Novel Internet of Things device

Abstract

The invention relates to the technical field of Internet of Things devices, and discloses a novel Internet of Things device, which can realize comprehensive and accurate data acquisition through various types of sensors and monitoring equipment, can provide comprehensive and accurate data support for agricultural production decision making, and utilizes a mode of combining a wireless communication technology with cloud computing and edge computing to improve the agricultural production decision making efficiency. Rapid transmission, real-time processing and deep analysis of data are ensured, the requirements of agricultural production for information timeliness and accuracy are met, optimal calculation is performed on the collected data through an algorithm, intelligent decision support is provided for users, and the user experience is improved. Automatic and precise control over agricultural operations such as irrigation, fertilization and pest control can be achieved in cooperation with an automatic sliding execution structure, scientificity and convenience of agricultural planting can be effectively improved, crop growth vigor, pest data and the like are analyzed and predicted, crop growth can be precisely controlled in cooperation with efficient and precise execution of an execution unit, and the crop quality is improved. And excessive use of water resources, fertilizers and pesticides can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things devices, and in particular to a novel Internet of Things device. Background Art

[0002] With the development of agricultural modernization, not only has the scale of agriculture continued to expand, but the management of agricultural crops has become increasingly refined and scientific. Compared with traditional agricultural production methods, it is difficult to meet the needs of efficient, precise, and sustainable agricultural development. To achieve agricultural modernization, agricultural Internet of Things systems are widely used in modern agricultural planting.

[0003] The agricultural Internet of Things system can assist in agricultural crop planting management decision-making through information collection and processing, and realize faster information management. The accuracy of management decision-making depends on the timeliness, comprehensiveness and accuracy of data collection. Information collection is affected by the area of ​​planting blocks and the comprehensiveness of data collection. The existing agricultural Internet of Things system still has many shortcomings in data processing, decision-making accuracy and resource optimization allocation. Therefore, a new type of Internet of Things device is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a novel Internet of Things device to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: a novel Internet of Things device, comprising an environmental monitoring module, a crop growth monitoring module, a data transmission module, a data processing and analysis module, and an intelligent decision-making and control module;

[0006] The environmental monitoring module is used to collect and monitor crop growth environment data. The environmental monitoring module includes a temperature monitoring unit, an air humidity monitoring unit, a light monitoring unit, a soil moisture monitoring unit, and a wind speed and direction monitoring unit;

[0007] The crop growth monitoring module is used for collecting and monitoring plant growth status data, and includes a macro growth monitoring unit, a fruit expansion monitoring unit, a disease and insect pest monitoring unit, and a field sampling unit;

[0008] The data transmission module is used for efficient and stable transmission of sampled data, and the data transmission module includes a wireless communication unit and a gateway device;

[0009] The data processing and analysis module is used for processing and analyzing uploaded data, and the data processing and analysis module includes a cloud computing unit and an edge computing unit;

[0010] The intelligent decision-making and control module is used for intelligent decision-making and control of crop management, and includes a decision control unit and an execution unit.

[0011] Preferably, the above-mentioned temperature monitoring unit, air humidity monitoring unit, light monitoring unit, soil moisture monitoring unit and wind speed and direction monitoring unit are respectively temperature sensors, air humidity sensors, light sensors, soil moisture sensors and wind speed and direction sensors, and the soil moisture sensors are distributed in a matrix shape and installed in the soil layer of the planting area.

[0012] Preferably, the above-mentioned macro-growth monitoring unit is used for macro-monitoring of crop growth conditions in the planting area. The macro-growth monitoring unit includes a drone platform, a multispectral sensor and a lidar, and the multispectral sensor and the lidar are both carried and installed by the drone platform.

[0013] Preferably, the multispectral sensor is carried by an unmanned aerial vehicle platform to complete the detection and calculation of the crop normalized vegetation index, ratio vegetation index and chlorophyll absorption ratio index through regional cruise scanning. For the calculation of the crop normalized vegetation index, the specific formula is:

[0014]

[0015] where N NIR is the reflectivity in the near-infrared band, N Red is the red light band reflectance, and NDVI is the calculated normalized vegetation index, which can indicate vegetation coverage and chlorophyll content.

[0016] For the calculation of the ratio vegetation index of crops, the specific formula is:

[0017]

[0018] where N NIR is the reflectivity in the near-infrared band, N Red is the red light band reflectance, and NDVI is the calculated ratio vegetation index. The ratio vegetation index can be used to accurately predict the emergence rate of crops in early crop growth monitoring.

[0019] For the chlorophyll absorption ratio index of crops, the specific formula is:

[0020]

[0021] where N Green is the reflectivity of green light band, N NIR is the near-infrared reflectivity, N Red is the reflectance of red light band, CARI is the calculated chlorophyll absorption ratio index, and the chlorophyll absorption ratio index can be used to monitor the photosynthetic efficiency and growth status of crops. The correlation between CARI and leaf nitrogen content can be used as a control reference for dynamically adjusting the amount of fertilizer applied to crops.

[0022] Preferably, the laser radar is carried by a UAV platform to complete the detection and calculation of crop height and growth distribution through regional cruise scanning. The specific formula for crop height calculation is:

[0023]

[0024] where d i is the flight height of the i-th measurement point of the lidar, θ i is the laser incident angle at the i-th measuring point, n is the total number of valid measuring points, and h is the calculated crop height. By scanning and detecting the entire planting area, a three-dimensional model of crop growth can be reconstructed to accurately monitor crop height and growth.

[0025] Preferably, the fruit expansion monitoring unit is used for collecting and monitoring the growth data of crop fruits. The fruit expansion monitoring unit includes a plurality of sets of high-definition macro cameras, which are distributed and installed at sampling points in the planting area. The high-definition macro cameras use image recognition technology to analyze the shape and color changes of the fruit and perform regular monitoring to obtain the growth data of the fruit.

[0026] The pest monitoring unit is used to monitor pests and diseases in crop planting areas. It includes a high-definition camera and a light attractor. It automatically completes the functions of attracting insects, taking high-definition photos, identifying insects, and counting insects through light attracting and high-definition capture video. After uploading the images to the cloud platform, the insect species can be automatically identified and a database can be generated. For the calculation of the pest index, the specific formula is:

[0027]

[0028] Among them, H is the pest index, W i is the weight coefficient of the i-th type of pests, N i is the average daily number of pests detected in the i-th category, T is the average daily temperature, and RH is the relative humidity. By weightedly calculating the population density of different pests and correcting the environmental impact by combining temperature and humidity, a pest index is generated to provide data support for subsequent pest control.

[0029] The field sampling unit is manually operated to take samples on the field for checking the growth data and sampling the soil in the crop growing area.

[0030] Preferably, the wireless communication unit adopts a hybrid networking technology of low-power Bluetooth and ZigBee. The low-power Bluetooth is suitable for short-distance, low-power data transmission, and the ZigBee network is used for long-distance data transmission. Multiple BLE subnets are connected to form a wireless sensor network covering the entire farmland.

[0031] The gateway device is deployed at the edge of the planting area. The gateway device serves as a bridge between the wireless communication unit and the external network. The gateway device has data caching, protocol conversion and preliminary data processing functions. After receiving the sensor data, it first performs data verification and packaging, and then sends the data to the data processing and analysis module through the wireless network.

[0032] Preferably, the cloud computing unit includes a cloud database and a cloud computer. The cloud database is used to store uploaded data and related computing support data. The cloud computer adopts a distributed cloud computing architecture and has powerful data processing capabilities. It can classify, clean, analyze, calculate and model a large amount of collected agricultural data, providing computing power support for management decision-making analysis.

[0033] The edge computing unit sets up edge computing nodes in local areas of farmland, which are close to the data source and can quickly process data with high real-time requirements.

[0034] Preferably, the decision control unit is connected to the cloud computing unit upward and the edge computing unit is connected to the decision control unit downward, and the decision control unit is used to control the work of the execution unit;

[0035] The execution unit includes an irrigation execution device, a fertilization execution device and a pest control execution device, which are respectively used for automated irrigation, fertilization and pest control work in the planting area.

[0036] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0037] 1. Based on IoT communication, a comprehensive and precise monitoring method is adopted. Through various types of sensors and monitoring equipment, real-time data on farmland environment and crop growth conditions can be obtained comprehensively and accurately. The data collection is comprehensive and accurate, which can provide comprehensive and accurate data support for agricultural production decision-making. The combination of wireless communication technology and cloud computing and edge computing ensures rapid data transmission, real-time processing and in-depth analysis, meeting the requirements of agricultural production for information timeliness and accuracy.

[0038] 2. Use algorithms to analyze and optimize data, optimize and calculate the collected data through algorithms, analyze and predict crop growth, pest and disease data, etc., cooperate with the efficient and accurate execution of the execution unit, and through precise agricultural management measures, crop growth can be accurately controlled, and excessive use of water resources, fertilizers and pesticides can be avoided, which is conducive to protecting the ecological environment and achieving sustainable agricultural development.

[0039] 3. Intelligent decision-making control adopts a distributed cloud computing architecture to store collected data and combine real-time data with historical data to perform efficient and accurate calculation and analysis of the data, providing users with intelligent decision-making support. Combined with the automatic sliding execution structure, it can realize the automation and precision control of agricultural operations such as irrigation, fertilization, and pest control, which can effectively improve the scientificity and convenience of agricultural planting, effectively improve resource utilization efficiency, and reduce production costs and management difficulty. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 This is a schematic diagram of the module composition and working of the IoT device;

[0042] Figure 2 This is a schematic diagram of the unit composition and working of this IoT device. DETAILED DESCRIPTION

[0043] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which this application can be implemented. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in this application without affecting the efficacy and purpose that can be achieved by this application.

[0045] Example

[0046] See also Figure 1-2 The present invention provides a technical solution: a new type of Internet of Things device, including an environmental monitoring module, a crop growth monitoring module, a data transmission module, a data processing and analysis module, and an intelligent decision-making and control module. Specifically:

[0047] The environmental monitoring module is used to collect and monitor crop growth environment data. The environmental monitoring module includes a temperature monitoring unit, an air humidity monitoring unit, a light monitoring unit, a soil moisture monitoring unit, and a wind speed and direction monitoring unit. The temperature monitoring unit, the air humidity monitoring unit, the light monitoring unit, the soil moisture monitoring unit, and the wind speed and direction monitoring unit are respectively a temperature sensor, an air humidity sensor, a light sensor, a soil moisture sensor, and a wind speed and direction sensor for real-time collection of environmental data in the planting area. In order to improve the accuracy of data collection, soil moisture sensors are distributed in a matrix and installed in the soil layer of the planting area. Specifically:

[0048] Temperature sensor: A high-precision thermistor temperature sensor is used with a measurement range of -20°C to 50°C and an accuracy of ±0.5°C. This is determined by calibration in a known temperature environment. The sensor is used to monitor farmland air temperature in real time, with a data acquisition frequency of once per second.

[0049] Air humidity sensor: This capacitive-based humidity sensor has a measurement range of 0% to 100% RH and an accuracy of ±3% RH. It is used to monitor farmland air humidity and has the same data acquisition frequency as the temperature sensor.

[0050] Light sensor: A silicon photocell light sensor with a measurement range of 0 to 200,000 lux and an accuracy of ±5% is used to obtain farmland light intensity information. The data collection frequency is once per second.

[0051] Soil moisture sensor: This sensor uses the frequency domain reflectometry (FDR) principle to measure moisture content in a range of 0 to 100% volumetric moisture with an accuracy of ±3%. It is buried at different depths in the field (e.g., 10 cm, 20 cm, 30 cm) to monitor soil moisture content in real time, with a data acquisition frequency of once per minute.

[0052] Wind speed and direction sensor: Using ultrasonic wind speed and direction sensor, the wind speed measurement range is 0 to 50m / s, the accuracy is ±0.3m / s, and it is used to monitor the wind speed and direction in farmland meteorological conditions. The data collection frequency is once per second.

[0053] Each sensor in the environmental monitoring module collects data such as temperature, humidity, light, soil moisture, wind speed and direction at a set frequency, and converts the analog signal into a digital signal. For example, the temperature sensor converts the collected temperature signal V T After being converted into a digital signal by an analog-to-digital conversion chip, it is sent to a nearby wireless communication unit.

[0054] The crop growth monitoring module is used to collect and monitor crop growth status data. The crop growth monitoring module includes a macro growth monitoring unit, a fruit expansion monitoring unit, a pest and disease monitoring unit, and a field sampling unit. Specifically:

[0055] Macro-growth monitoring unit: used for macro-monitoring of crop growth in the planting area. The macro-growth monitoring unit includes a UAV platform, a multispectral sensor, and a LiDAR. The multispectral sensor and LiDAR are both mounted on the UAV platform. The multispectral sensor is carried by the UAV platform and completes the detection and calculation of the crop normalized vegetation index, ratio vegetation index, and chlorophyll absorption ratio index through regional cruise scanning.

[0056] For the calculation of the normalized vegetation index of crops, the specific formula is:

[0057]

[0058] where N NIR is the near-infrared reflectivity, N Red is the red light band reflectance, and NDVI is the calculated normalized vegetation index, which can indicate vegetation coverage and chlorophyll content.

[0059] For the calculation of the ratio vegetation index of crops, the specific formula is:

[0060]

[0061] where N NIR is the near-infrared reflectivity, N Red is the red light band reflectance, and NDVI is the calculated ratio vegetation index. The ratio vegetation index can be used to accurately predict the emergence rate of crops in early crop growth monitoring.

[0062] For the chlorophyll absorption ratio index of crops, the specific formula is:

[0063]

[0064] where N Green is the reflectivity of green light band, N NIR is the near-infrared reflectivity, N Red is the red light reflectance, and CARI is the calculated chlorophyll absorption ratio index. The chlorophyll absorption ratio index can be used to monitor the photosynthetic efficiency and growth status of crops, and the correlation between CARI and leaf nitrogen content can be used as a control reference for dynamically adjusting the amount of fertilizer applied to crops.

[0065] The lidar is carried by the UAV platform and completes the detection and calculation of crop height and growth distribution through regional cruise scanning. The specific formula for crop height calculation is:

[0066]

[0067] where d iis the flight height of the i-th measurement point of the lidar, θ i is the laser incident angle at the i-th measuring point, n is the total number of valid measuring points, and h is the calculated crop height. By scanning and detecting the entire planting area, a three-dimensional model of crop growth can be reconstructed to accurately monitor crop height and growth.

[0068] Fruit expansion monitoring unit: used to collect and monitor crop fruit growth data. The fruit expansion monitoring unit includes multiple sets of high-definition macro cameras, which are distributed and installed at sampling points in the planting area. The high-definition macro cameras use image recognition technology to analyze the shape and color changes of the fruit, and regularly monitor the fruit growth data.

[0069] Pest and disease monitoring unit: used to monitor pests and diseases in crop planting areas. The unit includes a high-definition camera and a light attractant. Through light attraction and high-definition capture video, it automatically completes insect trapping, high-definition photo identification and counting functions. After uploading images to the cloud platform, it can automatically identify insect species and generate a database. For the calculation of the pest index, the specific formula is:

[0070]

[0071] Among them, H is the pest index, W i is the weight coefficient of the i-th type of pests, N i is the average daily number of pests detected in the i-th category, T is the average daily temperature, and RH is the relative humidity. By weightedly calculating the population density of different pests and correcting the environmental impact by combining temperature and humidity, a pest index is generated to provide data support for subsequent pest control.

[0072] The vegetation index data monitored by the crop growth monitoring module, the fruit growth data monitored by the fruit expansion monitoring unit, and the pest and disease monitoring data monitored by the pest and disease monitoring unit are also sent to the nearby wireless communication unit after integration and processing. The wireless communication node will preliminarily integrate and package the various sensor data received and send it to the gateway device via wireless transmission (BLE or ZigBee);

[0073] Field sampling unit: manual field sampling is used as a control and verification of growth data and to sample the soil in the crop growing area.

[0074] The data transmission module is used for efficient and stable transmission of sampled data. The data transmission module includes a wireless communication unit and a gateway device. Specifically:

[0075] Wireless communication unit: Using a hybrid networking technology of Bluetooth Low Energy and ZigBee. BLE is suitable for short-distance, low-power data transmission, such as communication between sensors and nearby aggregation nodes, with a communication distance of up to 50 meters. ZigBee networks are used for data transmission over longer distances. Multiple BLE subnets can be connected to form a wireless sensor network covering the entire farmland, with a communication distance of up to hundreds of meters. The data transmission rate is adjusted according to the amount of sensor data and transmission requirements, generally around 250kbps, to ensure the real-time and integrity of the data.

[0076] Gateway device: The gateway device is deployed at the edge of the planting area and serves as a bridge between the wireless sensor network and external networks (such as the Internet and cloud computing platforms). The gateway has data caching, protocol conversion and preliminary data processing functions. After receiving sensor data, it first performs data verification and packaging, and then sends the data to the data processing and analysis module via Ethernet or 4G / 5G network. The packet loss rate of data transmission is controlled below 5%.

[0077] The data processing and analysis module is used to process and analyze uploaded data. The data processing and analysis module includes a cloud computing unit and an edge computing unit. Specifically:

[0078] Cloud computing unit: includes cloud database and cloud computer. The cloud database is used to upload data and store related computing support data. The cloud computer adopts a distributed cloud computing architecture with powerful data processing capabilities. It can classify and clean the large amount of agricultural data collected. For example, sensor readings that are obviously beyond the reasonable range (such as the temperature suddenly exceeding 50°C or falling below -20°C) are marked as abnormal data and processed (such as elimination or correction), analyzed, calculated and modeled. For example, machine learning algorithms are used to establish crop growth models, and crop growth trends, the probability of pests and diseases, etc. are predicted based on historical data and real-time data; for example, based on historical meteorological data, soil data and crop growth data, crop growth models are trained to predict crop growth trends and yields under different environmental conditions. At the same time, pest and disease data are analyzed, and pest and disease occurrence prediction models are established to provide early warnings of pest and disease occurrences, providing computing power support for management decision-making analysis.

[0079] Edge computing units: Edge computing units set up edge computing nodes in local areas of farmland. Close to the data source, they can quickly process data with high real-time requirements. For example, when soil moisture falls below a set threshold, the edge computing device can immediately trigger irrigation control instructions without waiting for data to be uploaded to the cloud and then returning control commands. This reduces response time and improves the real-time performance of the system. The data processing capacity of the edge computing device depends on the number of sensors it is responsible for and the data processing tasks. Generally, it can process data from multiple sensors simultaneously and complete simple decision-making operations within seconds.

[0080] The intelligent decision-making and control module is used for intelligent decision-making and control of crop management. The intelligent decision-making and control module includes a decision control unit and an execution unit. Specifically:

[0081] Decision control unit: The decision control unit is connected to the cloud computing unit and the edge computing unit upwards and connected to the decision control unit downwards. The decision control unit is used to control the work of the execution unit;

[0082] The execution unit includes an irrigation execution device, a fertilization execution device, and a pest control execution device. The irrigation execution device, the fertilization execution device, and the pest control execution device are used for automated irrigation, fertilization, and pest control in the planting area, respectively. Specifically:

[0083] Irrigation actuator: The irrigation actuator is a dripper and sprinkler irrigation facility pre-installed in the planting area. Based on the soil moisture, meteorological data, and crop water requirement model provided by the data processing and analysis module, it uses a fuzzy control algorithm to determine whether irrigation is needed, as well as the intensity and timing of irrigation. For example, if the soil moisture is low in the morning and the weather is clear and rainless, the system determines that irrigation is needed. Based on the fuzzy reasoning results, it calculates the appropriate irrigation amount, generates irrigation control instructions, and performs irrigation operations.

[0084] Fertilization execution device: Based on manually sampled soil fertility and crop nutrient requirement models, it accurately calculates the amount of fertilizer to be applied and generates fertilization control instructions. For example, during the peak crop growth period, based on the insufficient nitrogen, phosphorus, and potassium content in the soil as reported by soil nutrient sensors, it calculates the amount of fertilizer needed and then sends the fertilization instructions to the fertilization equipment. The fertilization execution device is carried by a drone platform and can efficiently complete fertilization work through cruise operations.

[0085] Pest control execution device: The pest control execution device includes a drone spraying device and a fixed-point insect trap. According to the pest monitoring data and prediction model, specifically, by weighted calculation of the population density of different pests, combined with the temperature and humidity correction environmental impact, the pest index is generated. When the H threshold H>H 阈值 (such as H 阈值 =50) triggers an intervention warning. When the characteristic value of the pest exceeds the threshold, the corresponding prevention and control measures are initiated. For example, when the number of pests increases and reaches the warning threshold, the system automatically selects drones to spread pesticides and turns on insect traps. For pesticide dosage control, the specific methods are as follows:

[0086]

[0087] Where C is the dosage of the drug (kg / hm 2 ), H is the pest index, A is the crop area (hm 2), E is the control efficiency of the pesticide (such as high-efficiency pesticide E = 0.9, ordinary pesticide E = 0.7), and the dosage is adjusted dynamically according to the pest index. For example, H = 60, A = 10hm 2 , when E = 0.9, C = 6.7 kg / hm 2 , avoid overdose;

[0088] And the probability of insect pests is calculated based on meteorological data prediction. The specific formula is:

[0089]

[0090] Among them, P 虫害 is the probability of pest occurrence, T is the air temperature (℃) detected by the temperature sensor, RH is the air humidity detected by the air humidity sensor, I is the light intensity detected by the light sensor, a, b, c, d are the model regression coefficients (generated through historical data, such as a = 0.2, b = -0.1, c = -0.05, d = 1.5) to predict the probability of pest occurrence based on meteorological data. When P 虫害 When the value is >0.7, the system automatically sends an early warning to the plant protection drone to prepare for pest control.

[0091] In summary, a comprehensive and precise monitoring method based on IoT communication can comprehensively and accurately obtain real-time data on farmland environment and crop growth status through various types of sensors and monitoring equipment. The data collection is comprehensive and accurate, which can provide comprehensive and accurate data support for agricultural production decision-making. The combination of wireless communication technology and cloud computing and edge computing can ensure the rapid transmission, real-time processing and in-depth analysis of data, meet the requirements of agricultural production for information timeliness and accuracy, use algorithms to analyze and optimize data, optimize and calculate the collected data through algorithms, analyze and predict crop growth, pest and disease data, etc., and cooperate with the efficient and precise execution unit. Execution, through precise agricultural management measures, can accurately control the growth of crops, and avoid excessive use of water resources, fertilizers and pesticides, which is beneficial to protecting the ecological environment and achieving sustainable development of agriculture, intelligent decision-making control, using distributed cloud computing architecture, to store the collected data and combine real-time data and historical data to perform efficient and accurate calculation and analysis of the data, providing users with intelligent decision-making support, with the automatic sliding execution structure to realize the automation and precise control of agricultural operations such as irrigation, fertilization, pest control, etc., which can effectively improve the scientificity and convenience of agricultural planting, effectively improve resource utilization efficiency, and reduce production costs and management difficulty.

[0092] Those skilled in the art will appreciate that various combinations and / or combinations of features described in the various embodiments and / or claims of the present invention may be made, even if such combinations and / or combinations are not explicitly described in the present invention. In particular, various combinations and / or combinations of features described in the various embodiments and / or claims of the present invention may be made, without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

Claims

1. A novel Internet of Things device, comprising an environmental monitoring module, a crop growth monitoring module, a data transmission module, a data processing and analysis module, and an intelligent decision-making and control module, characterized in that: The environmental monitoring module is used to collect and monitor crop growth environment data. The environmental monitoring module includes a temperature monitoring unit, an air humidity monitoring unit, a light monitoring unit, a soil moisture monitoring unit, and a wind speed and direction monitoring unit; The crop growth monitoring module is used for collecting and monitoring plant growth status data, and includes a macro growth monitoring unit, a fruit expansion monitoring unit, a disease and insect pest monitoring unit, and a field sampling unit; The data transmission module is used for efficient and stable transmission of sampled data, and the data transmission module includes a wireless communication unit and a gateway device; The data processing and analysis module is used for processing and analyzing uploaded data, and the data processing and analysis module includes a cloud computing unit and an edge computing unit; The intelligent decision-making and control module is used for intelligent decision-making and control of crop management, and includes a decision control unit and an execution unit.

2. A novel Internet of Things device according to claim 1, characterized in that: The temperature monitoring unit, air humidity monitoring unit, light monitoring unit, soil moisture monitoring unit and wind speed and direction monitoring unit are respectively a temperature sensor, an air humidity sensor, a light sensor, a soil moisture sensor and a wind speed and direction sensor. The soil moisture sensors are distributed in a matrix shape and installed in the soil layer of the planting area.

3. The novel Internet of Things device according to claim 1, characterized in that: The macro-growth monitoring unit is used for macro-monitoring of crop growth conditions in a planting area. The macro-growth monitoring unit includes an unmanned aerial vehicle platform, a multispectral sensor, and a laser radar. The multispectral sensor and the laser radar are both mounted on the unmanned aerial vehicle platform.

4. A novel Internet of Things device according to claim 3, characterized in that: The multispectral sensor is carried by the UAV platform and completes the detection and calculation of the crop normalized vegetation index, ratio vegetation index and chlorophyll absorption ratio index through regional cruise scanning. The specific formula for calculating the crop normalized vegetation index is: where N NIR is the near-infrared reflectivity, N Red is the red light band reflectance, and NDVI is the calculated normalized vegetation index, which can indicate vegetation coverage and chlorophyll content. For the calculation of the ratio vegetation index of crops, the specific formula is: where N NIR is the near-infrared reflectivity, N Red is the red light band reflectance, and NDVI is the calculated ratio vegetation index. The ratio vegetation index can be used to accurately predict the emergence rate of crops in early crop growth monitoring. For the chlorophyll absorption ratio index of crops, the specific formula is: where N Green is the reflectivity of green light band, N NIR is the near-infrared reflectivity, N Red is the reflectance of red light band, CARI is the calculated chlorophyll absorption ratio index, and the chlorophyll absorption ratio index can be used to monitor the photosynthetic efficiency and growth status of crops. The correlation between CARI and leaf nitrogen content can be used as a control reference for dynamically adjusting the amount of fertilizer applied to crops.

5. The novel Internet of Things device according to claim 3, characterized in that: The laser radar is carried by the UAV platform and completes the detection and calculation of crop height and growth distribution through regional cruise scanning. The specific formula for crop height calculation is: where d i is the flight height of the i-th measurement point of the lidar, θ i is the laser incident angle at the i-th measuring point, n is the total number of valid measuring points, and h is the calculated crop height. By scanning and detecting the entire planting area, a three-dimensional model of crop growth can be reconstructed to accurately monitor crop height and growth.

6. The novel Internet of Things device according to claim 1, characterized in that: The fruit expansion monitoring unit is used to collect and monitor the growth data of crop fruits. The fruit expansion monitoring unit includes multiple groups of high-definition macro cameras, which are distributed and installed at sampling points in the planting area. The high-definition macro cameras use image recognition technology to analyze the shape and color changes of the fruits and perform regular monitoring to obtain the growth data of the fruits. The pest monitoring unit is used to monitor pests and diseases in crop planting areas. It includes a high-definition camera and a light attractor. It automatically completes the functions of attracting insects, taking high-definition photos, identifying insects, and counting insects through light attracting and high-definition capture video. After uploading the images to the cloud platform, the insect species can be automatically identified and a database can be generated. For the calculation of the pest index, the specific formula is: Among them, H is the pest index, W i is the weight coefficient of the i-th type of pests, N i is the average daily number of pests detected in the i-th category, T is the average daily temperature, and RH is the relative humidity. By weightedly calculating the population density of different pests and correcting the environmental impact by combining temperature and humidity, a pest index is generated to provide data support for subsequent pest control. The field sampling unit is manually operated to take samples on the field for checking the growth data and sampling the soil in the crop growing area.

7. The novel Internet of Things device according to claim 1, characterized in that: The wireless communication unit uses a hybrid networking technology of Bluetooth Low Energy and ZigBee. Bluetooth Low Energy is suitable for short-distance, low-power data transmission, while the ZigBee network is used for longer-distance data transmission. Multiple BLE subnets are connected to form a wireless sensor network covering the entire farmland. The gateway device is deployed at the edge of the planting area. The gateway device serves as a bridge between the wireless communication unit and the external network. The gateway device has data caching, protocol conversion and preliminary data processing functions. After receiving the sensor data, it first performs data verification and packaging, and then sends the data to the data processing and analysis module through the wireless network.

8. The novel Internet of Things device according to claim 1, characterized in that: The cloud computing unit includes a cloud database and a cloud computer. The cloud database is used to store uploaded data and related computing support data. The cloud computer adopts a distributed cloud computing architecture and has powerful data processing capabilities. It can classify, clean, analyze, calculate and model the large amount of collected agricultural data, providing computing power support for management decision-making analysis. The edge computing unit sets up edge computing nodes in local areas of farmland, which are close to the data source and can quickly process data with high real-time requirements.

9. The novel Internet of Things device according to claim 1, characterized in that: The decision control unit is connected to the cloud computing unit upward and the edge computing unit downward, and the decision control unit is used to control the work of the execution unit; The execution unit includes an irrigation execution device, a fertilization execution device and a pest control execution device, which are respectively used for automated irrigation, fertilization and pest control work in the planting area.

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