Tricholoma matsutake cultivation intelligent monitoring method and system of agricultural intelligent technology shelter

Abstract

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent monitoring method and system for tricholoma matsutake cultivation of an agricultural intelligent technology shelter. The method comprises the following steps: monitoring temperature data, humidity data, light environment data and gas concentration data corresponding to a tricholoma matsutake culture growth stage in real time in an agricultural intelligent science and technology shelter, and transmitting the data to a data processing center by using a wireless communication technology; carrying out growth stage classification and optimal environment screening on temperature data, humidity data, illumination environment data and gas concentration data corresponding to the tricholoma matsutake culture growth stage, and generating optimal tricholoma matsutake growth environment conditions corresponding to a hypha growth stage and a fruiting stage; and based on the optimal tricholoma matsutake growth environment conditions corresponding to the mycelium growth period and the fruiting period, performing tricholoma matsutake growth intelligent monitoring analysis in the corresponding growth stages in the tricholoma matsutake culture growth stages, and generating a corresponding tricholoma matsutake culture growth environment intelligent regulation and control decision. The method can provide suitable growth environment conditions for tricholoma matsutake.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and particularly to a method and system for intelligent monitoring of matsutake mushroom cultivation in an agricultural intelligent technology cabin. Background Art

[0002] With the rapid development of modern agricultural technology, agricultural intelligent and digital management has gradually become an important means to improve production efficiency, reduce costs, and enhance product quality. Especially in the field of edible mushroom cultivation, by means of advanced Internet of Things (IoT) technology, data collection and transmission, artificial intelligence (AI) analysis, etc., achieving precise control and real-time monitoring of environmental factors has become the trend of modern aquaculture development. However, in the traditional process of matsutake mushroom cultivation, there are many problems that are difficult to meet the growth requirements of matsutake mushrooms and achieve efficient cultivation. For example, during the mycelium growth period, traditional temperature control means cannot stably control the temperature within the narrow range of 16 - 25°C. Excessive temperature fluctuations are likely to affect the growth rate and vitality of the mycelium. During the fruiting period, if the temperature cannot be precisely controlled at 19 - 23°C, it will lead to a decrease in the fruiting rate and a decline in the quality of the mushroom bodies. For humidity management, the traditional manual spraying method is difficult to accurately maintain the air humidity at 80% - 90% and the soil humidity at 60% - 70%. Improper humidity is likely to cause diseases and pests, affecting the healthy growth of matsutake mushrooms. Moreover, traditional cultivation lacks scientific regulation of light and ventilation, and cannot simulate the scattered light environment required for the natural growth of matsutake mushrooms. Unreasonable light duration and intensity are not conducive to the development of fruiting bodies. Poor ventilation is likely to cause carbon dioxide accumulation, and the phenomenon of stuffy greenhouses occurs from time to time, thus seriously affecting the growth quality of matsutake mushrooms. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for intelligent monitoring of matsutake mushroom cultivation in an agricultural intelligent technology cabin to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for intelligent monitoring of matsutake mushroom cultivation in an agricultural intelligent technology cabin includes the following steps:

[0005] Step S1: Real-time monitor the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stage of matsutake mushroom cultivation in the agricultural intelligent technology cabin, and transmit them to the data processing center step by step using wireless communication technology;

[0006] Step S2: Use the data processing center to classify the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stages of matsutake cultivation to output the matsutake growth environment parameter conditions for each combination in the mycelium growth period and the fruiting period; obtain the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, and perform optimal environment screening on the corresponding matsutake growth environment parameter conditions based on the growth status of matsutake corresponding to the mycelium growth period and the fruiting period to generate the optimal growth environment conditions of matsutake corresponding to the mycelium growth period and the fruiting period;

[0007] Step S3: Based on the optimal growth environment conditions of matsutake corresponding to the mycelium growth period and the fruiting period, conduct intelligent monitoring and analysis of the growth stages within the matsutake cultivation growth stage to generate corresponding intelligent regulation decisions for the matsutake cultivation growth environment.

[0008] Further, Step S1 includes the following steps:

[0009] Step S11: Deploy temperature sensors inside the agricultural intelligent technology cabin to monitor the temperature data corresponding to the matsutake cultivation growth stage in real time;

[0010] Step S12: Deploy humidity sensors inside the agricultural intelligent technology cabin to monitor the air humidity and soil humidity corresponding to the matsutake cultivation growth stage in real time to obtain humidity data;

[0011] Step S13: Deploy light sensors inside the agricultural intelligent technology cabin to monitor the light intensity and duration corresponding to the matsutake cultivation growth stage in real time to obtain light environment data;

[0012] Step S14: Deploy gas sensors inside the agricultural intelligent technology cabin to monitor the oxygen and carbon dioxide concentrations corresponding to the matsutake cultivation growth stage in real time to obtain gas concentration data;

[0013] Step S15: Denoise and filter the temperature data, humidity data, light environment data, and gas concentration data corresponding to the matsutake cultivation growth stage to remove the corresponding noise interference in each data, correct the deviations and errors corresponding to each data, so as to obtain the preprocessed temperature data, humidity data, light environment data, and gas concentration data within the matsutake cultivation growth stage; use wireless communication technology to transmit the preprocessed temperature data, humidity data, light environment data, and gas concentration data to the data processing center through excellent steps.

[0014] Further, the matsutake cultivation growth stage described in Step S11 includes the mycelium growth period and the fruiting period corresponding to matsutake.

[0015] Further, the excellent step transmission described in step S15 specifically means setting corresponding high priorities for the temperature data and humidity data corresponding to the growth stage of matsutake mushroom cultivation to transmit them to the data processing center quickly and stably with priority, while for the non-urgent data such as the light environment data and gas concentration data corresponding to the growth stage of matsutake mushroom cultivation, they are transmitted to the data processing center asynchronously when the network is idle.

[0016] Further, step S2 includes the following steps:

[0017] Step S21: Using the data processing center to classify the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stage of matsutake mushroom cultivation to output the matsutake mushroom growth environment parameter conditions for each combination corresponding to the mycelium growth period and the fruiting period;

[0018] Step S22: Obtaining the growth rate of matsutake mushroom mycelium corresponding to the mycelium growth period;

[0019] Step S23: Obtaining the fruiting rate of matsutake mushroom corresponding to the fruiting period;

[0020] Step S24: Based on the growth rate of matsutake mushroom mycelium corresponding to the mycelium growth period and the fruiting rate of matsutake mushroom corresponding to the fruiting period, and using regression analysis to screen the best environment for the matsutake mushroom growth environment parameter conditions under the corresponding combination, to establish a regression equation between the corresponding matsutake mushroom growth environment parameter conditions, the growth rate of matsutake mushroom mycelium, and the fruiting rate of matsutake mushroom, and predict the best environment conditions corresponding to the mycelium growth period and the fruiting period according to the regression equation between the matsutake mushroom growth environment parameter conditions, the growth rate of matsutake mushroom mycelium, and the fruiting rate of matsutake mushroom, so as to generate the best growth environment conditions of matsutake mushroom corresponding to the mycelium growth period and the fruiting period, where for the mycelium growth period, the temperature is maintained at 16 - 25 °C, the air humidity is kept at 80% - 90%, the soil humidity is 60% - 70%, the sunshine duration of warm light LED simulated scattered light is 10 - 12 hours, the oxygen content is 21% - 23%, and the carbon dioxide should be less than 0.1%, while for the fruiting period, the temperature is maintained at 19 - 23 °C, the air humidity is kept at 80% - 90%, the soil humidity is 60% - 70%, the sunshine duration of warm light LED simulated scattered light is 10 - 12 hours, the oxygen content is 22% - 25%, and the carbon dioxide should be less than 0.05%.

[0021] Further, step S22 includes the following steps:

[0022] Obtaining the growth image of matsutake mushroom mycelium corresponding to the mycelium growth period;

[0023] Extracting the mycelium microscopic parameters of the matsutake mushroom mycelium growth image corresponding to the mycelium growth period at corresponding time intervals to extract the length, diameter, number of branches, and mycelium area of the matsutake mushroom mycelium within a period of time, and obtaining the mycelium growth microscopic structure parameter set corresponding to the mycelium growth period;

[0024] Calculate the macroscopic growth indexes according to the set of microscopic structure parameters of mycelium growth corresponding to the mycelium growth period, so as to obtain the mycelium length growth rate, the increase rate of the number of branches, and the mycelium coverage area growth rate corresponding to the mycelium growth period;

[0025] Quantify the growth rate according to the mycelium length growth rate, the increase rate of the number of branches, and the mycelium coverage area growth rate corresponding to the mycelium growth period, so as to obtain the growth rate of Tricholoma matsutake mycelium corresponding to the mycelium growth period.

[0026] Further, step S23 includes the following steps:

[0027] Step S231: Monitor the ecological factors of the Tricholoma matsutake growth environment corresponding to the fruiting period to monitor the corresponding temperature, humidity, light, soil pH, and microbial community structure, so as to obtain the set of Tricholoma matsutake growth ecological factors corresponding to the fruiting period;

[0028] Step S232: Analyze the characteristics of the ecological factors in the set of Tricholoma matsutake growth ecological factors corresponding to the fruiting period to obtain the mean value, extreme value, and fluctuation range corresponding to each ecological factor in the fruiting period;

[0029] Step S233: Based on the mean value, extreme value, and fluctuation range corresponding to each ecological factor in the fruiting period, analyze the ecological space distribution differences between the ecological factors in the corresponding Tricholoma matsutake growth ecological factor set, so as to obtain the spatial distribution differences between the Tricholoma matsutake ecological factors in the fruiting period;

[0030] Step S234: Divide the corresponding Tricholoma matsutake growth environment in the fruiting period into the corresponding sunny and humid micro-regions and shady and dry micro-regions according to the spatial distribution differences between the Tricholoma matsutake ecological factors in the fruiting period, and monitor the content of metabolites, the number of fruiting positions, and the enzyme activity intensity corresponding to the Tricholoma matsutake mycelium in different growth zones;

[0031] Step S235: Calculate the fruiting rate prediction according to the content of metabolites, the number of fruiting positions, and the enzyme activity intensity corresponding to the Tricholoma matsutake mycelium in different growth zones, so as to obtain the Tricholoma matsutake fruiting rate corresponding to the fruiting period.

[0032] Further, step S3 includes the following steps:

[0033] Step S31: Based on the temperature conditions in the optimal growth environment of Tricholoma matsutake corresponding to the mycelium growth period and the fruiting period, analyze the growth temperature regulation in the corresponding growth stage during the Tricholoma matsutake breeding growth stage, so as to generate the corresponding Tricholoma matsutake breeding growth temperature regulation decision;

[0034] Step S32: Based on the humidity, light, and gas conditions within the optimal growth environment for Tricholoma matsutake during the mycelium growth stage and the fruiting stage, conduct an analysis of the regulation of other growth parameters during the corresponding growth stages in the Tricholoma matsutake cultivation growth stage. Taking the optimal growth environment conditions for Tricholoma matsutake during the mycelium growth stage and the fruiting stage as the goal, adjust the humidity spraying time, light intensity and irradiation duration, and the ventilation frequency of the gas to generate corresponding decisions for the regulation of other growth of Tricholoma matsutake cultivation;

[0035] Step S33: Combine the corresponding temperature regulation decisions for Tricholoma matsutake cultivation growth and the decisions for the regulation of other growth of Tricholoma matsutake cultivation to generate corresponding intelligent regulation decisions for the growth environment of Tricholoma matsutake cultivation.

[0036] Further, the growth temperature regulation analysis described in step S31 includes the following steps:

[0037] Obtain the corresponding suitable temperature range for growth at the growth stage through the optimal growth environment conditions for Tricholoma matsutake during the mycelium growth stage and the fruiting stage;

[0038] Calculate the growth temperature deviation of the real-time temperature monitored at the corresponding growth stage based on the corresponding suitable temperature range for growth at the growth stage to obtain the deviation between the temperature sensor monitoring and the suitable temperature range for growth at the corresponding growth stage;

[0039] Calculate the corresponding cooling / heating power according to the deviation between the temperature sensor monitoring and the suitable temperature range for growth at the corresponding growth stage, and adjust the corresponding growth temperature in the agricultural intelligent technology cabin to the corresponding suitable temperature range for growth at the growth stage according to the corresponding cooling / heating power to generate the corresponding temperature regulation decision for Tricholoma matsutake cultivation growth.

[0040] Further, the present invention also provides a smart monitoring system for Tricholoma matsutake cultivation in an agricultural intelligent technology cabin, which is used to execute the smart monitoring method for Tricholoma matsutake cultivation in the agricultural intelligent technology cabin as described above. The smart monitoring system for Tricholoma matsutake cultivation in the agricultural intelligent technology cabin includes:

[0041] A Tricholoma matsutake growth environment monitoring module, which is used to monitor the temperature data, humidity data, light environment data, and gas concentration data corresponding to the Tricholoma matsutake cultivation growth stage in real time in the agricultural intelligent technology cabin, and transmit them to the data processing center in excellent step using wireless communication technology;

[0042] The optimal growth environment screening module is used to classify the growth stages by using the data processing center for the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stages of matsutake cultivation, so as to output the matsutake growth environment parameter conditions corresponding to each combination in the mycelium growth period and the fruiting period; obtain the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, and conduct optimal environment screening on the corresponding matsutake growth environment parameter conditions based on the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, so as to generate the optimal growth environment conditions of matsutake corresponding to the mycelium growth period and the fruiting period.

[0043] The intelligent matsutake growth monitoring module is used to conduct intelligent matsutake growth monitoring and analysis in the corresponding growth stages during the matsutake cultivation growth stage based on the optimal matsutake growth environment conditions corresponding to the mycelium growth period and the fruiting period, so as to generate the intelligent regulation decision for the matsutake cultivation growth environment.

[0044] Advantages of the present invention:

[0045] 1. The intelligent monitoring method for Tricholoma matsutake cultivation in the agricultural intelligent technology cabin proposed by the present invention, compared with the prior art, the beneficial effects of the present application are as follows. During the cultivation of Tricholoma matsutake, environmental factors such as temperature, humidity, light intensity, gas concentration, etc. play a crucial role in the growth of Tricholoma matsutake. By real-time monitoring these environmental parameters in the agricultural intelligent technology cabin, accurate data at each stage of the growth process of Tricholoma matsutake can be obtained, providing an accurate basis for subsequent analysis and regulation. The changes in temperature, humidity, light, and gas concentration directly affect the growth rate and quality of Tricholoma matsutake. Therefore, precise monitoring of these data is the prerequisite for ensuring the healthy growth of Tricholoma matsutake. In addition, through the application of wireless communication technology, these real-time data can be continuously transmitted to the data processing center. The advantage of wireless transmission is that it can ensure the continuity and timeliness of data transmission, ensuring that environmental data can be obtained in real time at every critical moment during the cultivation of Tricholoma matsutake, and then effectively regulating the environment. This data collection and transmission method greatly improves the intelligent level of agricultural cultivation, provides a scientific basis for the management of the growth stage of Tricholoma matsutake, which can not only reduce manual intervention but also achieve remote monitoring and regulation, improving the efficiency and accuracy of management. Secondly, by analyzing the real-time data transmitted from the agricultural intelligent technology cabin, the environmental conditions required by Tricholoma matsutake at different growth stages can be identified. These environmental conditions not only include temperature, humidity, light intensity, gas concentration, etc., but also cover various factors affecting the health of Tricholoma matsutake during the growth process. Through data classification, the differences in environmental requirements between the mycelium growth period and the fruiting period can be accurately understood, providing targeted references for subsequent environmental regulation. Specifically, the data processing center can output the optimal environmental combination corresponding to each stage by analyzing the environmental data at each growth stage. For example, during the mycelium growth period, the temperature required for the growth of Tricholoma matsutake is maintained at 16-25 °C, the air humidity is kept at 80%-90%, and the soil humidity is controlled at 60%-70%. These data can help cultivators accurately control the environmental conditions, thus promoting the growth and development of the mycelium. Similarly, for the fruiting period, the adjustment of temperature, humidity, oxygen content, etc. can also help Tricholoma matsutake smoothly enter the mature stage, ensuring the quality and yield of Tricholoma matsutake, and can also achieve scientific regulation of light and ventilation to simulate the scattered light environment required for the natural growth of Tricholoma matsutake. Through this step of analysis, the data processing center can not only provide the optimal environmental conditions for Tricholoma matsutake at different growth stages, but also screen environmental parameters based on the growth status of Tricholoma matsutake, further optimizing the cultivation environment of Tricholoma matsutake. This precise and personalized environmental control greatly improves the cultivation efficiency and growth quality of Tricholoma matsutake, reducing unnecessary resource waste.Then, based on the optimal environmental condition data obtained in the previous two steps, intelligent monitoring can be carried out for each growth stage of matsutake cultivation. The growth process of matsutake involves multiple stages, including mycelium growth, fruiting period, etc. Each stage has different requirements for the environment. Therefore, in this step, the environment of different growth stages will be monitored in real time and dynamically regulated to ensure that the environment of each stage can be maintained within the optimal range. Through the intelligent monitoring system, farmers can adjust environmental conditions such as temperature, humidity, and gas concentration according to real-time feedback to ensure the stability and consistency of the matsutake growth environment. This intelligent regulation can not only reduce the errors of manual operation but also make automatic adjustments according to real-time data to ensure that matsutake can grow in the most suitable environment. For example, when the system detects that the environmental conditions of a certain growth stage do not meet the requirements, it will immediately issue an alarm and give suggestions for optimized regulation to help farmers respond to changes in time and avoid adverse effects on matsutake growth. In this way, the changes in the matsutake growth environment can be more accurately grasped to ensure the healthy growth and high yield of matsutake.

[0046] 2. The intelligent monitoring system for matsutake cultivation in the agricultural intelligent technology shelter proposed by the present invention is generally composed of a matsutake growth environment monitoring module, an optimal growth environment screening module, and a matsutake growth intelligent monitoring module, and can implement the intelligent monitoring method for matsutake cultivation in any agricultural intelligent technology shelter described in the present invention. It is used to realize the intelligent monitoring method for matsutake cultivation in the agricultural intelligent technology shelter through the operation between computer programs running on each module. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient intelligent monitoring process for matsutake cultivation in the agricultural intelligent technology shelter, thereby simplifying the operation process of the intelligent monitoring system for matsutake cultivation in the agricultural intelligent technology shelter. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0048] Figure 1 It is a schematic flowchart of the steps of the intelligent monitoring method for matsutake cultivation in the agricultural intelligent technology shelter of the present invention;

[0049] Figure 2 For Figure 1 the detailed flowchart of step S1 in

[0050] Figure 3 For Figure 1 the detailed flowchart of step S2 in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0052] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0053] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0054] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a method for intelligent monitoring of matsutake cultivation in an agricultural intelligent technology cabin, and the method includes the following steps:

[0055] Step S1: Real-time monitor the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stage of matsutake cultivation in the agricultural intelligent technology cabin, and transmit them to the data processing center in advance using wireless communication technology;

[0056] Step S2: Use the data processing center to classify the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stage of matsutake cultivation to output the matsutake growth environment parameter conditions corresponding to each combination in the mycelium growth period and the fruiting period; obtain the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, and perform the best environment screening on the corresponding matsutake growth environment parameter conditions based on the growth status of matsutake corresponding to the mycelium growth period and the fruiting period to generate the best matsutake growth environment conditions corresponding to the mycelium growth period and the fruiting period;

[0057] Step S3: Based on the optimal environmental conditions for the growth of Tricholoma matsutake during the mycelium growth stage and the fruiting stage, conduct intelligent monitoring and analysis of Tricholoma matsutake during the corresponding growth stages in the Tricholoma matsutake cultivation growth stage to generate intelligent regulation decisions for the Tricholoma matsutake cultivation growth environment.

[0058] In the embodiment of the present invention, please refer to Figure 1 As shown in the figure, it is a schematic flowchart of the steps of the intelligent monitoring method for Tricholoma matsutake cultivation in the agricultural intelligent technology cabin of the present invention. In this example, the intelligent monitoring method for Tricholoma matsutake cultivation in the agricultural intelligent technology cabin includes the following steps:

[0059] Step S1: Real-time monitor the temperature data, humidity data, light environment data, and gas concentration data corresponding to the Tricholoma matsutake cultivation growth stage in the agricultural intelligent technology cabin, and use wireless communication technology to transmit them to the data processing center in the first step;

[0060] In the embodiment of the present invention, in the agricultural intelligent technology cabin, high-precision sensors are selected to monitor various types of data in real time. The temperature sensor uses a thermistor type and is evenly distributed in the cultivation area. The temperature data is collected every 10 minutes and connected to the data acquisition module through shielded twisted pair to convert the temperature data into an electrical signal for temporary storage. The humidity sensor includes a capacitive air humidity sensor and a soil humidity sensor based on the time domain reflectometry (TDR) principle. The air humidity sensor is installed in a well-ventilated area close to the growth of Tricholoma matsutake, and the soil humidity sensor is inserted into the appropriate depth of the soil at a density of one per square meter. The data is also collected and temporarily stored every 10 minutes. The light sensor uses a photoresistor type and is installed at the top of the cabin where there is no light obstruction. Combined with a timer and a microcontroller, the light intensity and duration data are recorded. The gas sensor uses the electrochemical principle to monitor the oxygen and carbon dioxide concentrations and is installed about 30 centimeters away from the Tricholoma matsutake growth area. The data is collected regularly. After the data acquisition module aggregates these temperature, humidity, light environment, and gas concentration data, using a 4G wireless communication module, according to the MQTT communication protocol, the temperature and humidity data are set as high priority and are preferentially transmitted to the data processing center quickly and stably; the light environment and gas concentration data are transmitted asynchronously when the network is idle. For example, at a certain moment, the collected temperature is 20°C, the air humidity is 75% RH, the soil humidity is 25%, the light intensity is 2000 lux, the light duration is 8 hours, the oxygen concentration is 21%, and the carbon dioxide concentration is 400 ppm. According to the priority setting, the temperature and humidity data are transmitted first, and the light and gas concentration data are then transmitted asynchronously to the data processing center.

[0061] Step S2: The data processing center classifies the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stages of matsutake cultivation to output the matsutake growth environment parameter conditions for each combination during the mycelium growth period and the fruiting period; obtain the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, and perform optimal environment screening on the corresponding matsutake growth environment parameter conditions based on the growth status of matsutake corresponding to the mycelium growth period and the fruiting period to generate the optimal growth environment conditions for matsutake corresponding to the mycelium growth period and the fruiting period;

[0062] In the embodiment of the present invention, in the data processing center, the received data is processed using the data analysis library pandas of Python. First, according to the time sequence and the record of the matsutake growth stage, the data is divided into the mycelium growth period and the fruiting period. For example, by consulting the cultivation log, it is determined that the 1st - 3rd months after inoculation are the mycelium growth period, and the 4th - 6th months are the fruiting period. The Boolean indexing function of pandas is used to screen out various environmental data corresponding to the corresponding stages and organize them into a data table of matsutake growth environment parameter conditions for each combination. For the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, it is obtained from the cultivation record database. For example, during the mycelium growth period, the microscopic parameters such as the mycelium length, diameter, and number of branches are measured regularly to calculate the mycelium growth rate; during the fruiting period, the number of fruiting bodies and the total number of inoculation points are counted to calculate the fruiting rate. The linear regression model in the statistical analysis library scikit - learn of Python is used, with the growth environment parameter conditions as independent variables and the growth rate and fruiting rate as dependent variables for fitting. For example, during the mycelium growth period, with temperature, air humidity, soil humidity, etc. as independent variables and the mycelium growth rate as the dependent variable, the influence degree of each parameter on the growth rate is determined through model calculation. According to the given range of optimal environment conditions (such as the mycelium growth period temperature is 16 - 25°C, etc.), the boundary values and intermediate values are substituted into the regression equation for calculation to find the environmental parameter combination that can make the growth rate and fruiting rate reach relatively high values. Finally, a report on the optimal growth environment conditions for matsutake corresponding to the mycelium growth period and the fruiting period is generated and stored in the data processing center.

[0063] Step S3: Based on the optimal growth environment conditions for matsutake corresponding to the mycelium growth period and the fruiting period, perform intelligent monitoring and analysis on the growth stage corresponding to the matsutake cultivation growth stage to generate the intelligent regulation decision for the corresponding matsutake cultivation growth environment.

[0064] In an embodiment of the present invention, by writing a program in Python in a data processing center, based on the optimal environmental conditions for the growth of Tricholoma matsutake during the mycelium growth period and the fruiting period, intelligent monitoring and analysis of the growth of Tricholoma matsutake are carried out during the corresponding growth stages in the Tricholoma matsutake cultivation and growth stage. Data is read from the data processing center storing the optimal environmental condition report to clarify the optimal temperature, humidity, light, and gas concentration ranges for different growth stages. At the same time, the real-time environmental data of the current growth stage is read from the real-time monitoring data storage area. Assuming that the current is in the mycelium growth period, the optimal temperature range is 16 - 25 °C, and the real-time average temperature is 14 °C. By calculation, the temperature deviation is 2 °C (16 - 14). For humidity, light, and gas concentration, deviation calculations are also carried out. According to the temperature deviation, combined with the relationship between the heating power of the heating equipment (such as electric heating wires) connected to the temperature control system in the cabin and the temperature change (every 50 watts of power can increase the average temperature in the cabin by 1 °C per hour), it is calculated that 100 watts of heating power is required to raise the temperature to the appropriate range. For the humidity, light, and gas concentration deviations, respectively, based on the relationship between the corresponding control equipment (humidity spraying system, light adjustment lamp group, ventilation equipment) in the cabin and the environmental parameter changes, the adjustment parameters are calculated, such as adjusting the humidity spraying time, light intensity and irradiation duration, gas ventilation frequency, etc. These control measures for different environmental parameters are sorted into the intelligent control decision for the growth environment of Tricholoma matsutake cultivation, such as "During the mycelium growth period, since the current temperature of 14 °C is lower than the optimal range with a deviation of 2 °C, adjust the power of the electric heating wire to 100 watts for heating; since the air humidity of 70% is lower than the optimal range, turn on the humidity spraying system for 5 minutes; since the light duration of 8 hours is insufficient, increase the light intensity from 2000 lux to 2500 lux and extend the irradiation duration to 10 hours; since the oxygen content of 20% and the carbon dioxide concentration of 450 ppm are within the optimal range, there is no need to adjust the ventilation frequency", which is stored in the main control decision database of the data processing center, and the decision information is sent to the control system of the agricultural intelligent technology cabin through the network to automatically execute the corresponding environmental control operations.

[0065] Further, step S1 includes the following steps:

[0066] Step S11: Deploy temperature sensors inside the agricultural intelligent technology cabin to monitor the temperature data corresponding to the Tricholoma matsutake cultivation and growth stage in real time;

[0067] Step S12: Deploy humidity sensors inside the agricultural intelligent technology cabin to monitor the air humidity and soil humidity corresponding to the Tricholoma matsutake cultivation and growth stage in real time to obtain humidity data;

[0068] Step S13: Deploy light sensors inside the agricultural intelligent technology cabin to monitor the light intensity and duration corresponding to the Tricholoma matsutake cultivation and growth stage in real time to obtain light environment data;

[0069] Step S14: Deploy gas sensors inside the agricultural intelligent technology shelter to monitor the oxygen and carbon dioxide concentrations corresponding to the growth stages of matsutake cultivation in real time, so as to obtain gas concentration data;

[0070] Step S15: Denoise and filter the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stages of matsutake cultivation to remove the corresponding noise interference in each data, correct the deviations and errors in each data, so as to obtain the pre-processed temperature data, humidity data, light environment data, and gas concentration data during the growth stages of matsutake cultivation; use wireless communication technology to transmit the pre-processed temperature data, humidity data, light environment data, and gas concentration data to the data processing center through excellent steps.

[0071] As an embodiment of the present invention, refer to Figure 2 shown, for Figure 1 the detailed step flow schematic diagram of step S1 in

[0072] Step S11: Deploy temperature sensors inside the agricultural intelligent technology shelter to monitor the temperature data corresponding to the growth stages of matsutake cultivation in real time;

[0073] In the embodiment of the present invention, inside the agricultural intelligent technology shelter, a thermistor temperature sensor with high sensitivity and stability is selected for real-time temperature monitoring. This type of sensor works based on the characteristic that the resistance value of the thermistor changes with temperature. Considering the different temperature sensitivities of matsutake during the mycelium growth stage and the fruiting stage, there is a focus on the layout of the cultivation area. During the mycelium growth stage, sensors are evenly distributed on the bottom, middle, and top layers of the cultivation rack, with three sensors on each layer, arranged in a triangle to ensure full-range monitoring. When entering the fruiting stage, the number of sensors is additionally increased in the dense fruiting area. For example, one more sensor is added in each corner where fruiting is concentrated. The sensors are wired to the data acquisition module through shielded twisted pair to prevent signal interference. The data acquisition module collects the resistance values of each sensor every 10 minutes according to the preset program. For example, at a certain moment during the mycelium growth stage, the resistance values of the three sensors on the bottom layer correspond to temperatures of 18.2 °C, 18.1 °C, and 18.3 °C respectively; at a certain fruiting corner sensor during the fruiting stage, the resistance value corresponds to a temperature of 20.5 °C. The module converts the resistance value into a temperature value according to the pre-calibrated accurate resistance-temperature correspondence table and temporarily stores it in its own cache for subsequent processing.

[0074] Step S12: Deploy humidity sensors inside the agricultural intelligent technology shelter to monitor the air humidity and soil humidity corresponding to the growth stages of matsutake cultivation in real time, so as to obtain humidity data;

[0075] In the embodiments of the present invention, for air humidity monitoring, a capacitive humidity sensor is adopted. Since it can accurately sense changes in air humidity, such sensors are installed in well-ventilated places close to the Tricholoma matsutake growth area. For example, one sensor is installed every 2 meters around the cultivation rack to ensure that changes in air humidity can be captured in a timely manner. For soil humidity monitoring, a soil humidity sensor based on the time domain reflectometry (TDR) principle is selected. It is vertically inserted into the cultivation soil at a density of one per 0.5 square meters, and the insertion depth is controlled at 6 - 8 cm to obtain accurate soil humidity information. The data acquisition module also collects the data of the humidity sensors at a time interval of 10 minutes. For example, during a certain collection, the air humidity sensor feeds back that the air humidity is 70% RH; the soil humidity sensor at a certain position measures the soil humidity to be 22% (volume water content). The collected data is stored in the cache of the data acquisition module and waits for further processing together with the temperature data to obtain humidity data.

[0076] Step S13: By deploying a light sensor inside the agricultural intelligent technology shelter to monitor the light intensity and duration corresponding to the growth stage of Tricholoma matsutake cultivation in real time, so as to obtain light environment data;

[0077] In the embodiments of the present invention, a photoresistive light sensor is selected to monitor the light intensity, and a timer and a microcontroller are combined to record the light duration. The light sensor is installed at a position on the top of the shelter that is light-transmissive and unobstructed to ensure that it can receive sufficient light. The resistance value of the photoresistor decreases as the light intensity increases. The data acquisition module measures the resistance value and converts it into a light intensity value according to the accurately calibrated curve in the early stage. The timer is triggered to start timing by a signal synchronized with the external clock at sunrise every day and stops timing when receiving the corresponding signal at sunset. The microcontroller records the timing result and the light intensity data at the same moment. For example, during the fruiting period of Tricholoma matsutake on a certain day, the recorded light duration is 10.5 hours, and the light intensity at a certain moment is 2200 lux. These light environment data are collected in the cache of the data acquisition module to provide a basis for subsequent analysis.

[0078] Step S14: By deploying gas sensors inside the agricultural intelligent technology shelter to monitor the oxygen and carbon dioxide concentrations corresponding to the growth stage of Tricholoma matsutake cultivation in real time, so as to obtain gas concentration data;

[0079] In an embodiment of the present invention, an electrochemical gas sensor is used to monitor the oxygen and carbon dioxide concentrations. The oxygen sensor operates based on the principle of an electrochemical cell, and measures the oxygen concentration by detecting the current generated by the chemical reaction of oxygen on the electrode. The carbon dioxide sensor is based on the principle of non-dispersive infrared absorption (NDIR), and calculates the carbon dioxide concentration by measuring the degree of absorption of infrared rays with a specific wavelength by carbon dioxide. The gas sensor is installed about 30 cm away from the matsutake growth area to ensure that the gas can fully diffuse to the sensor. The data acquisition module collects the data of the gas sensor every 10 minutes. For example, if the collected oxygen concentration is 20.9% and the carbon dioxide concentration is 420 ppm, the collected gas concentration data is stored in the cache of the data acquisition module and summarized with other environmental data.

[0080] Step S15: Denoise and filter the temperature data, humidity data, light environment data, and gas concentration data corresponding to the matsutake cultivation and growth stages to remove the corresponding noise interference in each data, correct the deviation and error of each data, so as to obtain the preprocessed temperature data, humidity data, light environment data, and gas concentration data during the matsutake cultivation and growth stages; use wireless communication technology to transmit the preprocessed temperature data, humidity data, light environment data, and gas concentration data to the data processing center through excellent steps.

[0081] In an embodiment of the present invention, after the data acquisition module completes data acquisition, a special data processing program is written in Python to process various types of data in the cache. For denoising, for the temperature data, humidity data, light environment data, and gas concentration data, a median filtering algorithm is uniformly used. For example, for a set of temperature data [19.8, 20.0, 25.6, 20.1, 20.2], 25.6 is obviously a noise point and is corrected to 20.0 after median filtering. The data deviation and error are corrected by setting a strict and reasonable value range. For example, it is stipulated that the temperature during the mycelium growth period of matsutake is 15 - 20 °C, and the temperature during the fruiting period is 18 - 23 °C; the air humidity is 65 - 75% RH during the mycelium growth period and 70 - 80% RH during the fruiting period, etc. In the data transmission link, a 4G wireless communication module is selected to transmit data according to the MQTT communication protocol. For the temperature data and humidity data, high-priority tags are set in the program to ensure that they are transmitted to the data processing center quickly, stably, and preferentially; for the light environment data and gas concentration data, they are set as low priority and transmitted asynchronously when the network is idle. After the data processing center receives the data, it stores them in a special database for subsequent analysis and for regulating the environment of the shelter.

[0082] Further, the matsutake cultivation and growth stage described in step S11 includes the mycelium growth period and the fruiting period corresponding to matsutake.

[0083] Further, the excellent step transmission described in step S15 specifically means setting corresponding high priorities for the temperature data and humidity data corresponding to the growth stage of matsutake cultivation to preferentially and rapidly and stably transmit them to the data processing center, while for the non-urgent data such as the light environment data and gas concentration data corresponding to the growth stage of matsutake cultivation, they are asynchronously transmitted to the data processing center when the network is idle.

[0084] Further, step S2 includes the following steps:

[0085] Step S21: Using the data processing center to classify the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stage of matsutake cultivation to output the matsutake growth environment parameter conditions corresponding to each combination in the mycelium growth period and fruiting period;

[0086] Step S22: Obtaining the growth rate of matsutake mycelium corresponding to the mycelium growth period;

[0087] Step S23: Obtaining the fruiting rate of matsutake corresponding to the fruiting period;

[0088] Step S24: Based on the growth rate of matsutake mycelium corresponding to the mycelium growth period and the fruiting rate of matsutake corresponding to the fruiting period, and using regression analysis to screen the best environment for the matsutake growth environment parameter conditions under the corresponding combination, to establish a regression equation between the corresponding matsutake growth environment parameter conditions, the growth rate of matsutake mycelium, and the fruiting rate of matsutake, and to predict the best environment conditions corresponding to the mycelium growth period and the fruiting period according to the regression equation between the matsutake growth environment parameter conditions, the growth rate of matsutake mycelium, and the fruiting rate of matsutake, so as to generate the best growth environment conditions of matsutake corresponding to the mycelium growth period and the fruiting period, where for the mycelium growth period, the temperature is maintained at 16 - 25 °C, the air humidity is kept at 80% - 90%, the soil humidity is 60% - 70%, the sunshine duration of warm light LED simulated scattered light is 10 - 12 hours, the oxygen content is 21% - 23%, and the carbon dioxide should be less than 0.1%, while for the fruiting period, the temperature is maintained at 19 - 23 °C, the air humidity is kept at 80% - 90%, the soil humidity is 60% - 70%, the sunshine duration of warm light LED simulated scattered light is 10 - 12 hours, the oxygen content is 22% - 25%, and the carbon dioxide should be less than 0.05%.

[0089] As an embodiment of the present invention, referring to Figure 3 shown, it is Figure 1 the detailed step flow schematic diagram of step S2 in

[0090] Step S21: Use the data processing center to classify the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stages of matsutake cultivation to output the matsutake growth environment parameter conditions for each combination during the mycelium growth period and the fruiting period;

[0091] In an embodiment of the present invention, in the data processing center, the data analysis library pandas of Python is used to classify the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stages of matsutake cultivation. First, various environmental data transmitted and stored are read from the database. These data all carry timestamps. According to the growth cycle record of matsutake cultivation, the time ranges of the mycelium growth period and the fruiting period are determined. For example, by consulting the cultivation log, it is determined that the 1st - 3rd months after inoculation are the mycelium growth period, and the 4th - 6th months are the fruiting period. Then, through the use of the boolean indexing function of pandas, the environmental data corresponding to the corresponding stages are screened according to the time range. For the mycelium growth period, the temperature, humidity, light, and gas concentration data within this time period are extracted; for the fruiting period, the data screening is also carried out in the same way. The screened data are sorted into new data tables according to different stages respectively, and the matsutake growth environment parameter conditions for each combination during the mycelium growth period and the fruiting period are output and stored in a special folder of the data processing center for subsequent analysis.

[0092] Step S22: Obtain the mycelium growth rate of matsutake corresponding to the mycelium growth period;

[0093] In an embodiment of the present invention, the mycelium growth rate of matsutake corresponding to the mycelium growth period is obtained from the cultivation record database of the agricultural intelligent technology shelter. During the cultivation process, the staff regularly (such as weekly) measure the growth length of matsutake mycelium. When measuring, a vernier caliper with a precision of 0.1 mm is used to measure the length of the mycelium extending outward from the inoculation point at multiple fixed observation points (such as setting 5 observation points per square meter). The results of each measurement are recorded in the cultivation log and entered into the database. A program is written in Python to read all the measurement data during the mycelium growth period from the database. By calculating the change in the mycelium growth length within the time interval between two adjacent measurements and then dividing by the time interval (converted to days), the daily mycelium growth rate is obtained. For example, in the 2nd and 3rd weeks of the mycelium growth period, the mycelium length at a certain observation point increases from 10 mm to 15 mm, and the time interval is 7 days. Then the mycelium growth rate of this observation point in this week is (15 - 10) ÷ 7 ≈ 0.71 mm / day. The data of all observation points are calculated in the same way, and then the average value is obtained to get the mycelium growth rate of matsutake corresponding to the entire mycelium growth period, which is stored in the analysis result table of the data processing center.

[0094] Step S23: Obtain the fruiting rate of matsutake corresponding to the fruiting period;

[0095] In an embodiment of the present invention, the mushroom fruiting rate of the Tricholoma matsutake corresponding to the mushroom fruiting period is obtained from the mushroom fruiting statistics database of the smart agricultural technology cabin. During the mushroom fruiting period, the staff counts the number of newly grown Tricholoma matsutake and the total number of inoculation points every day, and sets a fixed counting area in the breeding area (such as one counting area for every 10 square meters). The number of mushrooms produced is carefully counted in each counting area, and the number of mushrooms produced each day and the total number of inoculation points are recorded in the mushroom fruiting statistics log and entered into the database. A program is written in Python to read the data of the number of mushrooms produced and the total number of inoculation points during the entire mushroom fruiting period from the database. The formula for calculating the mushroom fruiting rate is: Mushroom fruiting rate = (number of mushrooms produced ÷ total number of inoculation points) × 100%. For example, on the 10th day of the mushroom fruiting period, the number of mushrooms produced in a counting area is 50, and the total number of inoculation points is 100. The mushroom fruiting rate of the area on that day is (50÷100)×100%=50%. The data of all counting areas are summarized and calculated, and finally the mushroom fruiting rate of the Tricholoma matsutake corresponding to the entire mushroom fruiting period is obtained, which is stored in the analysis result table of the data processing center.

[0096] Step S24: Based on the mycelium growth rate of the pine mushrooms corresponding to the mycelium growth period and the fruiting rate of the pine mushrooms corresponding to the fruiting period, the optimal environment parameter conditions of the pine mushroom growth under the corresponding combination are screened by regression analysis to establish a regression equation between the corresponding pine mushroom growth environment parameter conditions and the mycelium growth rate of the pine mushrooms and the fruiting rate of the pine mushrooms, and the optimal environment conditions corresponding to the mycelium growth period and the fruiting period are predicted according to the regression equation between the pine mushroom growth environment parameter conditions and the mycelium growth rate of the pine mushrooms and the fruiting rate of the pine mushrooms, so as to generate the optimal environment conditions for the growth of the pine mushrooms corresponding to the mycelium growth period and the fruiting period. During the mycelium growth period, the temperature is maintained at 16-25℃, the air humidity is maintained at 80%-90%, the soil humidity is 60%-70%, the warm light LED simulates scattered light sunshine for 10-12 hours, the oxygen content is 21%-23%, and the carbon dioxide should be less than 0.1%. During the mushroom fruiting period, the temperature is maintained at 19-23℃, the air humidity is maintained at 80%-90%, the soil humidity is 60%-70%, the warm light LED simulates scattered light sunshine for 10-12 hours, the oxygen content is 22%-25%, and the carbon dioxide should be less than 0.05%.

[0097] In an embodiment of the present invention, in a data processing center, by using a linear regression model in the statistical analysis library scikit-learn of Python, based on the growth rate of Tricholoma matsutake mycelium corresponding to the mycelium growth period and the fruiting rate of Tricholoma matsutake corresponding to the fruiting period, the optimal environment screening of the Tricholoma matsutake growth environment parameter conditions under the corresponding combination is carried out. First, the growth environment parameter condition data of the mycelium growth period and the fruiting period obtained previously are integrated with the corresponding mycelium growth rate data and fruiting rate data. The environmental parameter conditions are used as independent variables, such as temperature, humidity, light duration, etc.; the mycelium growth rate and fruiting rate are used as dependent variables, and a linear regression model is used for fitting. The coefficients of the regression equation are solved by the least squares method. For example, for the mycelium growth period, taking temperature, air humidity, soil humidity, etc. as independent variables and the mycelium growth rate as the dependent variable, the regression equation obtained through model calculation is: mycelium growth rate = 0.2×temperature + 0.1×air humidity - 0.05×soil humidity + constant term (the specific value is obtained through calculation). For the fruiting period, similarly, taking the corresponding environmental parameters as independent variables and the fruiting rate as the dependent variable for regression analysis. According to the coefficients of the regression equation, the influence degree of each environmental parameter on the mycelium growth rate and fruiting rate is analyzed. According to the given optimal environmental condition range of the mycelium growth period and the fruiting period, such as maintaining the temperature at 16 - 25°C during the mycelium growth period, etc., substitute the boundary values and intermediate values into the regression equation for calculation, and find out the environmental parameter combination that can make the mycelium growth rate and fruiting rate reach relatively high values, which is determined as the optimal environmental condition. Among them, for the mycelium growth period, the temperature is maintained at 16 - 25°C, the air humidity is kept at 80% - 90%, the soil humidity is 60% - 70%, the simulated scattered light sunshine duration of warm light LED is 10 - 12 hours, the oxygen content is 21% - 23% and the carbon dioxide should be lower than 0.1%. For the fruiting period, the temperature is maintained at 19 - 23°C, the air humidity is kept at 80% - 90%, the soil humidity is 60% - 70%, the simulated scattered light sunshine duration of warm light LED is 10 - 12 hours, the oxygen content is 22% - 25% and the carbon dioxide should be lower than 0.05%. And the optimal environmental conditions for the growth of Tricholoma matsutake corresponding to the mycelium growth period and the fruiting period are sorted into a report, stored in the data processing center, and fed back to the managers of the agricultural intelligent technology shelter for optimizing the breeding environment.

[0098] Further, step S22 includes the following steps:

[0099] Obtain the Tricholoma matsutake mycelium growth image corresponding to the mycelium growth period;

[0100] In the embodiment of the present invention, in the agricultural intelligent technology cabin, during the mycelium growth period, a professional industrial camera equipped with a high-resolution lens (such as 5 million pixels) is used to collect images of Tricholoma matsutake mycelium. The camera is fixed on an adjustable bracket and installed about 30 centimeters away from the cultivation surface to ensure that the growth area of Tricholoma matsutake mycelium can be clearly photographed. The camera is set to automatically take pictures every 3 days, and the shooting time is fixed at 9 am every day to ensure relatively stable lighting conditions. When shooting, the fill light installed in the cabin is turned on to provide uniform and soft light to avoid shadow interference. After each shooting, the image is automatically stored in the specified folder of the local server connected to the camera. The folder is named "mycelium growth period - date", such as "mycelium growth period - 20240501". For example, on the 10th day of the mycelium growth period (i.e., the 10th day after inoculation), the camera automatically takes a picture at 9 am, obtaining an image clearly showing Tricholoma matsutake mycelium. The image file name is "20240501 - 0900.jpg" and is successfully stored in the corresponding folder, providing basic data for subsequent analysis.

[0101] Preferably, the microscopic parameters of Tricholoma matsutake mycelium corresponding to the mycelium growth period are extracted at corresponding time intervals to extract the length, diameter, number of branches, and mycelium area of Tricholoma matsutake mycelium over a period of time, obtaining a set of microscopic structure parameters of mycelium growth corresponding to the mycelium growth period.

[0102] In the embodiment of the present invention, in the data processing center, the microscopic parameters of Tricholoma matsutake mycelium growth images taken at corresponding time intervals during the mycelium growth period are extracted using the OpenCV library of Python. The images are read from the specified folder of the local server, and image preprocessing functions of OpenCV are used. For example, grayscale processing is used to convert the color image into a grayscale image to simplify subsequent analysis. The edge detection algorithm, such as the Canny edge detection algorithm, is adopted. By setting appropriate thresholds (such as a low threshold of 50 and a high threshold of 150), the edge contour of Tricholoma matsutake mycelium is accurately identified, and the contour analysis function is used to calculate the length of the mycelium in the image in pixels. Through the pre-calibrated conversion relationship between pixels and actual length (such as 100 pixels = 1 millimeter), it is converted into the actual length. For the mycelium diameter, the contour width is measured at multiple positions and averaged, and then converted into the actual diameter in the same way. Through morphological operations, such as dilation and erosion, the mycelium branch points are identified, and the number of branches is counted. Using the image area calculation function and combining the calibration relationship, the mycelium area is obtained. For example, for an image taken on the 15th day after inoculation, after processing, the measured mycelium length is 12 millimeters, the average diameter is 0.2 millimeters, the number of branches is 5, and the mycelium area is 10 square millimeters. These data are sorted into a data record and stored in an Excel table in the data processing center in chronological order, forming a set of microscopic structure parameters of mycelium growth corresponding to the mycelium growth period.

[0103] Preferably, the macroscopic growth indexes are calculated according to the mycelium growth microscopic structure parameter set corresponding to the mycelium growth period, so as to obtain the mycelium length growth rate, the branch number increase rate and the mycelium coverage area growth rate corresponding to the mycelium growth period.

[0104] In the embodiment of the present invention, in the data processing center, by using the data analysis libraries pandas and numpy of Python, the macroscopic growth indexes are calculated according to the mycelium growth microscopic structure parameter set corresponding to the mycelium growth period. The data of the previously generated microscopic structure parameter set is read from the Excel table and sorted with time as the index. For the mycelium length growth rate, the change amount of the mycelium length within the time interval between two adjacent measurements is calculated, divided by the initial length, and then divided by the time interval (converted to days) to obtain the mycelium length growth rate per day. For example, on the 12th day after inoculation, the mycelium length is measured to be 10 mm, and on the 15th day, the length is measured to be 12 mm, and the time interval is 3 days, then the mycelium length growth rate is ((12 - 10)÷10)÷3≈0.067 / day. For the branch number increase rate, the increase amount of the branch number within the time interval between two adjacent measurements is calculated, divided by the initial branch number, and then divided by the time interval to obtain the branch number increase rate per day. For example, on the 12th day, the branch number is 3, and on the 15th day, the branch number is 5, then the branch number increase rate is ((5 - 3)÷3)÷3≈0.222 / day. For the mycelium coverage area growth rate, the change amount of the mycelium area within the time interval between two adjacent measurements is calculated, divided by the initial area, and then divided by the time interval to obtain the mycelium coverage area growth rate per day. Assume that on the 12th day, the mycelium area is 8 square millimeters, and on the 15th day, it is 10 square millimeters, then the mycelium coverage area growth rate is ((10 - 8)÷8)÷3≈0.083 / day. The calculated macroscopic growth index data are stored in a new Excel table for further analysis later.

[0105] Preferably, the growth rate is quantified according to the mycelium length growth rate, the branch number increase rate and the mycelium coverage area growth rate corresponding to the mycelium growth period, so as to obtain the Tricholoma matsutake mycelium growth rate corresponding to the mycelium growth period.

[0106] In an embodiment of the present invention, a program is written in Python in a data processing center to quantify the growth rate by using the hyphal length growth rate, the branch number increase rate, and the hyphal coverage area growth rate corresponding to the hyphal growth period, so as to read data from an Excel table storing macroscopic growth index data, assign different weights to the hyphal length growth rate, the branch number increase rate, and the hyphal coverage area growth rate, determine the weights through multiple experiments and analyses, for example, the weight of the hyphal length growth rate is 0.4, the weight of the branch number increase rate is 0.3, and the weight of the hyphal coverage area growth rate is 0.3. The growth rate of Tricholoma matsutake hyphae is calculated by weighted summation, and the formula is: Growth rate of Tricholoma matsutake hyphae = 0.4×Hyphal length growth rate + 0.3×Branch number increase rate + 0.3×Hyphal coverage area growth rate. For example, for a certain period, the hyphal length growth rate is 0.06 / day, the branch number increase rate is 0.2 / day, and the hyphal coverage area growth rate is 0.07 / day. Then, the growth rate of Tricholoma matsutake hyphae = 0.4×0.06 + 0.3×0.2 + 0.3×0.07 = 0.105 / day. The growth rates of Tricholoma matsutake hyphae in each period obtained by calculation are sorted into a data report and stored in the data processing center, and can be compared and analyzed with the hyphal growth rates obtained by traditional measurement methods before, providing a basis for more accurate assessment of the growth status of Tricholoma matsutake hyphae.

[0107] Further, step S23 includes the following steps:

[0108] Step S231: Monitor the ecological factors of the Tricholoma matsutake growth environment corresponding to the fruiting period to monitor the corresponding temperature, humidity, light, soil pH, and microbial community structure, and obtain the Tricholoma matsutake growth ecological factor set corresponding to the fruiting period;

[0109] In the embodiment of the present invention, in the agricultural intelligent technology cabin, ecological factor monitoring is carried out on the growth environment of Tricholoma matsutake during the fruiting period. For temperature monitoring, high-precision thermistor temperature sensors are selected and evenly distributed at different heights and positions in the cabin. For example, 3 sensors are arranged on each of the upper, middle and lower layers of the cultivation rack, and data is collected every 10 minutes to ensure comprehensive acquisition of temperature information. For humidity monitoring, a capacitive air humidity sensor and a soil humidity sensor based on the principle of time domain reflectometry (TDR) are used. The air humidity sensor is installed in a well-ventilated place near the growth area of Tricholoma matsutake, and one sensor is installed per 2 square meters; the soil humidity sensor is vertically inserted into the soil to a depth of 5-8 cm at a density of one sensor per 1 square meter, and data is also collected every 10 minutes. For light monitoring, a photoresistive light sensor is used and installed at a light-transmitting and unobstructed position on the top of the cabin. Combined with a timer and a microcontroller, the light intensity and duration are recorded. For soil pH monitoring, soil samples are collected at different positions every 3 days and measured using a pH meter. 5 sample points are selected in each area, mixed evenly and then measured to obtain accurate soil pH data. For the monitoring of the microbial community structure, samples are collected from the soil and the surrounding environment of Tricholoma matsutake every week, and high-throughput sequencing technology is used to extract, amplify and sequence the DNA of the samples in the laboratory to determine the types and abundances of microorganisms. These monitoring data are sorted and summarized, and finally the corresponding set of ecological factors for the growth of Tricholoma matsutake during the fruiting period is obtained and stored in the data storage device local to the cabin.

[0110] Step S232: Perform ecological factor characteristic analysis on the corresponding set of ecological factors for the growth of Tricholoma matsutake during the fruiting period to obtain the mean value, extreme value and fluctuation range corresponding to each ecological factor during the fruiting period;

[0111] In the embodiment of the present invention, in the data processing center, the ecological factor characteristic analysis is carried out on the corresponding set of ecological factors for the growth of Tricholoma matsutake during the fruiting period by using the data analysis libraries pandas and numpy of Python. The data is read from the local data storage device of the cabin, and pandas is used to organize the data into a suitable data structure, such as the DataFrame format. For temperature data, the mean() function of numpy is used to calculate the mean value, and the max() and min() functions are used to obtain the extreme values. Then, the fluctuation range is obtained by calculating the difference between the maximum value and the minimum value. For example, the mean value of the temperature data in a certain week is 21°C, the extreme values are 19°C and 23°C respectively, and the fluctuation range is 4°C. For data such as humidity, light, and soil pH, the same method is used for calculation. For the microbial community structure data, the mean value of the relative abundances of different microbial species, the highest and lowest abundance values (i.e., extreme values), and the fluctuation range of the abundances are analyzed. The mean values, extreme values and fluctuation ranges corresponding to each ecological factor are organized into a new table and stored in the database of the data processing center for subsequent analysis.

[0112] Step S233: Based on the mean values, extreme values, and fluctuation ranges of each ecological factor during the fruiting period, analyze the ecological spatial distribution differences among the ecological factors within the corresponding Tricholoma matsutake growth ecological factor set to obtain the spatial distribution differences among the Tricholoma matsutake ecological factors during the fruiting period;

[0113] In the embodiment of the present invention, based on the mean values, extreme values, and fluctuation ranges of each ecological factor obtained previously during the fruiting period, in the data processing center, use the data analysis and visualization libraries of Python (such as pandas, matplotlib) to analyze the ecological spatial distribution differences among the ecological factors within the corresponding Tricholoma matsutake growth ecological factor set. Taking temperature as an example, group the data collected by sensors at different positions according to spatial positions (such as different levels and different regions of the cultivation rack), which is achieved by using the groupby function of pandas, calculate the characteristics such as the mean value and extreme value of each group of data, and draw a three-dimensional bar chart through matplotlib to visually display the distribution differences of temperature at different spatial positions. For humidity, also group according to spatial positions, calculate the characteristic values, and then draw an isoline map to show the spatial distribution of humidity. For soil pH, based on the coordinates of the sampling points, use the interpolation algorithm to generate the spatial distribution map of soil pH and analyze the differences in soil pH in different regions. Through these analysis methods, finally obtain the spatial distribution differences among the Tricholoma matsutake ecological factors during the fruiting period, and store the analysis results in the data processing center in the form of charts and data reports.

[0114] Step S234: According to the spatial distribution differences among the Tricholoma matsutake ecological factors during the fruiting period, divide the corresponding Tricholoma matsutake growth environment during the fruiting period into corresponding sunny and humid micro-regions and shady and dry micro-regions, and based on the Tricholoma matsutake in different growth regions, monitor the content of metabolites, the number of fruiting positions, and the enzyme activity intensity corresponding to the Tricholoma matsutake mycelium in real time;

[0115] In the embodiment of the present invention, according to the spatial distribution differences among various Tricholoma matsutake ecological factors during the fruiting period obtained previously, a program is written in Python in the data processing center to divide the corresponding Tricholoma matsutake growth environment during the fruiting period into corresponding sunny and humid micro-regions and shady and dry micro-regions, and division rules are set. For example, a region with a light intensity higher than a certain threshold (such as 2500 lux) and an air humidity higher than another threshold (such as 80% RH) is divided into a sunny and humid micro-region; a region with a light intensity lower than a certain threshold (such as 1500 lux) and an air humidity lower than another threshold (such as 60% RH) is divided into a shady and dry micro-region. In the different divided micro-regions, special detection equipment is used to monitor in real time the content of metabolites corresponding to Tricholoma matsutake mycelium, the number of fruiting sites, and the enzyme activity intensity. The content of metabolites is detected using a high-performance liquid chromatograph (HPLC). Tricholoma matsutake mycelium samples are collected once a week and analyzed in the laboratory. The number of fruiting sites is counted manually at a fixed time every day and recorded in a special record form, and at the same time, it is input into the data processing center. The enzyme activity intensity is detected using the enzyme-linked immunosorbent assay (ELISA). Samples are collected once every 5 days for detection. The monitoring data in different micro-regions are stored in the corresponding folders in the data processing center for subsequent analysis.

[0116] Step S235: Calculate the fruiting rate prediction based on the content of metabolites corresponding to Tricholoma matsutake mycelium, the number of fruiting sites, and the enzyme activity intensity in different growth zones to obtain the Tricholoma matsutake fruiting rate corresponding to the fruiting period.

[0117] In the embodiment of the present invention, in the data processing center, a program is written in Python to calculate the fruiting rate prediction based on the content of metabolites corresponding to Tricholoma matsutake mycelium, the number of fruiting sites, and the enzyme activity intensity in different growth zones. By establishing a prediction model, for example, using a multiple linear regression model, with the content of metabolites, the number of fruiting sites, and the enzyme activity intensity as independent variables and the fruiting rate as the dependent variable, data is read from the monitoring data folders in different growth zones, sorted into the input format required by the model using pandas, and the coefficients of the regression equation are solved by the least squares method to obtain the prediction model. For example, for the sunny and humid micro-region, after calculation, the regression equation is obtained: fruiting rate = 0.3 × metabolite content + 0.4 × number of fruiting sites + 0.3 × enzyme activity intensity + constant term (the specific value is obtained through calculation). Substitute the current independent variable data in each micro-region into the regression equation to calculate the fruiting rate. For the shady and dry micro-region, the same calculation is performed. The fruiting rates calculated in different micro-regions are summarized to obtain the Tricholoma matsutake fruiting rate corresponding to the fruiting period, and a data report is formed and stored in the data processing center to provide a basis for optimizing the Tricholoma matsutake cultivation environment.

[0118] Further, step S3 includes the following steps:

[0119] Step S31: Analyze the growth temperature regulation during the corresponding growth stage in the matsutake cultivation growth stage based on the temperature conditions within the optimal growth environment for matsutake corresponding to the mycelium growth period and the fruiting period, so as to generate the corresponding matsutake cultivation growth temperature regulation decision;

[0120] In the embodiment of the present invention, in the data processing center, a program is written using Python to analyze the growth temperature regulation during the corresponding growth stage in the matsutake cultivation growth stage based on the temperature conditions within the optimal growth environment for matsutake corresponding to the mycelium growth period and the fruiting period. Read the data of the optimal growth environment conditions for matsutake corresponding to the mycelium growth period and the fruiting period generated from the database of the data processing center to clarify the suitable temperature range for different growth stages. For example, the temperature during the mycelium growth period is 16 - 25 °C, and the temperature during the fruiting period is 19 - 23 °C. At the same time, read the previously collected and stored real-time temperature data. Assume that the current is in the mycelium growth period and the average temperature in the agricultural intelligent technology shelter at a certain moment is 14 °C, which is lower than the lower limit of the optimal temperature range. Through analysis, the program determines that the temperature needs to be increased. In the agricultural intelligent technology shelter, the temperature regulation is achieved through an intelligent temperature control system. This system is connected to heating equipment (such as electric heating wires) and refrigeration equipment (such as air conditioners). The program calculates the additional heat required based on the temperature difference, sends an instruction to the temperature control system, starts the heating equipment, and sets the heating power and duration. For example, start the electric heating wire and run it at a power of 500 watts for 30 minutes, generating a matsutake cultivation growth temperature regulation decision such as "During the mycelium growth period, since the current temperature of 14 °C is lower than the optimal range, start the electric heating wire and run it at a power of 500 watts for 30 minutes to increase the temperature", and store it in the regulation decision table of the data processing center.

[0121] Step S32: Analyze the regulation of other growth parameters during the corresponding growth stage in the matsutake cultivation growth stage based on the humidity, light, and gas conditions within the optimal growth environment for matsutake corresponding to the mycelium growth period and the fruiting period. Taking the optimal growth environment conditions for matsutake corresponding to the mycelium growth period and the fruiting period as the goal, execute the adjustment of the humidity spraying time, light intensity and irradiation duration, and the ventilation frequency corresponding to the gas, so as to generate the corresponding matsutake cultivation other growth regulation decisions;

[0122] In the embodiment of the present invention, in the data processing center, a program is written in Python. Based on the humidity, light, and gas conditions within the optimal environmental conditions corresponding to the mycelium growth period and the fruiting period of Tricholoma matsutake, the regulation and analysis of other growth parameters are carried out for the corresponding growth stages during the Tricholoma matsutake cultivation growth stage. The optimal environmental condition data is read from the database. For example, during the mycelium growth period, the air humidity is 80%-90%, the soil humidity is 60%-70%, the warm light LED simulates the scattered light sunshine duration of 10-12 hours, the oxygen content is 21%-23%, and the carbon dioxide is less than 0.1%. The corresponding parameters during the fruiting period are also read, and at the same time, the previously collected real-time humidity, light, and gas concentration data are read. Assume that the current stage is the fruiting period, and the real-time air humidity is 75%, which is lower than the optimal range. The humidity regulation relies on the humidity spraying system installed in the shelter. The program calculates the amount of humidity that needs to be increased according to the humidity difference, sends an instruction to the spraying system, and adjusts the spraying time. For example, it is set that the spraying system is turned on for 5 minutes to increase the air humidity. For the light, if the real-time light duration is 8 hours, which is lower than the optimal range, the program controls the warm light LED lamp group installed on the top of the shelter to adjust the light intensity and irradiation duration. For example, the light intensity is increased from 2000 lux to 2500 lux, and the irradiation duration is extended to 12 hours. For the gas condition, if the real-time carbon dioxide concentration is 0.15%, which is higher than the optimal range, the program controls the ventilation equipment (such as an exhaust fan) to increase the ventilation frequency. For example, the exhaust fan that originally runs for 10 minutes per hour is adjusted to run for 15 minutes per hour. Through these operations, the regulation decision for the remaining growth of Tricholoma matsutake cultivation is generated, such as "During the fruiting period, because the current air humidity of 75% is lower than the optimal range, the humidity spraying system is turned on for 5 minutes; because the light duration of 8 hours is insufficient, the light intensity of the warm light LED lamp group is increased to 2500 lux, and the irradiation duration is extended to 12 hours; because the carbon dioxide concentration of 0.15% is higher than the optimal range, the ventilation frequency of the exhaust fan is adjusted to run for 15 minutes per hour", and it is stored in the regulation decision table in the data processing center.

[0123] Step S33: Merge the corresponding Tricholoma matsutake cultivation growth temperature regulation decision and the Tricholoma matsutake cultivation remaining growth regulation decision for decision support to generate the corresponding intelligent regulation decision for the Tricholoma matsutake cultivation growth environment.

[0124] In the embodiment of the present invention, in the data processing center, a program is written in Python to merge the corresponding temperature regulation decisions for matsutake mushroom cultivation growth and the remaining growth regulation decisions for matsutake mushroom cultivation for decision support. The previously generated growth temperature regulation decisions and the remaining growth regulation decision data are read from the regulation decision table in the data processing center. The program classifies and integrates these decisions according to the growth stage. For example, for the mycelium growth stage, the temperature regulation decision "Start the electric heating wire and run it at a power of 500 watts for 30 minutes to increase the temperature" and the remaining growth regulation decisions "Since the current air humidity of 78% is within the optimal range, there is no need to adjust the humidity spray; since the light duration of 11 hours is within the optimal range, there is no need to adjust the light; since the oxygen content of 22% and the carbon dioxide concentration of 0.08% are within the optimal range, there is no need to adjust the ventilation frequency" are merged and sorted in a unified format to generate a complete intelligent regulation decision report for the growth environment of matsutake mushroom cultivation, such as "Mycelium growth stage: Temperature regulation is to start the electric heating wire and run it at a power of 500 watts for 30 minutes to increase the temperature; Humidity regulation is not required; Light regulation is not required; Gas regulation is not required". The same merging operation is also performed for the fruiting stage. The intelligent regulation decisions for the growth environment of matsutake mushroom cultivation at different growth stages generated finally are stored in the main regulation decision database in the data processing center, and the decision information is sent to the control system of the agricultural intelligent technology cabin through the network to automatically execute the corresponding environment regulation operations, realizing the intelligent regulation of the growth environment of matsutake mushroom cultivation.

[0125] Further, the growth temperature regulation analysis described in step S31 includes the following steps:

[0126] Obtain the corresponding temperature growth suitable range at the growth stage through the optimal environmental conditions for matsutake mushroom growth corresponding to the mycelium growth stage and the fruiting stage;

[0127] In the embodiment of the present invention, in the data processing center, a data reading program is written in Python. From the database storing the previously generated optimal environmental condition data for matsutake mushroom growth, it is retrieved and classified according to the growth stage. When the data of the mycelium growth stage is retrieved, the corresponding temperature growth suitable range, such as 16 - 25 °C, is extracted; when the data of the fruiting stage is retrieved, its temperature growth suitable range, such as 19 - 23 °C, is obtained. The program sorts the corresponding temperature growth suitable ranges at these different growth stages into a dictionary form, with the keys being the growth stages ("mycelium growth stage", "fruiting stage") and the values being the corresponding temperature range lists ([16, 25], [19, 23]), and stores them in the temporary data storage area in the data processing center for convenient call in subsequent steps. For example, when the program runs, it successfully reads and sorts out the temperature growth suitable range for the mycelium growth stage as [16, 25] and for the fruiting stage as [19, 23] from the database and stores them in the temporary data storage area.

[0128] Preferably, based on the temperature growth suitable range corresponding to the growth stage, the growth temperature deviation is calculated for the real-time temperature monitored at the corresponding growth stage, so as to obtain the deviation between the temperature sensing monitoring and the temperature growth suitable range at the corresponding growth stage;

[0129] In the embodiment of the present invention, by using Python to write a deviation calculation program, which is executed in the data processing center, the real-time temperature data is read from the database storing the real-time temperature data previously according to the time range corresponding to the growth stage. At the same time, the temperature growth suitable range data sorted out previously is read from the temporary data storage area. Assuming that the current is the mycelium growth period, the real-time average temperature at a certain moment read from the database is 14°C, and the temperature growth suitable range for the mycelium growth period is obtained from the temporary data storage area as [16, 25]. Through calculation, the program obtains the temperature deviation. For the lower limit deviation, calculate 16 - 14 = 2°C; for the upper limit deviation, calculate 14 - 25 = -11°C (take the absolute value as 11°C). Finally, it is determined that the temperature deviation is 2°C (because the actual temperature is lower than the lower limit, so the deviation from the lower limit is taken as the standard). The program records the deviation between the temperature sensing monitoring and the temperature growth suitable range at each growth stage in the deviation record table of the data processing center according to the growth stage and the timestamp, such as recording "mycelium growth period - June 1, 2024 10:00, temperature deviation 2°C".

[0130] Preferably, the corresponding cooling / heating power is calculated according to the deviation between the temperature sensing monitoring and the temperature growth suitable range at the corresponding growth stage, and the corresponding growth temperature in the agricultural intelligent technology cabin is adjusted according to the corresponding cooling / heating power to be restored to the temperature growth suitable range corresponding to the growth stage, so as to generate a corresponding temperature regulation decision for Tricholoma matsutake cultivation.

[0131] In an embodiment of the present invention, in a data processing center, a power calculation and regulation program is written in Python to read the deviation data between the temperature sensing monitoring and the suitable temperature range for growth at the corresponding growth stage from the deviation record table. Assume that during the mycelium growth period, the deviation is 2°C and the temperature needs to be increased. Given the relationship between the thermal power of the heating equipment (electric heating wire) connected to the temperature control system in the agricultural intelligent technology cabin and the temperature change, for example, for every 50 watts of power, the average temperature in the cabin can be increased by 1°C per hour. According to the deviation, the corresponding heating power is calculated. A deviation of 2°C requires a heating power of 2×50 = 100 watts. The program sends the calculated heating power value to the temperature control system in the cabin, and the temperature control system adjusts the power of the electric heating wire according to the instruction to turn on the heating. At the same time, the program generates a temperature regulation decision record for the growth of matsutake, such as "During the mycelium growth period, because the current temperature of 14°C is lower than the optimal range, with a deviation of 2°C, the power of the electric heating wire is adjusted to 100 watts for heating to restore to the suitable temperature range for growth", and stores this regulation decision in the regulation decision table in the data processing center to achieve the generation and execution control of the temperature regulation decision for the growth of matsutake.

[0132] Furthermore, the present invention also provides a matsutake cultivation intelligent monitoring system for an agricultural intelligent technology cabin, which is used to execute the matsutake cultivation intelligent monitoring method for the agricultural intelligent technology cabin as described above. The matsutake cultivation intelligent monitoring system for the agricultural intelligent technology cabin includes:

[0133] A matsutake growth environment monitoring module, which is used to monitor the temperature data, humidity data, light environment data, and gas concentration data corresponding to the matsutake cultivation growth stage in real time in the agricultural intelligent technology cabin, and transmit them to the data processing center in real time using wireless communication technology;

[0134] A growth optimal environment screening module, which is used to classify the temperature data, humidity data, light environment data, and gas concentration data corresponding to the matsutake cultivation growth stage by the data processing center to output the matsutake growth environment parameter conditions for each combination corresponding to the mycelium growth period and the fruiting period; obtain the matsutake growth status corresponding to the mycelium growth period and the fruiting period, and perform optimal environment screening on the corresponding matsutake growth environment parameter conditions based on the matsutake growth status corresponding to the mycelium growth period and the fruiting period to generate the matsutake growth optimal environment conditions corresponding to the mycelium growth period and the fruiting period;

[0135] A matsutake growth intelligent monitoring module, which is used to perform intelligent monitoring and analysis of the matsutake growth during the matsutake cultivation growth stage based on the matsutake growth optimal environment conditions corresponding to the mycelium growth period and the fruiting period to generate corresponding intelligent regulation decisions for the matsutake cultivation growth environment.

[0136] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0137] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for intelligent monitoring of Tricholoma matsutake cultivation in an agricultural intelligent technology shelter, characterized in that, It includes the following steps: Step S1: Real-time monitor the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stage of matsutake cultivation in the agricultural intelligent technology cabin, and use wireless communication technology to transmit them to the data processing center in the first step; Step S2: Use the data processing center to classify the growth stages of the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stage of matsutake cultivation, so as to output the matsutake growth environment parameter conditions corresponding to each combination in the mycelium growth period and the fruiting period; Obtain the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, and perform the best environment screening on the corresponding matsutake growth environment parameter conditions based on the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, so as to generate the best matsutake growth environment conditions corresponding to the mycelium growth period and the fruiting period; Step S3: Based on the best matsutake growth environment conditions corresponding to the mycelium growth period and the fruiting period, conduct intelligent monitoring and analysis of the growth stage corresponding to the matsutake cultivation growth stage, so as to generate the intelligent control decision corresponding to the matsutake cultivation growth environment.

2. The intelligent monitoring method for pine mushroom breeding of the agricultural intelligent technology cabin according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Real-time monitor the temperature data corresponding to the growth stage of matsutake cultivation by deploying temperature sensors inside the agricultural intelligent technology cabin; Step S12: Real-time monitor the air humidity and soil humidity corresponding to the growth stage of matsutake cultivation by deploying humidity sensors inside the agricultural intelligent technology cabin to obtain humidity data; Step S13: Real-time monitor the light intensity and duration corresponding to the growth stage of matsutake cultivation by deploying light sensors inside the agricultural intelligent technology cabin to obtain light environment data; Step S14: Real-time monitor the oxygen and carbon dioxide concentrations corresponding to the growth stage of matsutake cultivation by deploying gas sensors inside the agricultural intelligent technology cabin to obtain gas concentration data; Step S15: Denoise and filter the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stage of matsutake cultivation to remove the corresponding noise interference in each data, correct the deviation and error corresponding to each data, so as to obtain the preprocessed temperature data, humidity data, light environment data, and gas concentration data during the growth stage of matsutake cultivation; Use wireless communication technology to transmit the preprocessed temperature data, humidity data, light environment data, and gas concentration data to the data processing center in the first step.

3. The intelligent monitoring method for Tricholoma matsutake cultivation in the intelligent agricultural technology cabin according to claim 2, characterized in that, The growth stage of matsutake cultivation described in Step S11 includes the mycelium growth period and the fruiting period corresponding to matsutake.

4. The intelligent monitoring method for matsutake cultivation of the agricultural intelligent technology cabin according to claim 2, wherein, The first-step transmission described in Step S15 specifically means setting corresponding high priorities for the temperature data and humidity data corresponding to the growth stage of matsutake cultivation to transmit them to the data processing center quickly and stably with priority, while the non-urgent data corresponding to the light environment data and gas concentration data corresponding to the growth stage of matsutake cultivation are transmitted to the data processing center asynchronously when the network is idle.

5. The intelligent monitoring method for Tricholoma matsutake cultivation of the agricultural intelligent technology cabin according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Use the data processing center to classify the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stages of matsutake cultivation to output the matsutake growth environment parameter conditions for each combination during the mycelium growth stage and the fruiting stage; Step S22: Obtain the growth rate of matsutake mycelium corresponding to the mycelium growth stage; Step S23: Obtain the fruiting rate of matsutake corresponding to the fruiting stage; Step S24: Based on the growth rate of matsutake mycelium corresponding to the mycelium growth stage and the fruiting rate of matsutake corresponding to the fruiting stage, and using regression analysis to screen the optimal environment for the matsutake growth environment parameter conditions under the corresponding combinations, establish a regression equation between the corresponding matsutake growth environment parameter conditions, the growth rate of matsutake mycelium, and the fruiting rate of matsutake, and predict the optimal environment conditions corresponding to the mycelium growth stage and the fruiting stage according to the regression equation between the matsutake growth environment parameter conditions, the growth rate of matsutake mycelium, and the fruiting rate of matsutake, so as to generate the optimal growth environment conditions of matsutake corresponding to the mycelium growth stage and the fruiting stage. Among them, for the mycelium growth stage, the temperature is maintained at 16 - 25 °C, the air humidity is kept at 80% - 90%, the soil humidity is 60% - 70%, the sunshine duration of warm light LED simulated scattered light is 10 - 12 hours, the oxygen content is 21% - 23%, and the carbon dioxide should be less than 0.1%. For the fruiting stage, the temperature is maintained at 19 - 23 °C, the air humidity is kept at 80% - 90%, the soil humidity is 60% - 70%, the sunshine duration of warm light LED simulated scattered light is 10 - 12 hours, the oxygen content is 22% - 25%, and the carbon dioxide should be less than 0.05%.

6. The intelligent monitoring method for matsutake mushroom cultivation of the agricultural intelligent technology cabin according to claim 5, characterized in that, Step S22 includes the following steps: Obtain the matsutake mycelium growth image corresponding to the mycelium growth stage; Extract the mycelium microscopic parameters from the matsutake mycelium growth images corresponding to the mycelium growth stage at corresponding time intervals to extract the length, diameter, number of branches, and mycelium area of the matsutake mycelium within a period of time, and obtain the mycelium growth microscopic structure parameter set corresponding to the mycelium growth stage; Calculate the macroscopic growth indexes according to the mycelium growth microscopic structure parameter set corresponding to the mycelium growth stage to obtain the mycelium length growth rate, the number of branch increase rate, and the mycelium coverage area growth rate corresponding to the mycelium growth stage; Quantify the growth rate according to the mycelium length growth rate, the number of branch increase rate, and the mycelium coverage area growth rate corresponding to the mycelium growth stage to obtain the growth rate of matsutake mycelium corresponding to the mycelium growth stage.

7. The intelligent monitoring method for matsutake mushroom cultivation in the agricultural intelligent technology cabin according to claim 5, characterized in that, Step S23 includes the following steps: Step S231: Monitor the ecological factors of the matsutake growth environment during the fruiting stage to monitor the corresponding temperature, humidity, light, soil pH, and microbial community structure, and obtain the matsutake growth ecological factor set corresponding to the fruiting stage; Step S232: Analyze the ecological factor characteristics of the matsutake growth ecological factor set corresponding to the fruiting stage to obtain the mean value, extreme value, and fluctuation range corresponding to each ecological factor during the fruiting stage; Step S233: Based on the mean values, extreme values, and fluctuation ranges of each ecological factor during the fruiting period, perform an analysis of the ecological spatial distribution differences among the ecological factors within the corresponding set of Tricholoma matsutake growth ecological factors to obtain the spatial distribution differences among the Tricholoma matsutake ecological factors during the fruiting period; Step S234: According to the spatial distribution differences among the Tricholoma matsutake ecological factors during the fruiting period, divide the corresponding Tricholoma matsutake growth environment during the fruiting period into corresponding sunny and humid micro-regions and shady and dry micro-regions, and monitor the metabolite content, the number of fruiting positions, and the enzyme activity intensity of the Tricholoma matsutake mycelium in real time according to the Tricholoma matsutake in different growth zones; Step S235: Perform a prediction calculation of the fruiting rate according to the metabolite content, the number of fruiting positions, and the enzyme activity intensity of the Tricholoma matsutake mycelium in different growth zones to obtain the Tricholoma matsutake fruiting rate corresponding to the fruiting period.

8. The intelligent monitoring method for matsutake cultivation of the intelligent agricultural technology cabin according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Based on the temperature conditions within the optimal growth environment of Tricholoma matsutake corresponding to the mycelium growth period and the fruiting period, perform an analysis of the growth temperature regulation during the corresponding growth stage within the Tricholoma matsutake cultivation growth stage to generate a corresponding Tricholoma matsutake cultivation growth temperature regulation decision; Step S32: Based on the humidity, light, and gas conditions within the optimal growth environment of Tricholoma matsutake corresponding to the mycelium growth period and the fruiting period, perform an analysis of the regulation of the remaining growth parameters during the corresponding growth stage within the Tricholoma matsutake cultivation growth stage. Taking the optimal growth environment conditions of Tricholoma matsutake during the mycelium growth period and the fruiting period as the goal, adjust the humidity spraying time, light intensity and irradiation duration, and the ventilation frequency of the corresponding gas, so as to generate a corresponding Tricholoma matsutake cultivation regulation decision for the remaining growth; Step S33: Combine the corresponding Tricholoma matsutake cultivation growth temperature regulation decision and the Tricholoma matsutake cultivation regulation decision for the remaining growth for decision support to generate a corresponding intelligent regulation decision for the Tricholoma matsutake cultivation growth environment.

9. The intelligent monitoring method for Tricholoma matsutake cultivation in the intelligent agricultural technology cabin according to claim 8, characterized in that, The growth temperature regulation analysis described in Step S31 includes the following steps: Obtain the corresponding suitable temperature range for growth at the growth stage through the optimal growth environment conditions of Tricholoma matsutake corresponding to the mycelium growth period and the fruiting period; Based on the corresponding suitable temperature range for growth at the growth stage, calculate the growth temperature deviation of the real-time temperature monitored at the corresponding growth stage to obtain the deviation between the temperature sensor monitoring and the suitable temperature range for growth at the corresponding growth stage; Calculate the corresponding cooling / heating power according to the deviation between the temperature sensor monitoring and the suitable temperature range for growth at the corresponding growth stage, and adjust the growth temperature in the agricultural intelligent technology cabin to the corresponding suitable temperature range for growth at the corresponding growth stage according to the corresponding cooling / heating power, so as to generate a corresponding Tricholoma matsutake cultivation growth temperature regulation decision.

10. A smart monitoring system for Tricholoma matsutake cultivation in an agricultural intelligent technology cabin, characterized in that, For implementing the intelligent monitoring method for Tricholoma matsutake cultivation in the agricultural intelligent technology cabin as claimed in Claim 1, the intelligent monitoring system for Tricholoma matsutake cultivation in the agricultural intelligent technology cabin includes: A Tricholoma matsutake growth environment monitoring module, which is used to monitor the temperature data, humidity data, light environment data, and gas concentration data corresponding to the Tricholoma matsutake cultivation growth stage in real time in the agricultural intelligent technology cabin, and transmit them to the data processing center in real time using wireless communication technology; The optimal growth environment screening module is used to classify the growth stages by using the data processing center for the temperature data, humidity data, light environment data, and gas concentration data corresponding to the growth stages of matsutake cultivation, so as to output the matsutake growth environment parameter conditions for each combination corresponding to the mycelium growth period and the fruiting period; obtain the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, and based on the growth status of matsutake corresponding to the mycelium growth period and the fruiting period, screen the optimal environment for the corresponding matsutake growth environment parameter conditions, so as to generate the optimal growth environment conditions of matsutake corresponding to the mycelium growth period and the fruiting period; The intelligent matsutake growth monitoring module is used to conduct intelligent monitoring and analysis of the growth of matsutake in the corresponding growth stages during the matsutake cultivation growth stage based on the optimal growth environment conditions of matsutake corresponding to the mycelium growth period and the fruiting period, so as to generate the intelligent regulation decision for the corresponding matsutake cultivation growth environment.

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