A greenhouse cucumber planting management system based on the Internet of Things

Abstract

The present invention discloses a greenhouse cucumber planting management system based on the Internet of Things, including a data acquisition and transmission subsystem, which includes a soil information acquisition module, an air environment information acquisition module and an image acquisition module, and the collected data is transmitted to a data analysis and processing subsystem through various sensors for processing; the data analysis and processing subsystem includes a standard cucumber growth model and a pest and disease model, and the collected data is analyzed and processed through the model, and then sent to the application device of the client; the intelligent application subsystem includes a water, fertilizer and medicine integrated control module, a cucumber production standardization program module, a greenhouse environment control module and an application system management module, and the analyzed and processed data is applied to actual production and planting. The present invention realizes rapid identification of cucumber pests and diseases, determination of the location of pests and diseases, and prediction of crop growth stages through the data acquisition and transmission subsystem, the data analysis and processing subsystem and the intelligent application subsystem, thereby ensuring the healthy growth of plants.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural Internet of Things, and in particular to a greenhouse cucumber planting management system based on the Internet of Things. Background Art

[0002] my country is a major country in facility horticulture, and the total area of ​​facility horticulture ranks first in the world. Cucumber is one of the most important crops cultivated in facilities. In my country's traditional agricultural production process, the management of crop fertilization, watering, pest control, etc. is determined by the experience of growers. For some inexperienced or inexperienced growers, unreasonable management will lead to slow growth of crops, low yield, poor quality, etc.

[0003] There are many existing crop management systems based on the Internet of Things on the market, but they have the following problems:

[0004] 1) Single functions, some are just scattered functions such as pest and disease detection and early warning, environmental testing, water and fertilizer integration, etc.

[0005] 2) The level of intelligence is low, and it is unable to identify pests and diseases, predict the growth stage of crops, and help users make correct decisions based on the current status of crops in a timely manner. Summary of the invention

[0006] In view of the above technical problems in the related art, the present invention proposes a greenhouse cucumber planting management system based on the Internet of Things, which can overcome the above shortcomings of the prior art methods.

[0007] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows:

[0008] A greenhouse cucumber planting management system based on the Internet of Things includes a data acquisition and transmission subsystem, a data analysis and processing subsystem, and an intelligent application subsystem, wherein:

[0009] The data acquisition and transmission subsystem includes a soil information acquisition module, an air environment information acquisition module and an image acquisition module. The soil information acquisition module includes a soil temperature sensor, a soil moisture sensor, a soil EC sensor and a soil pH sensor. The air environment information acquisition module includes an air temperature sensor, an air humidity sensor, a light sensor and a carbon dioxide concentration sensor. The image acquisition module includes an image sensor. All the collected data are regularly transmitted to the data analysis and processing subsystem for processing through various sensors.

[0010] The data analysis and processing subsystem includes a standard cucumber growth model and a pest and disease model, and analyzes and processes the data collected by the data acquisition and transmission subsystem through the model, and then sends it to the client application device. If the application running state or the received data is abnormal, an alarm is immediately sent to the client application device;

[0011] The intelligent application subsystem includes an integrated water, fertilizer and medicine control module, a cucumber production standardization program module, a greenhouse environment control module and an application system management module, which applies the analyzed data to actual production and planting. The integrated water, fertilizer and medicine control module starts the automatic irrigation equipment according to the types of fertilizers and medicines and the ratio of water, fertilizers and medicines displayed by the data analysis and processing subsystem. The cucumber production standardization module automatically generates production guidance opinions based on the scientific parameters sent by the data analysis and processing subsystem to accurately guide users in agricultural production. The greenhouse environment control module includes control of LED lights, film winding motors, CO2 releasers and atomizer equipment. The application system management module is used for daily operation and maintenance, and realizes the display of intelligent application terminals on the Web and mobile phones through 2G, 4G and NBIoT mobile networks.

[0012] Furthermore, the air temperature sensor, air humidity sensor, light sensor, image sensor and carbon dioxide concentration sensor are all equipped with GPS positioning and a unique device identifier. The device identifier is uniformly generated by the server and has a unique QR code corresponding to the device identifier. After the user scans the QR code through the APP and binds successfully, the location of each sensor can be determined.

[0013] Furthermore, the standard cucumber growth model is formed by the climate conditions, soil conditions and field management measures of the planting area, and the specific use includes the following steps:

[0014] S31: According to the characteristics of cucumber growth and development, the growth and development after planting is divided into five stages: planting and vine-drawing stage, flowering and fruiting stage, early melon-setting stage, peak melon-setting stage, and late melon-setting stage. The staged growth model of cucumber is established based on the effective accumulated temperature required to complete the development period and the geometric information obtained by machine vision measurement;

[0015] S32: based on the standardized growth period, obtain the required light, temperature, carbon dioxide and water for each stage, and input the standardized farming operation plan for each stage into the model accordingly;

[0016] S33: The data analysis and processing subsystem uses the environmental and cucumber growth data collected by various sensors in the actual greenhouse, combined with the cucumber growth model for comparative analysis and processing to output scientific adjustment parameters.

[0017] Furthermore, in step S31, the expression for obtaining the effective accumulated temperature is: Σ(Ti-Tb)=A, the average temperature Ti on the i-th day of the growth period minus the lower limit temperature of cucumber 10 degrees Celsius Tb, to obtain the effective accumulated temperature A required for the growth period.

[0018] Further, in step S32, during the standardized growth period, the expression for obtaining the required moisture is: ET is the actual crop water requirement, LAI is the leaf area index; Δ is the saturated water vapor temperature slope; R n It is the net radiation on the crop surface, which is measured by photosynthetic instrument to measure the effective radiation PAR in the greenhouse and calculated using the Penman-Monteth formula; P is the atmospheric pressure; T is the average temperature 2m above the ground; e a is the saturated water vapor pressure, e d is the actual water vapor pressure.

[0019] Furthermore, the pest and disease model is formed based on the occurrence time of pests and diseases in the past planting areas and the suitable environment. The specific use includes the following steps:

[0020] S61: First, screen pictures of cucumber diseases and insect pests by shooting in the field from multiple angles and writing a web crawler;

[0021] S62: The data acquisition and transmission subsystem sends the captured pest and disease images and positions to the data analysis and processing system through the image sensor, and determines the pest and disease type through the convolutional neural network algorithm;

[0022] S63: The data analysis and processing subsystem sends the pest and disease identification results and corresponding solutions to the intelligent application subsystem.

[0023] Furthermore, the greenhouse environment control module controls the opening and closing of LED lights, film winding motors, CO2 releasers and atomizers according to instructions issued by the data analysis and processing subsystem, thereby automatically adjusting the growth environment of cucumbers in the greenhouse.

[0024] Furthermore, the application system management module includes user management, role management, authority management, operation log, metadata management, template management and task management.

[0025] The beneficial effects of the present invention are as follows: through the data acquisition and transmission system, the data analysis and processing system and the intelligent application system, the cucumber diseases and insect pests can be quickly identified, the locations of the diseases and insect pests can be determined, and the crop growth stages can be predicted. In addition, the growth status of the plants entering the corresponding growth stages can be accurately controlled, thereby ensuring the healthy growth of the plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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 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.

[0027] Figure 1 It is a system structure diagram of a greenhouse cucumber planting management system based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in 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 belong to the scope of protection of the present invention. In order to facilitate the understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are described in detail below through specific usage methods.

[0029] like Figure 1 As shown, the greenhouse cucumber planting management system based on the Internet of Things according to the embodiment of the present invention includes a data acquisition and transmission subsystem, a data analysis and processing subsystem, and an intelligent application subsystem.

[0030] The data acquisition and transmission subsystem includes a soil information acquisition module, an air environment information acquisition module and an image acquisition module; the soil information acquisition module includes a soil temperature sensor, a soil moisture sensor, a soil EC sensor and a soil pH sensor; the air environment information acquisition module includes an air temperature sensor, an air humidity sensor, a light sensor and a carbon dioxide concentration sensor; the image acquisition module includes an image sensor, and all the collected data are regularly transmitted to the data analysis and processing subsystem through various sensors for processing.

[0031] The soil temperature sensor, soil moisture sensor, soil EC sensor, soil pH sensor, air temperature sensor, air humidity sensor, light sensor, image sensor and carbon dioxide concentration sensor are all equipped with GPS positioning and are provided with a unique device identification, which is uniformly generated by the server and has a unique QR code corresponding to the device identification. After the user scans the QR code through the APP and binds successfully, the location of each sensor can be determined.

[0032] The data analysis and processing subsystem includes a standard cucumber growth model and a pest and disease model. The data collected by the data acquisition and transmission subsystem is analyzed and processed through the model, and then sent to the client application device (App or Web). If the application running status or the received data is abnormal, an alarm is immediately issued to the client application device (App or Web).

[0033] The standard cucumber growth model is formed by the climate conditions, soil conditions and field management measures of the planting area, and the specific use includes the following steps:

[0034] 1) According to the characteristics of cucumber growth and development, the growth and development after planting is divided into five stages: planting and vine-drawing stage, flowering and fruiting stage, early fruit-bearing stage, peak fruit-bearing stage, and late fruit-bearing stage. Growth models are established for each stage. The staged growth model of cucumber is established based on the effective accumulated temperature required to complete the development and the geometric information obtained by machine vision measurement;

[0035] 2) Based on the standardized growth period, the required light, temperature, carbon dioxide and water for each stage are obtained, and the standardized farming operation plan for each stage is input into the model accordingly;

[0036] 3) The data analysis and processing subsystem collects environmental and cucumber growth data from sensors in the actual greenhouse, combines it with the cucumber growth model for comparative analysis and processing, and outputs scientific adjustment parameters.

[0037] As mentioned above, the expression for obtaining the effective accumulated temperature is:

[0038] Σ(Ti-Tb)=A

[0039] Among them, the average temperature Ti on the i-th day of the growth period minus the lower limit temperature of cucumber 10 degrees Celsius Tb can be obtained to obtain the effective accumulated temperature A required for the growth period.

[0040] During the standardized growth period, the expression for the required water content is:

[0041]

[0042] Among them, ET is the actual crop water requirement, LAI is the leaf area index; Δ is the saturated water vapor temperature slope; R n It is the net radiation on the crop surface, which is measured by photosynthetic instrument to measure the effective radiation PAR in the greenhouse and calculated using the Penman-Monteth formula; P is the atmospheric pressure; T is the average temperature 2m above the ground; e a is the saturated water vapor pressure, e d is the actual water vapor pressure.

[0043] The pest model is formed based on the occurrence time and suitable environment of pests and diseases in the planting area in the past. The specific use includes the following steps:

[0044] 1) First, screen and crawl pictures of cucumber pests and diseases by shooting in the field from multiple angles and programming web crawlers;

[0045] 2) The data acquisition and transmission subsystem sends the captured pest and disease images and locations to the data analysis and processing system through the image sensor, and determines the pest and disease type through the convolutional neural network algorithm;

[0046] 3) The data analysis and processing subsystem sends the pest and disease identification results and corresponding solutions to the intelligent application subsystem.

[0047] The intelligent application subsystem includes an integrated water-fertilizer-pesticide control module, a cucumber production standardization program module, a greenhouse environment control module and an application system management module, which applies the analyzed and processed data to actual production and planting. The integrated water-fertilizer-pesticide control module starts the automatic irrigation equipment based on the types of fertilizers and pesticides and the ratio of water-fertilizer-pesticides displayed by the data analysis and processing subsystem.

[0048] The cucumber production standardization module automatically generates production guidance opinions based on the scientific parameters sent by the data analysis and processing subsystem to accurately guide users in agricultural production; the greenhouse environment control module includes control of LED lights, film winding motors, CO2 releasers and atomizer equipment; the application system management module is used for daily operation and maintenance, and realizes the display of intelligent application terminals on the Web and mobile phones through 2G, 4G, and NBIoT mobile networks.

[0049] The greenhouse environment control module controls the opening and closing of LED lights, film rolling motors, CO2 releasers and atomizers according to the instructions issued by the data analysis and processing subsystem, thereby automatically adjusting the growth environment of cucumbers in the greenhouse.

[0050] The application system management module includes user management, role management, authority management, operation log, metadata management, template management and task management.

[0051] In summary, with the help of the above-mentioned technical scheme of the present invention, the data acquisition and transmission system, the data analysis and processing system and the intelligent application system can be used to quickly identify cucumber pests and diseases, determine the location of the pests and diseases, and predict the crop growth stage. The growth state of the plant entering the corresponding growth stage can be accurately controlled, thereby ensuring the healthy growth of the plant.

[0052] 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 principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A greenhouse cucumber planting management system based on the Internet of Things, characterized in that: It includes data acquisition and transmission subsystem, data analysis and processing subsystem, and intelligent application subsystem, among which: The data acquisition and transmission subsystem includes a soil information acquisition module, an air environment information acquisition module and an image acquisition module. The soil information acquisition module includes a soil temperature sensor, a soil moisture sensor, a soil EC sensor and a soil pH sensor. The air environment information acquisition module includes an air temperature sensor, an air humidity sensor, a light sensor and a carbon dioxide concentration sensor. The image acquisition module includes an image sensor. All the collected data are regularly transmitted to the data analysis and processing subsystem for processing through various sensors. The data analysis and processing subsystem includes a standard cucumber growth model and a pest and disease model. The data collected by the data acquisition and transmission subsystem is analyzed and processed through the model, and then sent to the app. If the application running state or the received data is abnormal, an alarm is immediately sent to the app; The intelligent application subsystem includes a water-fertilizer-pesticide integrated control module, a cucumber production standardization program module, a greenhouse environment control module and an application system management module, and applies the analyzed and processed data to actual production and planting. The water-fertilizer-pesticide integrated control module starts the automatic irrigation equipment according to the fertilizer and pesticide types and the water-fertilizer-pesticide ratio displayed by the data analysis and processing subsystem. The cucumber production standardization module automatically generates production guidance opinions based on the scientific parameters sent by the data analysis and processing subsystem to accurately guide users to carry out agricultural production. The greenhouse environment control module includes controlling LED lights, film rolling motors, CO2 releasers and atomizer equipment. The application system management module is used for daily operation and maintenance, and realizes the display of intelligent application terminals on PC and mobile phones through 2G, 4G, and NBIOT mobile networks; The standard cucumber growth model is formed by the climate conditions, soil conditions and field management measures of the planting area, and the specific use includes the following steps: S31: According to the characteristics of cucumber growth and development, the growth and development after planting is divided into five stages: planting and vine-drawing stage, flowering and fruiting stage, early melon-setting stage, peak melon-setting stage, and late melon-setting stage, and growth models are established respectively. The staged growth model of cucumber is established based on the effective accumulated temperature required to complete the development and the geometric information obtained by machine vision measurement; S32: based on the standardized growth period, obtain the required light, temperature, carbon dioxide and water for each stage, and input the standardized farming operation plan for each stage into the model accordingly; S33: The data analysis and processing subsystem uses the environment and cucumber growth data collected by various sensors in the actual greenhouse, combined with the cucumber growth model for comparative analysis and processing to output scientific adjustment parameters; In step S31, the expression for obtaining the effective accumulated temperature is: Σ(Ti-Tb)=A, the average temperature Ti on the i-th day of the growth period minus the lower line temperature of the cucumber 10 degrees Celsius Tb, to obtain the effective accumulated temperature A required for the growth period; In step S32, during the standardized growth period, the expression for obtaining the required moisture is: ET is the actual crop water requirement, LAI is the leaf area index; Δ is the saturated water vapor temperature slope; R n It is the net radiation on the crop surface, which is measured by photosynthetic instrument to measure the effective radiation PAR in the greenhouse and calculated using the Penman-Monteth formula; P is the atmospheric pressure; T is the average temperature 2m above the ground; e a is the saturated water vapor pressure, e d is the actual water vapor pressure; The pest model is formed based on the occurrence time of pests and diseases in the planting area and the suitable environment. The specific use includes the following steps: S61: First, screen pictures of cucumber diseases and insect pests by shooting in the field from multiple angles and writing a web crawler; S62: The data acquisition and transmission subsystem sends the captured pest and disease images and positions to the data analysis and processing system through the image sensor, and determines the pest and disease type through the convolutional neural network algorithm; S63: The data analysis and processing subsystem sends the pest and disease identification results and corresponding solutions to the intelligent application subsystem.

2. The greenhouse cucumber planting management system based on the Internet of Things according to claim 1 is characterized in that: The soil temperature sensor, soil moisture sensor, soil EC sensor, soil pH sensor, air temperature sensor, air humidity sensor, light sensor, image sensor and carbon dioxide concentration sensor are all equipped with GPS positioning and are provided with a unique device identification, which is uniformly generated by the server and has a unique QR code corresponding to the device identification. After the user scans the QR code through the APP and binds successfully, the location of each sensor can be determined.

3. The greenhouse cucumber planting management system based on the Internet of Things according to claim 1 is characterized in that: The greenhouse environment control module controls the opening and closing of LED lights, film rolling motors, CO2 releasers and atomizers according to the instructions issued by the data analysis and processing subsystem, thereby automatically adjusting the growth environment of cucumbers in the greenhouse.

4. The greenhouse cucumber planting management system based on the Internet of Things according to claim 1 is characterized in that: The application system management module includes user management, role management, authority management, operation log, metadata management, template management and task management.

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