Intelligent camellia oleifera forest management method and system, camellia oleifera planting mechanized intelligent management and agricultural machine remote monitoring management system and storage medium

Abstract

The invention discloses a camellia oleifera forest intelligent management method and system, a camellia oleifera planting mechanized intelligent management and agricultural machine remote monitoring management system and a storage medium, and belongs to the technical field of agricultural production management. The method comprises the following steps: acquiring current soil parameters; obtaining current crop growth data, and predicting the root depth of the current crop according to the current crop growth data; according to the current crop growth data and preset soil standard humidity data, the current soil required humidity is obtained; and obtaining a soil irrigation strategy according to the current required soil humidity, the current soil humidity and the root depth of the current crop. The system can accurately adapt to water demand differences of different growth stages and different growth vigor of camellia oleifera, effectively avoids poor crop growth vigor and yield reduction caused by insufficient water supply in traditional irrigation, remarkably improves the water resource utilization rate, reduces the irrigation cost, and promotes intelligence and refinement of camellia oleifera forest management.

Description

Technical Field

[0001] This invention relates to the field of agricultural production management technology, and in particular, to a smart management method, system, electronic device, and storage medium for camellia oleifera forests. Background Technology

[0002] Camellia oleifera, an important woody oil crop in my country, plays a vital strategic role in ensuring national food and oil security. However, with the continuous expansion of camellia oleifera planting, traditional forest management methods have gradually revealed numerous drawbacks and are no longer sufficient to meet the demands of modern, large-scale cultivation.

[0003] In traditional camellia oleifera forest management, irrigation operations rely heavily on the experience and judgment of growers, lacking scientific data support. Growers typically determine the timing and amount of irrigation based on intuitive feelings or single environmental indicators (such as weather conditions). This experience-based management approach is highly subjective and prone to over-reliance on data. Insufficient irrigation can lead to insufficient soil moisture to meet the growth needs of the camellia oleifera, especially during water-sensitive periods at different growth stages (such as budding and fruiting), resulting in poor crop growth and reduced yield. Excessive irrigation not only wastes valuable water resources but can also reduce soil aeration, causing root rot due to oxygen deficiency, and affecting the quality and yield of the camellia oleifera. Traditional management methods fail to develop targeted irrigation strategies, resulting in low irrigation efficiency and low water resource utilization. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a smart management method, system, electronic device, and storage medium for camellia oleifera forests.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A smart management method for camellia oleifera forests includes the following steps: S1, acquiring current soil parameters, wherein the soil parameters include at least soil moisture; S2, acquiring current crop growth data, and predicting the current crop root depth based on the current crop growth data; S3, acquiring the required soil moisture based on the current crop growth data and preset standard soil moisture data; S4, acquiring a soil irrigation strategy based on the required soil moisture, the current soil moisture, and the current crop root depth, wherein the soil irrigation strategy includes the number and depth of irrigation output points.

[0006] Further, step S4 specifically includes: S41, comparing the current soil moisture with the required soil moisture; S42, if the current soil moisture is greater than the required soil moisture, then no irrigation is performed; S43, if the current soil moisture is less than the required soil moisture, then calculating the moisture difference between the current soil moisture and the required soil moisture, and determining whether the moisture difference is less than a first preset threshold: if yes, then proceed to step S44; if no, then proceed to step S45; S44, then determining whether the current crop root depth is less than the depth of the lowest irrigation output point: if yes, then based on the current crop root depth, controlling the irrigation depth to be greater than the current crop root depth and closest to the current crop root depth. S45. Determine if the humidity difference is less than the second preset threshold: if yes, proceed to step S46; if no, control all irrigation output points to discharge water; S46. Determine if the root depth of the current crop is less than the depth of the lowest irrigation output point: if yes, control one or more irrigation output points with a depth greater than the root depth of the current crop and closest to the root depth of the current crop, and one or more irrigation output points with a depth less than the root depth of the current crop and closest to the root depth of the current crop to discharge water; if no, control one or more irrigation output points with the lowest position to discharge water.

[0007] Furthermore, the current crop growth data includes canopy coverage, leaf area index, and plant height.

[0008] Further, step S2 specifically includes: predicting the root depth corresponding to the current crop growth data based on historical data; the historical data includes crop growth data and the root depth of the crop corresponding to the crop growth data.

[0009] Furthermore, the irrigation output points are arranged in multiple sets at intervals along the depth direction.

[0010] Furthermore, the soil parameters also include soil pH and soil electrical conductivity.

[0011] Furthermore, it also includes sending current soil parameters, current crop growth data, current crop root depth, and soil irrigation strategies to the intelligent management platform for camellia oleifera planting.

[0012] This invention also provides a smart management system for camellia oleifera forests, comprising: a soil parameter acquisition module for acquiring current soil parameters, the soil parameters including at least soil moisture; a crop growth data acquisition and root depth prediction module for acquiring current crop growth data and predicting the current crop root depth based on the current crop growth data; a current soil moisture requirement acquisition module for acquiring the current soil moisture requirement based on the current crop growth data and preset soil standard moisture data; and an irrigation strategy generation module for acquiring a soil irrigation strategy based on the current soil moisture requirement, the current soil moisture, and the current crop root depth, the soil irrigation strategy including the depth and number of irrigation output points.

[0013] This invention also provides a mechanized intelligent management and remote monitoring and management system for camellia oleifera planting, including: a whole-process mechanized management module for camellia oleifera planting, including a camellia oleifera forest intelligent management system; and a remote monitoring and management module for camellia oleifera forest agricultural machinery, used for remote monitoring and data uploading of agricultural machinery.

[0014] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements the intelligent management method for camellia oleifera forests.

[0015] The present invention has the following beneficial effects: Step S1 accurately acquires soil parameters, including soil moisture; simultaneously, step S2 obtains crop growth data and predicts root depth, overcoming the shortcomings of traditional management methods that neglect differences in crop growth status and cannot accurately locate the water absorption range, thus laying a solid data foundation for subsequent targeted irrigation strategies. Step S3 combines real-time crop growth data with preset standard soil moisture data to dynamically determine the current soil moisture requirement, accurately adapting to the different water needs of camellia at different growth stages and under different growth statuses. This effectively avoids problems such as poor crop growth and reduced yield due to insufficient water supply in traditional irrigation, as well as problems such as poor soil permeability, root rot, and quality damage caused by excessive irrigation, providing a suitable water environment for the healthy growth of camellia. Step S4 integrates the current required soil moisture, actual soil moisture, and crop root depth to determine an irrigation strategy that includes the number and depth of irrigation output points, enabling targeted and quantitative water supply. This ensures that irrigation water reaches the crop root absorption area, improving water absorption efficiency, while avoiding water waste. Compared to traditional irrigation methods, this method significantly improves water resource utilization, reduces irrigation costs, and minimizes soil nutrient loss caused by excessive irrigation, thus helping to maintain soil ecological balance.

[0016] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the overall process of the method of the present invention; Figure 2 This is a flowchart illustrating step S4; Figure 3 This is a schematic diagram of agricultural machinery equipped with a remote monitoring and management module for camellia oil forest farm machinery. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0021] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0022] Please refer to Figure 1 The present invention provides a preferred embodiment of a smart management method for camellia oleifera forests, comprising steps S1, S2, S3, and S4.

[0023] S1. Obtain current soil parameters, including at least soil moisture; usually, soil pH value may also be included. These parameters can be uploaded to the monitoring platform to obtain real-time soil conditions, enabling scientific fertilization and irrigation.

[0024] S2, acquire current crop growth data, and predict the root depth of the current crop based on the current crop growth data. Crop growth data can be obtained by manually measuring the crops in the quadrat, or by analyzing images acquired by a camera.

[0025] S3, based on current crop growth data and preset soil moisture standards, obtains the required soil moisture level. Crop growth can be graded into multiple levels based on the data, and corresponding preset soil moisture standards are set for different crop levels based on experience. That is, different preset soil moisture standards are set for different crop growth levels, and the corresponding preset soil moisture standard is used as the required soil moisture level. For example, the preset soil moisture standards include a preset soil moisture standard for growth level 2. When the current crop growth data classifies the crop as growth level 2, the preset soil moisture standard for growth level 2 is used as the required soil moisture level. Of course, crop growth grading can be done manually based on experiments and experience, or it can be done through deep learning, using crop growth data as input and the grading level as output, utilizing artificial intelligence for intelligent grading.

[0026] S4. Based on the current soil moisture requirement, current soil moisture, and current crop root depth, obtain a soil irrigation strategy. The soil irrigation strategy includes the number and depth of irrigation output points.

[0027] This invention provides a smart management method for camellia oleifera forests. Step S1 accurately acquires soil parameters, including soil moisture; simultaneously, step S2 acquires crop growth data and predicts root depth, overcoming the shortcomings of traditional management methods that neglect differences in crop growth status and cannot accurately pinpoint the water absorption range. This lays a solid data foundation for subsequent targeted irrigation strategies. Step S3 combines real-time crop growth data with preset standard soil moisture data to dynamically determine the current soil moisture requirement. This accurately adapts to the different water needs of camellia oleifera at different growth stages and under different growth conditions, effectively avoiding problems such as poor crop growth and reduced yield due to insufficient water supply in traditional irrigation, and soil aeration deterioration, root rot, and quality damage caused by over-irrigation. This provides a suitable water environment for the healthy growth of camellia oleifera. Step S4 integrates the current required soil moisture, actual soil moisture, and crop root depth to determine an irrigation strategy that includes the number and depth of irrigation output points. This enables targeted and quantitative water supply, ensuring that irrigation water reaches the crop root absorption area, improving water absorption efficiency, and avoiding water waste. Compared to traditional irrigation methods, this significantly improves water resource utilization, reduces irrigation costs, and minimizes soil nutrient loss caused by excessive irrigation, thus helping to maintain soil ecological balance. It promotes the modernization of camellia oleifera forest management, enhancing both industrial and ecological benefits. It achieves a shift from experience-driven to data-driven management of camellia oleifera forests, improving the level of intelligent management and making it applicable to large-scale camellia oleifera forest planting and management, thereby contributing to improved overall planting and management efficiency.

[0028] Reference Figure 2 In some embodiments of the present invention, step S4 specifically includes steps S41, S42, S43, S44, S45 and S46.

[0029] S41 compares the current soil moisture with the required soil moisture. This directly distinguishes between two core scenarios: no irrigation required and irrigation needed, laying the foundation for precision irrigation decisions and reducing unnecessary water consumption and the risk of soil moisture imbalance.

[0030] S42, If the current soil moisture is greater than the required soil moisture, irrigation will not be carried out. When the soil moisture already meets or exceeds the crop's growth needs, irrigation operations will be terminated to avoid the negative effects of over-irrigation.

[0031] S43, If the current soil moisture is less than the required soil moisture, calculate the moisture difference between the current soil moisture and the required soil moisture, and determine whether the moisture difference is less than a first preset threshold: If yes, it means the water shortage is not severe, and proceed to step S44; otherwise, proceed to step S45. The degree of soil water shortage is classified, and different levels of precision irrigation strategies are matched according to the severity of water shortage to avoid the extensive operation of full irrigation when water is scarce.

[0032] S44 then determines whether the current root depth of the crop is less than the depth of the lowest irrigation output point: If so, it means that the depth of the crop's roots in the soil is less than the depth of the lowest irrigation output point, that is, the lowest position of the root system is higher than the height of the lowest irrigation output point. Based on the current root depth of the crop, control the irrigation output point with a depth greater than the current root depth and closest to the current root depth of the crop to discharge water.

[0033] If not, then control the minimum irrigation output point to discharge water.

[0034] Step S44 involves positioning the irrigation outlet as close to and below the root system as possible to guide root growth downwards. By prioritizing the use of deeper irrigation outlets or those close to root depth, a downward water-guiding effect is created, encouraging roots to actively grow into deeper soil layers. This enhances crop drought and lodging resistance, addressing the problem of shallow root distribution caused by traditional shallow irrigation. Furthermore, since the water shortage is not severe when step S44 is performed, only one irrigation outlet at a single depth needs to be activated.

[0035] S45, determine whether the humidity difference is less than the second preset threshold: If so, proceed to step S46; If not, it indicates a severe water shortage, and all irrigation output points should be controlled to discharge water.

[0036] The system achieves a two-tiered classification of water shortage levels. In cases of severe water shortage, water is dispensed from all irrigation outlets to quickly cover different soil depths, meeting the emergency water needs of crops and preventing poor growth due to prolonged water shortage. In cases of moderate water shortage, step S46 is performed for more refined regulation.

[0037] S46, Determine if the current root depth of the crop is less than the depth of the lowest irrigation output point: If so, then based on the current root depth of the crop, control one or more irrigation output points with a depth greater than the current root depth and closest to the current root depth, and one or more irrigation output points with a depth less than the current root depth and closest to the current root depth, to discharge water. If not, control the water output from one or more irrigation outlets at the lowest (deepest) location.

[0038] Step S46 involves coordinating water output from nearby irrigation outlets at depths below and above the root system when the root system is shallow. This rapidly replenishes the water around the roots while guiding them to grow deeper. Balancing rapid water replenishment with root guidance, the synergistic effect of multiple irrigation outlets at different depths ensures that soil moisture around the roots quickly reaches the required level while continuously guiding the roots downwards. When the root system depth has reached below the lowest irrigation outlet, one or more of the lowest outlets are prioritized to enhance deep irrigation, improve the crop's ability to absorb water and nutrients from deeper soil layers, and avoid water waste in moderately water-deficient scenarios, thus achieving the dual goals of water replenishment and root development.

[0039] Step S4, by classifying and judging the difference between soil moisture and required moisture, and combining this with crop root depth, establishes a refined irrigation output point control logic, achieving the core objectives of on-demand irrigation and targeted guidance. By comparing the moisture difference with first and second preset thresholds, the number of irrigation output points is precisely matched, avoiding water waste. When the moisture difference is small, only a small number of irrigation output points suitable for root depth are activated; when the moisture difference is large, the irrigation coverage is expanded, ensuring precise matching between water supply and soil water shortage. Furthermore, based on the correspondence between root depth and irrigation output point depth, priority is given to controlling water output from irrigation points below or close to root depth. Through targeted deep irrigation, the roots of the camellia oleifera are guided to actively extend downwards. Deeper roots enhance the camellia oleifera's ability to absorb moisture and nutrients from deeper soil layers, improving the plant's drought resistance and lodging resistance, while reducing resource waste caused by surface soil moisture evaporation, further optimizing the synergy between irrigation efficiency and crop growth.

[0040] In a specific embodiment of the present invention, the current crop growth data includes canopy coverage, leaf area index, and plant height. Canopy coverage reflects the vigor of crop population growth, leaf area index is directly related to crop photosynthetic efficiency, and plant height intuitively reflects the longitudinal growth status of the crop. The combination of these three can depict the actual growth status of Camellia oleifera from multiple dimensions, avoiding the problems of root depth prediction deviation and inaccurate judgment of required humidity caused by a single growth indicator.

[0041] In a specific embodiment of the present invention, step S2 specifically includes: predicting the root depth corresponding to the current crop growth data based on historical data; the historical data includes crop growth data and the root depth of the crop corresponding to the crop growth data. Specifically, the current crop growth data can be input into a deep learning model to obtain the current crop root depth; the deep learning model is trained based on historical data, which includes crop growth data and the root depth of the crop corresponding to the crop growth data; the input parameter of the deep learning model is the crop growth data, and the output parameter is the crop root depth. Of course, in some other embodiments, the crop growth can also be graded into multiple levels based on the crop growth data, and the corresponding crop root depth can be set for different levels of crops based on experience or experimental data. Of course, in some other embodiments, prediction can be performed using a multiple linear regression model or a gradient boosting decision tree model, and the multiple linear regression model or the gradient boosting decision tree model can be trained using historical data.

[0042] In a specific embodiment of the present invention, multiple sets of irrigation output points are spaced apart along the depth direction. This method uses water pipes buried in the soil for irrigation. These pipes have multiple irrigation output points, which are arranged at intervals along the depth direction, thus enabling irrigation output points at different depths. Each irrigation output point can be equipped with an electrically controlled valve to achieve electrical control, allowing the opening or closing of irrigation output points at different depths. This provides hardware layout support for deep irrigation and root guidance. It can meet the water supply needs of camellia at different growth stages and root depths. As the crop roots extend downwards with guidance, the spaced-apart deep irrigation output points can adapt in a timely manner, ensuring that irrigation water is always accurately delivered to the root absorption area, providing continuous water stimulation for root growth. Simultaneously, the layout of multiple sets of output points makes flexible adjustment of the irrigation range possible, achieving precise control of both irrigation depth and range, further improving water resource utilization and the operability of the irrigation strategy.

[0043] Specifically, soil parameters also include soil pH and soil electrical conductivity, which comprehensively reflect the physical and chemical properties of the soil and its suitability for growth. This provides basic data for the subsequent expansion of camellia oleifera forest management (such as precision fertilization and soil improvement).

[0044] In a specific embodiment of the invention, the method further includes sending current soil parameters, current crop growth data, current crop root depth, and soil irrigation strategy to the intelligent management platform for camellia oleifera planting. This adds the function of uploading data to the intelligent management platform for camellia oleifera planting, realizing data archiving and remote monitoring of camellia oleifera forest management. Synchronizing core information such as soil parameters, crop growth data, root depth, and irrigation strategies to the server allows managers to monitor the growth status and irrigation implementation of the camellia oleifera forest in real time, breaking the limitations of traditional management that relies primarily on on-site inspections and improving the management efficiency of large-scale camellia oleifera forests. Simultaneously, the centralized storage of all data provides a data foundation for subsequent data analysis and model optimization. For example, by analyzing the correlation between irrigation strategies and crop growth effects in different regions and time periods, parameters such as preset thresholds and standard humidity can be further optimized, enabling continuous iteration and upgrading of intelligent management methods. Furthermore, data-driven management also provides a basis for tracing the origins of camellia oleifera forest planting, enhancing the standardization and traceability of management.

[0045] This invention also provides a smart management system for camellia oleifera forests, comprising: a soil parameter acquisition module for acquiring current soil parameters, including at least soil moisture; a crop growth data acquisition and root depth prediction module for acquiring current crop growth data and predicting the current crop root depth based on the current crop growth data; a current soil moisture requirement acquisition module for acquiring the current soil moisture requirement based on the current crop growth data and preset standard soil moisture data; and an irrigation strategy generation module for generating a soil irrigation strategy based on the current soil moisture requirement, current soil moisture, and current crop root depth, wherein the soil irrigation strategy includes the depth and number of irrigation output points. This management system promotes the intelligent and standardized transformation of camellia oleifera forest management.

[0046] This invention also provides a mechanized intelligent management and remote monitoring and management system for camellia oleifera planting, including: a whole-process mechanized management module for camellia oleifera planting, including a camellia oleifera forest intelligent management system; and a remote monitoring and management module for camellia oleifera forest agricultural machinery, used for remote monitoring and data uploading of agricultural machinery.

[0047] The Camellia oleifera planting mechanized intelligent management and agricultural machinery remote monitoring and management system can be understood as an intelligent management platform for Camellia oleifera planting. The full-process mechanized management module for Camellia oleifera planting can also realize the following functions: sowing plan formulation and dynamic adjustment, fertilization record and formula optimization, irrigation control and threshold setting, harvest task coordination and machinery allocation, machinery and equipment scheduling and status tracking, maintenance history registration and component replacement, fault report submission and handling plan, real-time soil data monitoring and analysis, meteorological data integration and early warning configuration, moisture monitoring and automatic adjustment, role allocation and permission binding, module access control configuration, user account management and security strategy, yield prediction and historical comparison, optimization suggestion generation and effect evaluation, cost statistics and report generation.

[0048] The Camellia oleifera planting full-process mechanized management module is the central hub for mechanized control covering the entire life cycle of Camellia oleifera from planting to harvest. Its core objective is to achieve automated, precise, and efficient control throughout the entire planting process by integrating planting process management, agricultural machinery collaborative control, intelligent data analysis, and system access management functions. It directly undertakes the task of issuing instructions and controlling the operation of agricultural machinery. The sowing plan formulation and dynamic adjustment function can automatically generate an initial plan for sowing time, sowing density, and row spacing based on the characteristics of the Camellia oleifera variety, soil conditions of the planting area, and weather forecast data. During operation, it can dynamically adjust sowing parameters and operation progress based on real-time soil moisture and seedling emergence rate feedback data, avoiding a decline in sowing quality due to environmental changes. The fertilization record and formula optimization function can automatically record information such as the time, fertilizer type, application rate, and agricultural machinery used for each fertilization, forming a complete fertilization file. Based on soil nutrient monitoring data and the nutrient requirements of Camellia oleifera at different growth stages, it optimizes the fertilization formula through algorithms to reduce fertilizer waste and achieve precision fertilization. The irrigation control and threshold setting function allows users to set soil moisture content thresholds based on the water requirements of different growth stages of camellia oleifera. When soil moisture is detected to be below the threshold, an irrigation command is automatically triggered, controlling the irrigation machinery to start operation. The operation automatically stops once the threshold is reached, achieving on-demand irrigation. The harvest task coordination and machinery allocation function intelligently divides harvesting areas based on the maturity distribution of camellia oleifera, the terrain of the planting area, and the type and status of agricultural machinery. It matches the optimal agricultural machinery (such as harvesters and transporters) and creates a harvest task sequence schedule to avoid idle machinery or overlapping operations. The machinery scheduling and status tracking function can monitor the location, operating status, and remaining fuel / battery levels of all agricultural machinery involved in camellia oleifera planting in real time. Based on the priority of planting tasks, remote scheduling commands are issued to adjust the agricultural machinery's operating routes and task order, achieving optimal allocation of agricultural machinery resources. The maintenance history registration and parts replacement function automatically registers the daily maintenance, fault repair, and parts replacement history of agricultural machinery, establishing agricultural machinery maintenance files. Based on the service life of parts and operating time, it automatically reminds users to perform preventative maintenance and parts replacement. The fault report submission and handling function allows agricultural machinery operators or the system to automatically submit fault reports, recording information such as fault phenomena, occurrence time, and agricultural machinery serial number. The system has a built-in fault diagnosis knowledge base that can automatically push preliminary handling solutions based on the fault type, shortening fault diagnosis time. The real-time soil data monitoring and analysis function connects to soil sensors to collect real-time data on soil nutrients (nitrogen, phosphorus, potassium), pH value, and soil moisture. Algorithms analyze soil fertility trends, providing data support for optimizing fertilization and irrigation plans. The meteorological data integration and early warning configuration function integrates with weather stations or third-party meteorological data interfaces to obtain meteorological information such as temperature, humidity, rainfall, and wind speed. It supports setting extreme weather (such as heavy rain and frost) early warning thresholds, automatically pushing notifications when an early warning is triggered, assisting users in adjusting planting operation plans.The moisture monitoring and automatic adjustment function combines soil moisture sensors and meteorological data to monitor the moisture dynamics of the camellia oleifera planting area in real time. In addition to automatically triggering irrigation, it can also remind users to activate drainage equipment when rainfall is excessive to prevent waterlogging. The role assignment and permission binding function can divide users into different roles (such as administrators, agricultural machinery operators, and data analysts) based on their job responsibilities in camellia oleifera planting management, and bind corresponding functional operation permissions to each role to prevent unauthorized operations. The module access control configuration function supports configuring access permissions for functional sub-modules within a module; for example, only allowing administrators to modify sowing plan thresholds, while ordinary operators can only view plans and report operation data. The user account management and security policy function provides management functions such as user account creation, deletion, and password reset; it has built-in account security policies, such as password complexity requirements and login failure lockout mechanisms, to ensure system data security. The yield prediction and historical comparison function can predict the current year's camellia oleifera yield based on historical yield data, current year's planting environment data, and agricultural machinery operation quality data through machine learning models; it supports comparative analysis of predicted yields with historical data to identify factors affecting yield. The optimization suggestion generation and effect evaluation functions can automatically generate optimization suggestions for planting plans (such as adjusting fertilization cycles and optimizing irrigation times) based on the comprehensive analysis results of data from the entire planting process; record the planting effects after the suggestions are implemented, forming a closed-loop management of suggestions, implementation, and evaluation. The cost statistics and report generation functions can automatically calculate various costs such as fuel / electricity, fertilizer, maintenance, and labor during the planting process; support the generation of cost reports, yield reports, and operation progress reports with custom cycles, providing data basis for planting management decisions. Through soil sensors, weather sensors deployed in the planting area, and status sensors on the agricultural machinery, environmental data and agricultural machinery operation data are collected in real time; the collected data is cleaned, integrated, and stored to establish a camellia oil planting database and an agricultural machinery equipment archive; the built-in camellia oil planting model (such as the fertilizer requirement model and water requirement model of the growth cycle) and intelligent algorithms are used to analyze and process the data, generate operation plans such as sowing, fertilization, and irrigation, as well as agricultural machinery scheduling schemes; decision commands are issued to the corresponding agricultural machinery equipment to control the agricultural machinery to complete automated operations; at the same time, operation effect data is fed back in real time, and operation plans are dynamically adjusted based on the feedback data to form a closed-loop optimization.

[0049] like Figure 3As shown, the agricultural machinery is equipped with a remote monitoring and management module 100 for camellia oleifera forest machinery. This module features positioning and photography capabilities, displaying the work path and workload. It automatically captures work photos via a camera and includes an IoT card, enabling it to upload work data to the cloud system via 4G / 5G networks. The remote monitoring and management module provides the following functions: real-time location monitoring of the machinery, collection of operating parameters and anomaly alarms; support for task scheduling, progress tracking, and results statistics; provision of equipment maintenance plan formulation, work order dispatch, parts management, and fault analysis functions; and core management modules including user account management, role and permission allocation, equipment file maintenance, and operation log auditing, with access controlled based on a role-based permission model. The camellia oleifera planting full-process mechanization management module is used for controlling and managing the machinery; the camellia oleifera forest machinery remote monitoring and management module is used for remote monitoring of the machinery.

[0050] The real-time location monitoring function uses the GPS / BeiDou positioning terminal on the agricultural machinery to obtain the location information of the machinery in real time, accurately displaying the distribution and operation trajectory of the machinery on an electronic map; it supports setting up electronic fences, and automatically triggers a boundary crossing alarm when the machinery exceeds the preset operation area. The operation parameter collection and abnormal alarm function collects operating parameters such as engine speed, operating load, fuel / electricity consumption, and hydraulic system pressure; preset parameter safety thresholds are set, and when parameters exceed the thresholds (such as excessively high engine speed or excessively low fuel level), the system automatically triggers an audible and visual alarm or pushes a message to remind managers to handle the situation promptly. The task scheduling, progress tracking, and results statistics function receives task instructions from the upper-level management system (such as the full mechanization management module for camellia oil planting) or creates task tasks independently and distributes them to designated agricultural machinery; it tracks the progress of agricultural machinery operations in real time, recording data such as the operating area and operating time; after the operation is completed, it automatically calculates the operation results (such as the amount of harvested fruit and fertilizer applied) and generates an operation ledger. The equipment maintenance planning function automatically generates preventative maintenance plans (such as regular maintenance and seasonal maintenance) based on the agricultural machinery's model, operating hours, and maintenance history data, specifying maintenance time, items, and personnel. The work order dispatch function automatically generates maintenance work orders based on maintenance plans or fault reports and dispatches them to the corresponding repair personnel; it supports work order progress tracking, recording the entire process of work order receipt, processing, and completion. The parts management function establishes an agricultural machinery parts database, recording information such as parts model, inventory quantity, storage location, and purchase time; when a maintenance work order requires parts replacement, it automatically links to the parts inventory, supporting parts issuance and receipt registration to avoid parts shortages or stockpiles. The fault analysis function summarizes historical agricultural machinery fault data, using data mining technology to analyze high-incidence fault types, high-incidence periods, and high-incidence agricultural machinery models, identifying root causes (such as improper operation or component aging), providing a basis for optimizing maintenance plans and selecting agricultural machinery. The user account management function provides account registration, cancellation, and information modification functions, supporting multi-user login and distinguishing different management entities (such as planting bases and agricultural machinery service providers). The role-based access control function classifies users into roles such as administrators, maintenance personnel, and dispatchers, assigning corresponding operational permissions to each role (e.g., administrators can configure maintenance plans, while maintenance personnel can only view and process work orders). The equipment file maintenance function establishes complete agricultural machinery equipment files, recording basic information (model, manufacturing date, purchase time), technical parameters, work records, maintenance records, fault records, and other full lifecycle data. The operation log auditing function automatically records all user operations (such as login, parameter modification, and command issuance), generating operation logs; it supports log querying and auditing, facilitating the tracing of operational responsibility and ensuring the safe operation of the system.

[0051] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements a smart management method for camellia oil forests.

[0052] The current crop growth data is input into a deep learning model to obtain the root depth of the current crop. The deep learning model is trained based on historical data, which includes crop growth data and the root depth of the crop corresponding to the crop growth data. The input parameter of the deep learning model is the crop growth data, and the output parameter is the root depth of the crop.

[0053] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart management method for camellia oleifera forests, characterized in that, Includes the following steps: S1, Obtain current soil parameters, wherein the soil parameters include at least soil moisture; S2, Obtain current crop growth data, and predict the root depth of the current crop based on the current crop growth data; S3, based on the current crop growth data and the preset soil standard humidity data, obtain the current soil humidity requirement; S4. Based on the current soil moisture requirement, current soil moisture, and current crop root depth, obtain a soil irrigation strategy, which includes the number and depth of irrigation output points.

2. The intelligent management method for camellia oleifera forests according to claim 1, characterized in that, Step S4 specifically includes: S41, Compare the current soil moisture with the required soil moisture; S42, If the current soil moisture is greater than the required soil moisture, then irrigation will not be carried out; S43, If the current soil moisture is less than the required soil moisture, calculate the moisture difference between the current soil moisture and the required soil moisture, and determine whether the moisture difference is less than a first preset threshold: If yes, proceed to step S44; otherwise, proceed to step S45. S44 then determines whether the current root depth of the crop is less than the depth of the lowest irrigation output point: If so, then based on the current root depth of the crop, control the irrigation output point with a depth greater than the current root depth and closest to the current root depth to discharge water; If not, then control the minimum irrigation output point to discharge water; S45, determine whether the humidity difference is less than the second preset threshold: If so, proceed to step S46; If not, then control all irrigation output points to discharge water; S46, Determine if the current root depth of the crop is less than the depth of the lowest irrigation output point: If so, then based on the current root depth of the crop, control one or more irrigation output points with a depth greater than the current root depth and closest to the current root depth, and one or more irrigation output points with a depth less than the current root depth and closest to the current root depth, to discharge water. If not, control the lowest one or more irrigation output points to discharge water.

3. The intelligent management method for camellia oleifera forests according to claim 1, characterized in that, The current crop growth data includes canopy coverage, leaf area index, and plant height.

4. The intelligent management method for camellia oleifera forests according to claim 1, characterized in that, Step S2 specifically includes: Based on historical data, predict the root depth corresponding to the current crop growth data; the historical data includes crop growth data and the root depth of the crop corresponding to the crop growth data.

5. The intelligent management method for camellia oleifera forests according to claim 1, characterized in that, The irrigation output points are arranged in multiple sets at intervals along the depth direction.

6. The intelligent management method for camellia oleifera forests according to claim 1, characterized in that, The soil parameters also include soil pH and soil electrical conductivity.

7. The intelligent management method for camellia oleifera forests according to claim 1, characterized in that, It also includes sending current soil parameters, current crop growth data, current crop root depth, and soil irrigation strategies to the intelligent management platform for camellia oleifera planting.

8. A smart management system for camellia oleifera forests, characterized in that, include: Soil parameter acquisition module, acquires current soil parameters, the soil parameters including at least soil moisture; The crop growth data acquisition and root depth prediction module acquires the current crop growth data and predicts the current crop root depth based on the current crop growth data. The current soil humidity requirement acquisition module obtains the current soil humidity requirement based on the current crop growth data and preset soil standard humidity data. The irrigation strategy generation module obtains a soil irrigation strategy based on the current soil moisture requirement, the current soil moisture, and the current root depth of the crop. The soil irrigation strategy includes the depth and number of irrigation output points.

9. A mechanized intelligent management and remote monitoring and management system for camellia oleifera planting, comprising: A fully mechanized management module for camellia oleifera planting, including the intelligent management system for camellia oleifera forests as described in claim 8; The Camellia oleifera forest agricultural machinery remote monitoring and management module is used for remote monitoring and data uploading of agricultural machinery.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent management method for camellia oleifera forests as described in any one of claims 1 to 7.

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