Digital planting system and method for ginseng fruits

Abstract

The invention discloses a digital planting system and method for ginseng fruits. According to the system, floating cutting seedling raising of virus-free seedlings is achieved through a seedling breeding module, a foam seedling raising disc and a nutrient solution pool are adopted, and environmental parameters are automatically adjusted through a controller; air quality, irrigation water quality and soil quality indexes are monitored in real time through the environment monitoring module; a yellow-blue plate, a black light lamp, natural enemy release and biopesticide application are integrated through the disease and pest prevention and control module for comprehensive prevention and control; precise drip irrigation and fertilization are realized through the water and fertilizer management module based on soil moisture content and plant requirements. The method comprises the steps of virus-free seedling raising, environment monitoring, disease and pest prevention and control, water and fertilizer management and the like, and whole-process visual management is achieved through a data platform. According to the method, digital management and control of ginseng fruit planting are achieved, and the seedling quality, yield and quality are effectively improved.

Description

Technical Field

[0001] This invention relates to agricultural planting management technology, and in particular to a digital system and method that integrates seedling propagation, environmental monitoring, pest and disease control and water and fertilizer management, which is applicable to the large-scale and standardized planting of ginseng fruit. Background Technology

[0002] As a fruit with high nutritional value, ginseng fruit has always faced many technical bottlenecks in its large-scale cultivation. Traditional cultivation methods generally rely on manual experience and lack scientific and systematic management methods, resulting in low production efficiency and unstable quality.

[0003] In the seedling propagation stage, conventional cutting propagation methods are significantly affected by seasonal changes, resulting in large fluctuations in survival rates. Temperature, humidity, and light during the seedling process rely heavily on manual judgment and adjustment, making it difficult to achieve precise control of environmental parameters. This easily leads to inconsistent seedling quality and low uniformity.

[0004] In terms of planting environment management, ginseng fruit has specific requirements for its growing environment, including air quality, irrigation water quality, and soil conditions. Although industry standards have set requirements for the quality of the production area environment, existing technologies lack effective real-time monitoring methods and dynamic control mechanisms, making it difficult to ensure that the planting environment continuously meets the standard requirements, thus affecting the yield and safety of the fruit.

[0005] Pest and disease control is another key challenge. While existing technologies employ integrated approaches such as agricultural, physical, and biological control, they still rely on regular manual inspections, resulting in slow response times and inconsistent control effectiveness. Furthermore, the precise control of chemical pesticide usage poses a risk of pesticide residue exceeding safety limits.

[0006] In terms of water and fertilizer management, traditional irrigation and fertilization methods are relatively extensive, easily leading to water and fertilizer waste and soil pollution. Although green planting principles emphasize precision fertilization, in practice, there is a lack of precise control methods based on soil moisture and plant needs.

[0007] Furthermore, existing digital solutions are mostly limited to improvements in single aspects, failing to form a comprehensive integrated system covering the entire process from seedling propagation and environmental monitoring to pest and disease control and water and fertilizer management. The isolated data at each stage hinders information sharing and intelligent decision-making, thus restricting the modernization of ginseng fruit cultivation.

[0008] Therefore, there is an urgent need in this field for a digital system and method that can integrate the entire planting process, and achieve precision, standardization and efficiency in ginseng fruit planting through modular integration and intelligent decision-making. Summary of the Invention

[0009] The purpose of this invention is to provide a digital planting system and method for ginseng fruit, which solves the problems of low planting efficiency and inaccurate management in the prior art through modular integration and data analysis.

[0010] This invention system includes a seedling propagation module, an environmental monitoring module, a pest and disease control module, and a water and fertilizer management module. The seedling propagation module employs floating cutting propagation technology, combined with foam seedling trays and nutrient solution tanks. Environmental sensors and controllers enable automated and precise control of the seedling environment. The controller, based on a preset optimal environmental parameter model, generates control commands through data comparison and analysis, driving the environmental control equipment to perform corresponding operations, forming a continuous monitoring-decision-execution closed-loop control system to ensure robust seedlings and effective virus removal. The environmental monitoring module collects real-time data on air quality, irrigation water quality, and soil quality, comparing it with standard limits to achieve dynamic early warning. The pest and disease control module integrates multiple control methods, monitoring pest infestations through sensors and automatically triggering control measures. The water and fertilizer management module implements precise water and fertilizer application through a drip irrigation system based on soil moisture and plant needs.

[0011] The method of this invention includes the following steps: First, virus-free seedlings are propagated by floating cuttings through a seedling propagation module, including stem tip virus removal treatment and virus detection; second, environmental data is collected and analyzed in real time through an environmental monitoring module to dynamically adjust planting conditions; then, integrated pest management is implemented through a pest and disease control module to reduce reliance on chemical pesticides; finally, precise irrigation and fertilization are carried out through a water and fertilizer management module to ensure balanced plant nutrition.

[0012] The advantage of this invention is that through digital integration, it realizes the fully automated management of ginseng fruit planting, improves seedling survival rate, environmental compliance and resource utilization efficiency, while reducing labor costs and pest and disease risks. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the overall architecture and method of the digital ginseng fruit cultivation system of the present invention. Figure 2 This is a flowchart of the floating cutting propagation process of the seedling propagation module of the present invention; Figure 3 This is a schematic diagram illustrating the integrated deployment of the environmental monitoring and pest control module of the present invention; Figure 4 This is a logical flowchart illustrating the visualization management and intelligent decision-making capabilities of the entire planting process within the data platform of this invention. Detailed Implementation

[0014] The following detailed description of the ginseng fruit digital planting system and method of the present invention is provided through specific embodiments.

[0015] Figure 1This invention demonstrates the overall architecture and method of the digital ginseng fruit cultivation system, specifically including the following technical features: System architecture components: 102 Seedling propagation modules: foam seedling trays (made of 120-200-cell polystyrene), nutrient solution tank (water depth 10-15 cm, bottom lined with a plastic film of 0.08 mm or more), environmental sensor array (temperature, humidity, and light intensity monitoring), and intelligent controller (automatically adjusting environmental parameters based on sensor data). 104 Environmental monitoring modules: air quality monitoring (total suspended particulate matter ≤ 0.30 mg / m³). 3 Sulfur dioxide ≤0.15 mg / m³ 3 Nitrogen oxides ≤0.10 mg / m³ 3 Fluoride ≤7 μg / m 3 106. Irrigation water quality monitoring (pH 5.5-8.5, hardness 25-75 mg / L, heavy metal content meets standard limits), soil quality monitoring (heavy metal content controlled according to pH levels). 107. Pest and disease control module: physical control (30-35 yellow and blue sticky traps per 667 square meters, yellow to blue trap ratio 4:1), biological control (release of natural enemy insects, application of biological pesticides), monitoring system (real-time collection of pest and disease occurrence data by insect sensors). 108. Water and fertilizer management module: soil moisture monitoring (real-time monitoring of soil moisture content), plant nutrient requirement analysis (determining nutrient requirements based on growth stage), drip irrigation system (achieving precise application of integrated water and fertilizer), dynamic fertilizer formula (adjusted according to soil pH and heavy metal content). 110. Data platform: central decision-making hub (integrating data from various modules for intelligent decision-making), visual interface (displaying full-process planting data), early warning system (dynamic early warning based on NY / T 391 standard). Methodological Process Characteristics: Data Flow Process: The seedling propagation module transmits seedling environmental data to the data platform; the environmental monitoring module transmits environmental compliance data; the pest and disease control module transmits pest and disease monitoring data; and the water and fertilizer management module transmits soil moisture data. Control Flow Process: The data platform sends control commands to each module to achieve automatic adjustment of environmental parameters, triggering of pest and disease control measures, and precise application of water and fertilizer. Feedback Loop Process: The water and fertilizer management module provides irrigation feedback to the environmental monitoring module, and the pest and disease control module provides control suggestions to the data platform, forming a closed-loop optimized management system.

[0016] Figure 2This is a flowchart of the floating cutting propagation process of the seedling propagation module of the present invention, wherein: the starting node indicates the start of the process and the input of virus-free seedlings or cuttings; the hardware configuration section shows the specific parameter configuration of the foam seedling tray and the nutrient solution tank; the environmental sensor cluster is responsible for multi-dimensional environmental data collection; the controller is the core processing unit, with an embedded environmental parameter comparison and decision algorithm; the algorithm logic realizes the automatic comparison and analysis of real-time data and the optimal parameter model; the decision-making section generates increase, maintain or decrease control instructions based on the comparison results; the execution section drives each environmental control device to perform corresponding operations; the feedback loop forms a continuous environmental monitoring and control closed loop; and the output node represents the successfully cultivated robust virus-free seedlings.

[0017] Figure 3 The intelligent response logic flow of the environmental monitoring module and the pest and disease control module is demonstrated, specifically including the following components: 301 Start Data Acquisition Node, initiating a dual-path parallel data acquisition process; 302 Multi-source environmental sensors, including air sensors, water quality sensors, and soil sensors, for real-time acquisition of environmental parameters in the planting area; 303 Pest and disease monitoring equipment, including pest sensors, image recognition equipment, and manual inspection input interfaces, for acquiring pest and disease occurrence signals; 304 Central Data Processing and Early Warning Center, serving as the core processing unit of the system, receiving and analyzing all monitoring data; 305 Environmental Compliance Judgment Module, with a built-in environmental compliance judgment algorithm; 306 Pest and Disease Risk Assessment Module, with a built-in pest and disease risk assessment algorithm; 307 Environmental Early Warning Generation Unit, generating an environmental early warning signal when the environmental compliance judgment module identifies abnormal environmental data; 308 Environmental Control Equipment Trigger Unit, receiving environmental early warning signals. Upon receiving the alarm signal, environmental control devices such as ventilation and irrigation systems are activated; the 309 Comprehensive Prevention and Control Plan Generation Unit generates a comprehensive prevention and control plan containing multiple control methods when the pest and disease risk assessment module determines the risk level to be high; the 310 Prevention and Control Unit Triggering Device precisely triggers the corresponding physical or biological control equipment according to the comprehensive prevention and control plan; the 311 Yellow and Blue Sticky Traps Deployment Unit deploys trapping devices according to a standard of 30-35 traps per 667 square meters, with a yellow to blue sticky trap ratio of 4:1; the 312 Black Light Lamp Control Unit controls the opening and closing of the black light insect traps; the 313 Natural Enemy Release Unit controls the release of natural enemy insects such as ladybugs and lacewings; the 314 Biological Pesticide Application Unit controls the precise application of biological pesticides such as abamectin, matrine, azadirachtin, and Bacillus thuringiensis; and the 315 Effect Feedback Data Collection Node collects the effect data after the implementation of each prevention and control measure, forming a closed-loop feedback to the data collection starting point.

[0018] Figure 4The logical flowchart of the data platform of this invention for realizing visualized management and intelligent decision-making throughout the planting process specifically includes: a data aggregation layer, used to receive real-time data transmitted from the seedling propagation module 102, environmental monitoring module 104, pest and disease control module 106, and water and fertilizer management module 108; and a data processing and intelligent analysis layer, including a central database 402 of the data platform, which connects to a visualization presentation path 404 and a model analysis and prediction path 410. The visualization presentation path generates chart trend curve dashboards 408 through a data visualization engine 406, and the model analysis and prediction path is based on patented technology. The algorithm model for constructing technical features includes a growth trend prediction model 420, a pest and disease early warning model 422, and a water and fertilizer optimization model 424; the intelligent decision-making and command output layer is used to convert the analysis results into executable commands, including providing environmental parameter adjustment suggestions to the seedling propagation module 426, sending control measures activation commands to the pest and disease control module 428, and outputting precise fertilization and irrigation formulas to the water and fertilizer management module 430; the human-computer interaction layer is equipped with an administrator terminal 432, which is used to receive early warning information 434 and decision suggestions 436, and can realize human-computer collaborative management through remote control commands 438 and scheme confirmation 440. Example

[0019] This embodiment was implemented at the ginseng fruit planting base in Yinai Village, Yunnan Province, and the system and method were applied for large-scale planting management.

[0020] The seedling propagation module uses polystyrene foam seedling trays with a hole diameter ranging from 120 to 200 holes to accommodate different seedling densities. The nutrient solution pool is set to a depth of 10 to 15 centimeters, and the bottom of the pool is lined with a 0.10-millimeter-thick plastic film to prevent leakage and contamination.

[0021] The control system of the seedling propagation module executes the following automated control process: First, the environmental sensor cluster collects multi-dimensional data of the seedling environment in real time, including temperature, humidity, light intensity, nutrient solution conductivity, and pH value; second, the controller compares and analyzes the collected data with a preset optimal environmental parameter model, which is set as follows: water temperature 18-25 degrees Celsius, conductivity 1.2-1.8 millisiemens per centimeter, pH 5.5-6.5, and relative humidity 60%-80%; then, based on the comparison results, corresponding control commands are generated. When the monitored value is below the lower limit of the optimal range, enhanced control is activated; when it is above the upper limit, weakened control is activated; and when it is within the optimal range, the current state is maintained; finally, the control commands drive the corresponding environmental control equipment to perform actions, including activating heating or cooling devices to adjust water temperature, controlling the nutrient solution supply valve to adjust conductivity and pH value, operating shading nets or supplemental lighting equipment to adjust light intensity, and activating humidification or ventilation systems to adjust humidity.

[0022] This module obtains virus-free seedlings through shoot tip detoxification treatment. The shoot tips are no larger than 0.2 mm and have 1-2 leaf primordia. After being heat-treated at 38 degrees Celsius for 10 days, virus testing is performed. Once qualified, the seedlings are transferred to the floating cutting propagation stage. The entire control process forms a continuous monitoring-decision-execution closed loop to ensure that the seedling environment parameters are always at their optimal state.

[0023] The environmental monitoring module deploys a multi-parameter sensor network to monitor environmental indicators in the planting area in real time. Air quality sensors monitor the daily average concentration of total suspended particulate matter to be no more than 0.30 mg / m³. 3 Sulfur dioxide concentration not exceeding 0.15 mg / m³ 3 Nitrogen oxides not exceeding 0.10 mg / m³ 3 Fluoride content not exceeding 7 μg / m 3 Irrigation water quality monitoring points are set up at the water source inlet, testing pH values ​​(5.5-8.5), hardness (25-75 mg / L), and heavy metal content (e.g., total mercury not exceeding 0.001 mg / L, total cadmium not exceeding 0.005 mg / L). Soil monitoring points are distributed in a grid pattern, testing heavy metal content and controlling it according to soil pH limits; for example, cadmium content should not exceed 0.30 mg / kg when soil pH ≤ 6.5, and not exceed 0.40 mg / kg when pH ≥ 7.5. All monitoring data are transmitted to the central processor in real time and compared with the NY / T 391 standard. When data exceeds the threshold, the system automatically issues an early warning and activates control equipment.

[0024] In the specific implementation process, the central data processing and early warning center (304) performs environmental quality monitoring through a built-in environmental compliance judgment algorithm. The specific implementation of the algorithm includes the following steps: First, the system acquires real-time monitoring data collected by multi-source environmental sensors, including air quality parameters, irrigation water quality parameters, and soil quality parameters; second, the real-time monitoring data is compared one by one with the threshold values ​​of each indicator in the built-in NY / T 391 standard library; then, the heavy metal content is classified according to the soil pH value, specifically, when the soil pH value is not greater than 6.5, the cadmium content limit is 0.30 mg / kg, and when the soil pH value is not less than 7.5, the cadmium content limit is 0.40 mg / kg; finally, based on the comparison results, abnormal environmental data is identified and corresponding flags are generated. When any monitoring indicator exceeds the corresponding threshold, the system determines that the environmental data is abnormal.

[0025] The pest and disease control module integrates multiple control measures. Yellow and blue sticky traps are deployed at a ratio of 4:1 (32 traps per 667 square meters), evenly distributed throughout the planting area. Black light insect traps are activated at night, and sugar-vinegar solution traps are prepared at a ratio of 3:4:1:2 (sugar, vinegar, alcohol, and water), with 5 traps per 667 square meters. The biological control section regularly releases natural enemy insects such as ladybugs and lacewings, and rotates the application of biological pesticides including abamectin, matrine, and Bacillus thuringiensis. Insect sensors collect pest and disease occurrence data in real time; when insect population density exceeds the standard, the system automatically increases the deployment of traps or adjusts the frequency of biological pesticide spraying.

[0026] Meanwhile, the central data processing and early warning center (304) performs pest and disease risk assessment through a built-in pest and disease risk assessment algorithm. The specific implementation of the algorithm includes the following steps: First, the system acquires pest density data collected by pest and disease monitoring equipment, pest and disease identification confidence data collected by image recognition equipment, and manual inspection input data; Second, combined with the pest and disease occurrence rate data of the same period in the historical pest and disease occurrence model, a weighted calculation method is used to obtain the pest and disease occurrence risk score, where the pest sensor data has a weight of 0.4, the image recognition data has a weight of 0.35, and the historical data has a weight of 0.25; Then, the calculated risk score is compared with the preset risk threshold of 0.7; Finally, the risk level is determined based on the comparison result. When the risk score is greater than the risk threshold, it is judged as a high risk level; otherwise, it is judged as a low risk level.

[0027] When the system determines that the risk level of pests and diseases is high, the integrated prevention and control plan generation unit (309) generates an integrated prevention and control plan containing multiple control methods according to the preset strategy. Specifically, this includes: deploying yellow and blue sticky traps at a standard of 30-35 traps per 667 square meters with a yellow-to-blue trap ratio of 4:1; controlling the activation of black light traps based on nighttime pest monitoring data; releasing natural enemy insects such as ladybugs and lacewings; and rotating the application of biological pesticides such as abamectin, matrine, azadirachtin, and Bacillus thuringiensis. After each prevention and control measure is implemented, the system collects the prevention and control effect data through the effect feedback data collection node (315), forming a complete "monitoring-analysis-decision-execution" intelligent closed loop to achieve continuous optimization management of the planting environment.

[0028] The water and fertilizer management module implements precision irrigation based on soil sensor data. Soil moisture sensors monitor soil water content and nutrient status, and the drip irrigation system dynamically adjusts the water and fertilizer formula according to the plant's growth stage. For example, during the fruit enlargement period, the fertilizer formula is prepared according to a nitrogen, phosphorus, and potassium ratio of 15:15:15, while simultaneously controlling lead content to no more than 50 mg / kg and chromium content to no more than 120 mg / kg based on soil heavy metal testing results. Irrigation water volume is automatically adjusted based on real-time evaporation and soil moisture to ensure maximum water and fertilizer utilization efficiency.

[0029] The data platform integrates real-time data transmitted from various modules, displaying seedling growth status, environmental parameters, pest and disease occurrence, and water and fertilizer application records through a visual interface. The platform incorporates intelligent decision-making algorithms that generate optimization suggestions based on historical data and model predictions, such as automatically adjusting seedling environmental parameters or issuing warnings for peak pest and disease periods. Users can view the entire process data in real time and remotely control the operation of each module via terminal devices.

[0030] The data platform integrates seedling growth status, environmental parameters, pest and disease occurrence, and water and fertilizer application records transmitted from various modules through a central database 402. During data processing and intelligent analysis, based on the technical features described in claims 1-10, the platform transforms the environmental regulation logic of seedling propagation, the standard comparison mechanism for environmental monitoring, the integrated pest management strategy, and the precise application principles of water and fertilizer management into corresponding algorithm model construction processes.

[0031] Specifically, the platform constructs a growth trend prediction model 420 based on the technical characteristics of the controller in the seedling propagation module, which automatically adjusts the water temperature, nutrient solution EC value and pH value, light intensity, temperature and humidity in the seedling breeding module; it establishes a pest and disease early warning model 422 based on the monitoring indicators and limit requirements of air quality, irrigation water quality and soil quality in the environmental monitoring module, combined with the technical characteristics of the number and proportion of yellow and blue sticky traps and the types of biological pesticides used in the pest and disease control module; and it forms a water and fertilizer optimization model 424 based on the technical characteristics of dynamically adjusting the fertilizer formula according to the soil pH value and heavy metal content in the water and fertilizer management module.

[0032] The platform transforms collected data into charts, trend curves, and dashboards (408) through visualization path (404) for users to manage visually. Simultaneously, the intelligent decision-making results generated through model analysis and prediction path (410) are fed back to various execution modules via the intelligent decision-making and instruction output layer, enabling automatic adjustment of the planting environment, precise triggering of pest and disease control measures, and optimal configuration of water and fertilizer formulas.

[0033] Administrators can receive early warning information 434 and decision suggestions 436 from the platform through terminal device 432, and can adjust system operating parameters through remote control command 438 based on actual planting conditions, or authorize the system to execute recommended plans through plan confirmation 440, thereby realizing intelligent decision-making and precise management of the entire planting process.

[0034] This embodiment realizes digital management of ginseng fruit planting through the above system and method, increasing the seedling survival rate to over 95%, reducing the incidence of pests and diseases by 40%, increasing water and fertilizer utilization by 25%, and ensuring that the fruit quality meets the green food standards.

Claims

1. A ginseng fruit digital planting system, characterized in that, The application relates to a ginseng fruit seedling breeding system, which comprises the following parts: a seedling breeding module for realizing floating cutting seedling breeding of ginseng fruit virus-free seedlings, a foam seedling tray, a nutrient solution pool, an environmental sensor and a controller, the controller automatically adjusts the water temperature of the seedling pool, the EC value and the pH value of the nutrient solution, the indoor light intensity, the temperature and the humidity according to the sensor data; an environmental monitoring module for monitoring the air quality, the irrigation water quality and the soil quality of a planting area in real time, the air quality monitoring comprises total suspended particulates, sulfur dioxide, nitrogen oxides and fluorides, the irrigation water quality monitoring comprises the pH value, the hardness and the heavy metal content, and the soil quality monitoring comprises the heavy metal content and the pH value; a disease and pest control module which integrates agricultural control, physical control and biological control means, comprises yellow and blue board trapping, black light lamp trapping, sugar and vinegar liquid trapping, natural enemy releasing and biological pesticide application, and monitors the occurrence of diseases and pests through sensors; and a water and fertilizer management module which realizes water and fertilizer integrated precision application through a drip irrigation system based on soil moisture content and plant nutrient demand. In the seedling breeding module, the foam seedling tray is a 120-200-hole polystyrene tray, and the nutrient solution pool has a water depth of 10-15 cm and is paved with a plastic film with a thickness of more than 0.08 mm at the bottom. In the disease and pest control module, the number of yellow and blue boards arranged is 30-35 per 667 square meters, and the ratio of yellow boards to blue boards is 4:

1. In the water and fertilizer management module, the fertilization formula is dynamically adjusted according to the soil pH value and the heavy metal content, wherein the soil cadmium content is not higher than 0.30 mg / kg when the pH value is less than or equal to 6.5, and the soil cadmium content is not higher than 0.40 mg / kg when the pH value is greater than or equal to 7.

5. The application further comprises the following steps:

2. The ginseng digital plantation system according to claim 1, wherein, floating cutting seedling breeding of virus-free seedlings is carried out through the seedling breeding module, which comprises stem tip virus-free treatment, virus detection and floating pool seedling breeding, and the environmental parameters are automatically adjusted by the controller during the seedling breeding process; 3. The ginseng digital plantation system according to claim 1, wherein, The air quality monitoring index includes that the daily average total suspended particulate is not more than 0.30 mg / m 3 , sulfur dioxide is not more than 0.15 mg / m 3 , nitrogen oxide is not more than 0.10 mg / m 3 , fluoride is not more than 7 μg / m 3 .

4. The ginseng digital plantation system according to claim 1, wherein, air, water and soil data are collected in real time through the environmental monitoring module, and are compared with the NY / T 391 standard for dynamic early warning and regulation; 5. The ginseng digital plantation system according to claim 1, wherein, comprehensive prevention and control is implemented through the disease and pest control module, which comprises setting yellow and blue boards, black light lamps, sugar and vinegar liquid traps, releasing natural enemy insects and applying biological pesticides; 6. A method for digital cultivation of ginseng fruits, characterized by, precision drip irrigation and fertilization are implemented according to the monitoring data through the water and fertilizer management module, so that the plant nutrition is balanced and the soil environment is stable. In the seedling breeding step, the stem tip virus-free treatment comprises stem tip culture and heat treatment, the stem tip size is less than or equal to 0.2 mm with 1-2 leaf primordia, and the heat treatment temperature is 38 DEG C for 10 days. In the disease and pest control step, the biological pesticides comprise one or more of abamectin, matrine, azadirachtin and bacillus thuringiensis. In the water and fertilizer management step, the fertilization formula is dynamically adjusted according to the soil pH value and the heavy metal content, wherein the soil lead content is not more than 50 mg / kg, and the soil chromium content is not more than 120 mg / kg. The application further comprises integrating seedling, environmental, disease and pest and water and fertilizer data through a data platform to realize visual management and intelligent decision-making in the whole planting process.

7. The digital cultivation method for ginseng fruit as described in claim 6, characterized in that, ​ 8. The ginseng digital plantation method according to claim 6, wherein, ​ 9. The ginseng digital plantation method according to claim 6, wherein, ​ 10. The ginseng digital plantation method according to claim 6, wherein, ​

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