation index formula to calculate the optimal germination period deviation index. If the deviation index exceeds the threshold, it will be analyzed item by item. to to and to If there is a deviation, corresponding measures should be taken to control the air heater, air humidifier, light supplement lamp and water and fertilizer control valve until the deviation index is controlled within the threshold range;

[0058] S5. The plant growth monitoring drone sends herbaceous plant video images to the system server in real time, and uses image recognition to identify seed germination rate Y0, plant height Y1, canopy size Y2, leaf number Y3, leaf shape Y4, leaf color Y5, flowering rate Y6 and fruiting rate Y7;

[0059] S6. The system server compares the seed germination rate, plant height, canopy size, leaf number, leaf shape, leaf color, flowering rate and fruiting rate with the optimal growth period deviation index formula in real time. If any of the actual data is lower than the optimal growth period deviation index threshold value in the same period in history, the differences in historical meteorological conditions and water and fertilizer conditions between the actual growth period and the optimal growth period are analyzed, and corresponding measures such as air heating, air humidification, light supplementation and water and fertilizer addition are taken until the actual data is controlled within the optimal growth period deviation index threshold value;

[0060] S7. The pest control module sends video images of some leaves of herbaceous plants to the system server in real time, uses image recognition method to identify the number of mosaics, leaf spots, insect holes, aphids, wilting, and root rot in the video images, and uses quantity estimation method to estimate the actual number of mosaics N0, leaf spots N1, insect holes N2, aphids N3, wilting N4, and root rot N5. According to the pest index formula, the pest risk index is calculated. If the risk index threshold is exceeded, the system server sends a command to the pest control module to control the insect attractant lamp to turn on, and at the same time sends real-time data to the remote mobile terminal to remind the administrator to manually use sticky insect boards or drugs to disinfect.

[0061] Among them, the optimal germination period deviation index formula is and here, Indicates that the environmental monitoring module acquisition parameters correspond to the 0th to 8th actual average data. Indicates the expected average data of the 0th to 8th parameters affected in the next 14 days. Indicates the theoretical optimal data of the 0th to 8th parameters, α i Indicates the actual impact factor of the 0th to 8th parameters, β i represents the estimated impact factor of the 0th to 8th parameters, γ i Indicates the theoretical impact factors of the 0th to 8th parameters.

[0062] The image recognition method is one or more combinations of edge detection, corner detection, texture analysis, shape analysis, convolutional neural network, recurrent neural network, and generative adversarial network.

[0063] The optimal growth period deviation index formula is Among them, Y i Indicates the values ​​of the plant growth parameters from 0 to 7, P i Indicates the recognition accuracy corresponding to the 0th to 7th plant growth parameters, Indicates the ideal plant growth parameter values ​​corresponding to the 0th to the 7th.

[0064] The quantity estimation method is one of the proportion estimation method, trend prediction method, geometric estimation method and model estimation method;

[0065] The pest risk index is Where N i Corresponding to the number of pests and diseases from 0 to 7, λ i Indicates the severity of the 0th to 7th pests and diseases, P i Indicates the correct recognition rate of pests and diseases from the 0th to the 7th.

[0066] like Figure 4 As shown, the environmental monitoring module sends environmental information to the system server, the system server calculates the average environmental data and obtains 14 days of meteorological data from the meteorological website, substitutes the optimal germination period deviation index formula to calculate the optimal germination period deviation index, analyzes each item and takes corresponding measures, the plant growth monitoring drone sends video images to the system server, obtains herbaceous plant growth information, substitutes the optimal growth period deviation index formula to calculate the optimal growth period deviation index, analyzes each item and takes corresponding measures, the pest control module sends leaf video images to the system server, obtains herbaceous plant pest information, substitutes the pest index formula to calculate the pest risk index, and sends insect capture or disinfection measures to the remote mobile terminal according to the pest results.

[0067] The method for using the herbaceous plant planting monitoring system in this embodiment adopts a modular design method, using an environmental monitoring module, a plant growth monitoring drone, a pest and disease control module, a local meteorological website, a system server and a remote mobile terminal to realize the full process monitoring of herbaceous plant planting, and combines the environment, plant growth status, pest and disease status, and weather forecasts, and uses the data of the optimal germination period and the optimal growth period of herbaceous plants to compare and analyze one by one, thereby realizing the full process supervision and closed-loop feedback control of herbaceous plant planting to achieve the best planting effect.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A herbaceous plant planting monitoring system, characterized in that: It mainly includes environmental monitoring module, plant growth monitoring drone, pest control module, local meteorological website, system server and remote mobile terminal; The environmental monitoring module is connected to the 4G / 5G network of the system server, and is used to measure the data information of the herbaceous plant growth environment and send it to the system server, and receive the control command of the system server to perform heating, humidification, supplementary lighting and fertilization; The plant growth monitoring drone is connected to the system server 4G / 5G network to monitor the whole process data of herbaceous plants from germination, seedling growth, flowering and fruiting and send it to the system server; The pest control module is connected to the 4G / 5G network of the system server, and is used to monitor the data of herbaceous plant pests and diseases and send it to the system server, and receive the control command of the system server to kill insects; The local weather website is connected to the system server through the Internet, so that the local weather website provides local weather information to the system server; The system server is connected to the remote mobile terminal 4G / 5G network, and is used to receive data from the environmental monitoring module, the plant growth monitoring drone, the pest and disease control module and the local meteorological website. According to the optimal germination period and optimal growth period of the herbaceous plant, the optimal germination period deviation index formula, the optimal growth period deviation index formula and the pest and disease index formula are used to reversely deduce the difference between actual growth factors and theoretical growth factors, and real-time feedback control is performed to achieve the optimal planting effect, and specific information is sent to the remote mobile terminal to facilitate real-time query and management by the administrator.

2. A herbaceous plant planting monitoring system as claimed in claim 1, characterized in that: The environmental monitoring module includes an air temperature and humidity sensor, an air speed sensor, a CO2 concentration sensor, a light intensity sensor, a soil temperature and humidity sensor, a soil pH sensor, a nitrogen, phosphorus and potassium sensor, an RTU, an air heater, an air humidifier, a light fill lamp and a water and fertilizer control valve; The air temperature and humidity sensor, the air wind speed sensor, the CO2 concentration sensor, the light intensity sensor, the soil temperature and humidity sensor, the soil pH value sensor, and the nitrogen, phosphorus and potassium sensor are connected to the RTU 0-5V or 4-20mA signal to collect air temperature and humidity, air wind speed, CO2 concentration, light intensity, soil temperature and humidity, soil pH value, nitrogen, phosphorus and potassium data and send them to the system server through RTU with TCP Modbus protocol 4G / 5G signal; The RTU is electrically connected to the air heater, the air humidifier, the lighting fill light, and the water and fertilizer control valve TTL, and is used to receive the TCP Modbus protocol commands sent by the system server to complete the control of the air heater, the air humidifier, the lighting fill light, and the water and fertilizer control valve.

3. A herbaceous plant planting monitoring system as claimed in claim 1, characterized in that: The plant growth monitoring drone is equipped with a long-focus optical 4G / 5G camera, which can collect video images of herbaceous plant growth and send them to the system server to identify seed germination rate, plant height, canopy size, leaf number, leaf morphology, leaf color, flowering rate and fruiting rate; The pest control module includes multiple close-focus optical WiFi cameras, RTUs and insect traps. The close-focus optical cameras can collect video images of herbaceous plant leaves and send them to the system server through the RTU via 4G / 5G signals using the FTP protocol. The system server identifies the mosaic, leaf spots and insect holes on the leaves and sends TCP Modbus protocol commands to the RTU via 4G / 5G signals to control the insect trap. The local meteorological website can provide the system server with local 14-day forecast weather conditions, air temperature, humidity and wind force forecast data.

4. A method for using a herbaceous plant planting monitoring system as claimed in any one of claims 1 to 3, characterized in that: The steps include: S1. The environmental monitoring module receives the Modbus query command from the system server and sends the air temperature X0, air humidity X1, air speed X2, CO2 concentration X3, light intensity X4, soil temperature X5, soil humidity X6, soil pH value X7, nitrogen, phosphorus and potassium value X8 to the system server; S2. The system server averages the information X0-X8 in the unit time and calculates the actual average air temperature Actual average air humidity Actual average air speed Actual average CO2 concentration Actual average light intensity Actual average soil temperature Actual average soil moisture Actual Average Soil pH Actual average nitrogen, phosphorus and potassium values S3. The system server obtains the weather conditions, air temperature, humidity and wind forecast data for the past 14 days from the local meteorological website and calculates the expected average air temperature for the next 14 days Expected average air humidity Estimated average air speed Estimated average CO2 concentration Estimated average light intensity Estimated average soil temperature Estimated Average Soil Moisture Estimated Average Soil pH Estimated average NPK values S4. The system server substitutes the actual data and the estimated average data into the optimal germination period deviation index formula to calculate the optimal germination period deviation index. If the deviation index exceeds the threshold, it will be analyzed item by item. to to and to If there is a deviation, corresponding measures should be taken to control the air heater, air humidifier, light supplement lamp and water and fertilizer control valve until the deviation index is controlled within the threshold range; S5. The plant growth monitoring drone sends herbaceous plant video images to the system server in real time, and uses image recognition to identify seed germination rate Y0, plant height Y1, canopy size Y2, leaf number Y3, leaf shape Y4, leaf color Y5, flowering rate Y6 and fruiting rate Y7; S6. The system server compares the seed germination rate, plant height, canopy size, leaf number, leaf shape, leaf color, flowering rate and fruiting rate with the optimal growth period deviation index formula in real time. If any of the actual data is lower than the optimal growth period deviation index threshold value in the same period in history, the differences in historical meteorological conditions and water and fertilizer conditions between the actual growth period and the optimal growth period are analyzed, and corresponding measures such as air heating, air humidification, light supplementation and water and fertilizer addition are taken until the actual data is controlled within the optimal growth period deviation index threshold value; S7. The pest control module sends video images of some leaves of herbaceous plants to the system server in real time, uses image recognition method to identify the number of mosaics, leaf spots, insect holes, aphids, wilting, and root rot in the video images, and uses quantity estimation method to estimate the actual number of mosaics N0, leaf spots N1, insect holes N2, aphids N3, wilting N4, and root rot N5. According to the pest index formula, the pest risk index is calculated. If the risk index threshold is exceeded, the system server sends a command to the pest control module to control the insect attractant lamp to turn on, and at the same time sends real-time data to the remote mobile terminal to remind the administrator to manually use sticky insect boards or drugs to disinfect.

5. The method for using the herbaceous plant planting monitoring system according to claim 4, characterized in that: The step S2 specifically includes: The optimal germination period deviation index formula is and here, Indicates that the environmental monitoring module acquisition parameters correspond to the 0th to 8th actual average data. Indicates the expected average data of the 0th to 8th parameters affected in the next 14 days. Indicates the theoretical optimal data of the 0th to 8th parameters, α i Indicates the actual impact factor of the 0th to 8th parameters, β i represents the estimated impact factor of the 0th to 8th parameters, γ i Indicates the theoretical impact factors of the 0th to 8th parameters.

6. The method for using the herbaceous plant planting monitoring system according to claim 4, characterized in that: The steps S5 and S7 specifically include: The image recognition method is one or more combinations of edge detection, corner detection, texture analysis, shape analysis, convolutional neural network, recurrent neural network, and generative adversarial network.

7. The method for using the herbaceous plant planting monitoring system according to claim 4, characterized in that: The step S6 specifically includes: The optimal growth period deviation index formula is Among them, Y i Indicates the values ​​of the plant growth parameters from 0 to 7, P i Indicates the recognition accuracy corresponding to the 0th to 7th plant growth parameters, Indicates the ideal plant growth parameter values ​​corresponding to the 0th to the 7th.

8. The method for using the herbaceous plant planting monitoring system according to claim 4, characterized in that: The step S7 specifically includes: The quantity estimation method is one of the proportion estimation method, trend prediction method, geometric estimation method and model estimation method; The pest risk index is Where N i Corresponding to the number of pests and diseases from 0 to 7, λ i Indicates the severity of the 0th to 7th pests and diseases, P i Indicates the correct recognition rate of pests and diseases from the 0th to the 7th.

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