CN113934245A - BP neural network system and method for growth of greenhouse crops - Google Patents
BP neural network system and method for growth of greenhouse crops Download PDFInfo
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- CN113934245A CN113934245A CN202111212183.XA CN202111212183A CN113934245A CN 113934245 A CN113934245 A CN 113934245A CN 202111212183 A CN202111212183 A CN 202111212183A CN 113934245 A CN113934245 A CN 113934245A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
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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
- A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
- A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
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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
- A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
- A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
- A01G9/249—Lighting means
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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
- Y02A40/25—Greenhouse technology, e.g. cooling systems therefor
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- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Cultivation Of Plants (AREA)
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Abstract
The invention belongs to the technical field of agricultural intelligent planting, and provides a BP neural network system for growth of greenhouse crops. The method comprises the following steps: the device comprises a light source, a memory, a BP neural network algorithm component and a controller; meanwhile, the BP neural network growth method applied to greenhouse crops comprises the following steps: s1, acquiring plant growth images and corresponding data in the greenhouse every day; s2, comparing the growth image with the standard image by an image processing algorithm, and establishing a standard growth sample; s3, revising data corresponding to the samples in the greenhouse; s4, establishing new sample data; s5, training the BP neural network by using new sample data; s6, inputting sample data to perform BP training; s7, obtaining output data through calculation; and S8, the controller outputs data and makes corresponding adjustment. The system and the method thereof can be realized by utilizing an algorithm, control the light source and ensure the growth process of different crops. Is beneficial to agricultural popularization.
Description
Technical Field
The invention relates to the technical field of agricultural intelligent planting, in particular to a BP neural network system and a BP neural network method for growth of greenhouse crops.
Background
In the special planting of the greenhouse, illumination with specific color or wavelength is needed to irradiate crops in the greenhouse, so that the required crops can be obtained; the technology for cultivating the crops by using the Japanese special illumination technology is mature, and the growth process of the crops is influenced by using the dim light emitted by the diode.
But because the method utilizes a light source generated by a photoelectric technology to irradiate crops; this photoelectric technology adopts the mode of manual monitoring mostly in daily use, through the artifical crop growth condition of observing in the interval, adopts the light source of light and shade difference. Therefore, manual observation and statistics are needed, labor cost is increased, and popularization is not facilitated.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a BP neural network system applied to growth of greenhouse crops, so as to reduce the degree of manual intervention and really control the growth of the crops through a BP neural network algorithm.
In a first aspect, a BP neural network system for growing greenhouse crops, comprising: the light sources are arranged in the greenhouse, and can irradiate crops by utilizing light with different wavelengths; a memory having image information for different growth cycles of different crops; the BP neural network algorithm component is electrically connected with the memory and can output light source wavelength, light intensity, temperature and humidity through a BP neural network algorithm; one end of the controller is electrically connected with the BP neural network algorithm component, and the other end of the controller is electrically connected with the light source; wherein the controller transmits the light source wavelength output by the BP neural network algorithm to the light source for adjustment.
Further, the method also comprises the following steps: and the monitoring mechanism is arranged in the greenhouse and is electrically connected with the controller, and the monitoring mechanism uploads monitoring data to the controller in real time. In practical application, the uploaded data are used as input data, so that the BP neural network training model is conveniently constructed.
Further, monitoring mechanism includes humidity inductor, humidity inductor is used for the humidity in the control big-arch shelter. In the actual growth process of crops, humidity is the important consideration standard, can provide the water source for crops in the system, and can keep the loose state in the soil, effectively prevent soil hardening, more be favorable to crops to grow.
Further, the monitoring mechanism further comprises an oxygen sensor; the oxygen sensor is used for monitoring the oxygen concentration in the greenhouse. The main role of oxygen is, above all, that it is a living substance-these are all important constituents of cells. The growth and development of the plant are actually cell growth, new cells are difficult to form due to lack of new cells, and the growth of the plant is stopped. Therefore, by monitoring the oxygen concentration, the growth of root system and branch and leaf crops is facilitated.
Further, the monitoring mechanism still includes the carbon dioxide inductor, the carbon dioxide inductor is used for monitoring the carbon dioxide concentration in the big-arch shelter. In practical application, the increase of the carbon dioxide concentration in the greenhouse has an influence on the growth and development of crops, namely, the carbon dioxide has an effect on many important physiological processes of the crops, such as photosynthesis, respiration, transpiration and the like, so that the carbon dioxide concentration needs to be monitored as important input data.
Further, the monitoring mechanism further comprises a temperature sensor, and the temperature sensor can be used for monitoring the temperature in the greenhouse. Of course, the growth temperature of different crops is also an important reference standard for determining the growth situation of the crops.
According to the technical scheme, the BP neural network system applied to the growth of the greenhouse crops has the beneficial effects that:
(1) the crops are subjected to specific illumination by adjusting the wavelength of light, further adjusting the illumination color, and simultaneously adjusting the light intensity and the illumination duration, so that the growth of the crops is realized.
(2) The wavelength and the illumination duration of light recorded by the image information of the crops in the memory are used as actual reference values of output data, so that the light source can be adjusted in time through comparing differences; the image information of the crop is, of course, the image information of the optimum effect of the growth of the crop.
(3) The light source is an artificial light source, the wavelength of the artificial light source is adjusted by adopting a photoelectric technology, and the illumination time length of the artificial light source is controlled by the power supply time length, so that the change of the light source is easier to control;
(4) and the output of the light source wavelength is finished through a BP neural network algorithm element;
(5) finally, the control of the whole greenhouse system is realized through the controller.
In a second aspect, a BP neural network growth method applied to greenhouse crops comprises the following steps:
step 1, collecting plant growth images every day, and corresponding carbon dioxide concentration, oxygen concentration, temperature and humidity, light wavelength, light intensity and illumination duration in a greenhouse, and uploading the images to a memory;
step 2, comparing the growth image with a standard image by an image processing algorithm every month, and establishing a standard growth sample;
step 3, revising the corresponding carbon dioxide concentration, oxygen concentration, temperature and humidity, optical wavelength, light intensity and illumination duration in the greenhouse;
step 4, taking the concentration of the newly generated carbon dioxide, the concentration of oxygen, the temperature and humidity, the wavelength of light, the light intensity and the illumination duration as new sample data;
step 5, training the BP neural network by using new sample data;
step 6, inputting sample data to perform BP training;
step 7, obtaining output data through calculation;
and 8, correspondingly adjusting the wavelength, the light intensity and the illumination duration of the light by the controller through outputting data.
Further, in step 2, after being processed by an image processing algorithm every month, the images are manually compared and processed. Thus, the information collection is clearer in the actual processing.
According to the technical scheme, the BP neural network growth method applied to greenhouse crops has the beneficial effects that:
(1) the carbon dioxide concentration, the oxygen concentration, the temperature and humidity, the light wavelength, the light intensity and the illumination duration are used as original sample data to construct a training model of the BP neural network, and the light wavelength, the illumination duration and the illumination intensity are used as output data to be more fit with the growth reality of crops.
(2) And a corresponding BP neural network training method is adopted, and newly generated carbon dioxide concentration, oxygen concentration, temperature and humidity, light wavelength, light intensity and illumination duration are adopted as new sample data, so that optimal output data of different crops and different periods can be adjusted more conveniently.
(3) The light source is adjusted through the controller, so that the whole system is maintained more conveniently, and manual intervention is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention, reference will now be made briefly to the embodiments or to the accompanying drawings that are needed in the description of the prior art. In all the drawings, the elements or parts are not necessarily drawn to actual scale.
FIG. 1 is a schematic diagram of a BP neural network system for growing greenhouse crops according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a BP neural network growing method applied to greenhouse crops according to another embodiment of the present invention;
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
The embodiment is basically as shown in the attached figure 1:
example 1:
as shown in fig. 1, the BP neural network system for growing crops in a greenhouse provided in this embodiment can reduce the degree of manual intervention, and really control the growth of crops through a BP neural network algorithm. One of them is applied to growth BP neural network system of big-arch shelter crops, includes: the light sources are arranged in the greenhouse, and can irradiate crops by utilizing light with different wavelengths; a memory having image information for different growth cycles of different crops; the BP neural network algorithm component is electrically connected with the memory and can output light source wavelength, light intensity, temperature and humidity through a BP neural network algorithm; one end of the controller is electrically connected with the BP neural network algorithm component, and the other end of the controller is electrically connected with the light source; wherein the controller transmits the light source wavelength output by the BP neural network algorithm to the light source for adjustment.
In practical application, the wavelength of light is adjusted, so that the illumination color is adjusted, and meanwhile, the light intensity and the illumination duration are adjusted, the crops are subjected to specific illumination, and the growth of the crops is realized. The wavelength and the illumination duration of light recorded by the image information of the crops in the memory are used as actual reference values of output data, so that the light source can be adjusted in time through comparing differences; the image information of the crop is, of course, the image information of the optimum effect of the growth of the crop. The light source is an artificial light source, the wavelength of the artificial light source is adjusted by adopting a photoelectric technology, and the illumination time length of the artificial light source is controlled by the power supply time length, so that the change of the light source is easier to control; and the output of the light source wavelength is finished through a BP neural network algorithm element; finally, the control of the whole greenhouse system is realized through the controller.
In this embodiment, the method further includes: and the monitoring mechanism is arranged in the greenhouse and is electrically connected with the controller, and the monitoring mechanism uploads monitoring data to the controller in real time. In practical application, the uploaded data are used as input data, so that the BP neural network training model is conveniently constructed.
In this embodiment, the monitoring mechanism includes a humidity sensor for monitoring humidity in the greenhouse. In the actual growth process of crops, humidity is the important consideration standard, can provide the water source for crops in the system, and can keep the loose state in the soil, effectively prevent soil hardening, more be favorable to crops to grow.
In this embodiment, the monitoring mechanism further comprises an oxygen sensor; the oxygen sensor is used for monitoring the oxygen concentration in the greenhouse. The main role of oxygen is, above all, that it is a living substance-these are all important constituents of cells. The growth and development of the plant are actually cell growth, new cells are difficult to form due to lack of new cells, and the growth of the plant is stopped. Therefore, by monitoring the oxygen concentration, the growth of root system and branch and leaf crops is facilitated.
In this embodiment, the monitoring mechanism further comprises a carbon dioxide sensor for monitoring the concentration of carbon dioxide in the greenhouse. In practical application, the increase of the carbon dioxide concentration in the greenhouse has an influence on the growth and development of crops, namely, the carbon dioxide has an effect on many important physiological processes of the crops, such as photosynthesis, respiration, transpiration and the like, so that the carbon dioxide concentration needs to be monitored as important input data.
In this embodiment, the monitoring mechanism further comprises a temperature sensor, and the temperature sensor can be used for monitoring the temperature in the greenhouse. Of course, the growth temperature of different crops is also an important reference standard for determining the growth situation of the crops.
Example 2, the following:
as shown in fig. 2, in order to implement the BP neural network construction and facilitate control, the BP neural network growth method applied to greenhouse crops comprises the following steps:
step 1, collecting plant growth images every day, and corresponding carbon dioxide concentration, oxygen concentration, temperature and humidity, light wavelength, light intensity and illumination duration in a greenhouse, and uploading the images to a memory;
step 2, comparing the growth image with a standard image by an image processing algorithm every month, and establishing a standard growth sample;
step 3, revising the corresponding carbon dioxide concentration, oxygen concentration, temperature and humidity, optical wavelength, light intensity and illumination duration in the greenhouse;
step 4, taking the concentration of the newly generated carbon dioxide, the concentration of oxygen, the temperature and humidity, the wavelength of light, the light intensity and the illumination duration as new sample data;
step 5, training the BP neural network by using new sample data;
step 6, inputting sample data to perform BP training;
step 7, obtaining output data through calculation;
and 8, correspondingly adjusting the wavelength, the light intensity and the illumination duration of the light by the controller through outputting data.
In practical application, the carbon dioxide concentration, the oxygen concentration, the temperature and humidity, the light wavelength, the light intensity and the illumination duration are used as original sample data to construct a training model of the BP neural network, and the light wavelength, the illumination duration and the illumination intensity are used as output data to be more suitable for the actual growth of crops. And a corresponding BP neural network training method is adopted, and newly generated carbon dioxide concentration, oxygen concentration, temperature and humidity, light wavelength, light intensity and illumination duration are adopted as new sample data, so that optimal output data of different crops and different periods can be adjusted more conveniently. The light source is adjusted through the controller, so that the whole system is maintained more conveniently, and manual intervention is reduced.
In this embodiment, further, in step 2, after being processed by the image processing algorithm every month, the image is processed by manual comparison. Thus, the information collection is clearer in the actual processing.
In summary, the BP neural network system and the BP neural network method applied to greenhouse crops can be realized by using an algorithm to control a light source, so that the switching of different lights is realized, and the growth process of different crops is ensured. Is beneficial to agricultural popularization.
In the description of the present invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Claims (8)
1. A growth BP neural network system applied to greenhouse crops is characterized by comprising:
the light sources are arranged in the greenhouse, and can irradiate crops by utilizing light with different wavelengths;
a memory having image information for different growth cycles of different crops;
the BP neural network algorithm component is electrically connected with the memory and can output light source wavelength, light intensity, temperature and humidity through a BP neural network algorithm;
one end of the controller is electrically connected with the BP neural network algorithm component, and the other end of the controller is electrically connected with the light source; wherein the controller transmits the light source wavelength output by the BP neural network algorithm to the light source for adjustment.
2. The BP neural network system for growing greenhouse crops as claimed in claim 1, further comprising: and the monitoring mechanism is arranged in the greenhouse and is electrically connected with the controller, and the monitoring mechanism uploads monitoring data to the controller in real time.
3. The BP neural network system for growing greenhouse crops, according to claim 2, wherein the monitoring mechanism comprises a humidity sensor, an oxygen sensor, a carbon dioxide sensor and a temperature sensor, and the humidity sensor is used for monitoring humidity in the greenhouse.
4. The BP neural network system for growing greenhouse crops, according to claim 2, wherein the monitoring mechanism further comprises an oxygen sensor; the oxygen sensor is used for monitoring the oxygen concentration in the greenhouse.
5. The BP neural network system for growing greenhouse crops, according to claim 2, wherein the monitoring mechanism further comprises a carbon dioxide sensor for monitoring the concentration of carbon dioxide in the greenhouse.
6. The growing BP neural network system for greenhouse crops, according to claim 2, wherein the monitoring mechanism further comprises a temperature sensor, the temperature sensor can be used to monitor the temperature inside the greenhouse.
7. A BP neural network growth method applied to greenhouse crops is characterized by comprising the following steps:
s1, acquiring plant growth images every day, and acquiring carbon dioxide concentration, oxygen concentration, temperature and humidity, light wavelength, light intensity and illumination duration in the corresponding greenhouse, and uploading the images to a memory;
s2, comparing the growth image with the standard image by an image processing algorithm every month, and establishing a standard growth sample;
s3, revising the carbon dioxide concentration, the oxygen concentration, the temperature and humidity, the light wavelength, the light intensity and the illumination duration corresponding to the greenhouse;
s4, taking the concentration of newly generated carbon dioxide, the concentration of oxygen, the temperature and humidity, the light wavelength, the light intensity and the illumination duration as new sample data;
s5, training the BP neural network by using new sample data;
s6, inputting sample data to perform BP training;
s7, obtaining output data through calculation;
and S8, the controller correspondingly adjusts the wavelength, the light intensity and the illumination duration of the light by outputting the data.
8. The BP neural network method for growing greenhouse crops as claimed in claim 7, wherein in step 2, after being processed by image processing algorithm every month, the BP neural network method is processed by manual comparison.
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Application publication date: 20220114 |