A Method and System for Identifying the Maturity of Shiitake Mushroom Sticks Based on a Maturity Identification Device

Abstract

This invention provides a method and system for identifying the maturity of shiitake mushroom logs based on a maturity identification device, belonging to the field of agricultural information technology. The method includes: acquiring a set of shiitake mushroom log feature data; inputting the set of shiitake mushroom log feature data into a pre-trained maturity prediction model to obtain the maturity identification result of the shiitake mushroom logs; wherein the maturity prediction model is trained on a convolutional gated recurrent unit based on the shiitake mushroom log feature sample set and according to the shiitake mushroom log data classification labels. The system includes: an internal temperature sensor for the logs, a log environment acquisition device, a wireless base station, and a cloud platform. This invention proposes a non-destructive method for identifying the maturity of shiitake mushroom logs and constructs a shiitake mushroom log maturity identification system. It employs ultrasonic temperature measurement and a shiitake mushroom log mycelium maturity identification model utilizing deep fusion of multi-source data information, effectively improving the accuracy and efficiency of maturity identification.

Description

Technical Field

[0001] This invention relates to the field of agricultural information technology, and in particular to a method and system for identifying the maturity of shiitake mushroom logs based on a maturity identification device. Background Technology

[0002] In the cultivation of fungi, the incubation time for edible fungi, represented by shiitake mushrooms, is much longer than the fruiting time. After the mycelium reaches physiological maturity, it needs to be moved to the fruiting shed for fruiting. Therefore, accurately determining the physiological maturity time of the mycelium and timely fruiting can significantly shorten the production cycle, improve the quality of fruiting, and reduce energy consumption during the incubation stage.

[0003] Currently, standard testing methods for mycelial physiological maturity typically include biochemical testing, appearance assessment, and accumulated temperature methods. Biochemical testing primarily involves offline detection of biochemical indicators, requiring sampling in a laboratory, which is time-consuming and labor-intensive. Appearance assessment usually uses the density and robustness of mycelia, color change, and the degree of nodulation within the cultivation bag, as well as the firmness of the bag texture, as indicators of physiological maturity. This method is highly subjective and requires extensive experience from the grower. The accumulated temperature method suffers from a lack of methods for measuring the internal temperature of the substrate; mushroom farmers commonly use air temperature or the surface temperature of the substrate to represent the internal temperature, leading to significant measurement errors. Some farmers even use metal probes to measure internal temperature, which only represent a point temperature and cannot reflect the overall temperature of the substrate, while also disrupting the mycelial growth microenvironment. Furthermore, the lack of information technology means that various indicators of mycelial physiological maturity are not simultaneously and effectively acquired, resulting in incomplete measurement information.

[0004] Given the various shortcomings of the existing maturity detection methods, there is an urgent need to propose a new method and system for identifying the maturity of shiitake mushroom spawn. Summary of the Invention

[0005] This invention provides a method and system for identifying the maturity of shiitake mushroom logs based on a maturity identification device, which solves the shortcomings of existing technologies in detecting the maturity of shiitake mushroom logs, such as the large influence of subjective factors, easy damage to the logs, and insufficient accuracy of measurement results.

[0006] In a first aspect, the present invention provides a method for identifying the maturity of shiitake mushroom spawn based on a maturity identification device, comprising:

[0007] Obtain the feature data set of shiitake mushroom spawn;

[0008] The set of shiitake mushroom log feature data is input into a pre-trained maturity prediction model to obtain the maturity recognition result of the shiitake mushroom log; wherein the maturity prediction model is trained on a convolutional gated recurrent unit based on the set of shiitake mushroom log feature samples and the classification labels of shiitake mushroom log data.

[0009] The shiitake mushroom spawn maturity identification device includes:

[0010] Temperature sensor inside the mushroom log, mushroom log environment acquisition equipment, wireless base station and cloud platform;

[0011] The internal temperature sensor of the mushroom stick is installed on the shiitake mushroom stick and is used to collect internal temperature data of the mushroom stick;

[0012] The mushroom substrate environment acquisition device is installed inside the mushroom cultivation room and is used to collect external environmental data of the mushroom substrate.

[0013] The wireless base station is connected to the internal temperature sensor of the mushroom stick and the environmental acquisition device of the mushroom stick, respectively, and is used to receive the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick;

[0014] The cloud platform is connected to the wireless base station and is used to output the maturity identification result of the shiitake mushroom sticks based on the internal temperature data and external environmental data of the mushroom sticks, and send the maturity identification result of the shiitake mushroom sticks to the terminal device.

[0015] According to the present invention, a method for identifying the maturity of shiitake mushroom spawn based on a maturity identification device is provided, wherein the maturity prediction model is obtained through the following steps:

[0016] A set of internal temperature samples of the mushroom sticks and a set of external environmental samples were obtained by non-destructive measurement of internal temperature of the mushroom sticks.

[0017] The temperature sample set inside the mushroom stick and the environmental sample set outside the mushroom stick are used to construct a feature data time series according to the data dimension and the time dimension;

[0018] The time series of the characteristic data was measured using biochemical assays to obtain the classification labels for the shiitake mushroom spawn.

[0019] The time series of feature data labeled with the corresponding shiitake mushroom stick data classification tags are classified to generate several datasets for training.

[0020] Obtain the initial network model using the convolutional gated recurrent unit;

[0021] The aforementioned datasets are input into the initial network model for training to obtain the maturity prediction model.

[0022] According to the present invention, a method for identifying the maturity of shiitake mushroom logs based on a maturity identification device is provided, wherein obtaining a set of internal temperature samples of the logs through a non-destructive measurement method includes:

[0023] The average diameter of the mushroom logs, the attenuation characteristics of the ultrasonic energy amplitude in the mushroom logs, and the ultrasonic emission frequency were obtained.

[0024] Based on the average diameter of the mushroom stick, the attenuation characteristics of the ultrasonic energy amplitude, and the ultrasonic emission frequency, the ultrasonic emission power that can penetrate the average diameter of the mushroom stick is determined.

[0025] Based on the ultrasonic emission power, the average ultrasonic velocity in the mushroom stick is obtained;

[0026] By utilizing the correlation between the average velocity of the ultrasonic waves and the temperature of the substrate medium, a set of temperature samples inside the substrate is determined.

[0027] According to the present invention, a method for identifying the maturity of shiitake mushroom spawn based on a maturity identification device is provided, wherein constructing a feature data time series by arranging the set of internal temperature samples and the set of external environmental samples of the spawn according to data dimensions and time dimensions includes:

[0028] The set of internal temperature samples of the mushroom stick and the set of external environmental samples of the mushroom stick are connected in series to form a series sample set;

[0029] The cascaded sample set is extracted according to a preset time step, and the feature data time series is output.

[0030] According to the present invention, a method for identifying the maturity of shiitake mushroom spawn based on a maturity identification device, wherein the step of inputting the plurality of datasets into the initial network model for training to obtain the maturity prediction model includes:

[0031] After preprocessing, the datasets are input into the input layer of the initial model of the network.

[0032] The preprocessed datasets are subjected to preset high-dimensional feature extraction by the convolutional layer connected to the input layer, and the preset high-dimensional features are reduced in dimensionality by max pooling to obtain a global feature vector.

[0033] The global feature vector is input into a gated recurrent unit layer for learning. The learned global feature vector is processed using a preset activation function, and the output value is inversely normalized to obtain the maturity prediction model.

[0034] According to the present invention, a method for identifying the maturity of shiitake mushroom logs based on a maturity identification device is provided. The internal temperature sensor of the log includes a receiving transducer, a signal amplification circuit, a filtering circuit, an ultrasonic detection and generation unit, a microcontroller, a power amplification circuit, and a transmitting transducer.

[0035] The microcontroller, the ultrasonic detection and generation unit, the power amplifier circuit, and the transmitting transducer are connected in sequence.

[0036] The ultrasonic detection and generation unit sends the generated ultrasonic signal to the power amplifier circuit for amplification, and then sends the amplified ultrasonic signal to the transmitting transducer, which is connected through the middle of the mushroom stick.

[0037] The receiving transducer, the signal amplification circuit, the filtering circuit, the ultrasonic detection and generation unit, and the microcontroller are connected in sequence.

[0038] The receiving transducer is connected through the middle of the mushroom stick. After receiving the ultrasonic signal from the mushroom stick, the receiving transducer sends it to the signal amplification circuit for signal amplitude amplification, and then passes it through the filtering circuit for noise filtering. The ultrasonic signal is then sent to the ultrasonic detection and generation unit to obtain the ultrasonic speed. The ultrasonic speed is then transmitted to the microcontroller through a preset digital communication interface, and the microcontroller uploads the data through a preset network interface.

[0039] According to the present invention, a method for identifying the maturity of shiitake mushroom logs based on a maturity identification device is provided. The log environment acquisition device includes a microcontroller, an air temperature and humidity sensor, a carbon dioxide concentration sensor, a light intensity sensor, a transmission unit, and a power supply unit.

[0040] The microcontroller is connected to the air temperature and humidity sensor, the carbon dioxide concentration sensor, the light intensity sensor, and the transmission unit, respectively.

[0041] The air temperature and humidity sensor is used to collect temperature and humidity data in the air, the carbon dioxide concentration sensor is used to collect carbon dioxide concentration data in the air, and the light intensity sensor is used to collect light intensity data.

[0042] The transmission unit is used to transmit the temperature data, humidity data, carbon dioxide concentration data, and light intensity data to the wireless base station according to a preset cycle.

[0043] The power supply unit supplies power to the microcontroller, the air temperature and humidity sensor, the carbon dioxide concentration sensor, the light intensity sensor, and the transmission unit, respectively.

[0044] According to the present invention, a method for identifying the maturity of shiitake mushroom logs based on a maturity identification device is provided, wherein the wireless base station includes a microcontroller, a near-field transmission unit, a storage unit, and a remote transmission unit;

[0045] The microcontroller is connected to the near-field transmission unit, the storage unit, and the remote transmission unit, respectively.

[0046] The near-field transmission unit includes a long-range radio LoRa receiver unit and a LoRa transmitter unit. The LoRa receiver unit is used to receive the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick. The LoRa transmitter unit is used to send feedback information to the mushroom stick environmental acquisition device.

[0047] The storage unit is used to store the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick;

[0048] The remote transmission unit is used to transmit the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick to the cloud platform.

[0049] According to the present invention, a method for identifying the maturity of shiitake mushroom logs based on a maturity identification device is provided, wherein the cloud platform includes an access gateway layer, a device and data management layer, a data analysis and model management system, and a data storage system;

[0050] The data analysis and model management system is connected to the access gateway layer, the device and data management layer, and the data storage system, respectively.

[0051] The access gateway layer is used to connect to the wireless base station;

[0052] The device and data management layer is used for device management and pushes the maturity recognition results of shiitake mushroom sticks to the terminal devices;

[0053] The data analysis and model management system is used to execute the shiitake mushroom stick maturity identification method;

[0054] The data storage system is used to store backup intermediate data and result data.

[0055] Secondly, the present invention also provides a shiitake mushroom spawn maturity identification system based on a maturity identification device, comprising:

[0056] The acquisition module is used to acquire a set of characteristic data of shiitake mushroom spawn.

[0057] The identification module is used to input the feature data set of the shiitake mushroom logs into a pre-trained maturity prediction model to obtain the maturity identification result of the shiitake mushroom logs; wherein the maturity prediction model is trained on a convolutional gated recurrent unit based on the feature sample set of shiitake mushroom logs and the classification labels of the shiitake mushroom log data.

[0058] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the shiitake mushroom spawn maturity identification method as described above.

[0059] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the shiitake mushroom spawn maturity identification method as described above.

[0060] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the shiitake mushroom spawn maturity identification method as described above.

[0061] The present invention provides a method and system for identifying the maturity of shiitake mushroom logs based on a maturity identification device. By proposing a non-destructive method for identifying the maturity of shiitake mushroom logs and constructing a shiitake mushroom log maturity identification system, the method effectively improves the accuracy and efficiency of maturity identification by using ultrasonic temperature measurement and a shiitake mushroom log mycelium maturity identification model that utilizes deep fusion of multi-source data information. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0063] Figure 1 This is a flowchart illustrating the method for identifying the maturity of shiitake mushroom logs based on a maturity identification device provided by the present invention.

[0064] Figure 2 This is a schematic diagram of the experimental setup for identifying key parameters of the non-destructive temperature sensor for mushroom logs provided by the present invention.

[0065] Figure 3 This is a model structure diagram of the CNN-GRU network provided by the present invention;

[0066] Figure 4 This is an overall structural diagram of the mushroom spawn maturity identification device provided by the present invention;

[0067] Figure 5 This is a schematic diagram of the layout of the mushroom spawn maturity identification device provided by the present invention;

[0068] Figure 6 This is a schematic diagram of the internal temperature sensor of the mushroom stick provided by the present invention;

[0069] Figure 7 This is a schematic diagram of the structure of the mushroom stick environment collection device provided by the present invention;

[0070] Figure 8 This is a schematic diagram of the structure of the wireless base station provided by the present invention;

[0071] Figure 9 This is a schematic diagram of the cloud platform provided by the present invention;

[0072] Figure 10 This is a schematic diagram of the structure of the shiitake mushroom log maturity identification system based on the maturity identification device provided by the present invention;

[0073] Figure 11 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0075] Figure 1 This is a flowchart illustrating the method for identifying the maturity of shiitake mushroom spawn based on a maturity identification device provided by the present invention. Figure 1 As shown, it includes:

[0076] Step 100: Obtain the feature data set of shiitake mushroom spawn;

[0077] Step 200: Input the set of shiitake mushroom log feature data into the pre-trained maturity prediction model to obtain the shiitake mushroom log maturity recognition result; wherein the maturity prediction model is based on the set of shiitake mushroom log feature samples and the convolutional gated recurrent unit trained according to the shiitake mushroom log data classification label.

[0078] The shiitake mushroom spawn maturity identification device includes:

[0079] Temperature sensor inside the mushroom log, mushroom log environment acquisition equipment, wireless base station and cloud platform;

[0080] The internal temperature sensor of the mushroom stick is installed on the shiitake mushroom stick and is used to collect internal temperature data of the mushroom stick;

[0081] The mushroom substrate environment acquisition device is installed inside the mushroom cultivation room and is used to collect external environmental data of the mushroom substrate.

[0082] The wireless base station is connected to the internal temperature sensor of the mushroom stick and the environmental acquisition device of the mushroom stick, respectively, and is used to receive the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick;

[0083] The cloud platform is connected to the wireless base station and is used to output the maturity identification result of the shiitake mushroom sticks based on the internal temperature data and external environmental data of the mushroom sticks, and send the maturity identification result of the shiitake mushroom sticks to the terminal device.

[0084] It should be noted that the main body executing the shiitake mushroom log maturity identification method provided in this embodiment of the invention is the shiitake mushroom log maturity identification device.

[0085] This invention acquires a set of various characteristic data of the mushroom logs to be measured online in real time, including internal temperature and various external environmental parameters, and inputs the set of various characteristic data into the maturity prediction model to obtain the maturity identification result of the mushroom logs.

[0086] The maturity prediction model here is obtained by using a large set of shiitake mushroom log feature samples obtained from the shiitake mushroom log maturity recognition system, introducing a convolutional gated recurrent unit as the basic model, and training the model based on the shiitake mushroom log data classification labels.

[0087] It is understandable that, such as Figure 4 The mushroom spawn maturity identification device shown employs temperature sensors evenly distributed both longitudinally and laterally within the cultivation room. Corresponding environmental data acquisition devices are deployed around the adjacent spawn. The entire device dynamically collects air temperature and humidity, carbon dioxide levels, light intensity, and spawn temperature every half hour, transmitting the data wirelessly to a base station. Upon receiving the data, the base station uploads it to a cloud platform via 4G. The cloud platform calculates the maturity level using a mycelial maturity identification model and then pushes the result to the mushroom farmer's mobile phone. For detailed deployment instructions, please refer to [reference needed]. Figure 5 As shown.

[0088] The present invention provides a digital and online identification method for the physiological maturity of mycelium during the mycelial growth period. This method overcomes the limitations of traditional methods that rely on the subjective experience of mushroom farmers, enabling accurate prediction of the timing of transfer to the mushroom shed and simultaneously improving both the yield and quality of mushrooms. At the same time, by introducing ultrasonic technology into the internal temperature measurement of the mushroom logs, the method achieves accurate inversion of the overall temperature of the mushroom logs, providing technical support for the digital management of mycelial growth production. Combined with external environmental data, the method has high accuracy and strong objectivity.

[0089] Based on the above embodiments, the maturity prediction model is obtained through the following steps:

[0090] A set of internal temperature samples of the mushroom sticks and a set of external environmental samples were obtained by non-destructive measurement of internal temperature of the mushroom sticks.

[0091] The temperature sample set inside the mushroom stick and the environmental sample set outside the mushroom stick are used to construct a feature data time series according to the data dimension and the time dimension;

[0092] The time series of the characteristic data was measured using biochemical assays to obtain the classification labels for the shiitake mushroom spawn.

[0093] The time series of feature data labeled with the corresponding shiitake mushroom stick data classification tags are classified to generate several datasets for training.

[0094] Obtain the initial network model using the convolutional gated recurrent unit;

[0095] The aforementioned datasets are input into the initial network model for training to obtain the maturity prediction model.

[0096] The method for obtaining a set of internal temperature samples of the mushroom sticks through non-destructive measurement includes:

[0097] The average diameter of the mushroom logs, the attenuation characteristics of the ultrasonic energy amplitude in the mushroom logs, and the ultrasonic emission frequency were obtained.

[0098] Based on the average diameter of the mushroom stick, the attenuation characteristics of the ultrasonic energy amplitude, and the ultrasonic emission frequency, the ultrasonic emission power that can penetrate the average diameter of the mushroom stick is determined.

[0099] Based on the ultrasonic emission power, the average ultrasonic velocity in the mushroom stick is obtained;

[0100] By utilizing the correlation between the average velocity of the ultrasonic waves and the temperature of the substrate medium, a set of temperature samples inside the substrate is determined.

[0101] The step of constructing a feature data time series by combining the set of internal temperature samples and the set of external environmental samples of the mushroom sticks according to the data dimension and the time dimension includes:

[0102] The set of internal temperature samples of the mushroom stick and the set of external environmental samples of the mushroom stick are connected in series to form a series sample set;

[0103] The cascaded sample set is extracted according to a preset time step, and the feature data time series is output.

[0104] The step of inputting the plurality of datasets into the initial network model for training to obtain the maturity prediction model includes:

[0105] After preprocessing, the datasets are input into the input layer of the initial model of the network.

[0106] The preprocessed datasets are subjected to preset high-dimensional feature extraction by the convolutional layer connected to the input layer, and the preset high-dimensional features are reduced in dimensionality by max pooling to obtain a global feature vector.

[0107] The global feature vector is input into a gated recurrent unit layer for learning. The learned global feature vector is processed using a preset activation function, and the output value is inversely normalized to obtain the maturity prediction model.

[0108] Specifically, the maturity prediction model proposed in this invention is trained as follows:

[0109] The system acquires online data on the internal temperatures of shiitake mushroom spawn logs, as well as external environmental samples including air temperature and humidity, light intensity, and carbon dioxide concentration.

[0110] Specifically, for the temperature measurement inside the shiitake mushroom spawn, this invention employs a non-destructive internal temperature measurement technique, utilizing, for example... Figure 2 The high-speed oscilloscope shown acquires the sound velocity of ultrasound in the main media of mushroom substrates such as sawdust, cottonseed hulls, and bran under various initial moisture contents, and calculates the average velocity of ultrasound in the substrates under different initial moisture contents. Based on the average diameter of the substrates, a certain ultrasonic wave power is set to ensure it can penetrate the substrate. Different transmission frequencies are then set, and the energy amplitude attenuation characteristics of the ultrasound in the substrates are measured. Considering the low-frequency signal as the optimal choice, a suitable transmission frequency is further selected. Then, based on the optimal frequency, multiple gradient transmission powers are set, and the energy amplitude attenuation characteristics of the ultrasound in the main media of the substrates are measured. Combined with the circuit signal-to-noise ratio, a suitable transmission power is selected.

[0111] Then, the temperature sample set inside the mushroom stick and the environmental sample set outside the mushroom stick are used to construct a feature data time series according to the data dimension and time dimension. That is, a two-dimensional matrix is ​​constructed as input. A convolutional neural network is used to mine the effective information contained in the data, extract high-dimensional features that reflect the interrelationship of mycelial maturity data, and construct the extracted feature vectors as the time series input maturity prediction model for maturity identification.

[0112] This invention selects a Convolutional Neural Network-GateRecurrent Unit (CNN-GRU) as the network model, which includes an input layer, a convolutional CNN layer, and a gated recurrent unit (GRU) layer.

[0113] In the input layer, the temperature and humidity values, carbon dioxide concentration, indoor light intensity, and internal temperature of the mushroom logs at any given time in the incubation room are concatenated into a new time series feature vector. The historical climate data of the room is represented as a two-dimensional matrix of time step × feature vector, which is then preprocessed and input into the network model mentioned above.

[0114] In the CNN layer, deep temporal and spatial features are captured from the input historical sequence. This is based on the characteristics of the input data, such as nonlinearity, sparsity, and strong coupling. Figure 3 As shown, this invention relates to a four-layer Conv2D convolutional layer with kernel numbers of 16, 16, 32, and 32 respectively, using the ReLU activation function. MaxPooling2D is performed after every two consecutive convolutions to reduce the dimensionality of the extracted high-dimensional features, thereby compressing data and improving processing efficiency. Furthermore, to fully utilize existing indoor environmental distribution data and non-temporal multi-feature data, the kernel size is set to 3×3, and the pooling size is 2. Finally, the Flatten operation is used to convert the extracted deep abstract features into a global feature vector, which serves as the input to the GRU layer.

[0115] In the GRU layer, the global feature vectors extracted from the CNN layer are learned by the GRU layer. Through continuous improvement in experiments, it was found that the best prediction effect can be achieved when constructing a two-layer GRU structure. The activation function used here is the ReLU activation function, and the number of neurons is 64 and 128 respectively. Finally, the output of the fully connected layer is denormalized to obtain the mycelial maturity after a certain period of time.

[0116] It should be noted that in this invention, the feature sample set is divided into training and test sets according to a certain ratio during model training. For example, 80% of the samples are randomly selected to form the training set, and the remaining 20% ​​form the test set. The entire model is implemented based on the Keras toolkit, using Python as the programming language, and PyCharm as the integrated development environment.

[0117] This invention effectively improves the accuracy and efficiency of maturity identification by employing ultrasonic temperature measurement and a maturity identification model for shiitake mushroom spawn through deep fusion of multi-source data.

[0118] Based on the above embodiments, the internal temperature sensor of the mushroom stick includes a receiving transducer, a signal amplification circuit, a filtering circuit, an ultrasonic detection and generation unit, a microcontroller, a power amplification circuit, and a transmitting transducer;

[0119] The microcontroller, the ultrasonic detection and generation unit, the power amplifier circuit, and the transmitting transducer are connected in sequence.

[0120] The ultrasonic detection and generation unit sends the generated ultrasonic signal to the power amplifier circuit for amplification, and then sends the amplified ultrasonic signal to the transmitting transducer, which is connected through the middle of the mushroom stick.

[0121] The receiving transducer, the signal amplification circuit, the filtering circuit, the ultrasonic detection and generation unit, and the microcontroller are connected in sequence.

[0122] The receiving transducer is connected through the middle of the mushroom stick. After receiving the ultrasonic signal from the mushroom stick, the receiving transducer sends it to the signal amplification circuit for signal amplitude amplification, and then passes it through the filtering circuit for noise filtering. The ultrasonic signal is then sent to the ultrasonic detection and generation unit to obtain the ultrasonic speed. The ultrasonic speed is then transmitted to the microcontroller through a preset digital communication interface, and the microcontroller uploads the data through a preset network interface.

[0123] Specifically, such as Figure 6 As shown, the internal temperature sensor of the mushroom log includes a receiving transducer, a signal amplification circuit, a filtering circuit, an ultrasonic detection and generation unit, a microcontroller, a power amplification circuit, and a transmitting transducer. The ultrasonic detection and generation unit uses the TDC-GP21 model. After generating an ultrasonic signal of a certain frequency, it is amplified by the power amplification circuit and then transmitted through the transmitting transducer, passing through the middle of the mushroom log. The receiving transducer receives the ultrasonic signal, which is amplified by the signal amplification circuit and then filtered to remove noise before being sent to the ultrasonic detection and generation unit to detect the ultrasonic velocity. The microcontroller interacts with the ultrasonic detection and generation unit via a digital communication interface using the SPI protocol, and then uploads temperature data to a wireless base station via an RS485 interface.

[0124] Based on the above embodiments, the mushroom stick environment acquisition device includes a microcontroller, an air temperature and humidity sensor, a carbon dioxide concentration sensor, a light intensity sensor, a transmission unit, and a power supply unit;

[0125] The microcontroller is connected to the air temperature and humidity sensor, the carbon dioxide concentration sensor, the light intensity sensor, and the transmission unit, respectively.

[0126] The air temperature and humidity sensor is used to collect temperature and humidity data in the air, the carbon dioxide concentration sensor is used to collect carbon dioxide concentration data in the air, and the light intensity sensor is used to collect light intensity data.

[0127] The transmission unit is used to transmit the temperature data, humidity data, carbon dioxide concentration data, and light intensity data to the wireless base station according to a preset cycle.

[0128] The power supply unit supplies power to the microcontroller, the air temperature and humidity sensor, the carbon dioxide concentration sensor, the light intensity sensor, and the transmission unit, respectively.

[0129] Specifically, such as Figure 7 As shown, the mushroom stick environment acquisition device includes a microcontroller, an air temperature and humidity sensor, a carbon dioxide concentration sensor, a light intensity sensor, a transmission unit, and a power supply unit.

[0130] The microcontroller controls the acquisition of air temperature and humidity sensors, carbon dioxide concentration sensors, and light intensity sensors, and the data is uploaded to the wireless base station by the transmission unit according to the set transmission cycle. The power supply unit uses a high-capacity lithium battery to power the other modules.

[0131] Based on the above embodiments, the wireless base station includes a microcontroller, a near-field transmission unit, a storage unit, and a remote transmission unit;

[0132] The microcontroller is connected to the near-field transmission unit, the storage unit, and the remote transmission unit, respectively.

[0133] The near-field transmission unit includes a long-range radio LoRa receiver unit and a LoRa transmitter unit. The LoRa receiver unit is used to receive the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick. The LoRa transmitter unit is used to send feedback information to the mushroom stick environmental acquisition device.

[0134] The storage unit is used to store the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick;

[0135] The remote transmission unit is used to transmit the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick to the cloud platform.

[0136] Specifically, such as Figure 8 As shown, the wireless base station consists of a microcontroller, a near-field transmission unit, a storage unit, and a remote transmission unit, and is powered by 12V DC. The near-field transmission unit comprises a LoRa receiver and a LoRa transmitter. The LoRa receiver receives information from the mushroom substrate environmental acquisition device and the internal temperature sensor of the mushroom substrate. The LoRa transmitter responds to the wireless environmental acquisition device, arranges the received data according to a specified protocol, and stores the data in the storage unit. Finally, the data in the storage unit is remotely uploaded to the cloud platform at regular intervals via wireless methods such as GPRS / 4G.

[0137] Based on the above embodiments, the cloud platform includes an access gateway layer, a device and data management layer, a data analysis and model management system, and a data storage system;

[0138] The data analysis and model management system is connected to the access gateway layer, the device and data management layer, and the data storage system, respectively.

[0139] The access gateway layer is used to connect to the wireless base station;

[0140] The device and data management layer is used for device management and pushes the maturity recognition results of shiitake mushroom sticks to the terminal devices;

[0141] The data analysis and model management system is used to execute the shiitake mushroom stick maturity identification method;

[0142] The data storage system is used to store backup intermediate data and result data.

[0143] Specifically, such as Figure 9 As shown, the cloud platform consists of an access gateway layer, a device and data management layer, a data analysis and model management system, and a data storage system. The access gateway layer receives data from wireless base stations via GPRS / 4G, etc. The device and data management layer is responsible for managing user devices and devices within the same culture workshop, and pushing identification results to user mobile devices. The data analysis and model management system contains the code for implementing specific physiological maturity identification methods. The data storage system is responsible for storing and backing up intermediate data and results.

[0144] This invention constructs a shiitake mushroom spawn maturity identification device, which can comprehensively, accurately, and in real time identify the internal and external data of shiitake mushroom spawn.

[0145] The maturity identification system for shiitake mushroom spawn provided by this invention is described below. The maturity identification system for shiitake mushroom spawn described below can be referred to in correspondence with the maturity identification method for shiitake mushroom spawn described above.

[0146] Figure 10 This is a schematic diagram of the structure of the shiitake mushroom log maturity identification system based on a maturity identification device provided by the present invention, as shown below. Figure 10 As shown, it includes: an acquisition module 1001 and an identification module 1002, wherein:

[0147] The acquisition module 1001 is used to acquire a set of shiitake mushroom log feature data; the identification module 1002 is used to input the set of shiitake mushroom log feature data into a pre-trained maturity prediction model to obtain the maturity identification result of the shiitake mushroom log; wherein the maturity prediction model is trained on a convolutional gated recurrent unit based on the set of shiitake mushroom log feature samples and according to the shiitake mushroom log data classification label.

[0148] This invention proposes a non-destructive method for identifying the maturity of shiitake mushroom substrate and constructs a shiitake mushroom substrate maturity identification system. It adopts ultrasonic temperature measurement and a shiitake mushroom substrate mycelium maturity identification model that utilizes deep fusion of multi-source data information, which effectively improves the accuracy and efficiency of maturity identification.

[0149] Figure 11 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 11 As shown, the electronic device may include a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140. The processor 1110 can call logical instructions in the memory 1130 to execute a method for identifying the maturity of shiitake mushroom spawn. This method includes: acquiring a set of shiitake mushroom spawn feature data; inputting the set of shiitake mushroom spawn feature data into a pre-trained maturity prediction model to obtain a shiitake mushroom spawn maturity identification result; wherein the maturity prediction model is trained on a convolutional gated recurrent unit based on a set of shiitake mushroom spawn feature samples and according to the shiitake mushroom spawn data classification labels.

[0150] Furthermore, the logical instructions in the aforementioned memory 1130 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0151] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the shiitake mushroom spawn maturity recognition method provided by the above methods. The method includes: acquiring a set of shiitake mushroom spawn feature data; inputting the set of shiitake mushroom spawn feature data into a pre-trained maturity prediction model to obtain a shiitake mushroom spawn maturity recognition result; wherein the maturity prediction model is trained on a convolutional gated recurrent unit based on a set of shiitake mushroom spawn feature samples and according to the shiitake mushroom spawn data classification labels.

[0152] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the shiitake mushroom spawn maturity identification method provided by the above methods. The method includes: acquiring a set of shiitake mushroom spawn feature data; inputting the set of shiitake mushroom spawn feature data into a pre-trained maturity prediction model to obtain a shiitake mushroom spawn maturity identification result; wherein the maturity prediction model is obtained by training convolutional gated recurrent units based on a set of shiitake mushroom spawn feature samples and according to the shiitake mushroom spawn data classification labels.

[0153] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0154] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the maturity of shiitake mushroom spawn based on a maturity identification device, characterized in that, include: Obtain the feature data set of shiitake mushroom spawn; The set of shiitake mushroom log feature data is input into a pre-trained maturity prediction model to obtain the maturity recognition result of the shiitake mushroom log; wherein the maturity prediction model is trained on a convolutional gated recurrent unit based on the set of shiitake mushroom log feature samples and the classification labels of shiitake mushroom log data. The shiitake mushroom spawn maturity identification device includes: Temperature sensor inside the mushroom log, mushroom log environment acquisition equipment, wireless base station and cloud platform; The internal temperature sensor of the mushroom stick is installed on the shiitake mushroom stick and is used to collect internal temperature data of the mushroom stick; The mushroom substrate environment acquisition device is installed inside the mushroom cultivation room and is used to collect external environmental data of the mushroom substrate. The wireless base station is connected to the internal temperature sensor of the mushroom stick and the environmental acquisition device of the mushroom stick, respectively, and is used to receive the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick; The cloud platform is connected to the wireless base station and is used to output the maturity identification result of the shiitake mushroom sticks based on the internal temperature data and the external environmental data of the mushroom sticks, and send the maturity identification result of the shiitake mushroom sticks to the terminal device; The maturity prediction model is obtained through the following steps: A set of internal temperature samples of the mushroom sticks and a set of external environmental samples were obtained by non-destructive measurement of internal temperature of the mushroom sticks. The temperature sample set inside the mushroom stick and the environmental sample set outside the mushroom stick are used to construct a feature data time series according to the data dimension and the time dimension; The time series of the characteristic data was measured using biochemical assays to obtain the classification labels for the shiitake mushroom spawn. The time series of feature data labeled with the corresponding shiitake mushroom stick data classification tags are classified to generate several datasets for training. Obtain the initial network model using the convolutional gated recurrent unit; The aforementioned datasets are input into the initial network model for training to obtain the maturity prediction model.

2. The method for identifying the maturity of shiitake mushroom logs based on a maturity identification device according to claim 1, characterized in that, The method for obtaining a set of internal temperature samples of the mushroom logs using a non-destructive measurement method includes: The average diameter of the mushroom logs, the attenuation characteristics of the ultrasonic energy amplitude in the mushroom logs, and the ultrasonic emission frequency were obtained. Based on the average diameter of the mushroom stick, the attenuation characteristics of the ultrasonic energy amplitude, and the ultrasonic emission frequency, the ultrasonic emission power that can penetrate the average diameter of the mushroom stick is determined. Based on the ultrasonic emission power, the average ultrasonic velocity in the mushroom stick is obtained; By utilizing the correlation between the average velocity of the ultrasonic waves and the temperature of the substrate medium, a set of temperature samples inside the substrate is determined.

3. The method for identifying the maturity of shiitake mushroom logs based on a maturity identification device according to claim 1, characterized in that, The step of constructing a feature data time series by combining the set of internal temperature samples and the set of external environmental samples of the mushroom sticks according to the data dimension and the time dimension includes: The set of internal temperature samples of the mushroom stick and the set of external environmental samples of the mushroom stick are connected in series to form a series sample set; The cascaded sample set is extracted according to a preset time step, and the feature data time series is output.

4. The method for identifying the maturity of shiitake mushroom logs based on a maturity identification device according to claim 1, characterized in that, The step of inputting the plurality of datasets into the initial network model for training to obtain the maturity prediction model includes: After preprocessing, the datasets are input into the input layer of the initial model of the network. The preprocessed datasets are subjected to preset high-dimensional feature extraction by the convolutional layer connected to the input layer, and the preset high-dimensional features are reduced in dimensionality by max pooling to obtain a global feature vector. The global feature vector is input into a gated recurrent unit layer for learning. The learned global feature vector is processed using a preset activation function, and the output value is inversely normalized to obtain the maturity prediction model.

5. The method for identifying the maturity of shiitake mushroom logs based on a maturity identification device according to claim 1, characterized in that, The internal temperature sensor of the mushroom stick includes a receiving transducer, a signal amplification circuit, a filtering circuit, an ultrasonic detection and generation unit, a microcontroller, a power amplification circuit, and a transmitting transducer. The microcontroller, the ultrasonic detection and generation unit, the power amplifier circuit, and the transmitting transducer are connected in sequence. The ultrasonic detection and generation unit sends the generated ultrasonic signal to the power amplifier circuit for amplification, and then sends the amplified ultrasonic signal to the transmitting transducer, which is connected through the middle of the mushroom stick. The receiving transducer, the signal amplification circuit, the filtering circuit, the ultrasonic detection and generation unit, and the microcontroller are connected in sequence. The receiving transducer is connected through the middle of the mushroom stick. After receiving the ultrasonic signal from the mushroom stick, the receiving transducer sends it to the signal amplification circuit for signal amplitude amplification, and then passes it through the filtering circuit for noise filtering. The ultrasonic signal is then sent to the ultrasonic detection and generation unit to obtain the ultrasonic speed. The ultrasonic speed is then transmitted to the microcontroller through a preset digital communication interface, and the microcontroller uploads the data through a preset network interface.

6. The method for identifying the maturity of shiitake mushroom logs based on a maturity identification device according to claim 1, characterized in that, The mushroom stick environmental acquisition device includes a microcontroller, an air temperature and humidity sensor, a carbon dioxide concentration sensor, a light intensity sensor, a transmission unit, and a power supply unit. The microcontroller is connected to the air temperature and humidity sensor, the carbon dioxide concentration sensor, the light intensity sensor, and the transmission unit, respectively. The air temperature and humidity sensor is used to collect temperature and humidity data in the air, the carbon dioxide concentration sensor is used to collect carbon dioxide concentration data in the air, and the light intensity sensor is used to collect light intensity data. The transmission unit is used to transmit the temperature data, humidity data, carbon dioxide concentration data, and light intensity data to the wireless base station according to a preset cycle. The power supply unit supplies power to the microcontroller, the air temperature and humidity sensor, the carbon dioxide concentration sensor, the light intensity sensor, and the transmission unit, respectively.

7. The method for identifying the maturity of shiitake mushroom logs based on a maturity identification device according to claim 1, characterized in that, The wireless base station includes a microcontroller, a near-field transmission unit, a storage unit, and a remote transmission unit; The microcontroller is connected to the near-field transmission unit, the storage unit, and the remote transmission unit, respectively. The near-field transmission unit includes a long-range radio LoRa receiver unit and a LoRa transmitter unit. The LoRa receiver unit is used to receive the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick. The LoRa transmitter unit is used to send feedback information to the mushroom stick environmental acquisition device. The storage unit is used to store the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick; The remote transmission unit is used to transmit the internal temperature data of the mushroom stick and the external environmental data of the mushroom stick to the cloud platform.

8. The method for identifying the maturity of shiitake mushroom logs based on a maturity identification device according to claim 1, characterized in that, The cloud platform includes an access gateway layer, a device and data management layer, a data analysis and model management system, and a data storage system. The data analysis and model management system is connected to the access gateway layer, the device and data management layer, and the data storage system, respectively. The access gateway layer is used to connect to the wireless base station; The device and data management layer is used for device management and pushes the maturity recognition results of shiitake mushroom sticks to the terminal devices; The data analysis and model management system is used to execute the shiitake mushroom stick maturity identification method; The data storage system is used to store backup intermediate data and result data.

9. A shiitake mushroom spawn maturity identification system based on a maturity identification device, implementing the shiitake mushroom spawn maturity identification method according to any one of claims 1-8, characterized in that, include: The acquisition module is used to acquire a set of characteristic data of shiitake mushroom spawn. The identification module is used to input the feature data set of the shiitake mushroom logs into a pre-trained maturity prediction model to obtain the maturity identification result of the shiitake mushroom logs; wherein the maturity prediction model is trained on a convolutional gated recurrent unit based on the feature sample set of shiitake mushroom logs and the classification labels of the shiitake mushroom log data.

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