Intelligent drip irrigation method and system for greenhouse seedling culture of dendrobium nobile

Abstract

The invention provides an intelligent drip irrigation method and system for dendrobium greenhouse seedling raising, and relates to the technical field of intelligent drip irrigation. The method specifically comprises the following steps: collecting historical data in a plurality of groups of dendrobium seedling raising greenhouses; taking temperature, wind speed and dendrobe growth stage data as a training set, taking the corresponding moisture demand as a label, and establishing a dendrobe moisture demand prediction model based on a deep learning algorithm; collecting temperature, wind speed and dendrobe growth stage data in a greenhouse in real time, inputting the data into the model to obtain a predicted dendrobe moisture demand quantity, and correcting the predicted dendrobe moisture demand quantity by using soil humidity data and illumination intensity data to obtain a comprehensive moisture demand index; calculating the drip irrigation interval and automatically adjusting the drip irrigation time, and increasing the drip irrigation water quantity when the comprehensive moisture demand index is higher than a preset threshold value. According to the method, the drip irrigation interval and the drip irrigation water amount are dynamically adjusted, it is ensured that dendrobium obtains appropriate water supply in different growth stages and under different environment changes, the water utilization efficiency is improved, and the labor management cost and the labor intensity are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent drip irrigation, and in particular to an intelligent drip irrigation method and system for cultivating dendrobium seedlings in a greenhouse. Background Art

[0002] In modern agriculture and horticulture, Dendrobium is a high-value Chinese herbal medicine and ornamental plant, and its planting and seedling raising process has very strict requirements on water management. Traditional drip irrigation methods usually rely on manual experience to adjust the irrigation frequency and water volume. This method is not only time-consuming and labor-intensive, but also has problems such as water resource waste and uneven irrigation. In a greenhouse environment, multiple factors such as temperature, humidity, and light will affect the growth of Dendrobium, and simple manual adjustments are difficult to meet its changing growth needs. In addition, the water requirements of Dendrobium at different growth stages are also different. It is difficult for humans to accurately grasp the best irrigation time and water volume, resulting in uneven growth of Dendrobium and even affecting its quality.

[0003] Although some existing intelligent drip irrigation systems have begun to use sensors to monitor environmental parameters and soil moisture, these systems are usually based on simple threshold control and lack the ability to comprehensively consider and dynamically adjust multiple growth factors. For example, these systems may decide whether to turn on irrigation based on the data from the soil moisture sensor, but they cannot accurately adjust based on multiple variables such as real-time temperature, wind speed, and the growth stage of Dendrobium. This single-factor adjustment method is still difficult to meet the complex growth needs of Dendrobium, and may lead to over-irrigation or under-irrigation, affecting the growth health and final yield of Dendrobium. Therefore, there is an urgent need for an intelligent drip irrigation system that can comprehensively consider multiple environmental factors and plant growth status to more accurately meet the water needs of Dendrobium at different growth stages.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method and system for intelligent drip irrigation of Dendrobium greenhouse seedlings to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An intelligent drip irrigation method for growing dendrobium seedlings in a greenhouse, comprising the following specific steps:

[0008] Step 1: Use soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors, and plant growth status sensors to collect multiple sets of historical data in the Dendrobium seedling greenhouse. Each set of historical data includes temperature, wind speed, Dendrobium growth stage data, and the corresponding Dendrobium water demand;

[0009] Step 2: Using the temperature, wind speed and growth stage data of Dendrobium in the greenhouse as training sets, and the corresponding water demand as labels, a Dendrobium water demand prediction model is established based on a deep learning algorithm, and the model is trained to obtain a trained Dendrobium water demand prediction model;

[0010] Step 3: Use sensors to collect real-time data on the temperature, wind speed, and growth stage of Dendrobium officinale in the greenhouse, and input them into the water demand prediction model to obtain the predicted water demand of Dendrobium officinale output by the model. Use the collected soil moisture data and light intensity data to correct the predicted plant water demand and obtain a comprehensive water demand index;

[0011] Step 4: Based on the obtained comprehensive water demand index, the drip irrigation interval is calculated using a dynamic adjustment formula. According to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation time, and when the comprehensive water demand index is higher than the preset water demand threshold, the system will increase the drip irrigation water volume.

[0012] Furthermore, soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors and plant growth status sensors are installed in different areas of the greenhouse, specifically including:

[0013] In the greenhouse, sensors are installed according to the planting layout and environmental characteristics of Dendrobium officinale. Soil moisture sensors are installed near the roots of plants, temperature sensors are installed in the center of the greenhouse to avoid the influence of local heat sources, light intensity sensors are installed in a position that can represent the lighting conditions of the entire greenhouse, and wind speed sensors are installed at the ventilation openings in the greenhouse.

[0014] The water demand of the collected Dendrobium should be obtained based on historical irrigation data;

[0015] The growth stages of Dendrobium candidum are mainly divided into: seed stage, seedling stage, growth period, flowering stage, fruiting stage and senescence stage.

[0016] Furthermore, for the sensor data, ensure that the data format of all sensor outputs is unified, the data format includes timestamp, sensor type, and reading value, and establish a data storage and backup mechanism in the system to ensure that the collected data will not be lost due to system failure, and the data is regularly backed up to a secure storage medium.

[0017] Furthermore, the data from each sensor is preprocessed to ensure the quality of the data, including:

[0018] Remove data outliers: Calculate the standard deviation of the data and set 3 times the standard deviation as the threshold. Data points outside this range are considered outliers.

[0019] The formula used to calculate the standard deviation of the data is:

[0020]

[0021] Where σ is the standard deviation, N is the total number of data points, and x i is the i-th data point, μ is the mean of the data;

[0022] The formula for calculating the mean of the data is:

[0023]

[0024] Where μ is the mean of the data, N is the total number of data points, and x i is the i-th data point;

[0025] Data interpolation: For time series data, use linear interpolation to fill missing values: Assuming that the data changes linearly between two known points, the missing values ​​are estimated by calculating the linear relationship between the two points.

[0026] Furthermore, the process of establishing a dendrobium water demand prediction model based on a deep learning algorithm specifically includes:

[0027] A Dendrobium water demand prediction model was established based on the deep learning algorithm. The model consists of an input layer, a hidden layer, an activation layer, and an output layer. The temperature, wind speed, and Dendrobium growth stage data in the greenhouse were used as training sets, and the corresponding water demand was used as a label to train the model and obtain a trained Dendrobium water demand prediction model.

[0028] Furthermore, the predicted water demand H of Dendrobium officinale was corrected using the collected soil moisture data and light intensity data, and the formula based on which the comprehensive water demand index was obtained was as follows;

[0029]

[0030] Among them, M is the comprehensive water demand index, S is soil moisture (percentage), L is light intensity, H is the predicted water demand of Dendrobium, α is the preset proportional coefficient of soil moisture S, β is the preset proportional coefficient of light intensity L, γ is the preset proportional coefficient of the predicted water demand H of Dendrobium, α, β, and γ are all greater than zero, and α>β>γ, and C1 is a constant correction index.

[0031] Furthermore, based on the obtained comprehensive water demand index, the drip irrigation interval I(t) is calculated using a dynamic adjustment formula, and the formula is as follows:

[0032] I(t)=I0*e -k*M

[0033] Among them, I(t) is the calculated drip irrigation interval, I0 is the initial drip irrigation interval in hours, M is the comprehensive water demand index, k is the preset adjustment coefficient, and k is greater than zero.

[0034] Furthermore, according to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation water volume, including:

[0035] When the comprehensive water demand index M is higher than the preset water demand threshold M x , that is, M>M x When the drip irrigation water volume increases, the system will increase the drip irrigation water volume. The calculation formula of the drip irrigation water volume Q is as follows:

[0036] Q=Q base *(1+δ*M)

[0037] Where Q is the irrigation water volume, Q base is the basic irrigation volume in liters, M is the comprehensive water demand index, δ is the preset proportional coefficient of the comprehensive water demand index M, and δ is greater than zero.

[0038] The present invention further provides a dendrobium greenhouse seedling intelligent drip irrigation system, the dendrobium greenhouse seedling intelligent drip irrigation system is used to execute the above-mentioned dendrobium greenhouse seedling intelligent drip irrigation method, comprising:

[0039] The data acquisition module uses soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors, and plant growth status sensors to collect multiple sets of historical data in the greenhouse. Each set of historical data includes temperature, wind speed, dendrobium growth stage data, and the corresponding plant water demand;

[0040] The model building module uses the temperature, wind speed and growth stage data of the greenhouse as training sets, and the corresponding water demand as labels, establishes a water demand prediction model for Dendrobium based on a deep learning algorithm, trains the model, and obtains a trained water demand prediction model for Dendrobium;

[0041] The model correction module uses sensors to collect real-time data on the temperature, wind speed and growth stage of Dendrobium in the greenhouse, and inputs them into the water demand prediction model to obtain the predicted water demand of Dendrobium output by the model, and uses the collected soil moisture data and light intensity data to correct the predicted water demand of Dendrobium to obtain a comprehensive water demand index;

[0042] The comprehensive analysis module calculates the drip irrigation interval based on the obtained comprehensive water demand index using a dynamic adjustment formula. According to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation time, and when the comprehensive water demand index is higher than the preset water demand threshold, the system will increase the drip irrigation water volume.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention constructs an intelligent greenhouse plant water demand prediction and management system by integrating multiple sensors and deep learning algorithms. The system can collect environmental data and plant growth status in the greenhouse in real time, accurately calculate the water demand of the plant through a trained prediction model, and correct it in combination with soil moisture data and light intensity data to obtain a more accurate comprehensive water demand index. Based on this index, the system can dynamically adjust the drip irrigation interval and drip irrigation water volume to ensure that plants can obtain appropriate water supply at different growth stages and environmental changes. This intelligent management method not only improves water use efficiency and reduces the waste of water resources, but also helps to improve the growth quality and yield of crops, reduces the cost and labor intensity of manual management, and provides technical support for the sustainable development of greenhouse agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0046] Figure 2 It is a schematic diagram of the overall system module of the present invention. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0048] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0049] Example:

[0050] See also Figure 1 , the present invention provides a technical solution:

[0051] An intelligent drip irrigation method for growing dendrobium seedlings in a greenhouse, comprising the following specific steps:

[0052] Step 1: Use soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors, and plant growth status sensors to collect multiple sets of historical data in the Dendrobium seedling greenhouse. Each set of historical data includes temperature, wind speed, Dendrobium growth stage data, and the corresponding Dendrobium water demand;

[0053] In this embodiment, soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors and plant growth status sensors are installed in different areas of the greenhouse, specifically including:

[0054] In the greenhouse, sensors should be installed according to the planting layout and environmental characteristics of Dendrobium. The soil moisture sensor should be installed near the roots of Dendrobium. The temperature sensor should be installed in the center of the greenhouse to avoid the influence of local heat sources. The light intensity sensor should be installed at a position that can represent the lighting conditions of the entire greenhouse. The wind speed sensor should be installed at the vents in the greenhouse.

[0055] The water demand of the collected Dendrobium should be obtained based on historical irrigation data;

[0056] By installing a variety of sensors, comprehensive data on environmental parameters and plant growth status in the greenhouse can be collected. These data include but are not limited to soil moisture, temperature, light intensity, wind speed, and plant growth stage. The comprehensive analysis of these parameters can more accurately reflect the water needs of plants; the collected historical data provides rich training materials for the deep learning model, enabling the model to learn the complex patterns and dynamic changes in the water needs of Dendrobium. This data-driven decision-making process is more scientific and reliable than traditional methods that rely on experience and intuition;

[0057] The growth stages of Dendrobium candidum are mainly divided into: seed stage, seedling stage, growth stage, flowering stage, fruiting stage and senescence stage;

[0058] For the sensor data, ensure that the data format of all sensor outputs is unified, including timestamp, sensor type, and reading value, and establish a data storage and backup mechanism in the system to ensure that the collected data will not be lost due to system failure, and the data is regularly backed up to a secure storage medium;

[0059] Preprocess the data from each sensor to ensure data quality, including:

[0060] Remove data outliers: Calculate the standard deviation of the data and set 3 times the standard deviation as the threshold. Data points outside this range are considered outliers.

[0061] The formula used to calculate the standard deviation of the data is:

[0062]

[0063] Where σ is the standard deviation, N is the total number of data points, and x i is the i-th data point, μ is the mean of the data;

[0064] The formula for calculating the mean of the data is:

[0065]

[0066] Where μ is the mean of the data, N is the total number of data points, and x i is the i-th data point;

[0067] Data interpolation: For time series data, use linear interpolation to fill missing values: Assuming that the data changes linearly between two known points, the missing values ​​are estimated by calculating the linear relationship between the two points.

[0068] Step 2: Using the temperature, wind speed and growth stage data of Dendrobium in the greenhouse as training sets, and the corresponding water demand as labels, a Dendrobium water demand prediction model is established based on a deep learning algorithm, and the model is trained to obtain a trained Dendrobium water demand prediction model;

[0069] In this embodiment, the process of establishing a dendrobium water demand prediction model based on a deep learning algorithm specifically includes:

[0070] A Dendrobium water demand prediction model was established based on the deep learning algorithm. The model consists of an input layer, a hidden layer, an activation layer, and an output layer. The temperature, wind speed, and Dendrobium growth stage data in the greenhouse were used as training sets, and the corresponding water demand was used as a label to train the model and obtain a trained Dendrobium water demand prediction model.

[0071] The Dendrobium water demand prediction model based on the deep learning algorithm can realize the automatic prediction of Dendrobium water demand. This degree of automation is unmatched by traditional rule-based or experience-based methods. It can dynamically adjust the prediction results according to real-time data to achieve more intelligent water management. The deep learning model can learn complex nonlinear relationships from a large amount of data, which enables the model to capture the subtle correlation between temperature, wind speed, plant growth stage and water demand. This high-precision prediction capability enables the model to adapt to different environmental conditions and plant growth stages, and provide more personalized water management recommendations; through automatic learning and prediction of the model, the impact of human factors on water management decisions can be reduced, and the problem of insufficient or excessive water supply due to human judgment errors can be reduced.

[0072] Step 3: Use sensors to collect real-time data on the temperature, wind speed, and growth stage of Dendrobium officinale in the greenhouse, and input them into the water demand prediction model to obtain the predicted water demand of Dendrobium officinale output by the model. Use the collected soil moisture data and light intensity data to correct the predicted plant water demand and obtain a comprehensive water demand index;

[0073] In this embodiment, the predicted water demand H of Dendrobium officinale is corrected using the collected soil moisture data and light intensity data, and the formula for obtaining the comprehensive water demand index is as follows:

[0074]

[0075] Among them, M is the comprehensive water demand index, S is soil moisture (percentage), L is light intensity, H is the predicted water demand of Dendrobium, α is the preset proportional coefficient of soil moisture S, β is the preset proportional coefficient of light intensity L, γ is the preset proportional coefficient of the predicted water demand H of Dendrobium, α, β, γ are all greater than zero, and α>β>γ, C1 is a constant correction index; soil moisture is a key factor that directly affects the water absorption of Dendrobium and has a large weight in the model. Too low soil moisture will limit the water acquisition of plants, and too high soil moisture may cause root hypoxia. Soil moisture S usually accounts for 40-60% of the weight; light intensity L affects plant photosynthesis and transpiration, thereby indirectly affecting the water demand of plants. Plant transpiration is enhanced under high light, and water demand increases. Light intensity usually accounts for 20-40% of the weight; the predicted plant water demand H is obtained through historical water demand, which reflects the water demand of plants under past environmental conditions and can provide some prediction basis for current water demand. Historical water demand usually accounts for 10-30% of the weight, so α>β>γ; when soil moisture S increases, plants will reduce their absorption of water to avoid oversaturation, so the comprehensive water demand index M decreases; when light intensity L increases, the stomata on plant leaves will open wider to absorb more carbon dioxide for photosynthesis. At the same time, this also causes more water to evaporate from the leaf surface into the atmosphere through transpiration. The increase in transpiration means that plants need more water to replenish the water lost due to evaporation, so the comprehensive water demand index M will increase; when the predicted plant water demand H increases, the comprehensive water demand index M will increase accordingly; that is, the light intensity L, the predicted plant water demand H and the comprehensive water demand index M are positively correlated, and the soil moisture S is negatively correlated with the comprehensive water demand index M;

[0076] By collecting the environmental data and the growth status data of Dendrobium in the greenhouse in real time and inputting them into the water demand prediction model, the real-time prediction of the water demand of Dendrobium can be realized. This real-time performance enables the system to dynamically adjust the water supply according to the current environmental conditions and plant status, so as to meet the needs of the plants more accurately; in addition to the temperature, wind speed and Dendrobium growth stage data, the predicted water demand is corrected by combining the soil moisture data and light intensity data. This comprehensive consideration of multiple factors can more comprehensively reflect the water demand of Dendrobium and improve the accuracy of the prediction. The soil moisture data directly reflects the water status of the plant roots, while the light intensity data affects the transpiration and water use efficiency of the plant. By incorporating these key data into the correction process of the prediction model, the accuracy and reliability of the prediction can be significantly improved. The calculation of the comprehensive water demand index takes into account a variety of environmental factors and plant growth status, which enables the system to better adapt to different climatic conditions and plant growth stages, and enhances the adaptability and robustness of the system.

[0077] Step 4: Based on the obtained comprehensive water demand index, the drip irrigation interval is calculated using a dynamic adjustment formula. According to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation time, and when the comprehensive water demand index is higher than the preset water demand threshold, the system will increase the drip irrigation water volume;

[0078] In this embodiment, the drip irrigation interval I(t) is calculated using a dynamic adjustment formula based on the obtained comprehensive water demand index, and the formula is as follows:

[0079] I(t)=I0*e -k*M

[0080] Among them, I(t) is the calculated drip irrigation interval, I0 is the initial drip irrigation interval, in hours, M is the comprehensive water demand index, K is the preset adjustment coefficient, and the value range of k is usually [0.5, 1.5]. When the comprehensive water demand index M increases, the plant's demand for water increases, and the drip irrigation interval shortens accordingly, which means that the comprehensive water demand index M is negatively correlated with the drip irrigation interval, I(t);

[0081] According to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation water volume, including:

[0082] When the comprehensive water demand index M is higher than the preset water demand threshold M x , that is, M>M x When the drip irrigation water volume increases, the system will increase the drip irrigation water volume. The calculation formula of the drip irrigation water volume Q is as follows:

[0083] Q=Q base *(1+δ*M)

[0084] Where Q is the irrigation water volume, Q baseis the basic irrigation volume, in liters, M is the comprehensive water demand index, δ is the preset proportional coefficient of the comprehensive water demand index M, and the value range of δ is usually [0.85, 0.95]. When the comprehensive water demand index M increases, the plant's demand for water increases. If M is higher than the preset water demand threshold M x , the drip irrigation water volume Q will increase, which means that the comprehensive water demand index M is positively correlated with the drip irrigation water volume Q.

[0085] See also Figure 2 The present invention also provides a Dendrobium greenhouse seedling intelligent drip irrigation system, comprising:

[0086] The data acquisition module uses soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors, and plant growth status sensors to collect multiple sets of historical data in the greenhouse. Each set of historical data includes temperature, wind speed, dendrobium growth stage data, and the corresponding plant water demand;

[0087] The model building module uses the temperature, wind speed and growth stage data of the greenhouse as training sets, and the corresponding water demand as labels, establishes a water demand prediction model for Dendrobium based on a deep learning algorithm, trains the model, and obtains a trained water demand prediction model for Dendrobium;

[0088] The model correction module uses sensors to collect real-time data on the temperature, wind speed and growth stage of Dendrobium in the greenhouse, and inputs them into the water demand prediction model to obtain the predicted water demand of Dendrobium output by the model, and uses the collected soil moisture data and light intensity data to correct the predicted water demand of Dendrobium to obtain a comprehensive water demand index;

[0089] The comprehensive analysis module calculates the drip irrigation interval based on the obtained comprehensive water demand index using a dynamic adjustment formula. According to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation time, and when the comprehensive water demand index is higher than the preset water demand threshold, the system will increase the drip irrigation water volume.

[0090] The specific values ​​of α, β and γ in the formula are generally determined by technical personnel in this field according to actual conditions. The technical personnel in this field collect multiple groups of sample data, and set corresponding preset proportional coefficients for each group of sample data. The preset proportional coefficients and the collected sample data are substituted into the formula. Through repeated experiments and parameter adjustments, the accuracy of the model output and the rationality of the results are observed, and these factor coefficients are gradually adjusted. The performance and effect of the model under different parameter settings are compared to find the optimal coefficient combination, and the calculated factor coefficients are screened and averaged to obtain the values ​​of α, β and γ.

[0091] In addition, the size of the preset factor coefficient is to quantify each parameter to obtain a specific value. In order to facilitate subsequent comparison, the size of the coefficient depends on the amount of sample data and the preset proportional coefficient initially set by technical personnel in this field for each set of sample data. It is not unique as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0092] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0093] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An intelligent drip irrigation method for growing Dendrobium officinale seedlings in a greenhouse, characterized in that: The specific steps include: Step 1: Use soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors, and plant growth status sensors to collect multiple sets of historical data in the Dendrobium seedling greenhouse. Each set of historical data includes temperature, wind speed, Dendrobium growth stage data, and the corresponding Dendrobium water demand; Step 2: Using the temperature, wind speed and growth stage data of Dendrobium in the greenhouse as training sets, and the corresponding water demand as labels, a Dendrobium water demand prediction model is established based on a deep learning algorithm, and the model is trained to obtain a trained Dendrobium water demand prediction model; Step 3: Use sensors to collect real-time data on the temperature, wind speed, and growth stage of Dendrobium officinale in the greenhouse, and input them into the water demand prediction model to obtain the predicted water demand of Dendrobium officinale output by the model. Use the collected soil moisture data and light intensity data to correct the predicted plant water demand and obtain a comprehensive water demand index; Step 4: Based on the obtained comprehensive water demand index, the drip irrigation interval is calculated using a dynamic adjustment formula. According to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation time, and when the comprehensive water demand index is higher than the preset water demand threshold, the system will increase the drip irrigation water volume.

2. The intelligent drip irrigation method for growing Dendrobium officinale seedlings in a greenhouse according to claim 1, characterized in that: Soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors, and plant growth status sensors are installed in different areas of the greenhouse, including: In the greenhouse, sensors are installed according to the planting layout and environmental characteristics of Dendrobium officinale. Soil moisture sensors are installed near the roots of plants, temperature sensors are installed in the center of the greenhouse to avoid the influence of local heat sources, light intensity sensors are installed in a position that can represent the lighting conditions of the entire greenhouse, and wind speed sensors are installed at the ventilation openings in the greenhouse. The water demand of the collected Dendrobium should be obtained based on historical irrigation data; The growth stages of the dendrobium are mainly divided into: seed stage, seedling stage, growth period, flowering stage, fruiting stage and aging stage.

3. The intelligent drip irrigation method for growing Dendrobium officinale seedlings in a greenhouse according to claim 2, characterized in that: For the sensor data, ensure that the data format of all sensor outputs is unified, and the data format includes timestamp, sensor type, and reading value. Establish a data storage and backup mechanism in the system to ensure that the collected data will not be lost due to system failure, and the data is regularly backed up to a secure storage medium.

4. The intelligent drip irrigation method for growing Dendrobium officinale seedlings in a greenhouse according to claim 3, characterized in that: Preprocess the data from each sensor to ensure data quality, including: Remove data outliers: Calculate the standard deviation of the data and set 3 times the standard deviation as the threshold. Data points outside this range are considered outliers. The formula used to calculate the standard deviation of the data is: Where σ is the standard deviation, N is the total number of data points, and x i is the i-th data point, μ is the mean of the data; The formula for calculating the mean of the data is: Where μ is the mean of the data, N is the total number of data points, and x i is the u-th data point; Data interpolation: For time series data, use linear interpolation to fill missing values: Assuming that the data changes linearly between two known points, the missing values ​​are estimated by calculating the linear relationship between the two points.

5. The intelligent drip irrigation method for growing Dendrobium officinale seedlings in a greenhouse according to claim 1, characterized in that: The process of establishing a dendrobium water demand prediction model based on a deep learning algorithm specifically includes: A Dendrobium water demand prediction model was established based on the deep learning algorithm. The model consists of an input layer, a hidden layer, an activation layer, and an output layer. The temperature, wind speed, and Dendrobium growth stage data in the greenhouse were used as training sets, and the corresponding water demand was used as a label to train the model and obtain a trained Dendrobium water demand prediction model.

6. The intelligent drip irrigation method for growing Dendrobium officinale seedlings in a greenhouse according to claim 5, characterized in that: The predicted water demand H of Dendrobium officinale was corrected using the collected soil moisture data and light intensity data, and the formula for the comprehensive water demand index was obtained as follows; Among them, M is the comprehensive water demand index, S is soil moisture (percentage), L is light intensity, H is the predicted water demand of Dendrobium, α is the preset proportional coefficient of soil moisture S, β is the preset proportional coefficient of light intensity L, γ is the preset proportional coefficient of the predicted water demand H of Dendrobium, α, β, and γ are all greater than zero, and α>β>γ, and C1 is a constant correction index.

7. The intelligent drip irrigation method for growing Dendrobium officinale seedlings in a greenhouse according to claim 6, characterized in that: Based on the obtained comprehensive water demand index, the drip irrigation interval I(t) is calculated using a dynamic adjustment formula, and the formula is as follows: I(t)=I0*e -k*M Among them, I(t) is the calculated drip irrigation interval, I0 is the initial drip irrigation interval in hours, M is the comprehensive water demand index, k is the preset adjustment coefficient, and k is greater than zero.

8. The intelligent drip irrigation method for growing Dendrobium officinale seedlings in a greenhouse according to claim 1, characterized in that: According to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation water volume, including: When the comprehensive water demand index M is higher than the preset water demand threshold M x , that is, M>M x When the drip irrigation water volume Q is increased, the system will increase the drip irrigation water volume. The calculation formula of the drip irrigation water volume Q is as follows: Q=Q base *(1+δ*M) Where Q is the irrigation water volume, Q base is the basic irrigation volume in liters, M is the comprehensive water demand index, δ is the preset proportional coefficient of the comprehensive water demand index M, and δ is greater than zero.

9. An intelligent drip irrigation system for Dendrobium greenhouse seedling cultivation, characterized by: The intelligent drip irrigation system for growing seedlings in a greenhouse of dendrobium is used to implement the intelligent drip irrigation method for growing seedlings in a greenhouse of dendrobium according to any one of claims 1 to 8, comprising: The data acquisition module uses soil moisture sensors, temperature sensors, light intensity sensors, wind speed sensors, and plant growth status sensors to collect multiple sets of historical data in the Dendrobium seedling greenhouse. Each set of historical data includes temperature, wind speed, Dendrobium growth stage data, and the corresponding plant water demand; The model building module uses the temperature, wind speed and growth stage data of the greenhouse as training sets, and the corresponding water demand as labels, establishes a water demand prediction model for Dendrobium based on a deep learning algorithm, trains the model, and obtains a trained water demand prediction model for Dendrobium; The model correction module uses sensors to collect real-time data on the temperature, wind speed and growth stage of Dendrobium in the greenhouse, and inputs them into the water demand prediction model to obtain the predicted water demand of Dendrobium output by the model, and uses the collected soil moisture data and light intensity data to correct the predicted water demand of Dendrobium to obtain a comprehensive water demand index; The comprehensive analysis module calculates the drip irrigation interval based on the obtained comprehensive water demand index using a dynamic adjustment formula. According to the calculated drip irrigation interval, the system automatically adjusts the drip irrigation time, and when the comprehensive water demand index is higher than the preset water demand threshold, the system will increase the drip irrigation water volume.

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