Crop management and control method and apparatus for smart greenhouse, and device and medium

Abstract

The present application relates to the technical field related to greenhouse planting, and particularly relates to a crop management and control method and apparatus for a smart greenhouse, and a device and a medium. The method comprises: acquiring environmental data and crop data; inputting the environmental data and the crop data into a preset regulation and control model to obtain an optimal planting scheme and / or an optimal environment control strategy; and on the basis of the environment control strategy, regulating and controlling preset smart remote control devices in a smart greenhouse. By means of the configuration in the solution provided in the present application, accurate control of a planting environment can be performed by collecting environmental data and crop data in a timely manner, thereby increasing the crop growth rate and improving the production efficiency. Furthermore, a preset regulation and control model is used in the present application, so as to automatically optimize a planting scheme and an environment control strategy on the basis of different environmental conditions and crop requirements, thereby reducing manual intervention and management costs, and thus improving the production efficiency and economic benefits.

Description

Smart greenhouse crop management method, device, equipment and medium Technical Field

[0001] The present application relates to the technical field related to greenhouse cultivation, and specifically to a smart greenhouse crop management method, device, equipment and medium. Background Art

[0002] With the advancement of agricultural modernization, more and more agricultural production is adopting intelligent equipment and technologies. In greenhouse cultivation, environmental factors play a vital role in the growth and development of crops. Traditional greenhouse control methods rely mainly on manual experience and simple mechanical equipment. Different devices are controlled individually, resulting in low efficiency, poor accuracy, and high labor intensity. Summary of the Invention

[0003] In view of this, the embodiments of the present application are dedicated to providing a smart greenhouse crop management method, device, equipment and medium.

[0004] The first aspect of the present application provides a smart greenhouse crop management method, comprising:

[0005] Obtain environmental and crop data;

[0006] Inputting the environmental data and the crop data into a preset control model to obtain an optimal planting plan and / or an optimal environmental control strategy;

[0007] Based on the environmental control strategy, each intelligent remote control device preset in the smart greenhouse is regulated.

[0008] In some embodiments, obtaining environmental data includes:

[0009] Based on preset sensors, real-time monitoring of environmental data inside and outside the greenhouse;

[0010] The environmental data includes: air temperature, air humidity, light intensity, soil temperature, soil humidity and rain and snow information.

[0011] In some embodiments, obtaining crop data includes:

[0012] The crop planting area of ​​the smart greenhouse crops is photographed based on a preset camera device to obtain an image of the crop planting area;

[0013] determining crop data based on the crop planting area image;

[0014] The crop data includes: crop types and crop growth status.

[0015] In some embodiments, the control model is built using data analysis and machine learning algorithms, and is used to process and analyze the collected data to automatically generate an optimal planting plan and environmental control strategy based on environmental data and crop data.

[0016] In some embodiments, the various intelligent remote control devices include: ventilation equipment, insulation equipment, sunshade nets, heating equipment, and irrigation systems.

[0017] In some embodiments, it further includes:

[0018] When the environmental data reaches a preset index, an alarm is issued.

[0019] In some embodiments, it further includes:

[0020] Based on the preset human-computer interaction interface, obtain the user input instructions;

[0021] Based on the instructions, each intelligent remote control device preset in the smart greenhouse is controlled.

[0022] This application also provides a smart greenhouse crop management and control device, including:

[0023] Acquisition module, used to obtain environmental data and crop data;

[0024] An input module, configured to input the environmental data and the crop data into a preset control model to obtain an optimal planting plan and / or an optimal environmental control strategy;

[0025] The control module is used to control various intelligent remote control devices preset in the smart greenhouse based on the environmental control strategy.

[0026] The present application also provides an electronic device, comprising:

[0027] A processor, and a memory for storing a program executable by the processor;

[0028] The processor is used to implement the above-mentioned smart greenhouse crop management method by running the program in the memory.

[0029] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes the above-mentioned smart greenhouse crop management method.

[0030] The present application provides a method for controlling crops in a smart greenhouse. The method first obtains environmental data and crop data; inputs the environmental data and crop data into a preset control model to obtain an optimal planting plan and / or an optimal environmental control strategy; and based on the environmental control strategy, controls each preset intelligent remote control device in the smart greenhouse. With such a configuration, the solution provided by the present application can accurately control the planting environment by timely collecting environmental data and crop data, thereby improving the growth rate and production efficiency of crops. Furthermore, the present application adopts a preset control model, which can automatically optimize the planting plan and environmental control strategy according to different environmental conditions and crop requirements, reduce manual intervention and management costs, and improve production efficiency and economic benefits. In addition, by fine-tuning the intelligent remote control device, accurate control of the planting environment can be achieved, improving the quality and yield of greenhouse crops, while also reducing energy consumption and resource waste. Therefore, the method has good friendliness and practicality, and can contribute to agricultural production and food safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0032] FIG1 is a flow chart of a smart greenhouse crop management method according to an embodiment of the present application.

[0033] FIG2 is a schematic structural diagram of a smart greenhouse crop management and control device provided in one embodiment of the present application.

[0034] FIG3 is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] Application Overview

[0037] With the advancement of agricultural modernization, more and more agricultural production is adopting intelligent equipment and technologies. In greenhouse cultivation, environmental factors play a vital role in the growth and development of crops. Traditional greenhouse control methods rely mainly on manual experience and simple mechanical equipment. Different devices are controlled individually, resulting in low efficiency, poor accuracy, and high labor intensity.

[0038] In order to solve the above problems, the present application provides a solution, including: obtaining environmental data and crop data; inputting the environmental data and the crop data into a preset control model to obtain the optimal planting plan and / or the optimal environmental control strategy; based on the environmental control strategy, regulating each preset intelligent remote control device in the smart greenhouse.

[0039] Specifically, in the solution provided by this application, data analysis and machine learning algorithms are used to process and analyze the collected data, and the optimal planting plan and environmental control strategy are automatically generated based on the growth analysis of the crops and environmental conditions. Furthermore, all environmental equipment in the greenhouse, such as ventilation equipment, insulation equipment, sunshade nets, heating equipment, irrigation systems, etc., are integrated, and according to the results of the algorithm decision, the environmental conditions inside the greenhouse are automatically adjusted, such as turning on or off ventilation equipment, sunshade nets, irrigation systems, etc., to achieve refined control of crop growth. Furthermore, when the environmental parameters exceed the appropriate range or the equipment fails, the system automatically sends an alarm signal, automatically processes the problem or reminds the user to process it.

[0040] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0041] Exemplary Methods

[0042] Figure 1 is a flow chart of a smart greenhouse crop management method provided by one embodiment of the present application. As shown in Figure 1, the method includes the following contents.

[0043] S101, acquiring environmental data and crop data;

[0044] S102, inputting the environmental data and the crop data into a preset control model to obtain an optimal planting plan and / or an optimal environmental control strategy;

[0045] S103: Based on the environmental control strategy, each preset intelligent remote control device in the smart greenhouse is regulated.

[0046] Specifically, in the smart greenhouse crop management and control system, regulating the intelligent remote control devices based on the environmental control strategy refers to automatically regulating the various intelligent remote control devices preset in the greenhouse based on the control model and environmental data. These intelligent remote control devices include ventilation equipment, humidification equipment, sunshade nets, integrated water and fertilizer equipment, etc. The solution provided in this application is used to manage crops in the greenhouse based on the optimal planting plan, so that the crops grow under the optimal planting plan and / or the optimal environmental control strategy. The above process is repeated continuously during the growth of the crops, so that the various devices in the smart greenhouse can quantitatively and regularly control the environmental parameters in the greenhouse based on the actual growth conditions and environmental conditions of the crops, thereby ensuring the safe growth of crops and high yield and high quality.

[0047] With such a configuration, the solution provided by this application can accurately control the planting environment by timely collecting environmental data and crop data, thereby improving the growth rate and production efficiency of crops. Furthermore, this application adopts a preset control model that can automatically optimize the planting plan and environmental control strategy according to different environmental conditions and crop requirements, reduce manual intervention and management costs, and improve production efficiency and economic benefits. In addition, through fine-tuning of the intelligent remote control equipment, accurate control of the planting environment can be achieved, improving the quality and yield of greenhouse crops, while also reducing energy consumption and waste of resources. Therefore, this method has good friendliness and practicality, and can contribute to agricultural production and food safety.

[0048] In some embodiments, obtaining environmental data includes:

[0049] Based on preset sensors, the environmental data inside and outside the greenhouse are monitored in real time; wherein, the environmental data includes: air temperature, air humidity, light intensity, soil temperature, soil humidity and rain and snow information.

[0050] Specifically, smart greenhouse crop management systems typically use pre-set sensors to monitor environmental data inside and outside the greenhouse. These sensors are located in various locations inside and outside the greenhouse and can obtain data on various environmental parameters in real time. Common environmental parameters include:

[0051] Air temperature: The sensor monitors the air temperature in real time to help users adjust the temperature in the greenhouse in a timely manner to ensure the most suitable temperature range for crop growth.

[0052] Air humidity: The sensor monitors the air humidity in real time to help users adjust the humidity in the greenhouse in time to ensure the most suitable humidity range for crop growth.

[0053] Light intensity: The sensor monitors light intensity in real time, helping users adjust the lighting conditions in the greenhouse in a timely manner to ensure the light intensity and photoperiod required for crop growth.

[0054] Soil temperature: The sensor monitors soil temperature in real time, helping users adjust the soil temperature in the greenhouse in a timely manner to ensure the most suitable temperature range for crop root growth.

[0055] Soil moisture: The sensor monitors soil moisture in real time, helping users adjust the amount and frequency of irrigation in the greenhouse in a timely manner to ensure the optimal humidity range required for crop root growth.

[0056] Rain and snow information: The sensor monitors rainfall and snowfall in real time, helping users to adjust ventilation and irrigation measures in the greenhouse in a timely manner to protect crops and ensure the environmental quality of the greenhouse.

[0057] By monitoring these environmental parameters in real time, the smart greenhouse crop management and control system can provide accurate environmental data, helping users to timely understand the growth status of crops and automatically optimize planting plans and environmental control strategies based on crop and market demands.

[0058] In some embodiments, obtaining crop data includes:

[0059] The crop planting area of ​​the smart greenhouse crops is photographed based on a preset camera device to obtain an image of the crop planting area; based on the image of the crop planting area, crop data is determined; wherein the crop data includes: the type of crop and the growth status of the crop.

[0060] Specifically, the smart greenhouse crop management system usually uses a preset camera device to capture the crop planting area in the greenhouse to obtain crop data. The advantage of obtaining crop data in this way is that it can obtain relevant information about the crops without touching the crops, and it can also save manpower and material resources. Specifically, the process includes the following steps:

[0061] The smart greenhouse's crop planting area is captured using a pre-set camera device, generating images of the crop planting area. The camera device can be installed at appropriate locations within the greenhouse to ensure comprehensive monitoring of the crop planting area. Generally, the camera device is installed at appropriate locations inside or outside the greenhouse to achieve panoramic monitoring of the crops.

[0062] Crop data is determined based on the crop planting area image. By processing, analyzing, and identifying the crop planting area image, relevant crop information can be extracted, including crop type, growth status, leaf area, fruit size, and pest and disease status. Specifically, computer vision technology can be used to implement automatic crop identification and analysis using methods such as image processing algorithms and machine learning algorithms.

[0063] The crop data includes crop type and crop growth status. Crop type refers to the variety and type of crop. Different crop types have different requirements for environmental conditions such as temperature, humidity, and light, so the greenhouse environment needs to be carefully controlled according to the crop type. Crop growth status refers to parameters such as crop growth rate, growth form, leaf color, flowering and fruiting period, etc. This data is very important for crop management and regulation, and can help users adjust the crop growth environment in a timely manner to ensure crop growth and yield.

[0064] In some embodiments, the control model is built using data analysis and machine learning algorithms, and is used to process and analyze the collected data to automatically generate an optimal planting plan and environmental control strategy based on environmental data and crop data.

[0065] Specifically, the control model is a deep learning model built using data analysis and machine learning algorithms. This model is the core of the entire system, automatically generating optimal planting plans and environmental control strategies by processing and analyzing collected environmental and crop data. Specifically, the model has the following characteristics:

[0066] Deep Learning Model. This control model is based on a deep learning algorithm. It can automatically learn and extract features and patterns from large amounts of data through training and learning. This model has good generalization capabilities and can adapt to different environments and crop requirements, thereby generating more accurate and feasible planting plans and environmental control strategies.

[0067] The control model utilizes data analysis and machine learning algorithms to automatically generate optimal planting plans and environmental control strategies by processing and analyzing collected data. These algorithms include KNN, decision tree, neural network, and support vector machine algorithms, which can be selected and combined based on actual conditions.

[0068] Automatically generate optimal planting plans and environmental control strategies. The control model processes environmental and crop data and automatically generates optimal planting plans and environmental control strategies based on the characteristics and patterns of these data. This data includes parameters such as air temperature, humidity, light intensity, soil temperature, moisture, crop type, and growth status. By rationally matching these parameters, the greenhouse environment can be finely regulated, providing optimal growing conditions for crops, thereby improving crop yield and quality.

[0069] Automatically optimize planting plans and environmental control strategies. The control model can automatically optimize planting plans and environmental control strategies based on environmental and crop changes. This means that users don't need to make manual adjustments or interventions; the system automatically learns and adapts to changing environmental and crop needs, providing more accurate and efficient greenhouse crop management services.

[0070] In some embodiments, the various intelligent remote-controlled devices include ventilation equipment, thermal insulation equipment, sunshade nets, heating equipment, and irrigation systems. Correspondingly, the solution provided in this application utilizes cross-device and intelligent control: all greenhouse environmental equipment, such as ventilation equipment, thermal insulation equipment, sunshade nets, heating equipment, and irrigation systems, are integrated. Based on the algorithmic decision-making results, the environmental conditions within the greenhouse are automatically adjusted, such as turning ventilation equipment, sunshade nets, and irrigation systems on or off, to achieve refined control over crop growth.

[0071] Furthermore, the solution provided by the present application includes: issuing an alarm when the environmental data reaches a preset indicator.

[0072] In the smart greenhouse crop management system, when environmental data reaches preset indicators, an alarm mechanism is triggered, promptly notifying the user or relevant staff to take countermeasures. This function ensures environmental safety and crop growth quality in the greenhouse.

[0073] Specifically, the process of implementing an alarm when environmental data reaches preset indicators includes the following steps: Presetting environmental indicators. In the greenhouse crop management system, users can preset warning values ​​for various environmental indicators based on specific circumstances. These environmental indicators include parameters such as temperature, humidity, light intensity, and carbon dioxide concentration. Monitoring environmental data. Using sensors or cameras, various environmental parameters within the greenhouse are monitored in real time. The collected data is uploaded to the greenhouse crop management system for processing and analysis (it should be noted that monitoring environmental data is a relevant step in the above-mentioned smart greenhouse crop management method). Analyzing environmental data. The collected environmental data is processed and analyzed using a control model, compared with preset environmental indicators, and determined whether the environmental data has reached the preset indicators. Triggering an alarm mechanism. When environmental data reaches the preset indicators, the system automatically triggers the alarm mechanism and sends an alert message to the user or relevant staff, notifying them to take timely action. For example, when the temperature within the greenhouse exceeds the preset warning value, the system automatically sounds an alarm. The user or relevant staff can receive the alarm message via a mobile phone or computer application and take appropriate countermeasures based on the specific situation. Handling abnormal situations. After receiving the alarm information, the user or relevant staff needs to deal with the abnormal situation in a timely manner, such as increasing the ventilation volume, adjusting the operating time of the humidification equipment, etc., to ensure the environmental safety and crop growth quality in the greenhouse.

[0074] In some embodiments, the solution provided by the present application further includes: obtaining user input instructions based on a preset human-computer interaction interface; and regulating various preset intelligent remote control devices in the smart greenhouse based on the instructions.

[0075] In the solution provided in this application, the system provides a user-friendly operation interface, which facilitates users to monitor environmental parameters and equipment status in real time in the greenhouse, and facilitates users to realize unified monitoring and operation of multiple devices on one control screen.

[0076] Furthermore, the solution provided by this application has good scalability, and equipment can be added or modified according to user needs to adapt to greenhouses of different sizes and types.

[0077] In summary, the solution provided by this application uses data analysis and machine learning algorithms to process and analyze the collected data, and automatically generates the best planting plan and environmental control strategy based on the growth analysis of the crops and environmental conditions. It integrates all greenhouse environmental equipment, such as ventilation equipment, insulation equipment, sunshade nets, heating equipment, irrigation systems, etc., and automatically adjusts the environmental conditions inside the greenhouse based on the results of the algorithm decision-making, such as turning on or off ventilation equipment, sunshade nets, irrigation systems, etc., to achieve refined control of crop growth. When the environmental parameters exceed the appropriate range or the equipment fails, the system automatically sends an alarm signal and automatically handles or reminds the user to handle it.

[0078] The solution proposed in this application is applied to smart greenhouses. As a connector between high-quality algorithms and real-world agriculture, this evolvable smart greenhouse (evolvable through hardware additions and deletions, systems and algorithms, and production models and efficiency) serves as a foundation for serving the most fundamental agricultural infrastructure. This greenhouse can eliminate the need for manual labor for basic operations and evolve to allow systems, algorithms, and AI to assist with basic greenhouse and crop management, freeing up human resources and enabling them to contribute more effectively to facility agriculture.

[0079] Furthermore, a single smart greenhouse has the ability to complete the analog-to-digital transformation of agriculture, completing the agricultural digitization of greenhouse production at the agricultural production level, which makes large-scale agricultural digital management truly feasible (analog information is difficult to effectively manage digitally). At the same time, it can also be effectively connected with many digital productivity tools (AI, algorithms, recognition, etc.). More importantly, it provides regional managers with convenient tools and effective results for more effective large-scale agricultural management.

[0080] Exemplary devices

[0081] The device embodiments of this application can be used to execute the method embodiments of this application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application.

[0082] FIG2 is a block diagram of a smart greenhouse crop management device according to an embodiment of the present application. As shown in FIG2 , the smart greenhouse crop management device includes:

[0083] An acquisition module 21 is used to acquire environmental data and crop data;

[0084] An input module 22, configured to input the environmental data and the crop data into a preset control model to obtain an optimal planting plan and / or an optimal environmental control strategy;

[0085] The control module 23 is used to control various intelligent remote control devices preset in the smart greenhouse based on the environmental control strategy.

[0086] In some embodiments, obtaining environmental data includes:

[0087] Based on preset sensors, real-time monitoring of environmental data inside and outside the greenhouse;

[0088] The environmental data includes: air temperature, air humidity, light intensity, soil temperature, soil humidity and rain and snow information.

[0089] In some embodiments, obtaining crop data includes:

[0090] The crop planting area of ​​the smart greenhouse crops is photographed based on a preset camera device to obtain an image of the crop planting area;

[0091] determining crop data based on the crop planting area image;

[0092] The crop data includes: crop types and crop growth status.

[0093] In some embodiments, the control model is built using data analysis and machine learning algorithms, and is used to process and analyze the collected data to automatically generate the best planting plan and environmental control strategy based on environmental data and crop data. Deep learning model.

[0094] In some embodiments, the various intelligent remote control devices include: ventilation equipment, insulation equipment, sunshade nets, heating equipment, and irrigation systems.

[0095] In some embodiments, the smart greenhouse crop management device is further used to:

[0096] When the environmental data reaches a preset index, an alarm is issued.

[0097] In some embodiments, the smart greenhouse crop management device is further used to:

[0098] Based on the preset human-computer interaction interface, obtain the user input instructions;

[0099] Based on the instructions, each intelligent remote control device preset in the smart greenhouse is controlled.

[0100] Exemplary electronic devices

[0101] The electronic device according to an embodiment of the present application is described below with reference to Figure 3. Figure 3 illustrates a block diagram of the electronic device according to an embodiment of the present application.

[0102] As shown in FIG. 3 , the electronic device 300 includes one or more processors 310 and a memory 320 .

[0103] The processor 310 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0104] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may execute the program instructions to implement the smart greenhouse crop management method of each embodiment of the present application described above and / or other desired functions. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0105] In one example, the electronic device 300 may further include an input device 330 and an output device 340 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0106] In addition, the input device 330 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 340 may output various information to the outside, including analysis results, etc. The output device 340 may include, for example, a display, a speaker, a printer, a communication network and its connected remote output device, etc.

[0107] Of course, for the sake of simplicity, FIG3 only shows some of the components of the electronic device related to the present application, omitting components such as a bus, input / output interface, etc. In addition, the electronic device may further include any other appropriate components depending on the specific application.

[0108] Exemplary computer program products and computer-readable storage media

[0109] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the smart greenhouse crop management method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0110] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0111] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the smart greenhouse crop management method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0112] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0113] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A smart greenhouse crop management method, characterized in that: include: Obtain environmental and crop data; Inputting the environmental data and the crop data into a preset control model to obtain an optimal planting plan and / or an optimal environmental control strategy; Based on the environmental control strategy, each intelligent remote control device preset in the smart greenhouse is regulated.

2. The smart greenhouse crop management method according to claim 1, characterized in that: Obtain environmental data, including: Based on preset sensors, real-time monitoring of environmental data inside and outside the greenhouse; The environmental data includes: air temperature, air humidity, light intensity, soil temperature, soil humidity and rain and snow information.

3. The smart greenhouse crop management method according to claim 1, characterized in that: Get crop data, including: The crop planting area of ​​the smart greenhouse crops is photographed based on a preset camera device to obtain an image of the crop planting area; determining crop data based on the crop planting area image; The crop data includes: crop types and crop growth status.

4. The smart greenhouse crop management method according to claim 1, characterized in that: The control model is built using data analysis and machine learning algorithms, and is used to process and analyze the collected data to automatically generate the optimal planting plan and environmental control strategy based on environmental data and crop data.

5. The smart greenhouse crop management method according to claim 1, characterized in that: The various intelligent remote control devices include: ventilation equipment, heat preservation equipment, sunshade net, heating equipment, and irrigation system.

6. The smart greenhouse crop management method according to claim 1, characterized in that: Also includes: When the environmental data reaches a preset index, an alarm is issued.

7. The smart greenhouse crop management method according to claim 1, characterized in that: Also includes: Based on the preset human-computer interaction interface, obtain the user input instructions; Based on the instructions, each intelligent remote control device preset in the smart greenhouse is controlled.

8. A smart greenhouse crop management and control device, characterized in that: include: Acquisition module, used to obtain environmental data and crop data; An input module, configured to input the environmental data and the crop data into a preset control model to obtain an optimal planting plan and / or an optimal environmental control strategy; The control module is used to control various intelligent remote control devices preset in the smart greenhouse based on the environmental control strategy.

9. An electronic device, characterized in that: include: A processor, and a memory for storing a program executable by the processor; The processor is configured to implement the smart greenhouse crop management method according to any one of claims 1 to 7 by running the program in the memory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the smart greenhouse crop management method according to any one of claims 1 to 7.

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