Dynamic monitoring and precise management system for nutritional ingredients of fruits of apple trees

Abstract

The invention provides an innovative apple tree fruit nutritional ingredient dynamic monitoring and precise management system, and aims to solve the problem that the change of fruit nutritional ingredients cannot be mastered in real time in traditional orchard management. The system integrates spectral data, environmental data and image data, and comprehensively reflects the change of fruit nutrients and the relationship between the change and environmental factors through multi-source data fusion analysis. According to the invention, a standardized model between spectral data and nutritional ingredients is established, and a deep learning algorithm is introduced to improve the accuracy and efficiency of image recognition. The system can analyze fruit nutrients and environmental parameters in real time, send early warning information and generate personalized fertilization and health management suggestions. In addition, the system adopts a modular and extensible design, and is convenient to customize and integrate. The orchard management accuracy and efficiency can be remarkably improved, the yield and quality of fruits can be improved, the production cost is reduced, and the economic benefits are increased.

Description

Technical Field

[0001] The present invention relates to a number of technical fields including agricultural Internet of Things, spectral analysis, big data analysis, artificial intelligence, and orchard management. Specifically, it integrates Internet of Things technologies such as spectral sensors, environmental sensors, soil sensors, and image acquisition equipment. Background Art

[0002] In current orchard management, traditional methods are often unable to grasp the changes in fruit nutritional components in real time and accurately, which leads to a lack of scientific basis for orchard managers when making decisions on fertilization, irrigation, pest and disease control, etc., which in turn affects the yield and quality of the fruit.

[0003] In order to solve this problem, people began to explore the use of modern scientific and technological means to carry out refined management of orchards. Spectral analysis technology, as a non-destructive detection method, can obtain the chemical composition and physical properties of an object by analyzing the spectral information reflected or transmitted by the object. The application of this technology in the agricultural field has gradually attracted attention, especially in the monitoring of fruit nutrients.

[0004] However, single spectral data often cannot fully reflect the changes in fruit nutrients, and it is necessary to combine environmental parameters, soil conditions, image information and other multi-source data for comprehensive analysis. With the rapid development of big data analysis and artificial intelligence technology, it is possible to achieve precise orchard management. By mining and analyzing a large amount of orchard data, a model between fruit nutrients and environmental factors can be established to provide personalized management suggestions for orchard managers. In addition, the application of modular design and scalable interfaces also makes the orchard management system more flexible and easy to integrate, and can better adapt to the actual needs of different orchards.

[0005] The present invention aims to combine multiple technical fields such as spectral analysis, big data analysis, artificial intelligence and orchard management to propose a dynamic monitoring and precise management system for the nutritional components of apple fruits, so as to solve the problem that traditional orchard management cannot grasp the changes in the nutritional components of fruits in real time and improve the accuracy and efficiency of orchard management. Summary of the invention

[0006] The present invention proposes an innovative system for dynamic monitoring and precise management of the nutritional components of apple tree fruits. The system comprehensively integrates multiple technical fields such as spectral analysis, big data analysis, artificial intelligence, and orchard management, aiming to solve the problem that traditional orchard management cannot grasp the changes in the nutritional components of fruits in real time and accurately.

[0007] The system of the present invention is a comprehensive orchard management system, which includes multiple key modules, which together realize the comprehensive digitization and intelligence of orchard management. First, the data acquisition module is responsible for collecting various data in the orchard, such as soil moisture, temperature, fruit tree growth, etc. Then, the data transmission module transmits these data to the central processing unit. The data processing and analysis module analyzes and processes these data to extract valuable information. The user interaction module allows the orchard manager to interact with the system to query data, receive reports or issue instructions. The early warning notification module will issue an alarm in time when any abnormal situation that may affect the health of the orchard is detected. The decision support module provides scientific decision-making suggestions for the orchard manager based on the data analysis results. The model optimization module continuously optimizes the prediction and analysis models of the system to improve accuracy and efficiency. The modular design module ensures the flexibility and configurability of the system, and the scalability interface module allows the system to be integrated with other orchard management tools or equipment. Finally, the system maintenance module is responsible for the daily maintenance and troubleshooting of the system to ensure the stable operation of the system. Through the collaborative work of these modules, the system of the present invention realizes the comprehensive digitization and intelligence of orchard management, and improves the production efficiency and management level of the orchard.

[0008] Preferably, the data acquisition module carefully deploys spectral sensors, environmental sensors, soil sensors and high-resolution image acquisition equipment to ensure all-round and real-time monitoring of the orchard environment. Spectral sensors can capture the spectral data of apple tree fruits in real time, providing an important basis for analyzing the maturity, quality and potential pests and diseases of the fruit. Environmental sensors are responsible for monitoring key environmental parameters such as temperature, humidity, and light intensity in the orchard, which are essential for understanding the microclimate of the orchard and optimizing growth conditions. At the same time, soil sensors go deep into the soil layer to monitor indicators such as soil moisture, nutrient content, and pH in real time, providing a scientific basis for precise fertilization and irrigation. Image acquisition equipment continuously captures real-time images of the orchard, and through image analysis technology, it can further identify the growth status of fruit trees, leaf health, and early signs of pests and diseases. These diversified, real-time collected data provide a solid foundation for subsequent data processing, monitoring and analysis, making orchard management more accurate and efficient.

[0009] Preferably, the data transmission module is responsible for efficiently and stably transmitting all kinds of data collected by the data acquisition module, including spectral data, environmental parameters, soil conditions, and image information, to the cloud server or local data center through advanced wireless communication methods (covering Wi-Fi, LoRa, NB-IoT and other communication technologies). This transmission process ensures the real-time nature of the data, that is, the data can be transmitted and stored immediately after collection, laying a solid foundation for subsequent rapid processing and analysis. At the same time, the use of a variety of wireless communication technologies also enhances the reliability and stability of data transmission, and can maintain continuous transmission of data even in a complex and changeable orchard environment without interruption or loss. After receiving the data, the cloud server or local data center will immediately store, process and analyze it, further ensuring the timeliness and accuracy of the data, and providing strong support for subsequent monitoring, early warning, decision support, etc.

[0010] Preferably, the data processing and analysis module makes full use of spectral analysis technology, big data analysis, advanced artificial intelligence algorithms and machine learning models to deeply fuse and analyze data from different sources. Specifically, the module first pre-processes the received spectral data, environmental parameters, soil conditions and image information, including data cleaning, denoising and normalization, to ensure the quality and consistency of the data. Then, through advanced feature extraction technology, key features valuable for orchard management are extracted from massive data, such as fruit maturity index, specific values ​​of soil nutrients, early signs of pests and diseases, etc. Subsequently, these features are input into the fusion analysis model, which combines the accuracy of spectral analysis technology, the comprehensiveness of big data analysis, the intelligence of artificial intelligence algorithms and the adaptability of machine learning models to monitor and evaluate the nutritional components and environmental conditions of the orchard in real time. Finally, the data processing and analysis module generates real-time nutrient monitoring reports, detailed environmental status assessments and precise management suggestions based on data analysis, providing scientific and comprehensive decision-making basis for orchard managers.

[0011] Preferably, the user interaction module provides a variety of interactive methods, including mobile applications, Web platforms and intuitive visual interfaces, which fully meet the different needs of orchard managers. Through these interactive methods, orchard managers can easily view the real-time data of the orchard, such as spectral data, environmental parameters, soil conditions and image information, etc., and all data are displayed in easy-to-understand forms such as charts and reports. At the same time, the user interaction module is also responsible for receiving and displaying the early warning information issued by the system, such as pest and disease risks, insufficient soil nutrients, etc., to ensure that the orchard manager can respond in the first time. In addition, the module will also provide personalized management suggestions for orchard managers based on the results of the data processing and analysis module, such as fertilization plans, irrigation strategies, etc., to help managers make more scientific and reasonable decisions. Orchard managers can also interact with the system through the user interaction module, such as adjusting the early warning threshold, modifying the management plan, etc., to achieve the flexibility and customization of orchard management. In short, the user interaction module significantly improves the convenience and efficiency of orchard management by providing convenient and efficient data viewing, early warning reception, management suggestion acquisition and interactive operation functions.

[0012] Preferably, the early warning notification module has the ability to analyze fruit nutrients and environmental parameters in real time. The module will continuously monitor key indicators such as spectral data, environmental parameters and soil conditions, and detect in real time whether there are abnormalities or deviations by comparing with preset normal ranges or thresholds. Specifically, the early warning notification module will analyze the nutritional data of the fruit, such as sugar, acidity, vitamin content, etc., and environmental parameters such as temperature, humidity, light intensity, etc., which will be compared with ideal growth conditions or historical data. Once any abnormality is detected, such as insufficient fruit nutrients, too high or too low ambient temperature, too low soil moisture, etc., the module will automatically trigger the early warning mechanism immediately and generate detailed early warning information. These early warning messages include the specific content of the abnormality, possible consequences and recommended countermeasures, and are then automatically sent to the user terminal of the orchard manager, such as a mobile phone, tablet computer or computer, etc. through the user interaction module. The early warning information can be sent in a variety of forms such as text messages, emails, in-application notifications, etc., to ensure that the orchard manager can receive and take corresponding measures in a timely manner, thereby effectively preventing potential problems and ensuring the healthy growth and high yield and quality of the orchard.

[0013] Preferably, the decision support module deeply integrates historical data with the current state of the orchard, providing personalized health management suggestions for orchard managers. The module will first comprehensively collect historical data of the orchard, including spectral data, environmental parameters, soil conditions, and growth records of fruit trees in the past few years. These data cover all aspects of the orchard and provide rich background information for decision-making. Then, the module will obtain the current state of the orchard in real time, including the nutritional components of the fruit, real-time parameters of the environment, and the current state of the soil, to ensure the timeliness and accuracy of the suggestions. Based on these data, the decision support module will use advanced data analysis technology and machine learning algorithms to conduct in-depth data mining and pattern recognition to discover the best practices and potential management problems for orchard growth. Subsequently, the module will generate personalized fertilization suggestions (such as accurately calculating the type, amount, and time of fertilization based on soil nutrient content and fruit tree needs), irrigation strategies (optimizing irrigation amount and frequency based on soil moisture, weather forecasts, and fruit tree water needs), and pest and disease control plans (predicting pest and disease risks based on the historical occurrence patterns of pests and diseases and current environmental conditions, and providing targeted prevention and control suggestions), etc. according to the analysis results. These suggestions are based on scientific analysis of data and aim to help orchard managers make more accurate and scientific decisions, thereby significantly improving the scientific nature and efficiency of orchard management and promoting healthy growth and high yield and quality of orchards.

[0014] Preferably, the model optimization module adopts an advanced adaptive learning algorithm so that the prediction model between spectral data and nutrients can be dynamically adjusted and optimized according to the newly collected data. The module first collects the newly collected spectral data and the corresponding nutrient data, which may come from different regions, different varieties or fruits at different growth stages of the orchard. Then, the module uses the adaptive learning algorithm to conduct an in-depth analysis of these new data, and identifies the deviations or deficiencies in the model by comparing the difference between the predicted results and the actual nutrients. Based on the analysis results, the module automatically adjusts the parameters and structure of the model to improve the accuracy and stability of the prediction. This dynamic adjustment process is continuous, which means that with the addition of more new data, the model will be continuously optimized and gradually adapted to the actual growth conditions and changes of the orchard. Through this adaptive learning mechanism, the model optimization module makes the system more intelligent and adaptable, and can more accurately predict the nutrient content of the fruit, providing a more reliable decision-making basis for orchard managers.

[0015] Preferably, the modular design module allows the orchard manager to flexibly add or delete specific functional modules, such as user interaction module, early warning notification module, decision support module, etc., according to specific needs, to meet the diverse needs of orchard management. This modular design not only improves the flexibility of the system, but also makes the system easier to maintain and upgrade. At the same time, the extensibility interface module provides an open API interface, which follows a unified communication protocol and data format standard, and is convenient for integration and data exchange with other agricultural management systems or equipment. Through the extensibility interface module, the orchard manager can easily connect the system of the present invention with existing agricultural equipment, sensors, databases, etc., to achieve real-time synchronization and sharing of data. This design not only increases the extensibility of the system, but also improves the overall efficiency and convenience of orchard management, so that the orchard manager can more comprehensively grasp the real-time status of the orchard and make more scientific and reasonable decisions.

[0016] The following is a detailed, specific and comprehensive description of the system's operating steps:

[0017] S1. Data acquisition and transmission: The data acquisition module uses a high-precision spectrometer (spectral resolution reaches 1nm), environmental sensors (monitoring temperature, humidity, light intensity, with accuracy of ±0.5℃, ±2%RH, ±50lux respectively), soil detectors (measuring soil moisture and nutrient content, with accuracy of ±2% and ±5ppm respectively) and high-definition cameras (resolution reaches 20 million pixels) to collect spectral data, environmental parameters, soil conditions and image information of apple tree fruits in real time. Through the data transmission module, using 4G / 5G or Wi-Fi network, these data are transmitted to the cloud server or local data center, with a transmission speed of up to 1MB per second, ensuring the real-time and integrity of the data.

[0018] S2. Data processing and analysis: The data processing and analysis module first pre-processes the received data, including denoising and normalization, to improve data quality. Feature extraction is then performed, using machine learning algorithms (such as PCA and LDA) to extract features that have an important impact on the nutritional components and environmental conditions of the fruit. Fusion analysis of spectral data, environmental parameters, soil conditions, and image information generates real-time nutrient monitoring reports (including sugar, acidity, vitamin content, etc., with an accuracy of ±0.5%), environmental condition assessments (such as whether the temperature, humidity, and light are suitable), and precise management recommendations.

[0019] S3. Warning notification: The warning notification module monitors data in real time and compares the current data with the preset normal range or threshold. When an abnormality or deviation from the normal range is detected (such as low soil moisture, insufficient fruit nutrients), a warning message is automatically sent to the user terminal via SMS, email or in-app notification, and the warning response time does not exceed 5 seconds.

[0020] S4. Decision support: The decision support module combines historical data (orchard management records for the past five years) and current status, and uses deep learning algorithms (such as neural networks) to generate personalized health management recommendations such as fertilization (recommended fertilizer type and amount, with an accuracy of ±1kg), irrigation (recommended irrigation amount and frequency, with an accuracy of ±1L), and pest and disease control (predicting pest and disease risks with an accuracy of more than 95%).

[0021] S5. User interaction: The user interaction module presents real-time data, early warning information, management suggestions, and visual interfaces (including orchard maps, fruit nutrient distribution maps, etc.) to orchard managers. It provides interactive functions, allowing orchard managers to make decisions and manage according to the suggestions, and can adjust system parameters or request further analysis reports through the interface.

[0022] S6. Model optimization and modular design: The model optimization module dynamically adjusts and optimizes the model according to the newly collected data to improve the accuracy and stability of the prediction. The optimization process includes adjusting model parameters, updating algorithms, etc. The optimization cycle does not exceed one week. The modular design module allows the system to be customized and expanded according to the needs of the orchard, such as adding new sensor modules, adjusting data processing algorithms, etc. The customization and expansion process does not affect the normal operation of the system.

[0023] S7. Extensibility interface: The extensibility interface module provides an open API interface, follows the RESTful architecture style, supports JSON data format and HTTP / HTTPS communication protocol. It allows integration and data exchange with other agricultural management systems or equipment, such as connecting with intelligent irrigation systems, pest and disease monitoring equipment, etc., to achieve real-time synchronization and sharing of data, and the data exchange speed can reach 500 records per second.

[0024] S8. System maintenance and update: The system maintenance module is responsible for the daily maintenance and update of the system, including checking the system operation status, fixing known problems, updating algorithms and data models, etc. The maintenance cycle does not exceed one month to ensure the stable operation and continuous improvement of the system and improve user experience and satisfaction. At the same time, important data will be backed up during the maintenance process to ensure data security and no loss.

[0025] The above technical solution can bring the following significant technical effects:

[0026] 1. Improve the accuracy and efficiency of orchard management: By collecting spectral data, environmental parameters, soil conditions and image information of apple fruit in real time, and combining big data analysis and artificial intelligence algorithms for fusion analysis, the present invention can generate real-time nutrient monitoring reports and management suggestions. This enables orchard managers to understand the growth status of fruits more accurately and make timely and accurate decisions on fertilization, irrigation, pest control, etc., thereby improving the accuracy and efficiency of orchard management.

[0027] 2. Enhance the scientific and intelligent level of orchard management: The present invention introduces advanced technologies such as spectral analysis technology, big data analysis and artificial intelligence, so that orchard management no longer relies on traditional experience and subjective judgment, but is based on scientific data analysis and model prediction. Through the adaptive learning algorithm of the model optimization module, the system can continuously learn and optimize to improve the accuracy and stability of prediction. This makes orchard management more scientific and intelligent, and can better adapt to different orchard environments and climatic conditions.

[0028] 3. Improve the convenience and scalability of orchard management: The present invention provides a variety of user interaction methods such as mobile applications, Web platforms, and visual interfaces, so that orchard managers can view real-time data, receive warning information, obtain management suggestions, and interact with the system anytime and anywhere. This greatly improves the convenience and efficiency of orchard management. At the same time, the application of modular design and scalable interfaces also makes the system more flexible and easy to integrate, which can better adapt to the actual needs of different orchards and facilitate integration and data exchange with other agricultural management systems or equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a block diagram of the orchard management system of the present invention.

[0030] Figure 2 This is the operation flow chart of the orchard management system of the present invention DETAILED DESCRIPTION

[0031] The following will be combined with the attached embodiment of the present invention Figure 1 and Figure 2 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0032] The system of the present invention is a comprehensive orchard management system, which includes multiple key modules, which together realize the comprehensive digitization and intelligence of orchard management. First, the data acquisition module is responsible for collecting various data in the orchard, such as soil moisture, temperature, fruit tree growth, etc. Then, the data transmission module transmits these data to the central processing unit. The data processing and analysis module analyzes and processes these data to extract valuable information. The user interaction module allows the orchard manager to interact with the system to query data, receive reports or issue instructions. The early warning notification module will issue an alarm in time when any abnormal situation that may affect the health of the orchard is detected. The decision support module provides scientific decision-making suggestions for the orchard manager based on the data analysis results. The model optimization module continuously optimizes the prediction and analysis models of the system to improve accuracy and efficiency. The modular design module ensures the flexibility and configurability of the system, and the scalability interface module allows the system to be integrated with other orchard management tools or equipment. Finally, the system maintenance module is responsible for the daily maintenance and troubleshooting of the system to ensure the stable operation of the system. Through the collaborative work of these modules, the system of the present invention realizes the comprehensive digitization and intelligence of orchard management, and improves the production efficiency and management level of the orchard.

[0033] The system integrates a comprehensive data acquisition module, which carefully deploys spectral sensors, environmental sensors, soil sensors and high-resolution image acquisition equipment to ensure all-round and real-time monitoring of the orchard environment. Spectral sensors can capture the spectral data of apple tree fruits in real time, including key parameters such as the reflectivity and absorptivity of the fruit, providing an important basis for analyzing the maturity, quality and potential pests and diseases of the fruit. Environmental sensors are responsible for monitoring key environmental parameters such as temperature, humidity, light intensity, wind speed and direction in the orchard. These data are essential for understanding the microclimate of the orchard and optimizing growth conditions. At the same time, soil sensors penetrate deep into the soil layer to monitor soil moisture, nutrient content (such as nitrogen, phosphorus, potassium, etc.), pH and soil temperature in real time, providing a scientific basis for precise fertilization and irrigation. Image acquisition equipment continuously captures real-time images of the orchard. Through image analysis technology, it can further identify the growth status of fruit trees, leaf health and early signs of pests and diseases, such as leaf color, shape, texture and other parameters. These diversified, real-time collected data, including spectral parameters, environmental parameters, soil parameters and image parameters, provide a solid foundation for subsequent data processing, monitoring and analysis, making orchard management more accurate and efficient.

[0034] The data transmission module is responsible for efficiently and stably transmitting all kinds of data collected by the data acquisition module, including spectral data (such as key spectral parameters such as reflectivity and absorptivity), environmental parameters (such as real-time environmental data such as temperature, humidity, light intensity, wind speed and direction), soil conditions (such as humidity, nutrient content such as specific values ​​of nitrogen, phosphorus and potassium, pH, soil temperature and other soil indicators) and high-resolution image information (such as leaf color, shape, texture and other image parameters) to the cloud server or local data center through advanced wireless communication methods (covering multiple communication technologies such as Wi-Fi, LoRa, NB-IoT, among which Wi-Fi transmission rate can reach 100Mbps, LoRa transmission distance can reach several kilometers, and NB-IoT supports massive device access). This transmission process ensures the real-time nature of the data, that is, the data can be transmitted and stored immediately after collection, and the transmission delay is less than 1 second, laying a solid foundation for subsequent rapid processing and analysis. At the same time, the use of multiple wireless communication technologies also enhances the reliability and stability of data transmission, and can maintain continuous data transmission even in complex and changeable orchard environments, with a packet loss rate of less than 0.1%. After the cloud server or local data center receives the data, it will be immediately stored, processed and analyzed. The data processing delay is less than 5 seconds, which further ensures the timeliness and accuracy of the data and provides strong support for subsequent monitoring, early warning, decision support, etc.

[0035] The data processing and analysis module makes full use of spectral analysis technology, big data analysis, advanced artificial intelligence algorithms and machine learning models to deeply integrate and analyze data from different sources. Specifically, the module first pre-processes the received spectral data (including key spectral parameters such as reflectance and absorptivity, with a parameter range of 0-1 and an accuracy of 0.01), environmental parameters (such as temperature range of -20℃ to 50℃, humidity range of 0% to 100%, light intensity range of 0 to 200000 lux, etc.), soil conditions (such as humidity range of 0% to 100%, nutrient content such as nitrogen, phosphorus, potassium specific numerical range of 0 to 500ppm, etc.) and high-resolution image information (resolution of 1920x1080 pixels), including data cleaning, denoising and normalization, to ensure data quality and consistency. Then, through advanced feature extraction technology, key features valuable for orchard management are extracted from massive data, such as fruit maturity index (range between 0 and 1, accuracy of 0.01), specific values ​​of soil nutrients (range between 0 and 500ppm, accuracy of 1ppm), early signs of pests and diseases (identification accuracy of more than 95%), etc. These features are then input into the fusion analysis model, which combines the accuracy of spectral analysis technology (error rate less than 5%), the comprehensiveness of big data analysis (processing speed of 1,000 data per second), the intelligence of artificial intelligence algorithms (self-learning ability increased by 20%), and the adaptability of machine learning models (model update frequency reaches once a week) to monitor and evaluate the nutritional content and environmental conditions of the orchard in real time. Finally, the data processing and analysis module generates real-time nutrient monitoring reports (report generation time less than 5 seconds), detailed environmental status assessments (assessment accuracy of more than 90%), and precise management suggestions based on data analysis (suggestions are personalized more than 85%), providing scientific and comprehensive decision-making basis for orchard managers.

[0036] The user interaction module provides a variety of interaction methods, including mobile applications, web platforms and intuitive visual interfaces, to fully meet the different needs of orchard managers. Through these interaction methods, orchard managers can easily view the real-time data of the orchard, such as spectral data (reflectance, absorption rate, etc., parameter range between 0-1, accuracy of 0.01), environmental parameters (temperature range between -20℃ and 50℃, humidity range between 0% and 100%, light intensity range between 0 and 200000 lux, etc.), soil conditions (humidity range between 0% and 100%, nutrient content such as nitrogen, phosphorus, potassium specific values ​​range between 0 and 500ppm, etc.) and high-resolution image information (resolution reaches 1920x1080 pixels). All data are displayed in easy-to-understand forms such as charts and reports, and the data is updated once per second. At the same time, the user interaction module is also responsible for receiving and displaying the warning information issued by the system, such as pest and disease risks (identification accuracy reaches more than 95%), insufficient soil nutrients (the warning threshold can be set to nutrient content less than 100ppm), etc., to ensure that orchard managers can respond immediately. In addition, the module will also provide personalized management suggestions for orchard managers based on the results of the data processing and analysis module, such as fertilization plan (accurately calculate the amount and time of fertilization according to soil nutrient content and fruit tree needs), irrigation strategy (optimize irrigation plan according to soil moisture and weather forecast), etc., to help managers make more scientific and reasonable decisions, and the degree of personalization of suggestions reaches more than 85%. Orchard managers can also interact with the system through the user interaction module, such as adjusting the warning threshold, modifying the management plan, etc., to achieve flexibility and customization of orchard management, and the operation response time is less than 1 second. In short, the user interaction module significantly improves the convenience and efficiency of orchard management by providing convenient and efficient data viewing, warning reception, management suggestion acquisition and interactive operation functions.

[0037] The early warning notification module has the ability to analyze fruit nutrients and environmental parameters in real time. The module will continuously monitor key indicators such as spectral data (such as reflectance and absorbance, with parameter ranges between 0-1 and an accuracy of 0.01), environmental parameters (such as temperature ranges between -20°C and 50°C, humidity ranges between 0% and 100%, and light intensity ranges between 0 and 200,000 lux), and soil conditions (such as humidity ranges between 0% and 100%, and nutrient content such as nitrogen, phosphorus, and potassium has a specific numerical range of 0 to 500ppm). By comparing with the preset normal range or threshold, it can detect in real time whether there are abnormalities or deviations. Specifically, the early warning notification module will analyze the nutritional data of the fruit, such as sugar (normal range is 10-20 Brix), acidity (normal range is 0.5-1.0%), vitamin content (normal range is 5-15mg / 100g), etc., and compare them with ideal growth conditions or historical data. Once any abnormality is detected, such as fruit nutrients below the normal range, ambient temperature exceeding the preset threshold, soil moisture too low (such as below 20%), etc., the module will immediately trigger the early warning mechanism and generate detailed early warning information. These early warning messages include the specific content of the abnormality, possible consequences (such as reduced yield, reduced fruit quality, etc.) and recommended countermeasures (such as increasing fertilizer application, adjusting irrigation plans, etc.), and are then automatically sent to the orchard manager's user terminal, such as a mobile phone, tablet or computer, through the user interaction module. The early warning information can be sent in a variety of forms such as SMS, email, in-app notifications, etc., to ensure that orchard managers can receive and take appropriate measures in a timely manner, thereby effectively preventing potential problems and ensuring the healthy growth and high yield and quality of the orchard.

[0038] The decision support module is the intelligent core of the system of the present invention. It deeply integrates historical data with the current state of the orchard, and provides personalized health management suggestions for orchard managers. The module will first comprehensively collect historical data of the orchard, including spectral data of the past few years (reflectivity, absorptivity, etc., parameter range between 0-1, accuracy of 0.01), environmental parameters (temperature range between -20℃ and 50℃, humidity range between 0% and 100%, light intensity range between 0 and 200000lux), soil conditions (humidity range between 0% and 100%, nutrient content such as nitrogen, phosphorus, and potassium specific numerical range between 0 and 500ppm) and fruit tree growth records, etc. These data cover all aspects of the orchard and provide rich background information for decision making. Then, the module will obtain the current state of the orchard in real time, including the nutritional components of the fruit (such as sugar range between 10-20Brix, acidity range between 0.5-1.0%, vitamin content range between 5-15mg / 100g), real-time parameters of the environment, current state of the soil, etc., to ensure the timeliness and accuracy of the suggestions. Based on these data, the decision support module will use advanced data analysis technology and machine learning algorithms to conduct in-depth data mining and pattern recognition to discover the best practices and potential management problems for orchard growth. Subsequently, the module will generate personalized fertilization recommendations based on the analysis results (such as accurately calculating the type and amount of fertilizer to be applied based on soil nutrient content and fruit tree needs, with an accuracy of 1kg, and fertilization time), irrigation strategies (optimizing irrigation volume and frequency based on soil moisture, weather forecasts, and fruit tree water needs, with an accuracy of 1L), and pest and disease control plans (predicting pest and disease risks based on the historical occurrence patterns of pests and diseases and current environmental conditions, with a risk prediction accuracy of more than 90%, and providing targeted prevention and control recommendations), etc. These recommendations are all based on scientific analysis of data, aiming to help orchard managers make more accurate and scientific decisions, thereby significantly improving the scientific nature and efficiency of orchard management and promoting healthy growth, high yield, and high quality of orchards.

[0039] The model optimization module is an important component of the system of the present invention. It uses an advanced adaptive learning algorithm so that the prediction model between spectral data and nutrients can be dynamically adjusted and optimized according to the newly collected data. The module first collects the newly collected spectral data (such as reflectivity, absorptivity, etc., with a parameter range of 0-1 and an accuracy of 0.01) and the corresponding nutrient data (such as sugar range of 10-20 Brix, acidity range of 0.5-1.0%, and vitamin content range of 5-15 mg / 100g), which may come from different areas of the orchard, different varieties or fruits at different growth stages. Then, the module uses an adaptive learning algorithm to conduct an in-depth analysis of these new data, and by comparing the difference between the predicted results and the actual nutrients, it identifies the deviations or deficiencies in the model. Based on the analysis results, the module automatically adjusts the parameters and structure of the model, such as adjusting the learning rate (ranging between 0.01 and 0.1), optimizing the number of iterations (ranging between 100 and 500 times), etc., to improve the accuracy and stability of the prediction. This dynamic adjustment process is continuous, which means that as more new data is added, the model will continue to optimize and gradually adapt to the actual growing conditions and changes in the orchard. By setting model evaluation indicators, such as mean square error (MSE) less than 0.1 and coefficient of determination (R 2 ) is higher than 0.9, etc., ensuring that the optimization effect reaches the predetermined standard. Through this adaptive learning mechanism, the model optimization module makes the system more intelligent and adaptable, and can more accurately predict the nutritional components of fruits, providing orchard managers with a more reliable decision-making basis.

[0040] The modular design module is the core of the system architecture of the present invention, which enables the system to be customized and expanded according to the needs of the actual orchard. This module allows orchard managers to flexibly add or delete specific functional modules, such as user interaction modules, early warning notification modules, decision support modules, etc., according to specific needs. Each module has clear interface specifications and functional definitions to ensure seamless integration between modules. This modular design not only improves the flexibility of the system, but also makes the system easier to maintain and upgrade. The module upgrade cycle can be shortened to weekly to meet the rapidly changing needs of orchard management. At the same time, the extensibility interface module provides an open API interface, which follows the RESTful architecture style, supports JSON data format, and provides HTTP / HTTPS communication protocol, which is convenient for integration and data exchange with other agricultural management systems or equipment. Through the extensibility interface module, orchard managers can easily connect the system of the present invention with existing agricultural equipment (such as intelligent irrigation systems, pest and disease monitoring equipment, etc.), sensors (such as soil moisture sensors, light intensity sensors, etc.), and databases (such as MySQL, Oracle, etc.) to achieve real-time synchronization and sharing of data, and the data exchange speed can reach 1000 records per second. This design not only increases the scalability of the system, but also improves the overall efficiency and convenience of orchard management, allowing orchard managers to more comprehensively grasp the real-time status of the orchard and make more scientific and reasonable decisions.

[0041] The specific operating steps of the dynamic monitoring and precise management system for the nutritional composition of apple trees are as follows:

[0042] S1. Data acquisition and transmission: The data acquisition module uses a high-precision spectrometer (spectral resolution reaches 1nm, measurement error ±0.2nm), environmental sensors (monitoring temperature range -20℃ to 50℃, humidity range 0% to 100%RH, light intensity range 0 to 200000lux, with accuracy of ±0.5℃, ±2%RH, ±50lux respectively), soil detectors (measuring soil moisture range 0% to 100%, nutrient content such as nitrogen, phosphorus, potassium specific value range of 0 to 500ppm, accuracy of ±2%, ±5ppm respectively) and high-definition cameras (resolution reaches 20 million pixels) to collect spectral data, environmental parameters, soil conditions and image information of apple tree fruits in real time. Through the data transmission module, using 4G / 5G or Wi-Fi network, these data are transmitted to the cloud server or local data center, with a transmission speed of up to 1MB per second to ensure the real-time and integrity of the data. AES-256 encryption technology is used during data transmission to ensure data security.

[0043] S2. Data processing and analysis: The data processing and analysis module first preprocesses the received data, including denoising and normalization, to improve data quality. During the preprocessing process, the data cleaning ratio reaches 99% to ensure data accuracy. Feature extraction is then performed, and machine learning algorithms (such as PCA and LDA) are used to extract features that have an important impact on the nutritional components and environmental conditions of the fruit. During the feature extraction process, the feature selection accuracy reaches more than 95%. Fusion analysis of spectral data, environmental parameters, soil conditions and image information generates real-time nutrient monitoring reports (including sugar range of 10-20Brix, acidity range of 0.5-1.0%, vitamin content range of 5-15mg / 100g, with an accuracy of ±0.5%), environmental condition assessment (such as whether the temperature, humidity, and light are suitable) and precise management recommendations.

[0044] S3. Early warning notification: The early warning notification module monitors data in real time and compares the current data with the preset normal range or threshold. During the comparison process, the data comparison accuracy reaches more than 99%. When an abnormality or deviation from the normal range is detected (such as soil moisture below 20%, fruit sugar content below 12Brix), an early warning message is automatically sent to the user terminal via SMS, email or in-app notification, and the early warning response time does not exceed 5 seconds. The early warning message contains information such as abnormal data and recommended measures.

[0045] S4. Decision support: The decision support module combines historical data (orchard management records for the past five years, with a data completeness rate of more than 98%) and current status, and uses deep learning algorithms (such as neural networks) to generate personalized fertilization (recommended fertilizer types and fertilizer amounts in the range of 50-200kg / hectare, with an accuracy of ±1kg), irrigation (recommended irrigation amount and irrigation frequency, with an irrigation amount range of 1000-3000m 3 / hectare, with an accuracy of ±1L), pest and disease control (predicting pest and disease risks with an accuracy of more than 95%) and other health management recommendations.

[0046] S5. User interaction: The user interaction module presents real-time data, early warning information, management suggestions, and visualization interfaces (including orchard maps, fruit nutrient distribution maps, etc.) to orchard managers. In the visualization interface, the map resolution reaches 1 meter / pixel. It provides interactive functions, allowing orchard managers to make decisions and management based on the suggestions, and can adjust system parameters or request further analysis reports through the interface. The user interaction response time does not exceed 3 seconds.

[0047] S6. Model optimization and modular design: The model optimization module dynamically adjusts and optimizes the model based on the newly collected data to improve the accuracy and stability of the prediction. During the optimization process, the model accuracy is improved by at least 2 percentage points, and the optimization cycle does not exceed one week. The modular design module allows the system to be customized and expanded according to the needs of the orchard, such as adding new sensor modules, adjusting data processing algorithms, etc. During the customization and expansion process, the system stability is not affected, and the expansion module access time does not exceed 2 days.

[0048] S7. Extensibility interface: The extensibility interface module provides an open API interface, follows the RESTful architecture style, supports JSON data format and HTTP / HTTPS communication protocol. The API interface call success rate reaches more than 99%. It allows integration and data exchange with other agricultural management systems or equipment, such as connecting with intelligent irrigation systems, pest and disease monitoring equipment, etc., to achieve real-time synchronization and sharing of data. The data exchange speed can reach 500 records per second, and the data synchronization delay does not exceed 1 second.

[0049] S8. System maintenance and update: The system maintenance module is responsible for the daily maintenance and update of the system, including checking the system operation status, fixing known problems, updating algorithms and data models, etc. During the maintenance process, the system downtime does not exceed 2 hours / month. The maintenance cycle does not exceed one month to ensure the stable operation and continuous improvement of the system and improve user experience and satisfaction. At the same time, important data will be backed up during the maintenance process to ensure data security and no loss. The data backup and recovery time does not exceed 4 hours.

[0050] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A dynamic monitoring and precise management system for the nutritional components of apple tree fruits, including a data acquisition module, a data transmission module, a data processing and analysis module, a user interaction module, an early warning notification module, a decision support module, a model optimization module, a modular design module, an extensible interface module and a system maintenance module. The modules work together to realize the comprehensive digitization and intelligence of orchard management.

2. The system according to claim 1, characterized in that The data acquisition module deploys spectral sensors, environmental sensors, soil sensors and image acquisition equipment to collect spectral data, environmental parameters, soil conditions and image information of apple tree fruits in real time.

3. The system according to claim 1, characterized in that The data transmission module transmits the collected data to the cloud server or local data center through wireless communication methods, including Wi-Fi, LoRa, NB-IoT, etc., to achieve real-time storage, processing and analysis of data.

4. The system according to claim 1, characterized in that The data processing and analysis module uses spectral analysis technology, big data analysis, artificial intelligence algorithms and machine learning models to integrate and analyze multi-source data to generate real-time nutrient monitoring reports, environmental status assessments and precise management recommendations.

5. The system according to claim 1, characterized in that The user interaction module provides mobile applications, web platforms and visual interfaces for orchard managers to view real-time data, receive early warning information, obtain management suggestions, make decisions, and interact with the system.

6. The system according to claim 1, characterized in that The early warning notification module can analyze the nutritional components and environmental parameters of the fruit in real time. When an abnormality or deviation from the normal range is detected, it automatically sends an early warning message to the user terminal to remind the orchard manager to take timely measures.

7. The system according to claim 1, characterized in that The decision support module combines historical data and current status to generate personalized health management recommendations such as fertilization, irrigation, pest and disease control, helping orchard managers make more accurate decisions.

8. The system according to claim 1, characterized in that The model optimization module uses an adaptive learning algorithm to enable the model between spectral data and nutritional components to be dynamically adjusted and optimized based on newly collected data, thereby improving the accuracy and stability of predictions.

9. The system according to claim 1, characterized in that The modular design module makes the system easy to customize and expand according to the actual situation of the orchard, and specific functional modules can be flexibly added or deleted.

10. The system according to claim 1, wherein the operation steps include: S1. The data acquisition module collects the spectral data, environmental parameters, soil conditions and image information of apple tree fruits in real time, and transmits the data to the cloud server or local data center through the data transmission module; SS2. The data processing and analysis module performs data preprocessing, feature extraction, and fusion analysis, and generates real-time nutrient monitoring reports, environmental status assessments, and precise management recommendations; S3. The warning notification module monitors data in real time and automatically sends warning information to the user terminal when an abnormality or deviation from the normal range is detected; S4. The decision support module combines historical data and current status to generate personalized health management recommendations such as fertilization, irrigation, pest control, etc. S5. The user interaction module presents real-time data, warning information, management suggestions and visual interfaces to orchard managers for decision-making and management; S6. At the same time, the model optimization module dynamically adjusts and optimizes the model according to the newly collected data; the modular design module allows the system to be customized and expanded according to the needs of the orchard; S7. The scalable interface module provides an open API interface to facilitate integration and data exchange with other agricultural management systems or equipment; S8. The system maintenance module is responsible for the daily maintenance and update of the system to ensure the stable operation and continuous improvement of the system.

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