Method and system for monitoring temperature and humidity under mulching film for tobacco planting

Abstract

The invention provides a method and system for monitoring temperature and humidity under a mulching film for tobacco planting, and the method comprises the steps: arranging multi-point distributed temperature and humidity sensors in a target mulching film coverage area, and collecting temperature and humidity data through the temperature and humidity sensors; acquiring historical planting data and real-time meteorological data of the target plastic film mulching area; performing data cleaning and de-noising processing on the temperature and humidity data to obtain pre-processed data; performing feature extraction on the historical planting data and the real-time meteorological data to obtain a time feature, an environment feature and a planting feature; performing feature fusion on the time feature, the environment feature and the planting feature to obtain a fused feature; inputting the fusion features and the preprocessed data into a constructed temperature and humidity regulation and control model to obtain a regulation and control strategy; and controlling the execution module of the target plastic film mulching area according to the regulation and control strategy. The method not only improves the management level of the planting environment, but also has important significance for improving the yield and quality of tobacco and reducing the planting cost.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural planting, and in particular to a method and system for monitoring temperature and humidity under ground film for tobacco planting. Background Art

[0002] In the process of tobacco cultivation, mulching technology is widely used to increase soil temperature, maintain soil moisture and inhibit weed growth. However, mulching also brings the problem that temperature and humidity are difficult to monitor and control in real time, especially in high temperature or high humidity environments, which can easily lead to tobacco root diseases or poor growth.

[0003] Therefore, there is an urgent need for a technical method that can monitor and dynamically regulate the temperature and humidity under the mulch in real time to optimize the tobacco growing environment. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method and system for monitoring temperature and humidity under mulch film for tobacco planting, which improves the management level of the planting environment and is of great significance to improving the yield and quality of tobacco and reducing the planting cost.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for monitoring temperature and humidity under mulch film for tobacco planting, comprising:

[0007] Arrange multiple distributed temperature and humidity sensors in the target mulch film coverage area, and use the temperature and humidity sensors to collect temperature and humidity data;

[0008] Acquire historical planting data and real-time meteorological data of the target mulch-film covered area;

[0009] Performing data cleaning and denoising on the temperature and humidity data to obtain preprocessed data;

[0010] Extracting features from the historical planting data and the real-time meteorological data to obtain time features, environmental features, and planting features;

[0011] Performing feature fusion on the time feature, the environmental feature and the planting feature to obtain a fusion feature;

[0012] Inputting the fusion features and the preprocessed data into the constructed temperature and humidity control model to obtain a control strategy;

[0013] The execution module of the target ground film covering area is controlled according to the control strategy.

[0014] Preferably, at least two of the temperature and humidity sensors are arranged at each monitoring point in the target mulch film coverage area.

[0015] Preferably, the temperature and humidity data are cleaned and denoised to obtain pre-processed data, including:

[0016] Filling missing values ​​in the temperature and humidity data to obtain cleaned data;

[0017] A connection degree model is constructed according to the spatial distance between each temperature sensor; the connection degree model is: Wherein, σ represents the average value of the temperature difference or humidity difference between the temperature and humidity sensor at point m and the temperature and humidity sensor at point n in the historical time period, dis(m,n) represents the spatial distance between the temperature and humidity sensor at point m and the temperature and humidity sensor at point n, and R represents the preset parameter;

[0018] Using the connection degree model to construct a temperature and humidity denoising function;

[0019] The temperature and humidity denoising function is used to remove abnormal values ​​of the cleaned data at each monitoring point to generate the preprocessed data.

[0020] Preferably, the temperature and humidity denoising function is constructed using the connection degree model, including:

[0021] The denoising threshold is constructed using the weighted value of the temperature or humidity at each monitoring point; wherein the denoising threshold is: Among them, Y i represents the i-th temperature or humidity value collected by the temperature and humidity denoising function, W i Represents Y i The weighted value of

[0022] A temperature and humidity denoising function is constructed based on the denoising threshold and the connection degree model; the temperature and humidity denoising function is: in, represents the denoised temperature or humidity information of the corresponding monitoring point at time t, x(m,t) represents the measurement value of the temperature and humidity sensor at point m at time t, x(n,t) represents the measurement value of the temperature and humidity sensor at point n at time t, ρ represents the adjustable threshold, |N m | indicates the number of temperature and humidity sensors at the same monitoring point.

[0023] Preferably, the time characteristics include: season, date and time period; the environmental characteristics include temperature, humidity, rainfall and light intensity; the planting characteristics include soil moisture, temperature and humidity under the mulch and irrigation frequency.

[0024] Preferably, the method for constructing the temperature and humidity control model includes:

[0025] Obtain the preset fusion feature dataset and temperature and humidity dataset;

[0026] Build a convolutional neural network;

[0027] The convolutional neural network is trained according to the fusion feature data set and the temperature and humidity data set to obtain a trained first classifier and a second classifier;

[0028] Cascading the trained first classifier and the second classifier to obtain a classification network;

[0029] Connecting the trained LSTM neural network after the classification network to obtain a CNN-LSTM combined neural network model;

[0030] The control strategy is obtained by performing strategy generation on the fusion features and the preprocessed data according to the CNN-LSTM combined neural network model.

[0031] Preferably, the execution module comprises an irrigation system, a ventilation device and a heating device.

[0032] A temperature and humidity monitoring system under mulch film for tobacco planting, comprising:

[0033] A first data collection unit is used to arrange multi-point distributed temperature and humidity sensors in the target ground film coverage area, and collect temperature and humidity data using the temperature and humidity sensors;

[0034] A second data acquisition unit is used to obtain historical planting data and real-time meteorological data of the target mulch film covered area;

[0035] A data processing unit, used for performing data cleaning and denoising on the temperature and humidity data to obtain pre-processed data;

[0036] A feature extraction unit, used for extracting features from the historical planting data and the real-time meteorological data to obtain time features, environmental features and planting features;

[0037] A feature fusion unit, used for fusing the time feature, the environmental feature and the planting feature to obtain a fused feature;

[0038] A strategy generating unit, used for inputting the fusion features and the pre-processed data into the constructed temperature and humidity control model to obtain a control strategy;

[0039] A temperature and humidity control unit is used to control the execution module of the target ground film covering area according to the control strategy.

[0040] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0041] The present invention provides a method and system for monitoring temperature and humidity under film for tobacco planting, the method comprising: arranging multi-point distributed temperature and humidity sensors in the target film-covered area, and using the temperature and humidity sensors to collect temperature and humidity data; obtaining historical planting data and real-time meteorological data of the target film-covered area; performing data cleaning and denoising on the temperature and humidity data to obtain pre-processed data; performing feature extraction on the historical planting data and the real-time meteorological data to obtain time features, environmental features and planting features; performing feature fusion on the time features, the environmental features and the planting features to obtain fusion features; inputting the fusion features and the pre-processed data into a constructed temperature and humidity control model to obtain a control strategy; and controlling the execution module of the target film-covered area according to the control strategy. The present invention not only improves the management level of the planting environment, but also has important significance for improving the yield and quality of tobacco and reducing the planting cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of the system structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The purpose of the present invention is to provide a method and system for monitoring temperature and humidity under mulch film for tobacco planting, which not only improves the management level of the planting environment, but also has important significance for improving the yield and quality of tobacco and reducing the planting cost.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Figure 1A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a method for monitoring temperature and humidity under mulch film for tobacco planting, comprising:

[0049] Step 100: Arrange multi-point distributed temperature and humidity sensors in the target mulch film coverage area, and use the temperature and humidity sensors to collect temperature and humidity data;

[0050] Step 200: Acquire historical planting data and real-time meteorological data of the target mulch-film covered area;

[0051] Step 300: performing data cleaning and denoising on the temperature and humidity data to obtain pre-processed data;

[0052] Step 400: extracting features from historical planting data and real-time meteorological data to obtain time features, environmental features, and planting features;

[0053] Step 500: Fusing the time feature, the environmental feature and the planting feature to obtain a fused feature;

[0054] Step 600: Input the fusion features and pre-processed data into the constructed temperature and humidity control model to obtain a control strategy;

[0055] Step 700: Controlling the execution module of the target mulch covering area according to the control strategy.

[0056] Preferably, at least two of the temperature and humidity sensors are arranged at each monitoring point in the target mulch film coverage area.

[0057] Specifically, step 100 of this embodiment includes:

[0058] Step 101: Sensor Placement

[0059] First, multi-point distributed temperature and humidity sensors are deployed in the target mulch-covered area. The sensor locations are reasonably selected according to the area, shape and terrain characteristics of the mulch-covered area. Generally, sensors should be deployed near the roots of tobacco plants and in different areas covered by mulch to ensure that the entire planting area can be fully covered to avoid monitoring blind spots caused by local environmental changes. The number and distribution density of sensors can be adjusted according to the specific planting scale and climatic conditions to ensure the comprehensiveness and accuracy of data collection.

[0060] Step 102: Sensor Selection and Installation

[0061] When selecting a temperature and humidity sensor, you should give priority to high-precision, low-power, and anti-interference sensor types, such as digital temperature and humidity sensors (such as DHT22 or SHT31). During installation, you need to ensure that the sensor is installed in a position that can effectively reflect the environmental conditions under the mulch and avoid direct exposure to sunlight or rain to reduce the impact of external factors on data collection. The sensor should be fixed at an appropriate height to ensure that it can accurately measure the temperature and humidity in the area covered by the mulch, while avoiding direct impact of soil moisture changes on the sensor.

[0062] Step 103: Data Collection and Transmission

[0063] Once the temperature and humidity sensors are installed, the system will start collecting temperature and humidity data in real time. Each sensor transmits the collected data to the central control system through a built-in wireless module (such as LoRa, ZigBee or Wi-Fi). The frequency of data transmission can be set according to specific needs, such as collecting once every minute or every hour. The central control system is responsible for receiving, storing and processing data from each sensor, providing a basis for subsequent data cleaning and analysis. This process ensures the real-time and reliability of temperature and humidity data, laying a solid foundation for the formulation of subsequent control strategies.

[0064] Specifically, step 200 of this embodiment includes:

[0065] Step 201: Acquisition of historical planting data

[0066] First, historical planting data can be obtained through a variety of channels, including agricultural management systems, databases of local agricultural departments, farmers' records, and relevant agricultural research institutions. Specifically, agricultural management software or database systems can be used to query historical planting records related to the target mulch-covered area. These records usually include planting time, crop growth stages, fertilization and irrigation records, pest and disease occurrence, and final yield and quality data in the past few years. In addition, manual records of farmers or growers can also be used as a supplementary data source to ensure the comprehensiveness and accuracy of historical data. By integrating these data, a historical database containing rich information can be established to provide a basis for subsequent analysis.

[0067] Step 202: Acquisition of real-time weather data

[0068] The acquisition of real-time meteorological data mainly depends on weather stations, weather service APIs and online meteorological platforms. By establishing a cooperative relationship with the local meteorological bureau or meteorological service provider, real-time meteorological data of the target mulch-covered area can be obtained, including information such as temperature, humidity, rainfall, wind speed and light intensity. In addition, using open meteorological data APIs (such as OpenWeather, WeatherAPI, etc.), automated data acquisition can be achieved and real-time meteorological data can be updated regularly. These real-time data will be combined with historical planting data to provide the necessary environmental background information for subsequent data analysis and model building, helping to optimize temperature and humidity control strategies.

[0069] Preferably, the temperature and humidity data are cleaned and denoised to obtain pre-processed data, including:

[0070] Filling missing values ​​in the temperature and humidity data to obtain cleaned data;

[0071] A connection degree model is constructed according to the spatial distance between each temperature sensor; the connection degree model is: Wherein, σ represents the average value of the temperature difference or humidity difference between the temperature and humidity sensor at point m and the temperature and humidity sensor at point n in the historical time period, dis(m,n) represents the spatial distance between the temperature and humidity sensor at point m and the temperature and humidity sensor at point n, and R represents the preset parameter;

[0072] Using the connection degree model to construct a temperature and humidity denoising function;

[0073] The temperature and humidity denoising function is used to remove abnormal values ​​of the cleaned data at each monitoring point to generate the preprocessed data.

[0074] In the present invention, the temperature and humidity sensors at point m and point n are neighboring sensors. The closer the positions of the two temperature and humidity sensors are, the greater the correlation between the two temperature and humidity sensors. Therefore, the present invention measures the degree of connection between the two temperature and humidity sensors based on the spatial distance between them. In practical applications, since the temperature and humidity sensors may be affected by their own parameters or environmental factors, the measured values ​​collected by the temperature and humidity sensors at a certain moment may deviate greatly from the actual values. Therefore, the present invention introduces R to correct the problem of deviation in sensor correlation caused by the influence of the temperature and humidity sensors' own parameters or environmental factors. In general, the value of R is 1.

[0075] Preferably, the temperature and humidity denoising function is constructed using the connection degree model, including:

[0076] The denoising threshold is constructed using the weighted value of the temperature or humidity at each monitoring point; wherein the denoising threshold is: Among them, Y irepresents the i-th temperature or humidity value collected by the temperature and humidity denoising function, W i Represents Y i The weighted value of

[0077] A temperature and humidity denoising function is constructed based on the denoising threshold and the connection degree model; the temperature and humidity denoising function is: in, represents the denoised temperature or humidity information of the corresponding monitoring point at time t, x(m,t) represents the measurement value of the temperature and humidity sensor at point m at time t, x(n,t) represents the measurement value of the temperature and humidity sensor at point n at time t, ρ represents the adjustable threshold, |N m | indicates the number of temperature and humidity sensors at the same monitoring point.

[0078] In the present invention, W can be set by referring to the model or position of each temperature and humidity sensor. i If the models of the temperature and humidity sensors are the same, W i Set to 1.

[0079] Preferably, the time characteristics include: season, date and time period; the environmental characteristics include temperature, humidity, rainfall and light intensity; the planting characteristics include soil moisture, temperature and humidity under the mulch and irrigation frequency.

[0080] Specifically, step 400 of this embodiment includes:

[0081] Step 401: Extraction of time features

[0082] First, extract time features from historical planting data and real-time meteorological data. Specifically, time information can be converted into features such as season, date, and time period. Seasonal features can be classified by date information, such as spring, summer, autumn, and winter, to reflect the impact of different seasons on crop growth. Date features can directly use the date field in the data, while time period features can divide a day into multiple time periods (such as morning, noon, evening, etc.) according to specific needs, so as to analyze the impact of environmental conditions on planting in different time periods. These time features will help the model understand the impact of seasonal changes and intraday changes on tobacco planting.

[0083] Step 402: Extraction of environmental features

[0084] Next, environmental features are extracted from real-time meteorological data. Environmental features include key indicators such as temperature, humidity, rainfall, and light intensity, which directly affect the growth environment under the mulch. Temperature and humidity can be directly extracted from meteorological data, while rainfall needs to focus on the frequency and intensity of rainfall events. Light intensity can be obtained through installed light sensors or estimated through relevant indicators in meteorological data. Integrating these environmental features into a feature vector helps the model identify the potential impact of different environmental conditions on tobacco growth during analysis.

[0085] Step 403: Extraction of planting features

[0086] Finally, planting features are extracted from historical planting data. These features include soil moisture, temperature and humidity under the mulch, and irrigation frequency. Soil moisture can be obtained through the data of soil moisture sensors to reflect the soil moisture status; temperature and humidity under the mulch are monitored by arranged temperature and humidity sensors to ensure the real-time and accuracy of the data. Irrigation frequency can be extracted through recorded irrigation logs, showing the number of irrigation operations and water volume in a specific time period. These planting features provide the model with specific environmental conditions for crop growth, which helps to further analyze and optimize temperature and humidity control strategies. By integrating time features, environmental features, and planting features, a comprehensive feature set can be formed, laying the foundation for subsequent data analysis and model training.

[0087] In step 500, the implementation process of feature fusion involves integrating the extracted time features, environmental features and planting features to form a unified feature vector. Specifically, the present embodiment standardizes various features to ensure that they are on the same dimension, thereby avoiding the model training deviation caused by different feature scales. Then, the standardized time features (such as season, date and time period), environmental features (such as temperature, humidity, rainfall and light intensity) and planting features (such as soil moisture, temperature and humidity under the ground film and irrigation frequency) are spliced ​​to form a high-dimensional feature vector. This feature vector will contain all relevant information and can fully reflect the various factors that affect tobacco growth. Finally, this fused feature vector will be used as input data for subsequent temperature and humidity control model to train and predict, thereby optimizing the tobacco planting environment.

[0088] Preferably, the method for constructing the temperature and humidity control model includes:

[0089] Obtain the preset fusion feature dataset and temperature and humidity dataset;

[0090] Build a convolutional neural network;

[0091] The convolutional neural network is trained according to the fusion feature data set and the temperature and humidity data set to obtain a trained first classifier and a second classifier;

[0092] Cascading the trained first classifier and the second classifier to obtain a classification network;

[0093] Connecting the trained LSTM neural network after the classification network to obtain a CNN-LSTM combined neural network model;

[0094] The control strategy is obtained by performing strategy generation on the fusion features and the preprocessed data according to the CNN-LSTM combined neural network model.

[0095] Specifically, step 600 of this embodiment includes:

[0096] Step 601: Acquisition and preparation of data set

[0097] First, obtain the preset fusion feature dataset and temperature and humidity dataset. These datasets should contain historical planting data, real-time meteorological data, and feature vectors after feature extraction and fusion processing. The fusion feature dataset includes time features, environmental features, and planting features, while the temperature and humidity dataset contains the temperature and humidity monitoring data under the target mulch. In the process of preparing the dataset, it is necessary to ensure the integrity and consistency of the data, remove outliers, and perform necessary standardization so that the data can be effectively used in subsequent model training.

[0098] Step 602: Constructing a Convolutional Neural Network (CNN)

[0099] Next, a convolutional neural network (CNN) is constructed to process the fused feature dataset. The structure of a CNN usually includes multiple convolutional layers, pooling layers, and fully connected layers. The convolutional layer is used to extract features, the pooling layer is used to reduce feature dimensions and computational complexity, and the fully connected layer is used to map the extracted features to output categories. By designing a suitable network architecture (such as convolution kernel size, number of layers, etc.), CNN can effectively capture the spatial relationships and patterns in the fused features, thus laying the foundation for subsequent classification tasks.

[0100] Step 603: Training the classifier

[0101] After building the CNN, the CNN is trained based on the fusion feature dataset and the temperature and humidity dataset to obtain the trained first and second classifiers. During the training process, the cross entropy loss function is used to evaluate the performance of the model, and the network parameters are optimized through the back propagation algorithm. The training data should be divided into a training set and a validation set to monitor the overfitting of the model during the training process, and adjust the hyperparameters (such as learning rate, batch size, etc.) according to the performance of the validation set to ensure the generalization ability of the classifier.

[0102] Step 604: Cascade classifier

[0103] After completing the training of the classifier, the trained first classifier and the second classifier are cascaded to form a classification network. The cascade process is to use the output of the first classifier as the input of the second classifier, thereby achieving multi-level feature learning and classification. This structure can better capture complex feature relationships and improve the classification accuracy of the model. The cascaded classification network will provide richer feature information for subsequent time series prediction.

[0104] Step 605: Connect LSTM and generate control strategy

[0105] Finally, the trained LSTM (long short-term memory) neural network is connected after the classification network to form a CNN-LSTM combined neural network model. LSTM can effectively process time series data and capture the time dependency in the data. The fusion features and preprocessed data are input into the combined model, and the model will generate corresponding control strategies based on the input data. These strategies will guide the execution modules (such as irrigation systems and ventilation devices) to dynamically control the temperature and humidity environment under the mulch to ensure the healthy growth of tobacco. By continuously iterating and optimizing the model, efficient temperature and humidity control will eventually be achieved.

[0106] Exemplarily, the control strategy of this embodiment includes:

[0107] 1. Temperature and humidity control instructions

[0108] (1) Temperature control: Instructions on how to adjust the temperature under the mulch, such as raising or lowering the temperature through heating equipment or ventilation devices.

[0109] (2) Humidity control: Instructions on how to adjust the humidity under the mulch, such as increasing soil moisture through an irrigation system or reducing humidity through ventilation.

[0110] 2. Irrigation strategy

[0111] (1) Irrigation frequency: The recommended irrigation interval, such as the number of irrigations per day, every two days, or per week.

[0112] (2) Irrigation volume: The amount of water required for each irrigation, usually calculated based on soil moisture and plant needs.

[0113] (3) Irrigation method: Choose an appropriate irrigation method (such as drip irrigation, sprinkler irrigation, etc.) to ensure even distribution of water.

[0114] 3. Ventilation strategy

[0115] (1) Ventilation time: The recommended ventilation period, for example, when to turn on the ventilation device when the temperature is too high or the humidity is too high.

[0116] (2) Ventilation intensity: the degree to which the ventilation device is opened, such as fully open, partially open, or closed.

[0117] (3) Ventilation frequency: The frequency of ventilation operation to ensure air circulation to maintain a suitable growing environment.

[0118] 4. Fertilization and nutrition management

[0119] (1) Fertilization time: The recommended time for fertilization, usually combined with irrigation and growth stage.

[0120] (2) Fertilizer amount: The amount of fertilizer required for each fertilization should be adjusted according to the soil nutrient conditions and plant needs.

[0121] (3) Fertilization method: Choose the appropriate fertilization method (such as base fertilizer, topdressing, etc.) to ensure effective absorption of nutrients.

[0122] Preferably, the execution module comprises an irrigation system, a ventilation device and a heating device.

[0123] Specifically, step 700 of this embodiment includes:

[0124] Step 701: Integration and communication of execution modules

[0125] First, ensure the integration and communication capabilities of execution modules such as irrigation systems, ventilation devices, and heating devices. Each execution module should be equipped with corresponding controllers and sensors to receive instructions from the central control system in real time. Through wireless communication protocols (such as LoRa, ZigBee, or Wi-Fi), the central control system can exchange data with each execution module to ensure timely communication and execution of instructions. In addition, the execution module should have a status feedback function, which can feedback the current operating status (such as water pump start status, fan speed, heater temperature, etc.) to the central control system for real-time monitoring and adjustment.

[0126] Step 702: Execution of control strategy

[0127] According to the generated control strategy, the central control system sends specific operation instructions to each execution module. For example, when the control strategy indicates that soil moisture needs to be increased, the system will send an opening instruction to the irrigation system and set the duration and amount of irrigation; if the temperature under the mulch needs to be lowered, the system will send an opening instruction to the ventilation device and adjust the fan speed to increase air circulation; if the temperature needs to be increased, the system will control the opening and closing of the heating device to ensure that the temperature reaches the set value. All these operations should be dynamically adjusted based on real-time monitoring data and preset thresholds to ensure that environmental conditions are always kept within the optimal range.

[0128] Step 703: Feedback and optimization adjustment

[0129] During the execution process, the central control system will continue to receive status feedback information from each execution module and compare and analyze it with the real-time monitoring data. If it is found that the actual environmental conditions fail to meet the expected goals, the system will automatically adjust the control strategy. For example, if the soil moisture is still insufficient after irrigation, the system may increase the irrigation frequency or water volume; if the ventilation effect is not obvious, the system may increase the operating intensity of the ventilation device. Through this closed-loop control mechanism, the system can achieve precise management of the target mulch covering area, ensuring the stability of the tobacco planting environment and the healthy growth of crops.

[0130] Corresponding to the above method, such as Figure 2 As shown, this embodiment also provides a temperature and humidity monitoring system under mulch film for tobacco planting, including:

[0131] A first data collection unit is used to arrange multi-point distributed temperature and humidity sensors in the target ground film coverage area, and collect temperature and humidity data using the temperature and humidity sensors;

[0132] A second data acquisition unit is used to obtain historical planting data and real-time meteorological data of the target mulch film covered area;

[0133] A data processing unit, used for performing data cleaning and denoising on the temperature and humidity data to obtain pre-processed data;

[0134] A feature extraction unit, used for extracting features from the historical planting data and the real-time meteorological data to obtain time features, environmental features and planting features;

[0135] A feature fusion unit, used for fusing the time feature, the environmental feature and the planting feature to obtain a fused feature;

[0136] A strategy generating unit, used for inputting the fusion features and the pre-processed data into the constructed temperature and humidity control model to obtain a control strategy;

[0137] A temperature and humidity control unit is used to control the execution module of the target ground film covering area according to the control strategy.

[0138] The beneficial effects of the present invention are as follows:

[0139] (1) The present invention arranges multiple distributed temperature and humidity sensors in the target film coverage area, so as to obtain the temperature and humidity data in the area in real time, ensure the comprehensiveness and accuracy of monitoring, and avoid data deviation caused by single-point monitoring.

[0140] (2) The present invention combines historical planting data and real-time meteorological data, and uses data cleaning and denoising processing to ensure the data quality of the input model, thereby improving the accuracy of the model prediction; feature extraction and fusion enable the model to better understand the impact of different factors on temperature and humidity, and form a scientific control strategy.

[0141] (3) By constructing a temperature and humidity control model, the present invention can dynamically adjust the control strategy according to real-time monitoring data and environmental changes, ensuring that the temperature and humidity under the mulch are always maintained within the optimal range, thereby promoting the healthy growth of tobacco.

[0142] (4) The present invention controls the execution modules (such as irrigation, ventilation, etc.) according to the regulation strategy, which can achieve precise moisture and air management, avoid excessive irrigation or ventilation, and save water resources and energy costs.

[0143] (5) Through real-time monitoring and dynamic regulation, the present invention can effectively prevent the occurrence of diseases caused by abnormal temperature and humidity, reduce the risk of diseases in the tobacco planting process, and improve the growth health of crops.

[0144] (6) The present invention promotes the uniform growth of tobacco by optimizing the growth environment, thereby ultimately improving the yield and quality of tobacco and increasing economic benefits.

[0145] (7) The automated and intelligent management of the present invention reduces manual intervention, lowers labor costs, and improves management efficiency, allowing farmers to focus more on other important agricultural activities.

[0146] (8) The present invention can be adjusted according to different climatic conditions and planting environments, has strong adaptability, and is suitable for tobacco planting needs in different regions.

[0147] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0148] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for monitoring temperature and humidity under mulch film for tobacco planting, characterized in that: include: Arrange multiple distributed temperature and humidity sensors in the target mulch film coverage area, and use the temperature and humidity sensors to collect temperature and humidity data; Acquire historical planting data and real-time meteorological data of the target mulch-film covered area; Performing data cleaning and denoising on the temperature and humidity data to obtain preprocessed data; Extracting features from the historical planting data and the real-time meteorological data to obtain time features, environmental features, and planting features; Performing feature fusion on the time feature, the environmental feature and the planting feature to obtain a fusion feature; Inputting the fusion features and the preprocessed data into the constructed temperature and humidity control model to obtain a control strategy; The execution module of the target ground film covering area is controlled according to the control strategy.

2. The method for monitoring temperature and humidity under mulch film for tobacco planting according to claim 1, characterized in that: At least two of the temperature and humidity sensors are arranged at each monitoring point in the target mulch film covered area.

3. The method for monitoring temperature and humidity under mulch film for tobacco planting according to claim 1, characterized in that: The temperature and humidity data are cleaned and denoised to obtain pre-processed data, including: Filling missing values ​​in the temperature and humidity data to obtain cleaned data; A connection degree model is constructed according to the spatial distance between each temperature sensor; the connection degree model is: Wherein, σ represents the average value of the temperature difference or humidity difference between the temperature and humidity sensor at point m and the temperature and humidity sensor at point n in the historical time period, dis(m,n) represents the spatial distance between the temperature and humidity sensor at point m and the temperature and humidity sensor at point n, and R represents the preset parameter; Using the connection degree model to construct a temperature and humidity denoising function; The temperature and humidity denoising function is used to remove abnormal values ​​of the cleaned data at each monitoring point to generate the preprocessed data.

4. The method for monitoring temperature and humidity under mulch film for tobacco planting according to claim 3, characterized in that: The temperature and humidity denoising function is constructed using the connection degree model, including: The denoising threshold is constructed using the weighted value of the temperature or humidity at each monitoring point; wherein the denoising threshold is: Among them, Y i represents the i-th temperature or humidity value collected by the temperature and humidity denoising function, W i Represents Y i The weighted value of A temperature and humidity denoising function is constructed based on the denoising threshold and the connection degree model; the temperature and humidity denoising function is: in, represents the denoised temperature or humidity information of the corresponding monitoring point at time t, x(m,t) represents the measurement value of the temperature and humidity sensor at point m at time t, x(n,t) represents the measurement value of the temperature and humidity sensor at point n at time t, ρ represents the adjustable threshold, |N m | indicates the number of temperature and humidity sensors at the same monitoring point.

5. The method for monitoring temperature and humidity under mulch film for tobacco planting according to claim 1, characterized in that: The time characteristics include: season, date and time period; the environmental characteristics include temperature, humidity, rainfall and light intensity; the planting characteristics include soil moisture, temperature and humidity under the mulch and irrigation frequency.

6. The method for monitoring temperature and humidity under mulch film for tobacco planting according to claim 1, characterized in that: The method for constructing the temperature and humidity control model includes: Obtain the preset fusion feature dataset and temperature and humidity dataset; Build a convolutional neural network; The convolutional neural network is trained according to the fusion feature data set and the temperature and humidity data set to obtain a trained first classifier and a second classifier; Cascading the trained first classifier and the second classifier to obtain a classification network; Connecting the trained LSTM neural network after the classification network to obtain a CNN-LSTM combined neural network model; The control strategy is obtained by performing strategy generation on the fusion features and the preprocessed data according to the CNN-LSTM combined neural network model.

7. The method for monitoring temperature and humidity under mulch film for tobacco planting according to claim 1, characterized in that: The execution module includes an irrigation system, a ventilation device and a heating device.

8. A temperature and humidity monitoring system under mulch film for tobacco planting, characterized in that: include: A first data collection unit is used to arrange multi-point distributed temperature and humidity sensors in the target ground film coverage area, and collect temperature and humidity data using the temperature and humidity sensors; A second data acquisition unit is used to obtain historical planting data and real-time meteorological data of the target mulch film covered area; A data processing unit, used for performing data cleaning and denoising on the temperature and humidity data to obtain pre-processed data; A feature extraction unit, used for extracting features from the historical planting data and the real-time meteorological data to obtain time features, environmental features and planting features; A feature fusion unit, used for fusing the time feature, the environmental feature and the planting feature to obtain a fused feature; A strategy generating unit, used for inputting the fusion features and the pre-processed data into the constructed temperature and humidity control model to obtain a control strategy; A temperature and humidity control unit is used to control the execution module of the target ground film covering area according to the control strategy.

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