Camellia planting intelligent drip irrigation system and method based on deep learning

Abstract

The invention discloses a camellia planting intelligent drip irrigation system and method based on deep learning, and relates to the technical field of planting management, and the system comprises a data collection module, a data analysis module, a prediction module and a regulation and control irrigation module; the data acquisition module is used for acquiring environment data and growth data of a camellia planting area in real time; the data analysis module is used for analyzing the environment data and the growth data to obtain growth requirements and prediction reference data; the prediction module performs irrigation prediction based on the prediction reference data; the regulation and control irrigation module performs drip irrigation on the planting area based on the irrigation prediction and the growth demand, and records and regulates and controls the drip irrigation result; the method is used for solving the problem that when an existing camellia irrigation technology is used for irrigating camellia, due to the fact that data is insufficient, irrigation is insufficient or excessive, and water shortage of the camellia or water source waste is caused.

Description

Technical Field

[0001] The present invention relates to the field of planting management technology, and specifically to a camellia planting intelligent drip irrigation system and method based on deep learning. Background Art

[0003] In the existing technology, camellia planting areas are mostly distributed in mountainous and hilly areas with complex terrain, relatively poor soil and poor irrigation conditions. Traditional ditch irrigation cannot be used, and most of them use external mechanical equipment for sprinkler irrigation. However, this sprinkler irrigation method wastes water resources and causes inaccurate irrigation. The existing camellia irrigation technology also has the problem of insufficient data collection and inability to judge the water shortage of camellia when irrigating camellia, resulting in insufficient or excessive irrigation, leading to water shortage or waste of water resources for camellia. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent, by deeply learning historical data, accurately predicting soil temperature and soil moisture, and then judging the growth needs of camellia by obtaining the growth period of camellia, and drip irrigation regulating the predicted soil temperature and soil moisture based on the growth needs of camellia; so as to solve the problem of insufficient or excessive irrigation due to insufficient data when irrigating camellia with existing camellia irrigation technology, resulting in water shortage or water waste for camellia.

[0005] To achieve the above objectives, in a first aspect, the present application provides a camellia planting smart drip irrigation system based on deep learning, including a data acquisition module, a data analysis module, a prediction module and an irrigation control module;

[0006] The data acquisition module is used to obtain environmental data and growth data of the camellia planting area in real time;

[0007] The data analysis module is used to analyze environmental data and growth data to obtain growth demand and prediction reference data;

[0008] The prediction module performs irrigation prediction based on prediction reference data;

[0009] The irrigation control module performs drip irrigation on the planting area based on irrigation prediction and growth demand, and records and controls the drip irrigation results.

[0010] Furthermore, the data collection module includes a growth collection unit, and the growth collection unit is configured with a growth collection strategy. The growth collection strategy includes obtaining the planting time of camellia, and dividing the camellia into seedling camellia, growing camellia, flowering camellia and fruiting camellia based on the planting time of the camellia number, collectively referred to as growth data.

[0011] Furthermore, the data collection module further includes an environment collection unit, the environment collection unit is configured with an environment collection strategy, the environment collection strategy includes dividing the camellia planting area based on the distribution of seedling stage camellia, growing stage camellia, flowering stage camellia and fruiting stage camellia, and obtaining the seedling stage collection area, the growing stage collection area, the flowering stage collection area and the fruiting stage collection area;

[0012] Soil moisture sensors and temperature sensors are set in different collection areas, and the readings of the sensors are obtained in real time to obtain the soil moisture and soil temperature in different collection areas;

[0013] Obtain daily precipitation in camellia growing areas from meteorological observation stations;

[0014] The soil moisture, soil temperature and daily precipitation are collectively referred to as environmental data.

[0015] Further, the data analysis module includes a growth analysis unit, the growth analysis unit is configured with a growth analysis strategy, the growth analysis strategy includes obtaining soil water requirements of camellia in the seedling stage, camellia in the growth stage, camellia in the flowering stage and camellia in the fruiting stage from the database, and setting them as seedling water requirements, growth water requirements, flowering water requirements and fruiting water requirements;

[0016] The soil temperature requirements of camellia in the seedling stage, camellia in the growing stage, camellia in the flowering stage and camellia in the fruiting stage are obtained from the database respectively, and are set as seedling temperature requirement, growth temperature requirement, flowering temperature requirement and fruiting temperature requirement; the seedling water requirement, growth water requirement, flowering water requirement, fruiting water requirement, seedling temperature requirement, growth temperature requirement, flowering temperature requirement and fruiting temperature requirement are collectively referred to as growth requirement.

[0017] Further, the data analysis module also includes an environmental analysis unit, the environmental analysis unit is configured with an environmental analysis strategy, the environmental analysis strategy includes obtaining daily precipitation, soil moisture and soil temperature from historical data to draw a curve graph, which is set as a precipitation temperature and humidity change graph;

[0018] The precipitation, temperature and humidity change graph is divided into two periods when the daily precipitation is 0 for two consecutive days or more as the drought period, the period when the daily precipitation is not 0 for two consecutive days or more as the rainy period, and the period when the daily precipitation is not 0 and the daily precipitation is 0 for the adjacent days as the normal period;

[0019] Get the duration from the beginning to the end of all drought periods, set it as the drought duration, get the soil temperature and humidity at the beginning and end of the corresponding drought period, set them as drought initial temperature, drought initial humidity, drought end temperature and drought end humidity;

[0020] Substitute the drought initial temperature, drought end temperature and drought duration into the drought temperature change value formula The drought temperature change value is calculated, where v T is the drought temperature change value, T0 is the drought initial temperature, T J is the drought end temperature, s is the drought duration, and n is the number of drought periods;

[0021] Substitute the initial humidity of drought, the end humidity of drought and the duration of drought into the formula of drought humidity change value The drought humidity change value is calculated, where v H is the change value of drought humidity, H0 is the initial humidity of drought, H J is the drought end humidity, s is the drought duration, and n is the number of drought periods;

[0022] Get the duration from the start to the end of all normal periods, set it as the normal duration, get the soil temperature, humidity and daily precipitation at the start and end of the corresponding normal period, set it as normal initial temperature, normal initial humidity, normal end temperature, normal end humidity and normal daily precipitation;

[0023] Substitute the normal initial temperature, normal ending temperature and normal daily precipitation into the precipitation temperature change formula The precipitation temperature change value is calculated, where kT is the precipitation temperature change value, T1 is the normal initial temperature, and T C is the normal ending temperature, P is the normal daily precipitation, and m is the number of normal periods;

[0024] Substitute the normal initial humidity, normal ending humidity and normal daily precipitation into the precipitation humidity change formula The precipitation humidity change value is calculated, where kH is the normal humidity change value, H1 is the normal initial humidity, and H C is the normal end humidity, P is the daily precipitation, and m is the number of normal periods;

[0025] The drought temperature change value, drought humidity change value, precipitation temperature change value and precipitation humidity change value are collectively referred to as prediction reference data.

[0026] Furthermore, the prediction module is configured with a prediction strategy, which includes obtaining predicted precipitation from a meteorological monitoring station, where the predicted precipitation is the daily precipitation of the next day output by the meteorological monitoring station; obtaining the soil temperature and soil humidity at this time from the precipitation temperature and humidity change diagram and setting them as the initial soil temperature and initial soil humidity;

[0027] Fill the predicted precipitation into the precipitation temperature and humidity change diagram, determine the time period type at this time, and perform drought prediction or precipitation prediction on the soil temperature and humidity based on the time period type to obtain the predicted soil temperature and predicted soil humidity;

[0028] The drought prediction includes substituting the initial soil temperature and the drought temperature change value into the soil temperature prediction formula T when it is a drought period. Y =T2+v T The predicted soil temperature is calculated as follows: Y To predict soil temperature, T2 is the initial soil temperature, v T is the drought temperature change value;

[0029] Substitute the initial soil moisture and drought moisture change values ​​into the predicted soil moisture formula H Y =H2+v H The predicted soil moisture is calculated, where H Y To predict soil moisture, H2 is the initial soil moisture, v H is the drought humidity change value;

[0030] The precipitation prediction includes: when it is a precipitation period, substituting the initial soil temperature and the precipitation temperature change value into the soil temperature prediction formula T Y =T2+k T The predicted soil temperature is calculated as follows: Y To predict soil temperature, T2 is the initial soil temperature, k T is the precipitation temperature change value;

[0031] Substitute the initial soil moisture and precipitation moisture change values ​​into the predicted soil moisture formula H Y =H2+k H The predicted soil moisture is calculated, where H Y To predict soil moisture, H2 is the initial soil moisture, v H is the precipitation humidity change value.

[0032] Furthermore, the control irrigation module is configured with a control irrigation strategy, which includes determining the demand for predicted soil temperature and predicted soil moisture, including determining whether the predicted soil temperature meets the seedling temperature requirement, growth temperature requirement, flowering temperature requirement and fruiting temperature requirement. When there is no requirement, drip irrigation for the second time period is performed on the corresponding collection area.

[0033] Determine whether the predicted soil moisture meets the requirements of seedling moisture, growth moisture, flowering moisture and fruiting moisture. If the requirements are not met, drip irrigation for the second period of time is performed on the corresponding collection area.

[0034] The soil temperature and soil moisture of the collection area after drip irrigation are collected and set as real-time temperature and real-time humidity. Drought prediction or humidity prediction is performed based on the real-time temperature and real-time humidity to obtain secondary predicted humidity and secondary predicted temperature. Demand judgment is performed on the secondary predicted humidity and secondary predicted temperature until the growth needs of all areas are met.

[0035] In a second aspect, the present application provides a camellia planting smart drip irrigation method based on deep learning, comprising the following steps:

[0036] Obtain real-time environmental and growth data of camellia growing areas;

[0037] Analyze environmental data and growth data to obtain growth demand and forecast reference data;

[0038] Make irrigation forecasts based on forecast reference data;

[0039] Drip irrigation is carried out in the planting area based on growth needs and irrigation forecasts, and the drip irrigation results are recorded and regulated.

[0040] Beneficial effects of the present invention: The present invention obtains environmental data and growth data of a camellia planting area in real time, and then analyzes the environmental data and growth data to obtain growth demand and prediction reference data. The advantages are that the growth demand obtained by analyzing the growth data of camellia can be used as a reference for the water demand of camellia, and the environmental data can reflect the changing law of soil. The prediction reference data obtained by analyzing the environmental data is universal, which improves the accuracy and effectiveness of subsequent predictions.

[0041] The present invention also divides the growth period of camellia based on the planting time of camellia, and performs different irrigation controls on camellia in different growth periods. The advantage is that the water requirements of camellia in different growth periods are also different, and formulating different irrigation plans improves the rationality and accuracy of camellia irrigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a functional block diagram of the system of the present invention;

[0043] Figure 2 It is the precipitation temperature and humidity change diagram of the present invention;

[0044] Figure 3 The figure is a flow chart of the method steps 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] Example 1, please refer to Figure 1 As shown, the present application provides a camellia planting intelligent drip irrigation system based on deep learning, including a data acquisition module, a data analysis module, a prediction module and an irrigation control module;

[0047] The data acquisition module is used to obtain the environmental data and growth data of the camellia planting area in real time;

[0048] The data collection module includes a growth collection unit, and the growth collection unit is configured with a growth collection strategy. The growth collection strategy includes obtaining the planting time of camellia, and dividing the camellia into seedling-stage camellia, growing-stage camellia, flowering-stage camellia and fruit-bearing-stage camellia based on the planting time of the number of camellias, which are collectively referred to as growth data; in the specific implementation process, the camellia with a planting time of less than or equal to 8 months is divided into the seedling-stage camellia, the camellia with a planting time of more than 8 months and less than or equal to 36 months is divided into the growing-stage camellia, the camellia with a planting time of more than 36 months and less than or equal to 60 months is divided into the flowering-stage camellia, and the camellia with a planting time of more than 60 months is divided into the fruit-bearing camellia;

[0049] The data collection module also includes an environment collection unit, which is configured with an environment collection strategy. The environment collection strategy includes dividing the camellia planting area based on the distribution of seedling camellia, growing camellia, flowering camellia and fruiting camellia, and obtaining the seedling collection area, growing collection area, flowering collection area and fruiting collection area. In the specific implementation process, when planting camellia, camellia planted in the same period is often in the same area;

[0050] Soil moisture sensors and temperature sensors are set in different collection areas, and the sensor readings are obtained in real time to obtain the soil moisture and soil temperature in different collection areas; the daily precipitation in the camellia planting area is obtained from the meteorological observation station; soil moisture, soil temperature and daily precipitation are collectively referred to as environmental data;

[0051] The data analysis module is used to analyze environmental data and growth data to obtain growth demand and forecast reference data;

[0052] The data analysis module includes a growth analysis unit, which is configured with a growth analysis strategy. The growth analysis strategy includes obtaining the soil water requirements of camellia in the seedling stage, camellia in the growth stage, camellia in the flowering stage, and camellia in the fruiting stage from the database, respectively, and setting them as seedling water requirements, growth water requirements, flowering water requirements, and fruiting water requirements;

[0053] The soil temperature requirements of camellia in the seedling stage, camellia in the growing stage, camellia in the flowering stage and camellia in the fruiting stage are obtained from the database respectively, and are set as the seedling temperature requirement, the growing temperature requirement, the flowering temperature requirement and the fruiting temperature requirement;

[0054] Seedling water requirements, growth water requirements, flowering water requirements, fruiting water requirements, seedling temperature requirements, growth temperature requirements, flowering temperature requirements and fruiting temperature requirements are collectively referred to as growth requirements;

[0055] See also Figure 2 As shown, the data analysis module also includes an environmental analysis unit, which is configured with an environmental analysis strategy, which includes obtaining daily precipitation, soil moisture and soil temperature from historical data to draw a curve graph, which is set as a precipitation temperature and humidity change graph;

[0056] The precipitation temperature and humidity change graph is divided, and the period when the daily precipitation is 0 for two consecutive days or more is set as the drought period, the period when the daily precipitation is not 0 for two consecutive days or more is set as the rainy period, and the period when the daily precipitation is not 0 and the daily precipitation of the adjacent days is 0 is set as the normal period; in the specific implementation process, the effects of continuous precipitation, continuous no precipitation and intermittent precipitation on soil temperature and humidity are different, so it is necessary to divide the precipitation temperature and humidity change graph into time periods and analyze the effects of precipitation on soil temperature and humidity separately;

[0057] The duration from the beginning to the end of the drought period within the first duration is obtained, which is set as the drought duration; the soil temperature and humidity at the beginning and end of the corresponding drought period are obtained, which are set as the drought initial temperature, drought initial humidity, drought end temperature and drought end humidity; in the specific implementation process, the first duration is 60 days;

[0058] Substitute the drought initial temperature, drought end temperature and drought duration into the drought temperature change value formula The drought temperature change value is calculated, where v T is the drought temperature change value, T0 is the drought initial temperature, T J is the drought end temperature, s is the drought duration, and n is the number of drought periods. In the specific implementation process, for example, there are two drought periods within 60 days, and the drought initial temperature T0 of one drought period is 20℃, and the drought end temperature T JThe drought duration s is 15 days, the initial temperature of drought T0 in another drought period is 26°C, and the end temperature of drought T J is 32℃, and the drought duration s is 20 days, then the drought temperature change value v is calculated T is 0.41;

[0059] Substitute the initial humidity of drought, the end humidity of drought and the duration of drought into the formula of drought humidity change value The drought humidity change value is calculated, where v H is the change value of drought humidity, H0 is the initial humidity of drought, H J is the drought end humidity, s is the drought duration, and n is the number of drought periods; in the specific implementation process, for example, there are 2 drought periods within 60 days, and the initial drought humidity H0 of one of the drought periods is 85%, and the drought end humidity H J The initial humidity of the drought period is 90%, and the end humidity of the drought period is H0. J is 55%, and the drought duration s is 20 days, then the drought humidity change value v is calculated H 1.7%;

[0060] Get the duration from the start to the end of all normal periods, set it as the normal duration, get the soil temperature, humidity and daily precipitation at the start and end of the corresponding normal period, set it as normal initial temperature, normal initial humidity, normal end temperature, normal end humidity and normal daily precipitation;

[0061] Substitute the normal initial temperature, normal ending temperature and normal daily precipitation into the precipitation temperature change formula The precipitation temperature change value is calculated, where k T is the precipitation temperature change value, T1 is the normal initial temperature, T C is the normal ending temperature, P is the normal daily precipitation, and m is the number of normal periods. In the specific implementation process, for example, there are 2 normal periods in 60 days, and the normal initial temperature T1 of one normal period is 20℃, and the normal ending temperature T C The normal duration P is 10 days, the normal initial temperature T1 of another normal period is 26°C, and the normal ending temperature T C is 18℃, and the normal duration P is 12 days, then the precipitation temperature change value k is calculated T is 0.53;

[0062] Substitute the normal initial humidity, normal ending humidity and normal daily precipitation into the precipitation humidity change formula The precipitation humidity change value is calculated, where k H is the normal humidity change value, H1 is the normal initial humidity, HC is the normal ending humidity, P is the daily precipitation, and m is the number of normal periods. In the specific implementation process, for example, there are 2 normal periods in 60 days, and the normal initial humidity H1 of one normal period is 60%, and the normal ending humidity H C The normal duration P is 10 days, the normal initial humidity H1 of another normal period is 55%, and the normal ending humidity H C is 90%, and the normal duration P is 12 days, then the normal humidity change value k is calculated H 2.7%;

[0063] The drought temperature change value, drought humidity change value, precipitation temperature change value and precipitation humidity change value are collectively referred to as prediction reference data;

[0064] The prediction module performs irrigation prediction based on prediction reference data;

[0065] The prediction module is configured with a prediction strategy, which includes obtaining the predicted precipitation from the meteorological monitoring station, the predicted precipitation is the daily precipitation of the next day output by the meteorological monitoring station; obtaining the soil temperature and soil humidity at this time from the precipitation temperature and humidity change diagram and setting them as the initial soil temperature and initial soil humidity;

[0066] Fill the predicted precipitation into the precipitation temperature and humidity change diagram, determine the time period type at this time, and perform drought prediction or precipitation prediction on the soil temperature and humidity based on the time period type to obtain the predicted soil temperature and predicted soil humidity;

[0067] Drought prediction includes, when it is a drought period, substituting the initial soil temperature and drought temperature change value into the predicted soil temperature formula T Y =T2+v T The predicted soil temperature is calculated as follows: Y To predict soil temperature, T2 is the initial soil temperature, v T is the drought temperature change value; in the specific implementation process, for example, the initial soil temperature T2 obtained is 16°C, the drought temperature change value v T is 0.41, then the predicted soil temperature T Y 16.41℃;

[0068] Substitute the initial soil moisture and drought moisture change values ​​into the predicted soil moisture formula H Y =H2+v H The predicted soil moisture is calculated, where H Y To predict soil moisture, H2 is the initial soil moisture, v H is the drought humidity change value; in the specific implementation process, for example, the initial soil moisture H2 obtained is 82%, the drought humidity change value v His 1.7%, then the predicted soil moisture H is obtained Y 83.7%;

[0069] Precipitation prediction includes, when it is the precipitation period, substituting the initial soil temperature and precipitation temperature change into the soil temperature prediction formula T Y =T2+k T The predicted soil temperature is calculated as follows: Y To predict soil temperature, T2 is the initial soil temperature, k T is the precipitation temperature change value; in the specific implementation process, for example, the initial soil temperature T2 obtained is 16°C, and the rainfall temperature change value k T is 0.58, then the predicted soil temperature T Y is 16.58℃;

[0070] Substitute the initial soil moisture and precipitation moisture change values ​​into the predicted soil moisture formula H Y =H2+k H The predicted soil moisture is calculated, where H Y To predict soil moisture, H2 is the initial soil moisture, k H is the precipitation humidity change value; in the specific implementation process, for example, the initial soil humidity H2 obtained is 82%, and the drought humidity change value k H is 2.7%, then the predicted soil moisture H is obtained Y 84.7%;

[0071] The irrigation control module drips the planting area based on irrigation forecasts and growth needs, and records and controls the drip irrigation results;

[0072] The control irrigation module is equipped with a control irrigation strategy, which includes demand judgment on the predicted soil temperature and predicted soil moisture, including whether the predicted soil temperature meets the seedling temperature requirements, growth temperature requirements, flowering temperature requirements and fruiting temperature requirements. When there is a demand that does not meet the requirements, the corresponding collection area is drip irrigated for the second time. In the specific implementation process, the second time is set to 60 minutes. Too long a drip irrigation time will lead to too high soil moisture, so a short-term and multiple drip irrigation method is adopted until the soil moisture meets the requirements.

[0073] Determine whether the predicted soil moisture meets the requirements of seedling moisture, growth moisture, flowering moisture and fruiting moisture. If the requirements are not met, drip irrigation for the second period of time is performed on the corresponding collection area.

[0074] The soil temperature and soil moisture of the collection area after drip irrigation are collected and set as real-time temperature and real-time humidity. Drought prediction or humidity prediction is performed based on the real-time temperature and real-time humidity to obtain secondary predicted humidity and secondary predicted temperature. Demand judgment is performed on the secondary predicted humidity and secondary predicted temperature until the growth needs of all areas are met.

[0075] Example 2, please refer to Figure 3 As shown, in the second aspect, the present application provides a camellia planting intelligent drip irrigation method based on deep learning, comprising the following steps:

[0076] Step S1: Real-time acquisition of environmental data and growth data of camellia planting areas; including the following sub-steps:

[0077] Step S101: Acquire the planting time of camellia, and divide the camellia into seedling-stage camellia, growing-stage camellia, flowering-stage camellia, and fruit-bearing-stage camellia based on the planting time of the number of camellias, collectively referred to as growth data;

[0078] Step S102: Divide the camellia planting area based on the distribution of seedling stage camellia, growing stage camellia, flowering stage camellia and fruiting stage camellia to obtain seedling stage collection area, growing stage collection area, flowering stage collection area and fruiting stage collection area;

[0079] Step S103: setting soil moisture sensors and temperature sensors in different collection areas, obtaining the sensor readings in real time, and obtaining the soil moisture and soil temperature in different collection areas; obtaining the daily precipitation in the camellia planting area from the meteorological observation station;

[0080] Soil moisture, soil temperature, and daily precipitation are collectively referred to as environmental data;

[0081] Step S2: Analyze the environmental data and growth data to obtain growth requirements and prediction reference data; including the following sub-steps:

[0082] Step S201: obtaining soil water requirements of camellia in the seedling stage, camellia in the growing stage, camellia in the flowering stage and camellia in the fruiting stage from the database respectively, and setting them as seedling water requirement, growth water requirement, flowering water requirement and fruiting water requirement;

[0083] The soil temperature requirements of camellia in the seedling stage, camellia in the growing stage, camellia in the flowering stage and camellia in the fruiting stage are obtained from the database respectively and set as seedling temperature requirement, growth temperature requirement, flowering temperature requirement and fruiting temperature requirement; seedling water requirement, growth water requirement, flowering water requirement, fruiting water requirement, seedling temperature requirement, growth temperature requirement, flowering temperature requirement and fruiting temperature requirement are collectively referred to as growth requirement.

[0084] Step S202: Obtain daily precipitation, soil moisture and soil temperature from historical data and draw a curve graph, which is set as a precipitation temperature and humidity change graph;

[0085] Step S203: The precipitation temperature and humidity change graph is divided, and the period when the daily precipitation is 0 for two consecutive days or more is set as a drought period, the period when the daily precipitation is not 0 for two consecutive days or more is set as a rainy period, and the period when the daily precipitation is not 0 and the daily precipitation of the adjacent days is 0 is set as a normal period;

[0086] Step S204: Obtain the duration from the start to the end of all drought periods, set it as the drought duration, obtain the soil temperature and humidity at the start and end of the corresponding drought period, set it as the drought initial temperature, drought initial humidity, drought end temperature and drought end humidity;

[0087] Substitute the drought initial temperature, drought end temperature and drought duration into the drought temperature change value formula The drought temperature change value is calculated, where v T is the drought temperature change value, T0 is the drought initial temperature, T J is the drought end temperature, s is the drought duration, and n is the number of drought periods;

[0088] Substitute the initial humidity of drought, the end humidity of drought and the duration of drought into the formula of drought humidity change value The drought humidity change value is calculated, where v H is the change value of drought humidity, H0 is the initial humidity of drought, H J is the drought end humidity, s is the drought duration, and n is the number of drought periods;

[0089] Step S205: Obtain the duration from the start to the end of all normal periods, set it as the normal duration, obtain the soil temperature and humidity and daily precipitation at the start and end of the corresponding normal period, and set it as the normal initial temperature, normal initial humidity, normal end temperature, normal end humidity and normal daily precipitation;

[0090] Substitute the normal initial temperature, normal ending temperature and normal daily precipitation into the precipitation temperature change formula The precipitation temperature change value is calculated, where kT is the precipitation temperature change value, T1 is the normal initial temperature, and T C is the normal ending temperature, P is the normal daily precipitation, and m is the number of normal periods;

[0091] Substitute the normal initial humidity, normal ending humidity and normal daily precipitation into the precipitation humidity change formula The precipitation humidity change value is calculated, where kH is the normal humidity change value, H1 is the normal initial humidity, and H Cis the normal end humidity, P is the daily precipitation, and m is the number of normal periods;

[0092] The drought temperature change value, drought humidity change value, precipitation temperature change value and precipitation humidity change value are collectively referred to as prediction reference data;

[0093] Step S3: performing irrigation prediction based on prediction reference data; including the following sub-steps:

[0094] Step S301: Obtain predicted precipitation from a meteorological monitoring station, where the predicted precipitation is the daily precipitation for the next day output by the meteorological monitoring station; obtain the soil temperature and soil humidity at this time from the precipitation temperature and humidity change diagram and set them as the initial soil temperature and initial soil humidity;

[0095] Step S302: Fill the predicted precipitation into the precipitation temperature and humidity change diagram, determine the time period type at this time, and perform drought prediction or precipitation prediction on the soil temperature and humidity based on the time period type to obtain the predicted soil temperature and predicted soil humidity;

[0096] Drought prediction includes, when it is a drought period, substituting the initial soil temperature and drought temperature change value into the predicted soil temperature formula T Y =T2+v T The predicted soil temperature is calculated as follows: Y To predict soil temperature, T2 is the initial soil temperature, v T is the drought temperature change value;

[0097] Substitute the initial soil moisture and drought moisture change values ​​into the predicted soil moisture formula H Y =H2+v H The predicted soil moisture is calculated, where H Y To predict soil moisture, H2 is the initial soil moisture, v H is the drought humidity change value;

[0098] Precipitation prediction includes, when it is the precipitation period, substituting the initial soil temperature and precipitation temperature change into the soil temperature prediction formula T Y =T2+k T The predicted soil temperature is calculated as follows: Y To predict soil temperature, T2 is the initial soil temperature, k T is the precipitation temperature change value;

[0099] Substitute the initial soil moisture and precipitation moisture change values ​​into the predicted soil moisture formula H Y =H2+k H The predicted soil moisture is calculated, where H Y To predict soil moisture, H2 is the initial soil moisture, v His the precipitation humidity change value;

[0100] Step S4: drip irrigation is performed on the planting area based on growth demand and irrigation forecast, and the drip irrigation results are recorded and regulated; including the following sub-steps:

[0101] Step S401: Demand judgment is performed on the predicted soil temperature and predicted soil moisture, including whether the predicted soil temperature meets the seedling temperature requirement, the growth temperature requirement, the flowering temperature requirement and the fruiting temperature requirement. If the requirements are not met, drip irrigation is performed for the second time period on the corresponding collection area.

[0102] Step S402: determining whether the predicted soil moisture meets the seedling moisture requirement, the growth moisture requirement, the flowering moisture requirement and the fruiting moisture requirement, and if the requirement is not met, drip irrigation for the second time period is performed on the corresponding collection area;

[0103] Step S403: Collect the soil temperature and soil moisture in the collection area after drip irrigation, set them as real-time temperature and real-time humidity, perform drought prediction or humidity prediction based on the real-time temperature and real-time humidity, obtain secondary predicted humidity and secondary predicted temperature, and make demand judgment on the secondary predicted humidity and secondary predicted temperature until they meet the growth needs of all areas.

[0104] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0105] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms;

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A camellia planting intelligent drip irrigation system based on deep learning, characterized in that: It includes data collection module, data analysis module, prediction module and irrigation control module; The data acquisition module is used to obtain environmental data and growth data of the camellia planting area in real time; The data analysis module is used to analyze environmental data and growth data to obtain growth demand and prediction reference data; The prediction module performs irrigation prediction based on prediction reference data; The irrigation control module performs drip irrigation on the planting area based on irrigation prediction and growth demand, and records and controls the drip irrigation results.

2. According to claim 1, a camellia planting intelligent drip irrigation system based on deep learning is characterized in that: The data collection module includes a growth collection unit, which is configured with a growth collection strategy. The growth collection strategy includes obtaining the planting time of camellia, and dividing the camellia into seedling camellia, growing camellia, flowering camellia and fruiting camellia based on the planting time of the camellia number, collectively referred to as growth data.

3. The deep learning-based camellia planting intelligent drip irrigation system according to claim 2 is characterized in that: The data collection module also includes an environment collection unit, which is configured with an environment collection strategy. The environment collection strategy includes dividing the camellia planting area based on the distribution of seedling camellia, growing camellia, flowering camellia and fruiting camellia to obtain seedling collection areas, growing collection areas, flowering collection areas and fruiting collection areas; Soil moisture sensors and temperature sensors are set in different collection areas, and the readings of the sensors are obtained in real time to obtain the soil moisture and soil temperature in different collection areas; Obtain daily precipitation in camellia growing areas from meteorological observation stations; The soil moisture, soil temperature and daily precipitation are collectively referred to as environmental data.

4. The deep learning-based camellia planting intelligent drip irrigation system according to claim 3 is characterized in that: The data analysis module includes a growth analysis unit, and the growth analysis unit is configured with a growth analysis strategy, and the growth analysis strategy includes obtaining soil moisture requirements of camellia in the seedling stage, camellia in the growth stage, camellia in the flowering stage, and camellia in the fruiting stage from the database, respectively, and setting them as seedling moisture requirements, growth moisture requirements, flowering moisture requirements, and fruiting moisture requirements; The soil temperature requirements of camellia in the seedling stage, camellia in the growing stage, camellia in the flowering stage and camellia in the fruiting stage are obtained from the database respectively, and are set as seedling temperature requirement, growth temperature requirement, flowering temperature requirement and fruiting temperature requirement; the seedling water requirement, growth water requirement, flowering water requirement, fruiting water requirement, seedling temperature requirement, growth temperature requirement, flowering temperature requirement and fruiting temperature requirement are collectively referred to as growth requirement.

5. The deep learning-based camellia planting intelligent drip irrigation system according to claim 4, characterized in that: The data analysis module further includes an environmental analysis unit, the environmental analysis unit is configured with an environmental analysis strategy, the environmental analysis strategy includes obtaining daily precipitation, soil moisture and soil temperature from historical data to draw a curve graph, and setting it as a precipitation temperature and humidity change graph; The precipitation, temperature and humidity change graph is divided into two periods when the daily precipitation is 0 for two consecutive days or more as the drought period, the period when the daily precipitation is not 0 for two consecutive days or more as the rainy period, and the period when the daily precipitation is not 0 and the daily precipitation is 0 for the adjacent days as the normal period; Get the duration from the beginning to the end of all drought periods, set it as the drought duration, get the soil temperature and humidity at the beginning and end of the corresponding drought period, set them as drought initial temperature, drought initial humidity, drought end temperature and drought end humidity; Substitute the drought initial temperature, drought end temperature and drought duration into the drought temperature change value formula The drought temperature change value is calculated, where v T is the drought temperature change value, T0 is the drought initial temperature, T J is the drought end temperature, s is the drought duration, and n is the number of drought periods; Substitute the initial humidity of drought, the end humidity of drought and the duration of drought into the formula of drought humidity change value The drought humidity change value is calculated, where v H is the change value of drought humidity, H0 is the initial humidity of drought, H J is the drought end humidity, s is the drought duration, and n is the number of drought periods; Get the duration from the start to the end of all normal periods, set it as the normal duration, get the soil temperature, humidity and daily precipitation at the start and end of the corresponding normal period, set it as normal initial temperature, normal initial humidity, normal end temperature, normal end humidity and normal daily precipitation; Substitute the normal initial temperature, normal ending temperature and normal daily precipitation into the precipitation temperature change formula The precipitation temperature change value is calculated, where k T is the precipitation temperature change value, T1 is the normal initial temperature, T C is the normal ending temperature, P is the normal daily precipitation, and m is the number of normal periods; Substitute the normal initial humidity, normal ending humidity and normal daily precipitation into the precipitation humidity change formula The precipitation humidity change value is calculated, where k H is the normal humidity change value, H1 is the normal initial humidity, H C is the normal end humidity, P is the daily precipitation, and m is the number of normal periods; The drought temperature change value, drought humidity change value, precipitation temperature change value and precipitation humidity change value are collectively referred to as prediction reference data.

6. The deep learning-based camellia planting intelligent drip irrigation system according to claim 5, characterized in that: The prediction module is configured with a prediction strategy, which includes obtaining predicted precipitation from a meteorological monitoring station, where the predicted precipitation is the daily precipitation of the next day output by the meteorological monitoring station; obtaining the soil temperature and soil humidity at this time from the precipitation temperature and humidity change diagram and setting them as the initial soil temperature and initial soil humidity; Fill the predicted precipitation into the precipitation temperature and humidity change diagram, determine the time period type at this time, and perform drought prediction or precipitation prediction on the soil temperature and humidity based on the time period type to obtain the predicted soil temperature and predicted soil humidity; The drought prediction includes substituting the initial soil temperature and the drought temperature change value into the soil temperature prediction formula T when it is a drought period. Y =T2+v T The predicted soil temperature is calculated as follows: Y To predict soil temperature, T2 is the initial soil temperature, v T is the drought temperature change value; Substitute the initial soil moisture and drought moisture change values ​​into the predicted soil moisture formula H Y =H2+v H The predicted soil moisture is calculated, where H Y To predict soil moisture, H2 is the initial soil moisture, v H is the drought humidity change value; The precipitation prediction includes: when it is a precipitation period, substituting the initial soil temperature and the precipitation temperature change value into the soil temperature prediction formula T Y =T2+k T The predicted soil temperature is calculated as follows: Y To predict soil temperature, T2 is the initial soil temperature, k T is the precipitation temperature change value; Substitute the initial soil moisture and precipitation moisture change values ​​into the predicted soil moisture formula H Y =H2+k H The predicted soil moisture is calculated, where H Y To predict soil moisture, H2 is the initial soil moisture, v H is the precipitation humidity change value.

7. The deep learning-based camellia planting intelligent drip irrigation system according to claim 6, characterized in that: The control irrigation module is configured with a control irrigation strategy, which includes determining the demand for predicted soil temperature and predicted soil moisture, including determining whether the predicted soil temperature meets the seedling temperature requirement, growth temperature requirement, flowering temperature requirement, and fruiting temperature requirement. When there is no requirement that meets the requirement, drip irrigation for the second duration is performed on the corresponding collection area. Determine whether the predicted soil moisture meets the requirements of seedling moisture, growth moisture, flowering moisture and fruiting moisture. If the requirements are not met, drip irrigation for the second period of time is performed on the corresponding collection area. The soil temperature and soil moisture of the collection area after drip irrigation are collected and set as real-time temperature and real-time humidity. Drought prediction or humidity prediction is performed based on the real-time temperature and real-time humidity to obtain secondary predicted humidity and secondary predicted temperature. Demand judgment is performed on the secondary predicted humidity and secondary predicted temperature until the growth needs of all areas are met.

8. A camellia planting smart drip irrigation method based on deep learning, applicable to a camellia planting smart drip irrigation system based on deep learning according to any one of claims 1 to 7, characterized in that: The steps include: Obtain real-time environmental and growth data of camellia growing areas; Analyze environmental data and growth data to obtain growth demand and forecast reference data; Make irrigation forecasts based on forecast reference data; Drip irrigation is carried out in the planting area based on growth needs and irrigation forecasts, and the drip irrigation results are recorded and regulated.

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