Integrated system for fertilization and pest control for emblica cultivation

Abstract

The present invention relates to the technical field of agricultural planting management, and discloses an integrated system for fertilization and pest control for emblica planting, including: a soil sensing module for real-time monitoring of soil moisture, nutrients, pH value and temperature; a pest monitoring module for detecting the types and severity of pests and diseases of emblica plants through image acquisition and recognition and pest sensors; a central control module for generating fertilization and control strategies, and scheduling each module to perform corresponding operations; an intelligent fertilization module for generating and executing a fertilization plan based on data from a soil sensing module; a pest control module for generating a control plan based on data from a pest monitoring module; a data communication module for data interaction between modules; and a power management module for providing power. In the present invention, soil moisture, nutrients, pH value and temperature data are collected in real time through the soil sensing module, and a specific fertilization plan is generated to achieve accurate nutrient supply.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural planting management, and in particular to an integrated system for fertilizing and preventing and controlling diseases and insect pests for planting emblica. Background Art

[0002] As a fruit tree with important economic and nutritional value, Phyllanthus emblica L. is widely planted around the world. However, in the cultivation process of Phyllanthus emblica, reasonable fertilization management and effective pest and disease control are the key to ensure its healthy growth and improve yield and quality. The existing Phyllanthus emblica planting and management technology mainly relies on manual experience and traditional mechanical equipment in fertilization and pest and disease control, and there are many technical deficiencies.

[0003] First, in terms of fertilization management, traditional fertilization methods often rely on the experience of farmers, make a rough estimate of the nutritional needs of crops, and carry out artificial fertilization. This fertilization method lacks scientific basis and is prone to excessive or insufficient fertilization. On the one hand, excessive fertilization will cause fertilizer waste, increase planting costs, and even lead to soil nutrient imbalance and environmental pollution; on the other hand, insufficient fertilization cannot meet the nutritional needs of emblica at different growth stages, thereby affecting the quality and yield of the fruit. Due to the lack of real-time soil nutrient monitoring methods, traditional fertilization methods are difficult to accurately fertilize according to the actual needs of crops, resulting in low soil utilization efficiency and unsatisfactory planting results. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides an integrated system for fertilization and pest control for emblica planting, aiming to improve the traditional fertilization method which often relies on the experience of farmers and makes a rough estimate of the nutritional needs of crops. Such a fertilization method lacks scientific basis and easily leads to the problem of excessive or insufficient fertilization.

[0005] In a first aspect, the present invention provides the following technical solution: an integrated system for fertilization and pest control for emblica planting, comprising:

[0006] Soil sensor module, used to monitor soil moisture, nutrients, pH value and temperature in real time, and transmit monitoring data to the central control module;

[0007] Pest and disease monitoring module, used to detect the type and severity of pests and diseases of Phyllanthus emblica plants through image acquisition and recognition and pest and disease sensors;

[0008] The central control module is used to collect data from each module, process soil conditions, crop growth status, and pest and disease information, generate fertilization and prevention strategies, and schedule each module to perform corresponding operations;

[0009] Intelligent fertilization module, used to generate and execute fertilization plans based on the data from the soil sensor module, and control the storage and delivery of fertilizers;

[0010] The pest control module is used to generate a control plan based on the data from the pest monitoring module and to control pests by spraying pesticides;

[0011] Data communication module, used for data exchange between modules, supporting remote monitoring and control;

[0012] The power management module is used to provide power, store electrical energy and allocate power resources.

[0013] Preferably, the central control module includes:

[0014] A data processing unit is used to collect data from each module, process soil conditions, crop growth status and pest and disease information through data fusion technology, and generate fertilization and prevention strategies; the data processing unit adopts an embedded microcontroller and can perform data fusion and processing locally;

[0015] An intelligent decision-making unit, which uses a machine learning algorithm based on a neural network, combines historical planting data and real-time monitoring data to optimize fertilization and control plans. The intelligent decision-making unit performs real-time calculations through edge computing devices;

[0016] The control scheduling unit uses a real-time operating system to schedule the execution of each module, so that the fertilization and pest control operations are coordinated and executed on time.

[0017] Preferably, the soil sensing module comprises:

[0018] A humidity sensing unit, used for real-time monitoring of soil humidity and transmitting data to a central control module, wherein the humidity sensing unit adopts a capacitive humidity sensor;

[0019] A nutrient sensing unit, used to monitor the content of nitrogen, phosphorus and potassium nutrients in the soil, wherein the nutrient sensing unit uses an ion selective electrode sensor for detection;

[0020] The pH value sensing unit is used to detect the soil pH and uses an electrode-type pH sensor to monitor the soil pH;

[0021] The temperature sensing unit is used to detect soil temperature and adopts a digital temperature sensor to detect through a sensor node buried in the soil.

[0022] Preferably, the pest monitoring module includes:

[0023] An image acquisition unit, used for acquiring images of the Phyllanthus emblica plant by using a high-resolution camera;

[0024] Image recognition unit, used to analyze the collected images, determine the type and severity of pests and diseases, and use image processing algorithms based on convolutional neural networks to perform identification on the embedded platform;

[0025] The pest sensing unit is used to detect the presence of pests through odor and humidity parameters. The sensing unit uses a gas sensor and a humidity sensor to detect changes in the concentration of specific volatile organic compounds.

[0026] Preferably, the intelligent fertilization module comprises:

[0027] A fertilization strategy generation unit is used to generate a fertilization plan based on the data of the soil sensor module. The fertilization plan is generated by the algorithm calculation in the central control module to meet the needs of different growth stages of Phyllanthus emblica;

[0028] A fertilization execution unit is used to control the application position and amount of fertilizer. The execution unit adopts a solenoid valve control system and is responsible for the precise application of fertilizer;

[0029] A fertilizer storage unit, used to store different types of fertilizers, the storage unit comprising a plurality of independent storage tanks, which are switched by electric valves;

[0030] The fertilizer delivery unit is used to deliver fertilizer from the storage unit to the fertilizer application device, and uses a precision pump and a flow sensor to control the amount of fertilizer applied.

[0031] Preferably, the pest control module includes:

[0032] A control strategy generating unit, used to generate a pest control plan based on the data of the pest monitoring module, wherein the pest control plan is obtained through an expert system and data analysis;

[0033] A medicine spraying unit, used to control the spraying position and amount of the medicine, wherein the spraying unit adopts a spray device driven by a stepping motor;

[0034] The agent storage unit is used for storing different types of disease and insect pest control agents. The agent storage unit comprises a plurality of agent tanks which are connected to the spraying device through pipelines and valves.

[0035] Preferably, the data communication module includes:

[0036] Wireless communication unit, used for data exchange between modules, adopts LoRa wireless communication technology to support long-distance data transmission;

[0037] A remote monitoring unit, used to support farmers to view system status through mobile devices, and the monitoring unit performs remote access to data based on a cloud platform;

[0038] A remote control unit, used to support farmers to remotely adjust fertilization and control strategies, the control unit transmits encrypted data;

[0039] The power management module comprises:

[0040] A solar power supply unit, used to provide electricity through a solar panel, wherein the power supply unit has a maximum power point tracking function;

[0041] Energy storage unit, used to store excess electrical energy for emergency use, uses lithium battery packs and charging management circuits for long-term stable power supply;

[0042] The power dispatching unit is used to intelligently allocate power resources and is responsible for the normal operation of the module. The dispatching unit controls the power supply priority through the power distribution management system.

[0043] In a second aspect, the present invention provides the following technical solution, an integrated method for fertilizing and controlling pests and diseases for planting emblica, comprising the following steps:

[0044] S1. Soil data collection: The soil sensing module collects soil moisture, nutrients, pH value and temperature data in real time, and transmits these data to the central control module through the data communication module;

[0045] S2. Pest and disease data collection: The pest and disease monitoring module takes images of the Phyllanthus emblica plants through the image acquisition unit, and the pest and disease sensing unit detects environmental changes around the plants;

[0046] S3, data processing: The data processing unit of the central control module processes the soil data and pest and disease monitoring data, and the intelligent decision-making unit generates fertilization and prevention strategies based on historical data and real-time data;

[0047] S4, fertilization control: the fertilization strategy generation unit generates a fertilization plan suitable for the current growth state of Phyllanthus emblica according to the soil data;

[0048] The fertilization execution unit transports the fertilizer from the fertilizer storage unit to the vicinity of the root of the emblica emblica through the fertilizer transportation unit according to the fertilization plan, so as to carry out precise fertilization;

[0049] S5. Pest and disease control: The control strategy generation unit generates a control plan based on the data provided by the pest and disease monitoring module;

[0050] The pesticide spraying unit controls the spraying position and amount of the pesticide according to the prevention and control plan, and selects the appropriate pesticide for spraying through the pesticide storage unit;

[0051] S6. Data feedback and regulation: The execution status of all fertilization and prevention operations is fed back to the central control module through the data communication module; the central control module adjusts the subsequent fertilization and prevention strategies according to the feedback information;

[0052] Farmers can access the system through their mobile devices to view real-time data of their planting areas, including soil moisture, nutrient levels, and pest and disease conditions, and remotely adjust fertilization and prevention strategies;

[0053] S7, power management: the solar power supply unit provides power, and the energy storage unit stores excess power when there is sufficient sunlight;

[0054] The power dispatching unit intelligently allocates power according to the operating conditions of each module, giving priority to ensuring the normal operation of the central control module, soil sensor module and pest and disease monitoring module.

[0055] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the above-mentioned integrated method of fertilization and pest and disease control for emblica planting is implemented.

[0056] In a fourth aspect, the present invention provides the following technical solutions: a readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements the above-mentioned integrated method of fertilization and pest and disease control for emblica planting.

[0057] The present invention has the following beneficial effects:

[0058] 1. In the present invention, the system collects soil moisture, nutrients, pH value and temperature data in real time through the soil sensor module, and generates a specific fertilization plan to achieve accurate nutrient supply. Compared with the traditional fertilization method based on experience, this data-driven method can significantly reduce fertilizer waste, avoid soil pollution caused by excessive fertilization, ensure that crops absorb the most suitable nutrients, and thus improve yield and quality.

[0059] 2. In the present invention, through high-resolution cameras and odor sensors, the system can detect pests and diseases from image information and changes in volatile organic compounds (VOCs) concentrations. Combining image recognition algorithms and odor detection technology, compared with traditional pest and disease management methods that rely solely on manual observation and simple monitoring, pest and disease problems can be discovered earlier and more accurately, and targeted prevention and control measures can be taken in a timely manner.

[0060] 3. In the present invention, the convolutional neural network (CNN) model used in the system has a self-learning function, which can continuously improve the recognition accuracy of pest types, thereby reducing the need for manual intervention and improving the prevention and control efficiency. This adaptive learning feature enables the system to cope with the ever-changing pest threats in different regions and environments.

[0061] 4. In the present invention, the system automatically completes various operations such as soil monitoring, fertilization, and pest control, reducing the manpower input of farmers in field management. Compared with the traditional manual fertilization and spraying methods, farmers do not need to frequently enter the field for operation, which greatly saves labor and improves management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A system architecture diagram of the integrated system for fertilization and pest control for emblica planting proposed by the present invention;

[0063] Figure 2 The present invention provides a method flow chart of the integrated method for fertilizing and controlling diseases and insect pests for emblica planting. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be described clearly and completely below 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 creative work are within the scope of protection of the present invention.

[0065] Embodiment 1

[0066] Reference Figure 1 In a first embodiment of the present invention, the present invention provides an integrated system for fertilizing and controlling pests and diseases for planting emblica, comprising:

[0067] Soil sensor module, used to monitor soil moisture, nutrients, pH value and temperature in real time, and transmit monitoring data to the central control module;

[0068] Pest and disease monitoring module, used to detect the type and severity of pests and diseases of Phyllanthus emblica plants through image acquisition and recognition and pest and disease sensors;

[0069] The central control module is used to collect data from each module, process soil conditions, crop growth status, and pest and disease information, generate fertilization and prevention strategies, and schedule each module to perform corresponding operations;

[0070] Intelligent fertilization module, used to generate and execute fertilization plans based on the data from the soil sensor module, and control the storage and delivery of fertilizers;

[0071] The pest control module is used to generate a control plan based on the data from the pest monitoring module and to control pests by spraying pesticides;

[0072] Data communication module, used for data exchange between modules, supporting remote monitoring and control;

[0073] The power management module is used to provide power, store electrical energy and allocate power resources.

[0074] Specifically, the integrated system for fertilization and pest control for emblica cultivation realizes precision agricultural management. Through the soil sensing module, multi-level soil moisture, nutrients, pH value and temperature data are collected in real time. Combined with the image and odor recognition function of the pest monitoring module, it can monitor the growth environment of crops in multiple dimensions, generate data-driven fertilization and control plans, and achieve precise nutrient supply and pest control. Compared with traditional agricultural management methods that rely on experience, the system reduces the waste of fertilizers and pesticides and avoids environmental pollution. In addition, the system's automated operation and remote monitoring functions significantly reduce the labor input of farmers, making management more efficient and convenient, and reducing overall costs.

[0075] The central control module includes:

[0076] The data processing unit is used to collect data from various modules, process soil conditions, crop growth status and pest and disease information through data fusion technology, and generate fertilization and prevention strategies; the data processing unit uses an embedded microcontroller that can perform data fusion and processing locally;

[0077] The intelligent decision-making unit uses a machine learning algorithm based on a neural network, combined with historical planting data and real-time monitoring data, to optimize fertilization and prevention plans. The intelligent decision-making unit performs real-time calculations through edge computing devices;

[0078] The control scheduling unit uses a real-time operating system to schedule the execution of each module, so that the fertilization and pest control operations are coordinated and executed on time.

[0079] Specifically, the data processing unit:

[0080] Using STM32H7 series microcontroller, it has high-speed computing and multi-tasking capabilities. It uses real-time data fusion algorithm to integrate soil parameters, environmental factors, crop growth information and other data.

[0081] Data fusion uses the Kalman filter algorithm to perform weighted averaging of multi-source data, remove noise, and improve data credibility.

[0082] The data processing unit communicates with each sensor module through the CAN bus to ensure the real-time and anti-interference performance of data transmission.

[0083] Intelligent decision-making unit:

[0084] Use edge computing devices (such as NVIDIA Jetson Xavier NX) to run deep learning models. The model uses reinforcement learning algorithms and combines historical planting experience to optimize fertilization and prevention strategies.

[0085] The system is equipped with GPU accelerated computing, which can process large amounts of image and sensor data in a short time.

[0086] The decision-making process takes into account seasonal changes, climate data and crop-specific growth needs, ensuring that the generated decision solutions are not only real-time but also adaptable in the long term.

[0087] Control and dispatch unit:

[0088] FreeRTOS is used as the real-time operating system to manage various tasks of the central control module, including data collection, decision execution, and task feedback.

[0089] FreeRTOS schedules each task according to priority. Fertilization and control tasks take precedence over data recording tasks to ensure that real-time operations are not delayed by secondary tasks.

[0090] The control and scheduling unit monitors and adjusts the execution of tasks through communication with the fertilization module and the prevention and control module.

[0091] Beneficial effects:

[0092] Efficient integrated processing: Data fusion technology improves the accuracy of various sensor data, enabling the system to have a comprehensive understanding of the growth status of crops and reducing decision-making errors.

[0093] Intelligent learning and optimization: Through deep learning, fertilization and prevention strategies are continuously optimized to gradually adapt to different environmental conditions and individual differences of crops, and realize personalized agricultural management.

[0094] Real-time scheduling and response: The application of real-time operating system enables the system to remain stable and efficient in complex environments and ensures coordinated work among various subsystems.

[0095] The soil sensing module includes:

[0096] A humidity sensing unit is used to monitor soil humidity in real time and transmit data to a central control module. The humidity sensing unit uses a capacitive humidity sensor;

[0097] A nutrient sensing unit is used to monitor the content of nitrogen, phosphorus and potassium nutrients in the soil. The nutrient sensing unit uses an ion selective electrode sensor for detection;

[0098] The pH value sensing unit is used to detect the soil pH and uses an electrode-type pH sensor to monitor the soil pH;

[0099] The temperature sensing unit is used to detect soil temperature and adopts a digital temperature sensor to detect through a sensor node buried in the soil.

[0100] Specifically, the humidity sensing unit:

[0101] Use an embedded capacitive humidity sensor, such as the SHT10 series sensor. This sensor has a waterproof housing and is suitable for long-term burial in the soil.

[0102] The humidity sensor is connected to the STM32 microcontroller through an analog signal interface. The signal is sampled by a 12-bit ADC (analog-to-digital converter) with a sampling rate set to once every 10 seconds to ensure the timeliness of the data.

[0103] Sensors are installed at different depths to obtain moisture information at different levels in the soil, and these data are transmitted to the central control module via the I2C communication protocol.

[0104] Nutrient Sensing Unit:

[0105] An ion selective electrode (ISE) sensor, including nitrogen (N), phosphorus (P), and potassium (K) electrodes, is used. The sensor has a built-in micropump to extract the soil solution into the detection chamber.

[0106] The potential difference signal generated by the electrodes is amplified by a differential amplifier and sampled by a 16-bit ADC to ensure high accuracy.

[0107] Each acquisition unit is equipped with an automatic calibration module, which can automatically adjust the sensor sensitivity by introducing standard solutions to ensure long-term data accuracy.

[0108] pH sensor unit:

[0109] Glass electrode pH sensors (such as the HI1297D series) are suitable for long-term measurement of pH values ​​in soil.

[0110] After the signal is amplified, it is transmitted to the central control module through the RS485 interface to ensure that the signal is not interfered with in the case of long-distance wiring.

[0111] The pH data is processed by Fourier filtering in the central control module to remove possible electrical noise interference.

[0112] Temperature sensing unit:

[0113] Using a digital temperature sensor (such as DS18B20), which has a unique ID code, multiple temperature sensors can be connected to the same data line through a single-wire communication protocol.

[0114] Each sensor is buried in the soil surface and root zone to collect temperature data through polling.

[0115] When the temperature change exceeds the set threshold (e.g. ±2°C), an alarm message is automatically triggered and transmitted to the farmer's terminal.

[0116] Beneficial effects:

[0117] Real-time and precise monitoring: Through moisture collection at different depths and accurate nutrient detection, the system can fully understand the soil environment and support more precise fertilization and irrigation decisions.

[0118] Reduce errors: The automatic calibration function allows the nutrient sensor to maintain high accuracy during long-term use and reduce human errors.

[0119] Multi-level monitoring: Temperature and humidity data are combined to achieve environmental control of different soil layers, ensuring that the crop root system and surface environment are in the best condition.

[0120] The pest and disease monitoring module includes:

[0121] An image acquisition unit, used for acquiring images of the Phyllanthus emblica plant by using a high-resolution camera;

[0122] Image recognition unit, used to analyze the collected images, determine the type and severity of pests and diseases, and use image processing algorithms based on convolutional neural networks to perform identification on the embedded platform;

[0123] The pest sensing unit is used to detect the presence of pests and diseases through odor and humidity parameters. The sensing unit uses a gas sensor and a humidity sensor to detect changes in the concentration of specific volatile organic compounds.

[0124] Specifically, the image acquisition unit:

[0125] Equipped with a 5-megapixel IP camera with autofocus, night vision and anti-shake functions, it can work stably even in bad weather.

[0126] The camera is installed on a rotating pan-tilt head, which is controlled by a stepper motor to achieve 360-degree all-round monitoring.

[0127] The image acquisition frequency is adjustable, usually once every 30 minutes during the day, and the acquisition frequency is reduced at night due to the reduced activity of pests and diseases.

[0128] Image recognition unit:

[0129] Use a convolutional neural network (CNN) combined with the YOLOv5 object detection model and run it on an edge computing platform using TensorFlowLite.

[0130] The identification process is divided into two steps: first, preliminary detection of pests and diseases is carried out, and then further classification is carried out according to different types (such as aphids, leaf spot, etc.).

[0131] The system has self-learning capabilities. When new pest and disease types are manually marked, the model will update parameters to enhance its ability to identify new threats.

[0132] Pest and disease sensing unit:

[0133] Based on MOS gas sensor, it is used to detect volatile organic compounds (VOCs), combined with humidity sensor to detect humidity changes around plants.

[0134] By establishing a characteristic database of volatile substances, when a specific odor is detected, the system can determine whether specific pests and diseases are present.

[0135] The sensor data is transmitted to the central control module via a wireless network for decision-making on prevention and control strategies.

[0136] Beneficial effects:

[0137] Comprehensive monitoring: Through rotating cameras and VOCs sensors, the growth status of crops and early signs of pests and diseases in the environment can be monitored in real time, improving the ability to prevent pests and diseases.

[0138] Self-learning improvement: The image recognition module has a self-learning function. As the number of labeled samples increases, the system's recognition ability continues to improve, reducing manual intervention.

[0139] Multi-dimensional detection: Image recognition combined with odor detection provides a more comprehensive method for determining pests and diseases, reducing the possibility of missed detection and false detection.

[0140] The intelligent fertilization module includes:

[0141] A fertilization strategy generation unit is used to generate a fertilization plan based on the data from the soil sensor module. The fertilization plan is generated by the algorithm calculation in the central control module to meet the needs of different growth stages of Phyllanthus emblica.

[0142] The fertilization execution unit is used to control the application position and amount of fertilizer. The execution unit adopts a solenoid valve control system to be responsible for the precise application of fertilizer;

[0143] A fertilizer storage unit is used to store different types of fertilizers. The storage unit includes multiple independent storage tanks, which are switched by electric valves;

[0144] The fertilizer delivery unit is used to deliver fertilizer from the storage unit to the fertilizer application device, and uses a precision pump and a flow sensor to control the amount of fertilizer applied.

[0145] Specifically, the fertilization strategy generation unit:

[0146] The fertilization strategy is generated by a decision tree algorithm based on the soil data analyzed by the central control module, combined with the specific growth stage and nutritional requirements of Phyllanthus emblica.

[0147] The system takes into account the dissolution rate of different fertilizers, plant absorption efficiency and weather conditions to ensure that fertilization is carried out at the optimal time.

[0148] Fertilization execution unit:

[0149] Solenoid valves are used for fertilization control, and flow meters (such as turbine flow sensors) are equipped to monitor the amount of fertilizer applied each time.

[0150] The solenoid valve is assembled on the drip irrigation pipe, and the central control module adjusts the opening and closing time through the PWM (pulse width modulation) signal to ensure accurate fertilization amount.

[0151] Fertilizer storage unit:

[0152] Use multiple corrosion-resistant fertilizer storage tanks, switch fertilizer types through electric valves, and install liquid level sensors on each tank to monitor fertilizer inventory in real time.

[0153] The storage tank is equipped with a stirring device to prevent fertilizer sedimentation and ensure uniform fertilizer concentration.

[0154] Fertilizer delivery unit:

[0155] A high-precision peristaltic pump is used to transport fertilizer from the storage tank to the drip irrigation system. The peristaltic pump has an adjustable flow function to meet the needs of different fertilizer application amounts.

[0156] A flow sensor is built into the delivery pipeline, and the actual flow during each fertilization process is monitored by the central control module to ensure the accuracy and consistency of fertilization.

[0157] Beneficial effects:

[0158] Precise fertilization: The combination of flow meter and solenoid valve enables the amount of fertilizer to be strictly controlled, minimizing fertilizer waste.

[0159] Mixing and uniformity: The storage tank is equipped with a stirring device to avoid fertilizer sedimentation problems, ensure the uniformity of each fertilization, and improve the efficiency of crop absorption of nutrients.

[0160] Automated adjustment: Through the input of real-time environmental data, the system can dynamically adjust the fertilization strategy to ensure that crops can get the right amount of nutrients at different growth stages.

[0161] The pest control module includes:

[0162] A control strategy generation unit is used to generate a pest control plan based on the data from the pest monitoring module. The pest control plan is obtained through an expert system and data analysis;

[0163] The medicine spraying unit is used to control the spraying position and amount of the medicine. The spraying unit adopts a spray device driven by a stepping motor;

[0164] The agent storage unit is used for storing different types of disease and insect pest control agents. The agent storage unit comprises a plurality of agent tanks which are connected with the spraying device through pipelines and valves.

[0165] Specifically, the prevention and control strategy generation unit:

[0166] Based on expert system software (such as CLIPS), the rule base and reasoning engine are used to analyze the data transmitted by the pest and disease monitoring module, and the optimal prevention and control plan is generated in combination with the crop health status.

[0167] The system will determine the spraying time and frequency based on the type of pesticide, dosage and weather conditions (such as wind speed and humidity).

[0168] Chemical spraying unit:

[0169] The spraying arm is driven by a stepper motor. The spraying device can adjust the spraying angle, spraying distance and flow rate as needed to ensure uniform coverage of the agent.

[0170] The nozzle is equipped with an anti-clogging device. When the nozzle is detected to be clogged, the system automatically stops spraying and starts the cleaning program.

[0171] Drug storage unit:

[0172] Various chemicals are stored in multiple corrosion-resistant stainless steel chemical tanks, which are connected to the spraying units via pipes with pressure regulating functions.

[0173] Before each use, the system will check whether the stock of pesticides is sufficient based on the storage information, and send a notification to the farmer's terminal if it is insufficient.

[0174] Beneficial effects:

[0175] Precise control: The spraying position and angle are precisely controlled by a stepper motor to ensure that the agent only acts on the infected area, reducing the impact on the surrounding environment and harmless organisms.

[0176] Efficient management: The pesticide storage and automatic inspection functions help farmers understand the use of pesticides in real time to avoid insufficient or wasted pesticides.

[0177] The data communication module includes:

[0178] Wireless communication unit, used for data exchange between modules, adopts LoRa wireless communication technology to support long-distance data transmission;

[0179] Remote monitoring unit, which is used to support farmers to check the system status through mobile devices. The monitoring unit can access data remotely based on the cloud platform.

[0180] Remote control unit, used to support farmers to remotely adjust fertilization and control strategies, the control unit transmits encrypted data;

[0181] The power management module includes:

[0182] A solar power supply unit, used to provide electricity through a solar panel, and the power supply unit has a maximum power point tracking function;

[0183] Energy storage unit, used to store excess electrical energy for emergency use, uses lithium battery packs and charging management circuits for long-term stable power supply;

[0184] The power dispatching unit is used to intelligently allocate power resources and is responsible for the normal operation of the module. The dispatching unit controls the power supply priority through the power distribution management system.

[0185] Specifically, the wireless communication unit uses LoRa communication technology to achieve data interaction between modules through long-distance wireless transmission, which is suitable for large areas of farmland.

[0186] Remote monitoring unit: Based on the cloud platform, farmers can access the system data in real time through the App on their mobile phones or computers to monitor the growth status of crops and the operation of the system.

[0187] Remote control unit: supports farmers to send control commands to the central control module through mobile terminals, and uses encrypted communication protocols (such as HTTPS) to ensure data transmission security.

[0188] Beneficial effects:

[0189] The application of LoRa wireless communication ensures data synchronization and transmission over large areas of farmland, reducing wiring requirements and costs.

[0190] The cloud platform and remote monitoring functions enable farmers to understand the status of farmland anytime and anywhere, thus improving management efficiency.

[0191] The secure and encrypted remote control function enables farmers to flexibly adjust their planting strategies and respond to emergencies in a timely manner.

[0192] Solar power supply unit: Collects light energy through solar panels and is equipped with an MPPT (maximum power point tracking) controller to improve the efficiency of power conversion and ensure the operation of the system in low light conditions.

[0193] Energy storage unit: Use lithium battery packs and charging management modules to store excess electrical energy and manage the battery's charging and discharging process to avoid overcharging or over-discharging.

[0194] Power dispatching unit: adopts power distribution management system, intelligently allocates power resources, gives priority to supplying key equipment such as central control module and sensor module, and ensures continuous and stable operation of the system.

[0195] Beneficial effects:

[0196] The combination of solar energy and energy storage units realizes the use of renewable energy in the system, reduces dependence on external power sources, and adapts to application needs in remote areas.

[0197] Through intelligent power scheduling, the efficiency of power use is improved, ensuring that key components of the system can still operate stably when power is insufficient.

[0198] The management function of the energy storage module extends the battery life and reduces the long-term operating cost of the system.

[0199] Embodiment 2:

[0200] Reference Figure 2 In a second embodiment of the present invention, the present invention provides an integrated method for fertilizing and controlling pests and diseases for planting Phyllanthus emblica, comprising the following steps:

[0201] S1. Soil data collection

[0202] Detailed implementation:

[0203] Data acquisition equipment:

[0204] The soil sensing module includes humidity, nutrients, pH and temperature sensors. Humidity sensors (such as SHT10) are buried at different depths in the planting area to ensure comprehensive monitoring of soil moisture changes.

[0205] Nutrient sensors (such as nitrogen, phosphorus, and potassium ion-selective electrode sensors) use built-in micropumps to introduce soil solutions into the detection chamber for quantitative analysis.

[0206] pH sensors (such as the HI1297D series) are installed on the soil surface to monitor soil acidity and alkalinity in real time.

[0207] Temperature sensors (such as DS18B20) are embedded in different soil layers to detect temperature changes in the surface layer and root zone.

[0208] Data transmission:

[0209] All sensor data are transmitted to the STM32 microcontroller through the I2C interface. After preliminary processing of the data, the microcontroller transmits the data to the central control module through the LoRa wireless communication module.

[0210] The frequency of data collection can be set according to needs, usually set to collect data every 10 minutes to ensure the real-time nature of the data.

[0211] Beneficial effects:

[0212] Real-time and comprehensive: Multiple sensors collect data simultaneously at different depths to achieve comprehensive and real-time monitoring of the soil environment.

[0213] Basis for accurate decision-making: Provides detailed soil status information to support the central control module in making accurate fertilization and regulation decisions.

[0214] S2. Pest and disease data collection

[0215] Detailed implementation:

[0216] Image acquisition:

[0217] The image acquisition unit in the pest monitoring module uses a 5-megapixel IP camera to collect all-round images of crops through a rotating pan-tilt system. The pan-tilt system is controlled by a stepper motor and can be set to rotate and scan at a fixed time.

[0218] The camera has autofocus and night vision functions to ensure clear crop images in different lighting conditions. The image acquisition interval is set every 30 minutes, and extended to once an hour at night.

[0219] Environmental change monitoring:

[0220] The pest sensing unit uses MOS odor sensors to detect the concentration of volatile organic compounds (VOCs). The sensors are placed around the emblica plants to detect the special odor caused by pests and diseases.

[0221] The humidity sensor also monitors the humidity of the plant environment to assist in the identification of pests and diseases. All data is transmitted to the central control module via the RS485 interface.

[0222] Beneficial effects:

[0223] Multi-angle, multi-parameter: Combined with image and odor monitoring, it provides multi-dimensional information and increases the accuracy of pest and disease identification.

[0224] Early warning: Rapid and comprehensive data collection ensures that problems can be detected in the early stages of pests and diseases, allowing early intervention measures to be taken.

[0225] S3. Data processing

[0226] Detailed implementation:

[0227] Data reception and preliminary processing:

[0228] The data from all sensors and monitoring modules are transmitted to the STM32H7 microcontroller of the central control module. The data processing unit uses the Kalman filter algorithm to perform preliminary data processing to remove noise during the acquisition process and obtain reliable data input.

[0229] Intelligent decision making:

[0230] Using edge computing devices (such as NVIDIA Jetson Xavier NX), the filtered data is input into the reinforcement learning model for analysis. The model uses long-term training data to generate the best fertilization and prevention strategies through the reward mechanism of reinforcement learning.

[0231] The decision results include the amount of fertilizer, time of fertilizer application, type of pesticide and spraying location. All decision instructions are sent to the corresponding execution module through the scheduling unit.

[0232] Beneficial effects:

[0233] Data fusion processing: Kalman filtering effectively removes data noise, ensuring that subsequent processing is based on high-precision data.

[0234] Efficient and intelligent decision support: Continuously optimize fertilization and control plans through reinforcement learning algorithms, so that decisions are highly consistent with crop growth conditions.

[0235] S4. Fertilization control

[0236] Detailed implementation:

[0237] Fertilization strategy generation:

[0238] The central control module uses soil nutrient data and the growth stage of Phyllanthus emblica, and the fertilization strategy generation unit uses a decision tree algorithm to determine the amount and type of fertilizer. The generated plan includes the specific ratio of fertilizer, fertilization location and time.

[0239] Fertilization execution:

[0240] The fertilization execution unit is controlled by a solenoid valve and a peristaltic pump. The solenoid valve is responsible for opening and closing the fertilization flow channel, while the peristaltic pump ensures accurate fertilizer application by precisely adjusting the flow rate.

[0241] The fertilizer delivery unit delivers fertilizer to the roots of the emblica plant through a pressure pipe, and cooperates with the drip irrigation system to ensure uniform distribution. The flow sensor monitors the fertilizer flow in real time during the delivery process. Any deviation will trigger an alarm and stop fertilization.

[0242] Beneficial effects:

[0243] Precision fertilization: Through flow sensor and peristaltic pump control, the amount of fertilizer is precisely controlled to reduce waste and improve fertilizer utilization.

[0244] Intelligent adjustment: The fertilization plan can be dynamically adjusted according to the real-time condition of the soil to ensure that crops obtain the required nutrients at different times.

[0245] S5. Pest and disease control

[0246] Detailed implementation:

[0247] Prevention and control strategy generation:

[0248] The control strategy generation unit uses an expert system (such as CLIPS-based software) to combine historical pest and disease data and current monitoring information to generate the most appropriate control plan.

[0249] The plan includes the type of pesticide, concentration, spraying time and spraying range, and all decisions take into account current climate conditions (such as wind speed and humidity).

[0250] Spraying of chemicals:

[0251] The spraying unit is precisely positioned by a stepper motor driving the spraying arm. The spraying arm is equipped with an adjustable nozzle and the spraying volume is controlled by PWM (Pulse Width Modulation).

[0252] The agent storage unit includes a plurality of corrosion-resistant agent tanks. Before each spraying, the liquid level sensor in the agent tank will check the amount of agent to ensure that there is enough agent for this spraying.

[0253] Beneficial effects:

[0254] Efficient prevention and control: The intelligent generation of prevention and control strategies is combined with the precise control of the spraying unit to effectively improve the efficiency of the use of pesticides and reduce the impact on the environment.

[0255] Automatic detection and replenishment: The pesticide storage unit is equipped with a liquid level detection system to automatically remind farmers to replenish pesticides to ensure uninterrupted control operations.

[0256] S6. Data Feedback and Regulation

[0257] Detailed implementation:

[0258] Data Feedback:

[0259] After completing the fertilization and prevention tasks, all execution modules will feedback the execution status to the central control module through the LoRa communication module. The feedback data includes the execution time, fertilization amount, pesticide dosage, etc. All information is recorded in the system log.

[0260] During the data feedback process, the central control module analyzes the feedback data and adjusts subsequent operations, such as increasing or decreasing the amount of fertilizer or changing the frequency of prevention and control.

[0261] Remote monitoring and control:

[0262] Farmers can access the cloud platform through the App on their mobile devices to view the current soil moisture, nutrient level, pest and disease conditions, and the operating status of each module.

[0263] Farmers can send instructions on the App to adjust fertilization and control strategies, such as increasing the application amount of a certain fertilizer or changing the spraying time. All instructions are sent via HTTPS encryption to ensure the security of data transmission.

[0264] Beneficial effects:

[0265] Efficient management and tracking: Data feedback ensures that every step of the system operation can be tracked and evaluated, providing farmers with a reliable management basis.

[0266] Remote and flexible control: Farmers can adjust the system through mobile devices at any location, which improves the convenience and flexibility of agricultural management.

[0267] S7. Power Management

[0268] Detailed implementation:

[0269] Solar Powered:

[0270] Solar panels (e.g. based on monocrystalline silicon technology) are installed close to the growing site and are equipped with MPPT (maximum power point tracking) controllers to ensure maximum utilization of light energy under different lighting conditions.

[0271] Solar panels power the system every day and can fully meet the power needs of all modules when there is sufficient sunlight.

[0272] Energy storage unit:

[0273] The energy storage unit uses a lithium battery pack and is equipped with a charging management module to control the charging and discharging process of the battery, avoiding overcharging and over-discharging problems and extending the battery life.

[0274] The energy storage unit can provide continuous power to the system when there is insufficient light, ensuring stable operation of the system at night and on rainy days.

[0275] Power Scheduling:

[0276] The power dispatching unit intelligently distributes power according to the operating requirements of each module. The power distribution management system is used to give priority to key components such as the central control module, soil sensor module, and pest monitoring module.

[0277] When the power supply is insufficient, the system will reduce the energy consumption of non-critical modules (such as reducing the acquisition frequency) to ensure the continuous operation of core functions.

[0278] Beneficial effects:

[0279] Energy self-sufficiency: Through the combination of solar energy and energy storage, a completely self-sufficient energy solution is achieved, reducing dependence on external electricity.

[0280] Prioritize key tasks: The power dispatch unit ensures that key modules are given priority power supply in the event of energy shortage, avoiding data collection and operation interruptions due to power outages and improving system stability.

[0281] Embodiment 3

[0282] The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the integrated method for fertilizing and controlling pests and diseases for planting emblica of the above embodiment are implemented.

[0283] Embodiment 4

[0284] The fourth embodiment of the present invention is based on the same inventive concept. A computer device proposed by the present invention comprises: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the integrated method of fertilizing and controlling diseases and pests for planting emblica of the above embodiment.

[0285] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0286] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An integrated system for fertilization and pest control for emblica emblica planting, characterized in that: include: Soil sensor module, used to monitor soil moisture, nutrients, pH value and temperature in real time, and transmit monitoring data to the central control module; Pest and disease monitoring module, used to detect the type and severity of pests and diseases of Phyllanthus emblica plants through image acquisition and recognition and pest and disease sensors; The central control module is used to collect data from each module, process soil conditions, crop growth status, and pest and disease information, generate fertilization and prevention strategies, and schedule each module to perform corresponding operations; Intelligent fertilization module, used to generate and execute fertilization plans based on the data from the soil sensor module, and control the storage and delivery of fertilizers; The pest control module is used to generate a control plan based on the data from the pest monitoring module and to control pests by spraying pesticides; Data communication module, used for data exchange between modules, supporting remote monitoring and control; The power management module is used to provide power, store electrical energy and allocate power resources.

2. The integrated system for fertilizing and controlling pests and diseases for planting Phyllanthus emblica according to claim 1, characterized in that: The central control module comprises: A data processing unit is used to collect data from each module, process soil conditions, crop growth status and pest and disease information through data fusion technology, and generate fertilization and prevention strategies; the data processing unit adopts an embedded microcontroller and can perform data fusion and processing locally; An intelligent decision-making unit, which uses a machine learning algorithm based on a neural network, combines historical planting data and real-time monitoring data to optimize fertilization and control plans. The intelligent decision-making unit performs real-time calculations through edge computing devices; The control scheduling unit uses a real-time operating system to schedule the execution of each module, so that the fertilization and pest control operations are coordinated and executed on time.

3. The integrated system for fertilizing and controlling pests and diseases for planting Phyllanthus emblica according to claim 1, characterized in that: The soil sensing module comprises: A humidity sensing unit, used for real-time monitoring of soil humidity and transmitting data to a central control module, wherein the humidity sensing unit adopts a capacitive humidity sensor; A nutrient sensing unit, used to monitor the content of nitrogen, phosphorus and potassium nutrients in the soil, wherein the nutrient sensing unit uses an ion selective electrode sensor for detection; The pH value sensing unit is used to detect the soil pH and uses an electrode-type pH sensor to monitor the soil pH; The temperature sensing unit is used to detect soil temperature and adopts a digital temperature sensor to detect through a sensor node buried in the soil.

4. The integrated system for fertilizing and controlling pests and diseases for planting Phyllanthus emblica according to claim 1, characterized in that: The pest monitoring module includes: An image acquisition unit, used for acquiring images of the Phyllanthus emblica plant by using a high-resolution camera; Image recognition unit, used to analyze the collected images, determine the type and severity of pests and diseases, and use image processing algorithms based on convolutional neural networks to perform identification on the embedded platform; The pest sensing unit is used to detect the presence of pests through odor and humidity parameters. The sensing unit uses a gas sensor and a humidity sensor to detect changes in the concentration of specific volatile organic compounds.

5. The integrated system for fertilizing and controlling pests and diseases for planting Phyllanthus emblica according to claim 1, characterized in that: The intelligent fertilization module comprises: A fertilization strategy generation unit is used to generate a fertilization plan based on the data of the soil sensor module. The fertilization plan is generated by the algorithm calculation in the central control module to meet the needs of different growth stages of Phyllanthus emblica; A fertilization execution unit is used to control the application position and amount of fertilizer. The execution unit adopts a solenoid valve control system and is responsible for the precise application of fertilizer; A fertilizer storage unit, used to store different types of fertilizers, the storage unit comprising a plurality of independent storage tanks, which are switched by electric valves; The fertilizer delivery unit is used to deliver fertilizer from the storage unit to the fertilizer application device, and uses a precision pump and a flow sensor to control the amount of fertilizer applied.

6. The integrated system for fertilizing and controlling pests and diseases for planting Phyllanthus emblica according to claim 1, characterized in that: The pest control module includes: A control strategy generating unit, used to generate a pest control plan based on the data of the pest monitoring module, wherein the pest control plan is obtained through an expert system and data analysis; A medicine spraying unit, used to control the spraying position and amount of the medicine, wherein the spraying unit adopts a spray device driven by a stepping motor; The agent storage unit is used for storing different types of disease and insect pest control agents. The agent storage unit comprises a plurality of agent tanks which are connected to the spraying device through pipelines and valves.

7. The integrated system for fertilizing and controlling pests and diseases for planting Phyllanthus emblica according to claim 1, characterized in that: The data communication module comprises: Wireless communication unit, used for data exchange between modules, adopts LoRa wireless communication technology to support long-distance data transmission; A remote monitoring unit, used to support farmers to view system status through mobile devices, and the monitoring unit performs remote access to data based on a cloud platform; A remote control unit, used to support farmers to remotely adjust fertilization and control strategies, the control unit transmits encrypted data; The power management module comprises: A solar power supply unit, used to provide electricity through a solar panel, wherein the power supply unit has a maximum power point tracking function; Energy storage unit, used to store excess electrical energy for emergency use, uses lithium battery packs and charging management circuits for long-term stable power supply; The power dispatching unit is used to intelligently allocate power resources and is responsible for the normal operation of the module. The dispatching unit controls the power supply priority through the power distribution management system.

8. An integrated method for fertilizing and controlling pests and diseases for planting Phyllanthus emblica, characterized in that: The integrated system for fertilizing and controlling diseases and pests for planting Phyllanthus emblica according to any one of claims 1 to 7 comprises the following steps: S1. Soil data collection: The soil sensing module collects soil moisture, nutrients, pH value and temperature data in real time, and transmits these data to the central control module through the data communication module; S2. Pest and disease data collection: The pest and disease monitoring module takes images of the Phyllanthus emblica plants through the image acquisition unit, and the pest and disease sensing unit detects environmental changes around the plants; S3, data processing: The data processing unit of the central control module processes the soil data and pest and disease monitoring data, and the intelligent decision-making unit generates fertilization and prevention strategies based on historical data and real-time data; S4, fertilization control: the fertilization strategy generation unit generates a fertilization plan suitable for the current growth state of Phyllanthus emblica according to the soil data; The fertilization execution unit transports the fertilizer from the fertilizer storage unit to the vicinity of the root of the emblica emblica through the fertilizer transportation unit according to the fertilization plan, so as to carry out precise fertilization; S5. Pest and disease control: The control strategy generation unit generates a control plan based on the data provided by the pest and disease monitoring module; The pesticide spraying unit controls the spraying position and amount of the pesticide according to the prevention and control plan, and selects the appropriate pesticide for spraying through the pesticide storage unit; S6. Data feedback and regulation: The execution status of all fertilization and prevention operations is fed back to the central control module through the data communication module; the central control module adjusts the subsequent fertilization and prevention strategies according to the feedback information; Farmers can access the system through their mobile devices to view real-time data of their planting areas, including soil moisture, nutrient levels, and pest and disease conditions, and remotely adjust fertilization and prevention strategies; S7, power management: the solar power supply unit provides power, and the energy storage unit stores excess power when there is sufficient sunlight; The power dispatching unit intelligently allocates power according to the operating conditions of each module, giving priority to ensuring the normal operation of the central control module, soil sensor module and pest and disease monitoring module.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the integrated method for fertilizing and controlling pests and diseases for planting emblica as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the integrated method for fertilizing and controlling pests and diseases for planting emblica as described in any one of claims 1 to 7 is implemented.

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