CN115081713A - Plant yield optimization method and plant planting system - Google Patents
Plant yield optimization method and plant planting system Download PDFInfo
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- CN115081713A CN115081713A CN202210719113.1A CN202210719113A CN115081713A CN 115081713 A CN115081713 A CN 115081713A CN 202210719113 A CN202210719113 A CN 202210719113A CN 115081713 A CN115081713 A CN 115081713A
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Abstract
The embodiment of the invention provides a plant yield optimization method and a plant planting system, and belongs to the field of agricultural planting. The method comprises the following steps: the method utilizes a machine learning algorithm and database data, the database data comprising yield data and growth data of plants, the method comprising: s1, obtaining database data, wherein the yield data comprise target yields; s2, calculating theoretical optimal environment data and theoretical optimal soil data of plant growth according to the target yield through the machine learning algorithm, adjusting the environment data and the soil data of plants according to the theoretical optimal environment data and the theoretical optimal soil data, and acquiring the actual yield, the actual environment data and the actual soil data of the plants at the current stage; s3, comparing the actual yield with the target yield, and if the actual yield is less than the target yield, repeating the steps S1 and S2. The method improves plant yield, saves manpower and financial resources, and reduces error probability.
Description
Technical Field
The invention relates to the field of planting, in particular to a plant yield optimization method and a plant planting system.
Background
The existing planting method generally adopts two modes for control, one mode adopts theoretical growth environment data given by a seed company to control environment parameters required by plant growth, such as required temperature, humidity, soil pH value, illumination intensity and the like, and farmers control the plant growth according to the theoretical environment data given by the seed company; another farmer empirically maintains the environmental parameters of the plant growth within a certain range of values, for example, the optimal temperature for the tomato growth is 25-30 deg.C, so the farmer utilizes the automatic control device to maintain the environmental temperature for the tomato growth at 25-30 deg.C. These two control methods can only ensure that the set environmental data is maintained, thereby allowing the plants to reach the optimal growth state within the range of theoretical environmental parameters. However, it is often difficult to achieve the optimal yield of plants according to theoretical data or empirical data, and how to achieve the theoretical yield of plants or even expect higher yield is a great concern in the planting field.
Disclosure of Invention
An object of an embodiment of the present invention is to provide a method for optimizing plant yield and a plant growing system, which can partially or completely solve the above technical problems.
In order to achieve the above object, an embodiment of the present invention provides a plant yield optimization method, which utilizes a machine learning algorithm and database data, the database data including yield data and growth data of plants, the method including: s1, obtaining database data, wherein the yield data comprise target yield, and the growth data comprise environmental data and soil data; s2, calculating theoretical optimal environment data and theoretical optimal soil data of plant growth according to the target yield through the machine learning algorithm, adjusting the environment data and the soil data of plants according to the theoretical optimal environment data and the theoretical optimal soil data, and acquiring the actual yield, the actual environment data and the actual soil data of the plants at the current stage; s3, comparing the actual yield with the target yield, and if the actual yield is less than the target yield, repeating the steps S1 and S2; and if the actual yield is greater than or equal to the target yield, covering the actual yield, the actual environment data and the actual soil data on the data in the database for the next planting.
Optionally, the target yield further comprises a theoretical optimal yield, which is the theoretical optimal yield of the plant.
Optionally, the target yield further comprises a desired yield, the desired yield being greater than the theoretical optimal yield.
Optionally, the machine learning algorithm comprises at least one of a BP-GA based algorithm, a PSO-BP based algorithm, and an SAA-BP based algorithm.
Optionally, the environmental data comprises at least one of temperature data, humidity data, illumination intensity; the soil data includes at least one of water content and nutrient solution content.
In another aspect, the planting system is configured as a plant yield optimization method, and the system comprises a host computer system, a controller, a detection device and an execution device, wherein the detection device is used for detecting environmental data and soil data of plant growth in real time and sending the environmental data and the soil data to the controller; the executing device is used for adjusting the environmental data and the soil data of the plant growth; the upper computer system is used for executing the machine optimization algorithm and sending the calculated theoretical optimal environment data and the calculated theoretical optimal soil data to the controller; and the controller is used for adjusting the executing device according to the theoretical optimal environment data and the theoretical optimal soil data so as to adjust the environment data and the soil data of the plants.
Optionally, the detection device comprises at least one of a temperature detection device, a humidity detection device, an illumination intensity detection device, a moisture detection device, a nutrient solution detection device and a voice detection device; the executing device comprises at least one of a temperature adjusting device, a humidity adjusting device, an illumination intensity adjusting device, an irrigation device and a nutrient solution adjusting device; the voice detection device is used for detecting a control instruction of a planting site and sending the control instruction to the controller, and the controller adjusts the execution device according to the control instruction.
Optionally, the temperature detection device is a thermometer or a temperature sensor; the humidity detection device is a humidity sensor; the illumination intensity detection device is an illumination sensor; the moisture detection device is a soil moisture detection sensor or a soil humidity sensor; the nutrient solution detection device is a nutrient element sensor; the voice detection device is a voice recognition module.
Optionally, the plant is any one of vegetables, fruits and flowers.
Optionally, the plant growing system is used in a vertical farm.
According to the technical scheme, when the actual yield of the plants is smaller than the target yield, the machine learning algorithm is adopted, the target yield is used as an input variable, new environmental data and soil data can be rapidly calculated, the environmental data and the soil data of the plant growth are adjusted through the controller, and then the actual yield of the plants meets or even exceeds the target yield, so that the aim of optimizing the plant yield is fulfilled. In addition, the invention also provides an intelligent planting system, which can automatically control the environmental data of a planting field according to a planting optimization method without frequent manual interference, thereby saving manpower and financial resources and simultaneously reducing the error probability.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a first embodiment of a method for optimizing plant yield according to the present invention;
FIG. 2 is a flow chart of a second embodiment of the method for optimizing plant yield according to the present invention;
FIG. 3 is a flow chart of a third embodiment of the method for optimizing plant yield according to the present invention;
fig. 4 is a structural view of the plant growing system of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 shows a flow chart of a first embodiment of the plant yield optimization method of the present invention, which utilizes a machine learning algorithm and database data including yield data and growth data of plants in order to enable actual yields to reach theoretical production yields of plants during actual planting, comprising:
s110, obtaining database data, wherein the yield data comprise theoretical optimal yield, and the growth data comprise environmental data and soil data;
s111, calculating theoretical optimal environment data and theoretical optimal soil data of plant growth according to the theoretical optimal yield through the machine learning algorithm, adjusting the environment data and the soil data of plants according to the theoretical optimal environment data and the theoretical optimal soil data, and acquiring the actual yield, the actual environment data and the actual soil data of the plants in the current period;
specifically, the theoretical optimal yield is a theoretical value capable of obtaining the optimal yield, the actual yield obtained in actual planting often cannot reach the theoretical optimal yield, theoretical optimal environment data and theoretical optimal soil data are calculated by using the theoretical optimal yield as a target model through the machine learning algorithm, the environment data and the soil data of plant growth are adjusted according to the theoretical optimal environment data and the theoretical optimal soil data, the environment data and the soil data of plant growth are maintained at the theoretical optimal environment data and the theoretical optimal soil data, the actual yield, the actual environment data and the actual soil data of plants in the current period are obtained after planting is finished, and whether the actual yield meets the theoretical optimal yield is further judged.
S112, comparing the actual yield with the theoretical optimal yield, and if the actual yield is smaller than the theoretical optimal yield, repeating the steps S110 and S111; and if the actual yield is greater than or equal to the theoretical optimal yield, covering the actual yield, the actual environment data and the actual soil data on the data in the database for the next planting.
If the actual yield is less than the theoretical optimal yield, the steps S110 and S111 are repeated until the actual yield is greater than or equal to the theoretical optimal yield. Of course, in order to make the planting more flexible, the next stage planting can be suspended manually and the steps S110 and S111 are not repeated.
For example, in a theoretical situation, the theoretical optimal yield of the harvested tomatoes is 5000 jin/mu when the tomatoes are planted according to theoretical environmental data and theoretical soil data given by a seed company, but in actual planting, the theoretical planting environmental data and the theoretical soil data are used for controlling the production environmental data and the soil data of the tomatoes, the actual yield is 4500 jin/mu and does not reach 5000 jin/mu, and the theoretical environmental data and the soil data cannot meet the requirement that the planting yield reaches the theoretical optimal yield. Therefore, theoretical optimal environmental data and theoretical optimal soil data are calculated according to the target yield of 5000 jin/mu through a machine learning algorithm, the growth environmental parameters and the soil parameters of the tomatoes are adjusted according to the new parameters, the actual yield, the actual environmental parameters and the actual soil parameters in the current period are obtained after planting is completed, if the actual yield is below 5000 jin/mu, whether next-period planting is carried out or not can be manually selected, if the next-period planting is carried out, the machine learning algorithm step is continued until the actual yield is more than or equal to 5000 jin/mu, and the actual yield, the actual environmental parameters and the actual soil parameters cover the original database data for direct calling in the next-period planting.
In order to facilitate direct calling of yield data and growth data corresponding to different plants during planting, the database data can also comprise names of the plants such as tomatoes, yield data, environment data and soil data corresponding to the database data can be called out by inputting or searching the tomatoes in an upper computer system during planting of the tomatoes again, and the frequency of manual operation errors is reduced.
FIG. 2 shows a flow chart of a second embodiment of the method for optimizing plant yield according to the present invention, said method comprising:
s210, obtaining database data, wherein the yield data comprise expected yield, and the growth data comprise environment data and soil data;
s211, calculating theoretical optimal environmental data and theoretical optimal soil data of plant growth according to the expected yield through the machine learning algorithm, adjusting environmental data and soil data of plants according to the theoretical optimal environmental data and the theoretical optimal soil data, and obtaining actual yield, actual environmental data and actual soil data of the plants in the current period;
s212, comparing the actual yield with the expected yield, and if the actual yield is smaller than the expected yield, repeating the steps S210 and S211; and if the actual yield is greater than or equal to the expected yield, covering the actual yield, the actual environment data and the actual soil data on the data in the database for the next planting.
Specifically, the second embodiment differs from the first embodiment in that the desired yield is higher than the theoretically optimal yield, which is a yield desired to achieve higher than the theoretically optimal yield. The specific yield optimization method is similar to the first embodiment, except that the target yield through the machine learning algorithm is the desired yield, and is not described herein again.
Fig. 3 shows a flow chart of a third embodiment of the plant yield optimization method of the present invention, and the specific optimization method comprises:
s310, obtaining database data, wherein the yield data are theoretical optimal yields, and the growth data comprise environmental data and soil data;
s311, calculating theoretical optimal environmental data and theoretical optimal soil data of plant growth according to the theoretical optimal yield through the machine learning algorithm, adjusting the environmental data and the soil data of plants according to the theoretical optimal environmental data and the theoretical optimal soil data, and acquiring a first actual yield, first actual environmental data and first actual soil data of the plants in a first period;
s312, comparing the first actual yield with the theoretical optimal yield, and if the first actual yield is smaller than the theoretical optimal yield, repeating the steps S310 and S311; and if the actual yield is larger than or equal to the theoretical optimal yield, continuing the following steps to obtain the expected yield.
S313, calculating optimized environment data and optimized soil data of plant growth according to the expected yield through the learning algorithm, adjusting the environment data and the soil data of the plant according to the optimized environment data and the optimized soil data, and acquiring a second actual yield, second actual environment data and second actual soil data of the plant in a second period;
s314, comparing the second actual yield with the expected yield, and if the second actual yield is smaller than the expected yield, repeating the step S313; and if the second actual yield is greater than or equal to the expected yield, covering the database data for the next planting.
Specifically, the third embodiment of the present invention is based on a further improvement of the first and second embodiments, and the third embodiment of the present invention further finds the environmental parameters and soil parameters when the yield reaches the desired yield through a machine learning algorithm only when the actual yield reaches the theoretical optimum yield, and adjusts the environmental parameters and soil parameters of plant growth to make the actual yield reach the desired yield. For example, in theoretical conditions, the theoretical optimal yield of the received tomatoes is 5000 jin/mu when the tomatoes are planted according to theoretical environmental data and theoretical soil data, in actual planting, the theoretical planting environmental data and the theoretical soil data are used for controlling the tomato production environmental data and the soil data, the actual yield is 4500 jin/mu but not 5000 jin/mu, the theoretical optimal environmental data and the theoretical optimal soil data are calculated according to the target yield of 5000 jin/mu through a machine learning algorithm, the growth environmental parameters and the soil parameters of the tomatoes are adjusted according to the new set of parameters, the first actual yield, the first actual environmental parameters and the first actual soil parameters in the current period are obtained after planting is completed, if the first actual yield is below 5000 jin/mu, whether next-stage planting is carried out or not can be manually selected, if the next-stage planting is carried out, the machine learning algorithm step is continued, until the actual yield is more than or equal to 5000 jin/mu, planting tomatoes with the desired yield (for example, the desired yield is 5500 jin/mu) as the target yield;
if the first actual yield is more than 5000 jin/mu, the next-stage planting can be selected to continue, and the target yield of the next-stage planting is 5500 jin/mu, the optimized environmental data and the optimized soil data are solved through the machine learning algorithm, the tomato growth environmental data and the soil data are adjusted as the above, and the detailed description is omitted here, and if the second actual yield is less than 5500 jin/mu, the next-stage planting is selected manually or not.
And if the second actual yield is larger than or equal to 5500 jin/mu, covering the second actual yield, the second actual soil data and the second environment data on the original database data for the next planting.
It should be noted that the "first" and "second" described in the third embodiment of the present invention are only for distinguishing plants planted at different stages, and are not planted for the first time or the second time.
In the present application, the machine learning algorithm includes at least one of a BP-GA based algorithm, a PSO-BP based algorithm, and an SAA-BP based algorithm.
Among them, the bp (back propagation) neural network algorithm is a multi-layer feedforward neural network trained according to an error back propagation algorithm, and is one of the most widely applied neural network models.
GA Genetic Algorithm (GA) this Algorithm was designed based on the rules of evolution of organisms in nature. The method is a calculation model of the biological evolution process for simulating natural selection and genetic mechanism of Darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process.
PSO (particle Swarm optimization) is a particle Swarm optimization algorithm, a population-based stochastic optimization technique, proposed by Eberhart and Kennedy in 1995. Particle swarm algorithms mimic the clustering behavior of insects, herds, birds, and fish, etc., which find food in a cooperative manner, with each member of the population constantly changing its search pattern by learning its own experience and that of other members.
The saa (simulated annealing) algorithm is a general probability algorithm, which is used to find the optimal solution of a proposition in a large search space.
The GA algorithm, the PSO algorithm and the SAA algorithm are used for quickly updating the weights in the BP neural network algorithm to achieve quick convergence and seek an optimal solution. The GA algorithm has the advantages of macroscopic search capability, good global optimization performance and high convergence speed, so that the machine learning algorithm is preferably based on the BP-GA algorithm.
Specifically, a machine learning algorithm and database data are utilized, a target yield is used as an input variable, a functional relation between the target yield and environmental data and soil data is obtained through a BP neural network training model, and a weight factor in the BP neural network training model is optimized through a GA (genetic algorithm) or a PSO (particle swarm optimization) or an SAA (simulated annealing algorithm), so that the function can be rapidly converged. The BP-GA algorithm is preferably selected, the theoretical target yield is used as an input parameter, the functional relation between the environment data corresponding to the target yield and the soil data is calculated, the theoretical optimal environment data and the theoretical optimal soil data are calculated through the neural network, the environment data and the soil data for plant growth are rapidly adjusted through the controller, and therefore the actual yield of the plant can really meet the target yield.
Further, the environment data comprises at least one of temperature data, humidity data and illumination intensity of the environment required by plant growth; the soil data comprises at least one of water content and nutrient solution content of soil in the plant growth process.
Accordingly, the present invention also includes a plant growing system, and fig. 4 is a block diagram illustrating the plant growing system of the present invention, wherein the directions of arrows represent the transmission directions between signals and the connection relationship between systems. The planting system is configured to execute the plant yield optimization method and comprises an upper computer system, a controller, a detection device and an execution device, wherein the detection device is connected with the controller and is used for detecting environmental data and soil data of plant growth in real time and transmitting the controller; the execution device is connected with the controller and is used for adjusting the environmental data and the soil data of the plant growth; the upper computer system is connected with the controller and is used for executing the machine learning algorithm and sending the calculated theoretical optimal environment data and the theoretical optimal soil data to the controller; the controller adjusts the actuator according to the theoretically optimal data, thereby adjusting environmental data and soil data of plant growth.
The detection device comprises at least one of a temperature detection device, a humidity detection device, an illumination intensity detection device, a moisture detection device, a nutrient solution detection device and a voice detection device. The executing device comprises at least one of a temperature adjusting device, a humidity adjusting device, an illumination intensity adjusting device, an irrigation device and a nutrient solution adjusting device. Wherein the temperature detection device selects a thermometer or a temperature sensor; the humidity sensor is selected as a humidity sensor, and a temperature and humidity sensor can also be selected and detected together with the temperature; the nutrient solution detection device adopts a nutrient element sensor, is mainly used for detecting the content of various nutrient elements in the nutrient solution prepared in the soilless culture environment, and can also be used for detecting the content of soil nutrient elements in a common greenhouse and a greenhouse to determine whether fertilization is needed or not; the voice detection device adopts an existing AI voice recognition device and is connected with the controller, when the AI voice recognition device receives an instruction of a planting field, if the voice instruction sent by a farmer of the planting field is received to increase the humidity to 70%, the current humidity detected by the humidity detection device is 65% and is sent to the controller, and the controller adjusts the humidity adjusting device to increase the environmental humidity to 70% through a PID control algorithm. The upper computer system sends the calculated data such as temperature data, humidity data, illumination intensity, water content of soil and nutrient solution content to the controller through a machine learning algorithm, the controller adjusts the execution device and then adjusts the environmental data and the soil data of plant growth according to the environmental data and the soil data of plant growth detected by the detection device in real time through a PID algorithm, the actual yield of the plant reaches or even exceeds the target yield, and the controller preferably selects a PLC controller with high reliability, perfect function and high practicability.
The planting system is suitable for any one of vegetables, fruits and flowers, and is also suitable for vertical farms.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for plant yield optimization, the method utilizing a machine learning algorithm and database data, the database data including yield data and growth data for plants, the method comprising:
s1, obtaining database data, wherein the yield data comprise target yield, and the growth data comprise environment data and soil data;
s2, calculating theoretical optimal environment data and theoretical optimal soil data of plant growth according to the target yield through the machine learning algorithm, adjusting the environment data and the soil data of plants according to the theoretical optimal environment data and the theoretical optimal soil data, and acquiring the actual yield, the actual environment data and the actual soil data of the plants at the current stage;
s3, comparing the actual yield with the target yield, and if the actual yield is less than the target yield, repeating the steps S1 and S2; and if the actual yield is greater than or equal to the target yield, covering the actual yield, the actual environment data and the actual soil data on the data in the database for the next planting.
2. The method of plant yield optimization according to claim 1, wherein said target yield further comprises a theoretical optimal yield, said theoretical optimal yield being a theoretical optimal yield of plant yield.
3. The plant yield optimization method of claim 2, wherein the target yield further comprises a desired yield, the desired yield being greater than the theoretical optimal yield.
4. The plant yield optimization method of any one of claims 1-3, wherein the machine learning algorithm comprises at least one of a BP-GA-based algorithm, a PSO-BP-based algorithm, and a SAA-BP-based algorithm.
5. The plant yield optimization method of claim 1, wherein the environmental data comprises at least one of temperature data, humidity data, illumination intensity;
the soil data includes at least one of water content and nutrient solution content.
6. A plant growing system, wherein said growing system is configured to perform the plant yield optimization method of any one of claims 1 to 5, said system comprising a host computer system, a controller, a detection device and an execution device, wherein
The detection device is used for detecting the environmental data and the soil data of the plant growth in real time and sending the environmental data and the soil data to the controller;
the executing device is used for adjusting the environmental data and the soil data of the plant growth;
the upper computer system is used for executing the machine learning algorithm and sending the calculated theoretical optimal environment data and the theoretical optimal soil data to the controller;
and the controller is used for adjusting the executing device according to the theoretical optimal environment data and the theoretical optimal soil data so as to adjust the environment data and the soil data of the plants.
7. The plant growing system of claim 6, wherein the detection device comprises at least one of a temperature detection device, a humidity detection device, a light intensity detection device, a moisture detection device, a nutrient solution detection device, a voice detection device;
the executing device comprises at least one of a temperature adjusting device, a humidity adjusting device, an illumination intensity adjusting device, an irrigation device and a nutrient solution adjusting device;
the voice detection device is used for detecting a control instruction of a planting site and sending the control instruction to the controller, and the controller adjusts the execution device according to the control instruction.
8. The plant growing system according to claim 7, wherein the temperature detection device is a thermometer or a temperature sensor;
the humidity detection device is a humidity sensor;
the illumination intensity detection device is an illumination sensor;
the moisture detection device is a soil moisture detection sensor or a soil humidity sensor;
the nutrient solution detection device is a nutrient element sensor;
the voice detection device is a voice recognition module.
9. The plant growing system according to claim 6, wherein said plant is any one of vegetables, fruits, and flowers.
10. The plant growing system according to claim 6, wherein said plant growing system is used in a vertical farm.
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| CN117762078A (en) * | 2024-02-22 | 2024-03-26 | 连云港银丰食用菌科技有限公司 | Intelligent control method for edible fungus room environment |
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