Agricultural Internet of Things acquisition device convenient to apply

Abstract

The invention discloses an agricultural Internet of Things acquisition device convenient to apply, and relates to the technical field of agricultural Internet of Things, and the method comprises the steps: dividing a farmland region based on a farmland condition; connecting and using an agricultural Internet of Things acquisition device; acquiring farmland data according to the agricultural Internet of Things acquisition device; preprocessing the obtained farmland data to obtain preprocessed data; based on the preprocessed data, the irrigation amount and the total fertilization amount required by the farmland are obtained; decision support suggestions are given according to the required irrigation amount and the total fertilization amount of the farmland in combination with environmental factors. According to the invention, remote monitoring and intelligent management of the equipment are realized, the management efficiency of the equipment is improved, the operation and maintenance cost is reduced, more comprehensive and accurate information support is provided for farmland management, the collected farmland data can be efficiently processed and analyzed, precise agricultural management can be realized, and the management efficiency of the equipment is improved. And an intelligent decision support suggestion is given.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural Internet of Things, and in particular to an agricultural Internet of Things data collection device that is easy to use. Background Art

[0002] In today's agricultural sector, IoT technology has been widely adopted to improve agricultural production efficiency and crop quality. However, existing agricultural IoT data collection devices and technologies still have some shortcomings. Traditional agricultural IoT systems have limitations, making it difficult to achieve accurate monitoring and intelligent management of farmland environments.

[0003] 1. Traditional agricultural IoT systems typically rely on simple sensor networks to collect farmland data. These systems can only collect limited farmland parameters, such as soil moisture and temperature, while ignoring other factors that have a significant impact on crop growth. This lacks comprehensiveness and accuracy, and cannot provide sufficient decision support for farmland management.

[0004] 2. In existing technologies, the equipment connection and management methods are relatively backward, making it difficult to achieve remote monitoring and intelligent management of equipment. In addition, the existing system can only provide simple data display and alarm functions, and cannot provide decision support. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an easy-to-use agricultural Internet of Things data collection device to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an agricultural Internet of Things data collection device that is easy to use, comprising the following steps:

[0008] S1. Divide farmland areas based on farmland conditions;

[0009] S2. Connect and use the agricultural Internet of Things collection device;

[0010] S3. Obtain farmland data using the agricultural Internet of Things collection device;

[0011] S4. preprocessing the acquired farmland data to obtain preprocessed data;

[0012] S5. Based on the preprocessed data, the required irrigation amount and total fertilizer amount of the farmland are obtained;

[0013] S6. Provide decision support recommendations based on the amount of irrigation and total fertilizer required for farmland and environmental factors.

[0014] To further optimize this technical solution, the method for dividing the farmland area based on the farmland conditions in S1 includes:

[0015] According to the planting range of different types of crops in the farmland, the farmland is divided into different areas according to the crop types, and each area is numbered to distinguish the data of different areas.

[0016] To further optimize this technical solution, the method for connecting and using the agricultural Internet of Things collection device in S2 includes:

[0017] Use 5G network to connect agricultural IoT devices, locate and manage agricultural IoT devices based on the network on the control center equipment, and monitor the operating status of the equipment at the same time.

[0018] To further optimize this technical solution, the agricultural Internet of Things collection device includes:

[0019] Humidity sensors are used to measure soil moisture, which helps determine farmland irrigation needs and plant growth status;

[0020] Chemical sensors for real-time monitoring of soil chemical element content;

[0021] Soil pH sensor, used to monitor soil pH in real time;

[0022] Temperature sensor, used to monitor farmland temperature in real time;

[0023] Light sensor, used to monitor light intensity in real time;

[0024] Camera equipment for real-time viewing of crop growth status;

[0025] Fill light is used to meet the lighting needs of crops.

[0026] To further optimize this technical solution, the farmland data obtained in S3 includes:

[0027] Soil area;

[0028] Soil moisture;

[0029] Soil chemical element content;

[0030] Soil pH;

[0031] temperature;

[0032] light intensity,

[0033] Crop growth status diagram.

[0034] To further optimize this technical solution, the step of pre-processing the acquired farmland data in S4 includes:

[0035] Missing value handling;

[0036] Outlier handling;

[0037] Data standardization;

[0038] Data storage.

[0039] To further optimize this technical solution, the step of obtaining the required irrigation amount and total fertilization amount of the farmland based on the preprocessed data in S5 includes:

[0040] Calculate irrigation amounts for each zone:

[0041] ;

[0042] in:

[0043] represents the irrigation amount of the i-th region;

[0044] represents the potential evapotranspiration of the ith region, which is calculated using the potential evapotranspiration calculation formula;

[0045] represents the actual evapotranspiration of the i-th region, which is estimated by regularly measuring soil water loss using a lysimeter;

[0046] represents the soil area that needs to be irrigated in the i-th region;

[0047] represents the water absorption coefficient of the soil in the i-th region;

[0048] represents the soil moisture in the i-th region;

[0049] Indicates the maximum moisture content held by the soil in the area;

[0050] represents the difference between the potential evapotranspiration and the actual evapotranspiration of the ith region;

[0051] It represents the reciprocal of the soil's water absorption capacity and is used to adjust irrigation volume to suit the water absorption characteristics of different soils.

[0052] Indicates the degree of soil moisture deficiency and is used to determine the amount of water needed to achieve ideal soil moisture;

[0053] Calculate the amount of fertilizer to apply to each area:

[0054] Nitrogen fertilizer application rate:

[0055] ;

[0056] in:

[0057] represents the amount of nitrogen fertilizer applied in the i-th region;

[0058] represents the nitrogen requirement of crops in the i-th region;

[0059] represents the nitrogen content in the soil of the i-th region;

[0060] represents the nitrogen fertilizer utilization efficiency of crops in the i-th region;

[0061] represents the influence coefficient of soil pH on fertilizer utilization efficiency in the i-th region;

[0062] Similarly, calculate the amount of phosphorus fertilizer and potassium fertilizer;

[0063] Total fertilizer amount:

[0064] ;

[0065] in:

[0066] represents the amount of phosphorus fertilizer applied in the i-th region;

[0067] represents the amount of potassium fertilizer applied in the i-th region.

[0068] To further optimize this technical solution, the step of providing decision support suggestions in S6 based on the required irrigation amount and total fertilization amount of the farmland and combined with environmental factors includes:

[0069] Determine whether the crop water demand meets the standard based on soil moisture:

[0070] If the soil moisture is lower than the optimum moisture required for crop growth, the required irrigation amount is calculated and irrigation is carried out; if it is higher, the irrigation amount is reduced;

[0071] Judging whether soil conditions are suitable for crops based on the content of soil chemical elements:

[0072] If the content of a certain chemical element in the soil is lower than the amount required for crop growth, the required amount of fertilizer is calculated and applied; if it is higher, the addition of corresponding fertilizer is reduced;

[0073] Judging whether the lighting conditions of crops are suitable based on light intensity:

[0074] If the light intensity does not meet the requirements of crop growth, adjust the fill light to the appropriate range;

[0075] Judging whether the temperature conditions for crops are suitable based on temperature:

[0076] Too low or too high temperature will affect the normal growth of crops. Take warming or cooling measures according to the growth habits of crops.

[0077] Use image processing and recognition technology to determine the growth status of crops:

[0078] The obtained crop growth status diagram is processed using image processing and recognition technology, and the growth status of the crop is determined based on the processing results.

[0079] To further optimize this technical solution, the steps of the image processing and recognition technology are as follows:

[0080] Preprocessing of crop growth status graph;

[0081] Feature extraction;

[0082] Image recognition and classification;

[0083] Compare with standard images;

[0084] Output crop growth status results.

[0085] To further optimize this technical solution, the functional modules include:

[0086] Area division module, equipment connection module, data acquisition module, preprocessing module, data calculation module, and analysis module.

[0087] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of an agricultural Internet of Things collection device that is easy to use as described in the first aspect of the present invention are implemented.

[0088] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of an agricultural Internet of Things collection device that is easy to use as described in the first aspect of the present invention are implemented.

[0089] Compared with the existing technology, the present invention provides an agricultural Internet of Things data collection device that is easy to use and has the following beneficial effects:

[0090] This easy-to-use agricultural IoT data collection device integrates multiple sensors and cameras to comprehensively collect a wide range of farmland data, including soil moisture, soil chemical element content, soil pH, temperature, light intensity, and crop growth status maps. This data provides more comprehensive and accurate information support for farmland management, contributing to the implementation of precision agriculture management.

[0091] This device uses data preprocessing technology and image processing and recognition technology to efficiently process and analyze the collected farmland data, and provide intelligent decision support suggestions based on the collected data and calculated results.

[0092] This device uses 5G networks to connect agricultural IoT devices, enabling remote monitoring and intelligent management of these devices. The control center can view the operating status of the devices in real time, improving management efficiency and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 any creative work.

[0094] Figure 1 This is a flow chart of an agricultural Internet of Things data collection device that is easy to apply and proposed by the present invention;

[0095] Figure 2 This is a schematic diagram of the data preprocessing process of an agricultural Internet of Things data collection device that is easy to use and proposed by the present invention;

[0096] Figure 3 This is a schematic diagram of the image processing and recognition process of an agricultural Internet of Things collection device that is easy to use and proposed by the present invention;

[0097] Figure 4 This is a module schematic diagram of an agricultural Internet of Things data collection device that is easy to use and proposed by the present invention. DETAILED DESCRIPTION

[0098] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0099] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0100] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0101] Example 1:

[0102] Reference Figures 1 to 3 , which is the first embodiment of the present invention, provides an agricultural Internet of Things data collection device that is easy to use, including the following steps:

[0103] S1. Divide the farmland area based on the farmland conditions.

[0104] In this embodiment, the method for dividing farmland areas based on farmland conditions includes:

[0105] According to the planting range of different types of crops in the farmland, the farmland is divided into different areas according to the crop types, and each area is numbered to distinguish the data of different areas.

[0106] Dividing the areas and numbering them can help farmland managers accurately distinguish and manage data in each area, and can also improve the efficiency of data processing, ensuring the accuracy and pertinence of farmland management.

[0107] S2. Connect and use the agricultural Internet of Things collection device.

[0108] In this embodiment, the method of connecting and using the agricultural Internet of Things collection device includes:

[0109] Use 5G network to connect agricultural IoT collection devices, locate and manage agricultural IoT devices based on the network on the control center equipment, and monitor the operating status of the equipment at the same time.

[0110] Compared with other networks, 5G networks have the advantages of higher transmission speed, lower latency, larger connection capacity, higher reliability and stability, higher energy efficiency, and stronger security. They ensure data interoperability between agricultural IoT devices and control centers, making data real-time and providing a timely and accurate data source for data analysis and management. Today's 5G technology is relatively mature and easy to apply in farmland.

[0111] Furthermore, the agricultural Internet of Things data collection device includes:

[0112] Humidity sensors are used to measure soil moisture, which helps determine farmland irrigation needs and plant growth status;

[0113] Chemical sensors for real-time monitoring of soil chemical element content;

[0114] Soil pH sensor, used to monitor soil pH in real time;

[0115] Temperature sensor, used to monitor farmland temperature in real time;

[0116] Light sensor, used to monitor light intensity in real time;

[0117] Camera equipment for real-time viewing of crop growth status;

[0118] Fill light is used to meet the lighting needs of crops.

[0119] S3. Obtain farmland data based on the agricultural Internet of Things collection device.

[0120] In this embodiment, the acquired farmland data includes:

[0121] Soil area;

[0122] Soil moisture;

[0123] Soil chemical element content;

[0124] Soil pH;

[0125] temperature;

[0126] light intensity,

[0127] Crop growth status diagram.

[0128] Through agricultural Internet of Things collection devices, farmland managers can obtain various types of farmland data more comprehensively and easily, providing a data basis for decision-making support suggestions.

[0129] S4. Preprocess the acquired farmland data to obtain preprocessed data.

[0130] In this embodiment, the steps of pre-processing the acquired farmland data include:

[0131] Missing value handling;

[0132] Outlier handling;

[0133] Missing values ​​are processed using methods such as deletion, interpolation, and filling. Outliers are processed using statistical methods (such as box plots, etc.), domain knowledge judgment, and machine learning models (such as regression models, etc.) to reduce the impact on data integrity and accuracy.

[0134] Data standardization;

[0135] Data standardization can make data in different fields have the same dimension and value range. It can be processed through the Min-Max standardization method (converting the original data into data in the range of [0,1]) so that the acquired farmland data have the same dimension and value range, thereby improving the accuracy and efficiency of data analysis and calculation.

[0136] Data storage.

[0137] The acquired farmland data is stored in a database, and the data of each area is stored separately according to the divided numbers, saving the soil area, soil moisture, soil chemical element content, soil pH, temperature, and light intensity data of each area.

[0138] S5. Based on the preprocessed data, the required irrigation amount and total fertilizer amount of the farmland are obtained.

[0139] In this embodiment, the steps of obtaining the required irrigation amount and total fertilization amount of the farmland based on the preprocessed data include:

[0140] Calculate irrigation amounts for each zone:

[0141] ;

[0142] in:

[0143] represents the irrigation amount of the i-th region;

[0144] represents the potential evapotranspiration of the ith region, which is calculated using the potential evapotranspiration calculation formula;

[0145] represents the actual evapotranspiration of the i-th region, which is estimated by regularly measuring soil water loss using a lysimeter;

[0146] represents the soil area that needs to be irrigated in the i-th region;

[0147] represents the water absorption coefficient of the soil in the i-th region;

[0148] represents the soil moisture in the i-th region;

[0149] Indicates the maximum moisture content held by the soil in the area;

[0150] represents the difference between the potential evapotranspiration and the actual evapotranspiration of the ith region;

[0151] It represents the reciprocal of the soil's water absorption capacity and is used to adjust irrigation volume to suit the water absorption characteristics of different soils.

[0152] Indicates the degree of soil moisture deficiency and is used to determine the amount of water needed to achieve ideal soil moisture;

[0153] Calculate the amount of fertilizer to apply to each area:

[0154] Nitrogen fertilizer application rate:

[0155] ;

[0156] in:

[0157] represents the amount of nitrogen fertilizer applied in the i-th region;

[0158] represents the nitrogen requirement of crops in the i-th region;

[0159] represents the nitrogen content in the soil of the i-th region;

[0160] represents the nitrogen fertilizer utilization efficiency of crops in the i-th region;

[0161] represents the influence coefficient of soil pH on fertilizer utilization efficiency in the i-th region;

[0162] Similarly, calculate the amount of phosphorus fertilizer and potassium fertilizer;

[0163] Total fertilizer amount:

[0164] ;

[0165] in:

[0166] represents the amount of phosphorus fertilizer applied in the i-th region;

[0167] represents the amount of potassium fertilizer applied in the i-th region.

[0168] Through calculation, farmland managers can achieve precise management of farmland irrigation and fertilization, so that they can formulate reasonable irrigation and fertilization plans according to the actual situation of the farmland, avoid the waste of water and fertilizer resources, and improve resource utilization efficiency.

[0169] S6. Provide decision support recommendations based on the amount of irrigation and total fertilizer required for farmland and environmental factors.

[0170] In this embodiment, the steps of providing decision support suggestions based on the required irrigation amount and total fertilizer amount of the farmland and environmental factors include:

[0171] Determine whether the crop water demand meets the standard based on soil moisture:

[0172] If the soil moisture is lower than the optimum moisture required for crop growth, the required irrigation amount is calculated and irrigation is carried out; if it is higher, the irrigation amount is reduced;

[0173] Judging whether soil conditions are suitable for crops based on the content of soil chemical elements:

[0174] If the content of a certain chemical element in the soil is lower than the amount required for crop growth, the required amount of fertilizer is calculated and applied; if it is higher, the addition of corresponding fertilizer is reduced;

[0175] Judging whether the lighting conditions of crops are suitable based on light intensity:

[0176] If the light intensity does not meet the requirements of crop growth, adjust the fill light to the appropriate range;

[0177] Judging whether the temperature conditions for crops are suitable based on temperature:

[0178] Too low or too high temperature will affect the normal growth of crops. Take warming or cooling measures according to the growth habits of crops.

[0179] Use image processing and recognition technology to determine the growth status of crops:

[0180] The obtained crop growth status diagram is processed using image processing and recognition technology, and the growth status of the crop is determined based on the processing results.

[0181] Furthermore, the steps of the image processing and recognition technology are:

[0182] Preprocessing of crop growth status graph;

[0183] Feature extraction;

[0184] Image recognition and classification;

[0185] Compare with standard images;

[0186] Output crop growth status results.

[0187] Generally speaking, it is more appropriate to keep the relative humidity of the soil between 60% and 80%. Humidity within this range can ensure normal respiration and water absorption of crop roots, while avoiding root rot caused by excessive water or drought stress caused by too little water.

[0188] The daily lighting time should be no less than 8 hours, and the light intensity should reach above the light saturation point required for photosynthesis.

[0189] Generally speaking, the optimum temperature range for crop growth is between 15°C and 30°C. Within this temperature range, crop physiological activities such as photosynthesis, respiration, and material transport are at their best.

[0190] Generally speaking, the nitrogen content in the soil should be maintained between 0.1%-0.2%, the phosphorus content should generally be maintained between 0.05%-0.1%, and the potassium content should generally be maintained between 1%-2%.

[0191] Different crops may have different requirements, and the judgment conditions need to be appropriately adjusted according to the crop type and growth stage.

[0192] The collected crop growth status images may contain interference factors such as noise and shadows, so they need to be preprocessed. Preprocessing steps include denoising, contrast enhancement, stretching, and other operations to improve image quality and clarity.

[0193] Image processing techniques are used to extract crop features from pre-processed images. These features may include crop height, number of leaves, leaf color, fruit size, etc., which are key information for determining the growth status of crops.

[0194] The extracted features are identified and classified using image recognition algorithms, such as convolutional neural networks (CNN). These algorithms can identify and classify crop types, growth stages, pests and diseases, and other factors, thereby providing information on crop growth status.

[0195] After identification and classification, the obtained image is compared with the standard image to judge the growth status of the crop, including whether the crop needs fertilization or irrigation.

[0196] The judgment results are output in a visual format, such as generating reports, for easy review by farm managers. These results are an important basis for farmland management, helping farm managers understand crop growth, provide decision support, and develop scientific management plans.

[0197] Example 2:

[0198] Reference Figure 4, which is the second embodiment of the present invention, provides an agricultural Internet of Things data collection device that is easy to use, including the following functional modules:

[0199] Area division module, equipment connection module, data acquisition module, preprocessing module, data calculation module, and analysis module.

[0200] The regionalization module is responsible for dividing farmland into different zones based on crop types and planting areas. Each zone is assigned a unique number to facilitate data classification and management, improve data processing efficiency, facilitate the rational allocation and utilization of resources, and reduce farmland management costs.

[0201] The device connection module uses technologies such as 5G networks to connect agricultural IoT devices (such as humidity sensors, chemical sensors, etc.) with the control center. Through the control center, farmland managers can monitor the operating status of the equipment in real time and configure and manage it to ensure the accuracy and real-time nature of the data, facilitate remote management and maintenance of the equipment, and reduce operation and maintenance costs.

[0202] The data acquisition module collects various farmland data in real time through agricultural Internet of Things devices, including soil moisture, soil chemical element content, soil pH, temperature, light intensity, and crop growth status diagrams, thereby providing comprehensive data and reflecting the changes in farmland in real time.

[0203] The preprocessing module cleans, organizes, and standardizes the acquired farmland data to eliminate noise, outliers, and missing values ​​in the data, thereby improving the quality and reliability of the data, reducing data processing costs, improving data utilization efficiency, and providing support for subsequent analysis and decision-making.

[0204] The data calculation module uses algorithms and models based on preprocessed data to calculate the irrigation amount, total fertilizer amount and other data required for farmland, thereby providing guidance for the implementation of operations such as farmland irrigation and fertilization, optimizing resource utilization and reducing farmland management costs.

[0205] The analysis module combines farmland environmental data, calculation module results and other data to analyze the status of farmland and provide decision support suggestions to help farmland managers understand the actual situation of farmland, improve the intelligence level of farmland management, and reduce management difficulty and cost.

[0206] Example 3:

[0207] This embodiment also provides a computer device, which is suitable for an agricultural Internet of Things collection device that is easy to use, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement an agricultural Internet of Things collection device that is easy to use as proposed in the above embodiment.

[0208] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an easy-to-use agricultural Internet of Things collection device as proposed in the above embodiment is implemented.

[0209] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0210] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0211] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0212] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0213] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0214] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An agricultural Internet of Things data collection device that is easy to use, characterized in that: The following steps are involved: S1. Divide farmland areas based on farmland conditions; S2. Connect and use the agricultural Internet of Things collection device; S3. Obtain farmland data using the agricultural Internet of Things collection device; S4. preprocessing the acquired farmland data to obtain preprocessed data; S5. Based on the preprocessed data, the required irrigation amount and total fertilizer amount of the farmland are obtained; S6. Provide decision support recommendations based on the amount of irrigation and total fertilizer required for farmland and environmental factors.

2. The agricultural Internet of Things data collection device that is easy to use according to claim 1 is characterized in that: The method for dividing the farmland area based on the farmland conditions in S1 includes: According to the planting range of different types of crops in the farmland, the farmland is divided into different areas according to the crop types, and each area is numbered to distinguish the data of different areas.

3. The agricultural Internet of Things data collection device that is easy to use according to claim 1 is characterized in that: The method for connecting and using the agricultural Internet of Things collection device in S2 includes: Use 5G network to connect agricultural IoT collection devices, locate and manage agricultural IoT devices based on the network on the control center equipment, and monitor the operating status of the equipment at the same time.

4. The agricultural Internet of Things data collection device that is easy to use according to claim 3 is characterized in that: The agricultural Internet of Things collection device includes: Humidity sensors are used to measure soil moisture, which helps determine farmland irrigation needs and plant growth status; Chemical sensors for real-time monitoring of soil chemical element content; Soil pH sensor, used to monitor soil pH in real time; Temperature sensor, used to monitor farmland temperature in real time; Light sensor, used to monitor light intensity in real time; Camera equipment for real-time viewing of crop growth status; Fill light is used to meet the lighting needs of crops.

5. The agricultural Internet of Things data collection device that is easy to use according to claim 1 is characterized in that: The farmland data obtained in S3 includes: Soil area; Soil moisture; Soil chemical element content; Soil pH; temperature; light intensity, Crop growth status diagram.

6. The agricultural Internet of Things data collection device that is easy to use according to claim 1 is characterized in that: The step of pre-processing the acquired farmland data in S4 includes: Missing value handling; Outlier handling; Data standardization; Data storage.

7. The agricultural Internet of Things data collection device that is easy to use according to claim 1 is characterized in that: The step of obtaining the required irrigation amount and total fertilization amount of the farmland based on the pre-processed data in S5 includes: Calculate irrigation amounts for each zone: ； in: represents the irrigation amount of the i-th region; represents the potential evapotranspiration of the ith region, which is calculated using the potential evapotranspiration calculation formula; represents the actual evapotranspiration of the i-th region, which is estimated by regularly measuring soil water loss using a lysimeter; represents the soil area that needs to be irrigated in the i-th region; represents the water absorption coefficient of the soil in the i-th region; represents the soil moisture in the i-th region; Indicates the maximum moisture content held by the soil in the area; represents the difference between the potential evapotranspiration and the actual evapotranspiration of the ith region; It represents the reciprocal of the soil's water absorption capacity and is used to adjust irrigation volume to suit the water absorption characteristics of different soils. Indicates the degree of soil moisture deficiency and is used to determine the amount of water needed to achieve ideal soil moisture; Calculate the amount of fertilizer to apply to each area: Nitrogen fertilizer application rate: ； in: represents the amount of nitrogen fertilizer applied in the i-th region; represents the nitrogen requirement of crops in the i-th region; represents the nitrogen content in the soil of the i-th region; represents the nitrogen fertilizer utilization efficiency of crops in the i-th region; represents the influence coefficient of soil pH on fertilizer utilization efficiency in the i-th region; Similarly, calculate the amount of phosphorus fertilizer and potassium fertilizer; Total fertilizer amount: ； in: represents the amount of phosphorus fertilizer applied in the i-th region; represents the amount of potassium fertilizer applied in the i-th region.

8. The agricultural Internet of Things data collection device that is easy to use according to claim 1 is characterized in that: The step of providing decision support suggestions in S6 based on the required irrigation amount and total fertilization amount of the farmland and environmental factors includes: Determine whether the crop water demand meets the standard based on soil moisture: If the soil moisture is lower than the optimum moisture required for crop growth, the required irrigation amount is calculated and irrigation is carried out; if it is higher, the irrigation amount is reduced; Judging whether soil conditions are suitable for crops based on the content of soil chemical elements: If the content of a certain chemical element in the soil is lower than the amount required for crop growth, the required amount of fertilizer is calculated and applied; if it is higher, the addition of corresponding fertilizer is reduced; Judging whether the lighting conditions of crops are suitable based on light intensity: If the light intensity does not meet the requirements of crop growth, adjust the fill light to the appropriate range; Judging whether the temperature conditions for crops are suitable based on temperature: Too low or too high temperature will affect the normal growth of crops. Take warming or cooling measures according to the growth habits of crops. Use image processing and recognition technology to determine the growth status of crops: The obtained crop growth status diagram is processed using image processing and recognition technology, and the growth status of the crop is determined based on the processing results.

9. The agricultural Internet of Things data collection device that is easy to use according to claim 8 is characterized in that: The steps of the image processing and recognition technology are: Preprocessing of crop growth status graph; Feature extraction; Image recognition and classification; Compare with standard images; Output crop growth status results.

10. The easy-to-use agricultural Internet of Things data collection device according to claim 1, characterized in that: The functional modules of the agricultural Internet of Things collection device include: Area division module, equipment connection module, data acquisition module, preprocessing module, data calculation module, and analysis module.

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