CN116195495B - Method and system for water-saving irrigation based on Internet of things technology - Google Patents
Method and system for water-saving irrigation based on Internet of things technology Download PDFInfo
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- CN116195495B CN116195495B CN202310335459.6A CN202310335459A CN116195495B CN 116195495 B CN116195495 B CN 116195495B CN 202310335459 A CN202310335459 A CN 202310335459A CN 116195495 B CN116195495 B CN 116195495B
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/22—Improving land use; Improving water use or availability; Controlling erosion
Abstract
The invention relates to the technical field of agricultural irrigation, in particular to a method and a system for water-saving irrigation based on the technology of the Internet of things, wherein the method comprises the following steps: the data acquisition module acquires color images, infrared images and depth images of crops in irrigation lands under different water shortage states, and root depths of crops, water loss of crops, irrigation areas and water contents of different soil layers in the irrigation lands; the water shortage monitoring module uses the acquired image data to establish a water shortage detection model; the irrigation quantity calculation module determines the current irrigation quantity and predicts the next irrigation quantity by using other data; and the irrigation control module controls irrigation of crops according to the water shortage detection model, the current irrigation quantity and the next irrigation quantity. According to the invention, the irrigation quantity can be determined by using less data according to the actual conditions of crops, the accurate irrigation can be timely performed, the next irrigation quantity can be predicted, the irrigation reaction time is reduced, and the water-saving irrigation is realized.
Description
Technical Field
The invention relates to the technical field of agricultural irrigation, in particular to a method and a system for water-saving irrigation based on the technology of the Internet of things.
Background
Water is a source of life and is an indispensable resource for living organisms. Irrigation is to provide water for crops in a controlled irrigation mode to meet the demand that natural precipitation cannot meet, and modern agricultural requirements must improve the utilization efficiency of water resources, and at the same time, further adopts an irrigation mode which is favorable for agricultural environment and management. The traditional agriculture mode wastes much manpower and material resources, so that the traditional agriculture needs to be changed into intelligent agriculture, the Internet of things is an emerging high-new technology, has long-term development prospect, has great significance for promoting agricultural production when applied to agriculture, and is a research direction of a person skilled in the art how to realize agriculture water and energy conservation by utilizing the Internet of things technology and ensure reasonable growth of crops.
The traditional irrigation method and system based on the Internet of things are low in intelligent level, manual field control is needed in most cases when irrigation is carried out, the irrigation is laborious and inconvenient, irrigation cannot be carried out according to actual conditions of crops, water resource waste is caused, and the growth of the crops is not facilitated; the traditional irrigation method and system based on the Internet of things cannot control irrigation of each area through terminals such as mobile phones, cannot select irrigation modes, cannot realize reasonable utilization of irrigation water, and easily causes the problem of water waste; in addition, the traditional irrigation method and system based on the Internet of things need to collect a large amount of data when determining irrigation quantity, improves the complexity of water-saving irrigation, and is not beneficial to large-scale popularization.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method and a system for water-saving irrigation based on the technology of the Internet of things.
To achieve the above object, in a first aspect, the present invention provides a method for performing water-saving irrigation based on the internet of things technology, the method comprising the steps of: the data acquisition module acquires color images, infrared images and depth images of crops in irrigation lands under different water shortage states, and root depths of crops, water loss of crops, irrigation areas and water contents of different soil layers in the irrigation lands; the water shortage monitoring module establishes a water shortage detection model by using the color image, the infrared image and the depth image; the irrigation quantity calculation module determines current irrigation quantity and forecast next irrigation quantity by utilizing the root system depth of the crops, the water loss of the crops, the irrigation area and the water content; and the irrigation control module controls irrigation of crops according to the water shortage detection model, the current irrigation quantity and the next irrigation quantity. According to the method, the irrigation quantity can be determined by using less data according to the actual conditions of crops, accurate irrigation can be timely performed, the next irrigation quantity can be predicted, the irrigation reaction time is reduced, and water-saving irrigation is achieved.
Optionally, the data acquisition module acquires color images, infrared images and depth images of crops in the irrigation land under different water shortage states, and depth of root systems of crops, water loss of crops, irrigation area and water contents of different soil layers in the irrigation land, and the data acquisition module executes the following steps:
dividing the irrigated land into a plurality of irrigated areas;
acquiring the irrigation area of each irrigation area;
collecting the color image, the infrared image and the depth image of crops in different water shortage states, and the root system depth, the crop water loss and the irrigation area of the crops in different irrigation areas;
dividing the soil layer of the irrigation land into a surface layer and a deep layer according to the root system depth of crops;
and in different irrigation areas, measuring the water content of the surface layer to obtain the water content of the surface layer, and measuring the water content of the deep layer to obtain the water content of the deep layer.
Furthermore, the area of the irrigation land is often larger, so that the irrigation land is divided into different irrigation areas, and related data of each irrigation area are collected respectively, so that the water shortage condition and the crop water demand of each irrigation area are facilitated to be obtained, and accurate irrigation is carried out on the irrigation land according to the water shortage condition and the crop water demand of each irrigation area.
Optionally, the water shortage monitoring module establishes a water shortage detection model using the color image, the infrared image and the depth image, and the water shortage monitoring module performs the following steps:
creating an image dataset using the color image, the infrared image, and the depth image;
dividing the image data set into a training set, a verification set and a test set according to a proportion;
and establishing a water shortage detection model according to the training set, the verification set and the test set.
Further, the color image, the infrared image and the depth image are used for establishing a water shortage detection model, so that the condition of crops in the irrigation field is monitored in real time, the water shortage state of the crops is monitored, the irrigation is started when the water shortage is found, and healthy growth of the crops is promoted.
Optionally, the irrigation amount calculation module determines the current irrigation amount and predicts the next irrigation amount by using the root system depth of the crops, the water loss of the crops, the irrigation area and the water content, and the irrigation amount calculation module performs the following steps:
establishing an irrigation quantity calculation model by using the root system depth of the crops, the crop water loss, the surface water content, the deep water content and the irrigation area;
calculating the current irrigation quantity through the irrigation quantity calculation model;
establishing a water content prediction model by utilizing the surface water content and the deep water content;
and predicting the next irrigation amount according to the water content prediction model and the irrigation amount calculation model.
Furthermore, when the irrigation volume calculation model is built, less data needs to be acquired in advance, the internet of things irrigation system is simplified, meanwhile, the soil moisture content is directly related to whether crops lack water, and therefore the internet of things irrigation system is simplified, the irrigation volume can be accurately obtained according to the soil moisture content, and the irrigation water is saved.
Optionally, the establishing an irrigation quantity calculation model by using the root system depth of the crops, the water loss of the crops, the surface water content, the deep water content and the irrigation area comprises the following steps:
setting a time sequence of data measurement;
measuring the moisture loss of the crops according to the time sequence, and finishing to obtain a moisture loss data set;
measuring the surface water content according to the time sequence to obtain the variation of the surface water content;
measuring the deep water content according to the time sequence to obtain the change quantity of the deep water content;
and establishing an irrigation quantity calculation model by utilizing the root system depth of the crops, the water loss data set, the surface water content variation, the deep water content variation and the irrigation area.
Furthermore, under the condition that the area of the irrigation land is large, the water shortage states of crops in different irrigation areas can be different, so that the establishment of the irrigation quantity calculation model suitable for different irrigation areas is beneficial to irrigation according to the actual conditions of the irrigation areas, and the irrigation precision is improved.
Optionally, the irrigation quantity calculation model satisfies the following relationship:
wherein H is 1i +H 2i ＝cH i ，AIQ i T being the current irrigation quantity of the ith irrigation area i The water loss of the crops in the ith irrigation area, a, b and c are parameters,to correct the factor beta 1i The variation of the surface water content of the ith irrigation area, beta 2i For the variation of the deep water content of the ith irrigation area, S i For the irrigation area of the ith irrigation area, H 1i For the thickness of the surface layer in the ith irrigation area, H 2i For the thickness of the deep layer in the ith irrigation area, H i Is the root depth of the crop in the ith irrigation area.
Optionally, the step of establishing a water content prediction model by using the surface water content and the deep water content includes the following steps:
finishing the surface water content and the deep water content and manufacturing a water content data set;
dividing the water content data set into a training sample, a verification sample and a test sample according to a proportion;
and establishing the water content prediction model according to the training sample, the verification sample and the test sample.
Further, according to the water content data set, the water content prediction model is established by adopting a deep learning method.
Optionally, the predicting the next irrigation amount according to the water content prediction model and the irrigation amount calculation model includes the steps of:
predicting surface water content predicted values and deep water content predicted values of different irrigation areas after a period of time by using the water content predicted model;
and calculating the next irrigation quantity according to the surface water content predicted value and the deep water content predicted value and combining the irrigation quantity calculation model.
Optionally, the irrigation control module controls irrigation of crops according to the water shortage detection model, the current irrigation amount and the next irrigation amount, and the irrigation control module performs the following steps:
acquiring irrigation time according to the water shortage detection model;
and controlling the irrigation of crops in different irrigation areas according to the irrigation time and the current irrigation quantity, and preparing for the next crop irrigation in advance according to the next irrigation quantity.
Further, the irrigation control module controls irrigation of crops in different irrigation areas according to the irrigation time and the current irrigation quantity, including controlling irrigation time and irrigation mode. In addition, the irrigation control module can prepare for the next irrigation in advance according to the next irrigation quantity, so that the preparation time of the next irrigation is shortened.
In summary, the method provided by the invention can monitor the water shortage condition of crops in different areas in real time, accurately calculate the irrigation quantity by using less data according to the actual condition of the crops when the crops are in water shortage, and further accurately irrigate the crops, save irrigation water and simplify an Internet of things irrigation system; in addition, the method provided by the invention can also predict the irrigation quantity of the next irrigation after the current irrigation, so that the irrigation planning is performed in advance, the preparation time of irrigation is reduced, the adverse effect on crops caused by overlong water shortage time is reduced, and the yield and income of crops are promoted.
In a second aspect, the present invention provides a system for performing water-saving irrigation based on the internet of things technology, where the system uses the method for performing water-saving irrigation based on the internet of things technology provided by the present invention, and the system includes: the irrigation system comprises a data acquisition module, a water shortage monitoring module, an irrigation quantity calculation module and an irrigation control module; the data acquisition module acquires color images, infrared images and depth images of crops in irrigation lands under different water shortage states, and root depths of crops, water loss of crops, irrigation areas and water contents of different soil layers in the irrigation lands; the water shortage monitoring module establishes a water shortage detection model by using the color image, the infrared image and the depth image; the irrigation quantity calculation module determines current irrigation quantity and forecast next irrigation quantity by utilizing the root system depth of the crops, the water loss of the crops, the irrigation area and the water content; and the irrigation control module controls irrigation of crops according to the water shortage detection model, the current irrigation quantity and the next irrigation quantity.
The system provided by the invention is suitable for the method provided by the invention, has the same advantages as the method provided by the invention, and is beneficial to improving the intelligent degree of irrigation of the Internet of things.
In order to make the above objects, features and advantages of the present invention more comprehensible, alternative embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting in scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for performing water-saving irrigation based on the Internet of things technology according to an embodiment of the invention;
fig. 2 is a system structure diagram of water-saving irrigation based on the internet of things technology according to an embodiment of the invention.
Detailed Description
Specific embodiments of the invention will be described in detail below, it being noted that the embodiments described herein are for illustration only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been described in detail in order not to obscure the invention.
Throughout the specification, references to "one embodiment," "an embodiment," "one example," or "an example" mean: a particular feature, structure, or characteristic described in connection with the embodiment or example is included within at least one embodiment of the invention. Thus, the appearances of the phrases "in one embodiment," "in an embodiment," "one example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale.
It should be noted in advance that in an alternative embodiment, the same symbols or alphabet meaning and number are the same as those present in all formulas, except where separate descriptions are made.
In an alternative embodiment, referring to fig. 1, the present invention provides a method for performing water-saving irrigation based on the internet of things technology, the method comprising the following steps:
s1, a data acquisition module acquires color images, infrared images and depth images of crops in irrigation lands under different water shortage states, and root depths of crops, water loss of crops, irrigation areas and water contents of different soil layers in the irrigation lands.
In S1, the data acquisition module performs the following steps:
s11, dividing the irrigation land into a plurality of irrigation areas.
Specifically, in this embodiment, the irrigation area is divided according to the arrangement of the sensors in the irrigation area, the data acquisition module is wirelessly connected with a plurality of sensors in an external connection mode, the sensors comprise optical sensors and water content sensors, the arrangement mode of the sensors adopts a regular hexagon arrangement mode, the arrangement points of the sensors are located at six vertices of the regular hexagon and the geometric center of the regular hexagon, and the side lengths of the regular hexagon area satisfy the following relationship:
wherein d is the side length of the regular hexagon, and r is the sensing distance of the optical sensor.
Furthermore, each distribution point is respectively provided with one optical sensor and one water content sensor, and the optical sensor can sense the whole irrigation area by adopting a regular hexagon distribution mode, so that the real-time state of all crops can be conveniently determined.
Still further, in other alternative embodiments, other methods of dividing the irrigated land may be selected.
S12, acquiring the irrigation area of each irrigation area.
Specifically, in this embodiment, the data acquisition module is used to obtain DEM data of each irrigation area by using an external wireless external unmanned aerial vehicle flight platform, and each irrigation area of each irrigation area is obtained according to the DEM data, so that the influence of the gradient on the irrigation area can be reduced, the accuracy and reliability of the irrigation area are improved, and accurate irrigation is facilitated for each irrigation area.
Further, in other alternative embodiments, other methods of obtaining the irrigation area may be selected.
S13, collecting the color image, the infrared image and the depth image of crops in different water-deficient states, and the root system depth, the crop water loss and the irrigation area of the crops in different irrigation areas.
Specifically, in this embodiment, multiple crops are experimentally cultured, and different amounts of water are irrigated under the same conditions as those in the irrigated land to obtain crops in different water-deficient states, and the optical sensor is used to obtain the color image, the infrared image and the depth image of the crops in different water-deficient states.
Further, the water loss of the crops is obtained by using a weighing method, the root system depth of the crops is the maximum depth of the crops in the whole growth process, and the irrigation area is the surface area of soil for cultivating the crops.
Further, when crops are cultivated in a laboratory, the soil used should come from the irrigated land, and the thickness of the soil is large enough to ensure that the root system of the crops is bent in the direction vertical to the horizontal plane, thereby improving the reliability of the root system depth of the crops.
S14, dividing the soil layer of the irrigation land into a surface layer and a deep layer according to the root system depth of the crops.
Specifically, in this embodiment, according to the root system depth of the crop, a distance from the soil surface to half of the root system depth of the crop under the soil is taken as the surface layer, the soil under the surface layer is taken as the deep layer, and the thicknesses of the surface layer and the deep layer are integer multiples of the root system depth of the crop.
In particular, in other alternative embodiments, other methods may be utilized to divide the soil layer of the irrigated land into other soil layers.
S15, in different irrigation areas, measuring the water content of the surface layer to obtain the water content of the surface layer, and measuring the water content of the deep layer to obtain the water content of the deep layer.
Specifically, in this embodiment, the water content of the surface layer and the deep layer is measured by using the water content sensor according to the crop cultivated in step S13, so as to obtain the water content of the surface layer and the water content of the deep layer.
S2, a water shortage monitoring module establishes a water shortage detection model by using the color image, the infrared image and the depth image.
In S2, the water shortage monitoring module performs the steps of:
s21, utilizing the color image, the infrared image and the depth image to manufacture an image data set.
Specifically, in the present embodiment, the image data sets include a color image data set, an infrared image data set, and a depth image data set.
Further, according to the color image, the infrared image and the depth image obtained in the step S13, all the collected color images are classified together to form the color image dataset, all the collected infrared images are classified together to form the infrared image dataset, and all the collected depth images are classified together to form the depth image dataset.
S22, the image data set is divided into a training set, a verification set and a test set in proportion.
Specifically, in this embodiment, based on step S21, the color image dataset is divided into a color image training set, a color image verification set and a color image test set according to a ratio of 7:2:1, the infrared image dataset is divided into an infrared image training set, an infrared image verification set and an infrared image test set according to a ratio of 7:2:1, and the depth image dataset is divided into a depth image training set, a depth image verification set and a depth image test set according to a ratio of 7:2:1.
S23, establishing a water shortage detection model according to the training set, the verification set and the test set.
Specifically, in this embodiment, a deep learning method is used, training of the convolutional neural network is completed according to the color image training set, the color image verification set and the color image test set, the accuracy of the convolutional neural network is checked by setting a threshold value, and if the accuracy is greater than the threshold value, the neural network meets the requirement and can be used as a color image water shortage detection model.
Further, the same method is adopted to obtain an infrared image water shortage detection model according to the infrared image training set, the infrared image verification set and the infrared image test set, and a depth image water shortage detection model is obtained according to the depth image training set, the depth image verification set and the depth image test set.
More specifically, the color image water-shortage detection model, the infrared image water-shortage detection model and the depth image water-shortage detection model are combined to obtain the water-shortage detection model.
Further, the water shortage detection model is built by integrating the color image, the infrared image and the depth image of the crops, so that the water shortage state of the crops can be identified by integrating the color image information, the infrared image information and the depth image information of the crops in one detection process of the crops, the identification accuracy of the water shortage state of the crops is improved, misjudgment of the water shortage state of the crops is avoided, accurate determination of irrigation opportunity is facilitated, irrigation is timely carried out, and yield reduction caused by long-term water shortage of the crops is avoided.
S3, determining the current irrigation quantity and predicting the next irrigation quantity by using the root system depth of the crops, the water loss of the crops, the irrigation area and the water content by using the irrigation quantity calculation module.
In S3, based on S13-S15, the irrigation amount calculation module performs the steps of:
s31, establishing an irrigation quantity calculation model by using the root system depth of the crops, the water loss of the crops, the water content of the surface layer, the water content of the deep layer and the irrigation area.
The step S31 specifically further includes the following steps:
s311, setting a time sequence of data measurement.
Specifically, in this example, data were measured every 1 hour for a total of 48 measurements for the experimentally cultivated crop.
S312, measuring the moisture loss of the crops according to the time sequence, and finishing to obtain a moisture loss data set.
Specifically, in this embodiment, for the crops cultivated in the experiment, the moisture loss of the crops is obtained according to the time sequence by using a weighing method, and the moisture loss data set is obtained by arrangement.
S313, measuring the surface water content according to the time sequence to obtain the variation of the surface water content.
Specifically, in this embodiment, for the crop cultivated by experiment, the water content of the surface layer is measured by using the water content sensor according to the time sequence, and the water content of the surface layer measured by subtracting the water content of the surface layer measured by the previous time point from the water content of the surface layer measured by the previous time point is used to obtain the variation of the water content of the surface layer.
S314, measuring the deep water content according to the time sequence to obtain the change amount of the deep water content.
Specifically, in this embodiment, for the crop cultivated experimentally, the deep water content is measured by using the water content sensor according to the time series, and the deep water content measured by subtracting the previous time point from the deep water content measured by the previous time point is used to obtain the deep water content variation.
S315, establishing an irrigation quantity calculation model of different irrigation areas by utilizing the root system depth of the crops, the water loss data set, the surface water content variation, the deep water content variation and the irrigation area.
Specifically, in this embodiment, after the irrigation amount calculation model is obtained for the crops cultivated through experiments, parameters in the irrigation amount calculation model may be replaced by related parameters of the irrigation area, and the finally obtained irrigation amount calculation model satisfies the following relationship:
wherein H is 1i +H 2i ＝cH i ，AIQ i T being the current irrigation quantity of the ith irrigation area i The water loss of the crops in the ith irrigation area is represented by a, b and cThe number of the product is the number,to correct the factor beta 1i The variation of the surface water content of the ith irrigation area, beta 2i For the variation of the deep water content of the ith irrigation area, S i For the irrigation area of the ith irrigation area, H 1i For the thickness of the surface layer in the ith irrigation area, H 2i For the thickness of the deep layer in the ith irrigation area, H i Is the root depth of the crop in the ith irrigation area.
Further, for a certain irrigation area, the root depth of the crops is based on the maximum root depth of the crops in the area in the whole growth period.
Further, based on S11, the surface water content of a certain irrigation area at a certain moment is an average value of 7 surface water contents acquired by the water content sensor in the geometric center of the area and the adjacent 6 water content sensors; the method for acquiring the deep water content of a certain irrigation area at a certain moment is the same as the method for acquiring the surface water content of the irrigation area.
S32, calculating the current irrigation quantity through the irrigation quantity calculation model.
Specifically, in this embodiment, the water-deficient monitoring module is utilized to monitor crops in real time, and once the crops are detected to be in a water-deficient state, the irrigation quantity required by the area, that is, the current irrigation quantity, is calculated according to the surface water content variation and the deep water content variation in the irrigation area where the water-deficient crops are located.
S33, establishing a water content prediction model by utilizing the surface water content and the deep water content.
The step S33 specifically further includes the following steps:
s331, finishing the surface water content and the deep water content and manufacturing a water content data set;
specifically, in this embodiment, the water content data set includes a surface layer water content data set and a deep water content data set, the surface layer water content data set is made according to the surface layer water content obtained in S313, and the deep water content data set is made according to the deep water content obtained in S314.
S332, dividing the water content data set into a training sample, a verification sample and a test sample in proportion;
specifically, in this embodiment, the surface layer moisture content data set is divided into a first training sample, a first verification sample and a first test sample according to a ratio of 7:2:1, and the deep layer moisture content data set is divided into a second training sample, a second verification sample and a second test sample according to a ratio of 7:2:1.
S333, establishing the water content prediction model according to the training sample, the verification sample and the test sample.
Specifically, in this embodiment, a deep learning method is used, a surface layer moisture content prediction model is established according to the first training sample, the first verification sample and the first test sample, and the surface layer moisture content prediction model is used to implement prediction of the surface layer moisture content.
Further, a deep learning method is used, a deep water content prediction model is built according to the second training sample, the second verification sample and the second test sample, and the surface water content prediction model is utilized to predict the deep water content.
Furthermore, after one irrigation is completed, the water content prediction model can predict the water content of the soil surface layer and the soil deep layer of the irrigation land next time, and based on the prediction, a user can utilize the irrigation amount calculation model to schedule water for different irrigation areas in advance, so that the reaction time of the next crop irrigation is reduced.
In yet another alternative embodiment, the water content prediction model may also be built based on other data, such as adding meteorological data to the data applied based on the present embodiment.
S34, predicting the next irrigation quantity according to the water content prediction model and the irrigation quantity calculation model.
The step S34 specifically further includes the following steps:
s341, predicting surface water content predicted values and deep water content predicted values of different irrigation areas after a period of time by using the water content prediction model.
Specifically, in the present embodiment, the prediction of the surface water content and the deep water content is after the latest irrigation.
S342, calculating the next irrigation quantity according to the surface water content predicted value and the deep water content predicted value and combining the irrigation quantity calculation model.
Specifically, in this embodiment, the predicted values of the surface water content and the predicted values of the deep water content of different irrigation areas are brought into the irrigation amount calculation model, so that the next irrigation amount of the different irrigation areas can be calculated, and a user can schedule water for different irrigation areas in advance according to the next irrigation amount, so that the reaction time of next crop irrigation is reduced.
And S4, controlling irrigation of crops by an irrigation control module according to the water shortage detection model, the current irrigation quantity and the next irrigation quantity.
In S4, the irrigation control module performs the steps of:
s41, acquiring irrigation opportunity according to the water shortage detection model.
Specifically, in this embodiment, once the water shortage detection model detects the crops in the water shortage state at a certain moment, the irrigation control module determines that the moment is the irrigation opportunity.
And S42, controlling the irrigation of crops in different irrigation areas according to the irrigation time and the current irrigation quantity, and preparing for the next crop irrigation in advance according to the next irrigation quantity.
Specifically, in this embodiment, after determining the time of irrigation, the irrigation control module is connected to various irrigation devices, and according to the current irrigation amount calculated in S32, the irrigation control module may select an irrigation mode according to the irrigation amount to accurately irrigate the water-deficient irrigation area, and after irrigation, prepare for the next crop irrigation in advance according to the next irrigation amount predicted by the irrigation amount calculation module, so as to reduce the reaction time of the next crop irrigation.
In an alternative embodiment, referring to fig. 2, the invention further provides a system for performing water-saving irrigation based on the internet of things technology, wherein the system comprises a data acquisition module a, a water shortage monitoring module B, an irrigation quantity calculation module C and an irrigation control module D.
Specifically, in this embodiment, the specific function of the data acquisition module a may be referred to the content of step S1; the water shortage monitoring module B is connected with the data acquisition module A, the water shortage monitoring module B is used for detecting whether crops are deficient in water, and specific functions of the water shortage monitoring module B can be seen from the content of the step S2; the irrigation quantity calculating module C is connected with the water shortage monitoring module B, if the water shortage monitoring module B detects that crops are in a water shortage state, the irrigation quantity calculating module C starts to calculate the current irrigation quantity and predicts the next irrigation quantity, and the specific function of the irrigation quantity calculating module C can be seen from the content of the step S3; the irrigation control module D is connected with the water shortage monitoring module B and the irrigation quantity calculating module C, and specific functions of the irrigation control module D can be seen from the content of the step S4.
In yet another alternative embodiment, the system may further include a remote control terminal E, where the remote control terminal E is connected to the data acquisition module a, the water shortage monitoring module B, the irrigation amount calculation module C, and the irrigation control module D, respectively, and the remote control terminal E may query the data acquired by the data acquisition module a and the data calculated by the irrigation amount calculation module C in real time, and may modify the irrigation timing, the irrigation method, and the irrigation amount in the irrigation control module D as needed.
Further, the remote control terminal E may be a computer webpage or a mobile phone.
It should be noted that, in some cases, the actions described in the specification may be performed in a different order and still achieve desirable results, and in this embodiment, the order of steps is merely provided to make the embodiment more clear, and it is convenient to describe the embodiment without limiting it.
In summary, the method provided by the invention can monitor the water shortage condition of crops in different areas in real time by collecting the image information of the irrigation land and establishing the water shortage detection model, and accurately calculate the irrigation quantity according to the actual condition of the crops when the crops are deficient in water, so as to accurately irrigate the crops, save the irrigation water and simplify the Internet of things irrigation system; in addition, the method provided by the invention can also predict the irrigation quantity of the next irrigation after the current irrigation, so that the irrigation planning is performed in advance, the preparation time of irrigation is reduced, the adverse effect on crops caused by overlong water shortage time is reduced, and the yield and income of crops are promoted. The system provided by the invention is a system suitable for the method provided by the invention, has the same advantages as the method provided by the invention, and is beneficial to improving the intelligent degree of irrigation of the Internet of things.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (2)
1. The method for performing water-saving irrigation based on the technology of the Internet of things is characterized by comprising the following steps of:
the data acquisition module acquires color images, infrared images and depth images of crops in irrigation lands under different water shortage states, and the root depths of crops, the water loss of crops, the irrigation area and the water contents of different soil layers in the irrigation lands, and the data acquisition module executes the following steps:
dividing the irrigated land into a plurality of irrigated areas;
acquiring the irrigation area of each irrigation area;
collecting the color image, the infrared image and the depth image of crops in different water shortage states, and the root system depth, the crop water loss and the irrigation area of the crops in different irrigation areas;
dividing the soil layer of the irrigation land into a surface layer and a deep layer according to the root system depth of the crops;
in different irrigation areas, measuring the water content of the surface layer to obtain the water content of the surface layer, and measuring the water content of the deep layer to obtain the water content of the deep layer;
the water shortage monitoring module establishes a water shortage detection model by using the color image, the infrared image and the depth image, and the water shortage monitoring module executes the following steps:
creating an image dataset using the color image, the infrared image, and the depth image;
dividing the image data set into a training set, a verification set and a test set according to a proportion;
establishing a water shortage detection model according to the training set, the verification set and the test set;
the irrigation quantity calculation module determines current irrigation quantity and predicted next irrigation quantity by using the root system depth of the crops, the water loss of the crops, the irrigation area and the water content, and the irrigation quantity calculation module executes the following steps:
setting a time sequence of data measurement;
measuring the moisture loss of the crops according to the time sequence, and finishing to obtain a moisture loss data set;
measuring the surface water content according to the time sequence to obtain the variation of the surface water content;
measuring the deep water content according to the time sequence to obtain the change quantity of the deep water content;
establishing an irrigation quantity calculation model by utilizing the root system depth, the water loss data set, the surface water content variation, the deep water content variation and the irrigation area of the crops, wherein the irrigation quantity calculation model meets the following relation:
wherein H is 1i +H 2i ＝cH i ，AIQ i T being the current irrigation quantity of the ith irrigation area i The water loss of the crops in the ith irrigation area, a, b and c are parameters,to correct the factor beta 1i The variation of the surface water content of the ith irrigation area, beta 2i For the variation of the deep water content of the ith irrigation area, S i For the irrigation area of the ith irrigation area, H 1i For the thickness of the surface layer in the ith irrigation area, H 2i For the thickness of the deep layer in the ith irrigation area, H i Is the depth of the root system of the crops in the ith irrigation area;
calculating the current irrigation quantity through the irrigation quantity calculation model;
finishing the surface water content and the deep water content and manufacturing a water content data set;
dividing the water content data set into a training sample, a verification sample and a test sample according to a proportion;
establishing the water content prediction model according to the training sample, the verification sample and the test sample;
predicting surface water content predicted values and deep water content predicted values of different irrigation areas after a period of time by using the water content predicted model;
calculating the next irrigation quantity according to the surface water content predicted value and the deep water content predicted value and combining the irrigation quantity calculation model;
the irrigation control module controls irrigation of crops according to the water shortage detection model, the current irrigation quantity and the next irrigation quantity, and the irrigation control module executes the following steps:
acquiring irrigation time according to the water shortage detection model;
and controlling the irrigation of crops in different irrigation areas according to the irrigation time and the current irrigation quantity, and preparing for the next crop irrigation in advance according to the next irrigation quantity.
2. The utility model provides a system for carry out water-saving irrigation based on internet of things which characterized in that includes: the system comprises a data acquisition module, a water shortage monitoring module, an irrigation quantity calculation module and an irrigation control module, wherein the data acquisition module, the water shortage monitoring module, the irrigation quantity calculation module and the irrigation control module are respectively as claimed in claim 1.
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