CN116186392A - Citrus variety planting recommendation method and device, terminal equipment and storage medium - Google Patents
Citrus variety planting recommendation method and device, terminal equipment and storage medium Download PDFInfo
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Abstract
The application is suitable for the technical field of planting and provides a citrus variety planting recommendation method, a citrus variety planting recommendation device, terminal equipment and a storage medium. The citrus variety planting recommendation method specifically comprises the following steps: obtaining planting data, determining output results of a plurality of models according to the planting information and the plurality of models, wherein the plurality of models comprise a planting adaptability model, a citrus growing model, a price prediction model and a production environment related prediction analysis model, and fusing the output results of the plurality of models to obtain recommended results for planting and recommending the plurality of varieties. The embodiment of the application can improve the reliability of the planting recommendation result of the citrus varieties.
Description
Technical Field
The application belongs to the technical field of planting, and particularly relates to a citrus variety planting recommendation method, a citrus variety planting recommendation device, terminal equipment and a storage medium.
Background
Citrus varieties are directly related to sales prices, but the harvest prices of the varieties are affected by various factors such as seasons, time to market, supply and demand, etc. In the traditional technology, farmers often select the planted citrus varieties according to experience, the mode has larger blindness, therefore, some related technologies propose planting recommendation methods, which can combine factors such as soil, rainfall and the like to analyze the growth suitability degree so as to recommend the citrus varieties, but the mode does not consider the growth period of each citrus variety or the price factor of the citrus, so that the recommendation result is difficult to meet the demands of the farmers, and the reliability of the planting recommendation result of the citrus varieties is lower.
Disclosure of Invention
The embodiment of the application provides a citrus variety planting recommendation method, a citrus variety planting recommendation device, terminal equipment and a storage medium, which can solve the problem that the reliability of the current citrus variety planting recommendation result is insufficient.
An embodiment of the present application provides a method for recommending citrus varieties to plant, including: obtaining planting data, wherein the planting information comprises planting positions for citrus planting and production environment information related to harvest time and price; determining output results of the multiple models according to the planting information and the multiple models, wherein the multiple models comprise a planting adaptability model, a citrus growing model, a price prediction model and a production environment related prediction analysis model, the model output results of the planting adaptability model are the planting suitability of citrus of multiple varieties, the model output results of the citrus growing model are the harvest time and the yield of citrus of the multiple varieties, the model output results of the price prediction model are the predicted price of citrus of the multiple varieties, and the model output results of the production environment related prediction analysis model are the optimal picking time and the required agronomic operation of citrus of the multiple varieties; and fusing the output results of the multiple models to obtain a recommendation result of planting recommendation of the multiple varieties.
The second aspect of the embodiment of the present application provides a planting recommendation device for citrus varieties, including: the citrus planting system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring planting data, and the planting information comprises planting positions for citrus planting and production environment information related to harvest time and price; a determining unit, configured to determine output results of the multiple models according to the planting information and the multiple models, where the multiple models include a planting adaptability model, a citrus growing model, a price prediction model, and a production environment related prediction analysis model, and the model output result of the planting adaptability model is a planting suitability degree of citrus of multiple varieties, the model output result of the citrus growing model is a harvest time and yield of citrus of the multiple varieties, the model output result of the price prediction model is a predicted price of citrus of the multiple varieties, and the model output result of the production environment related prediction analysis model is an optimal picking time and a required agronomic operation of citrus of the multiple varieties; and the fusion unit is used for fusing the output results of the models to obtain a recommendation result for planting recommendation of the varieties.
A third aspect of the embodiments of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the steps of the method for recommending planting of citrus varieties are implemented when the processor executes the computer program.
A fourth aspect of the embodiments provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the citrus fruit variety planting recommendation method described above.
A fifth aspect of the embodiments of the present application provides a computer program product, which when run on a terminal device, causes the terminal device to perform the method for recommended planting of citrus varieties according to any of the first aspects described above.
In the embodiment of the application, the output results of the multiple models are determined according to the planting information and the multiple models, and the output results of the multiple models are fused to obtain the recommended results for planting and recommending the multiple varieties, wherein the multiple models comprise a planting adaptability model, a citrus growing model, a price prediction model and a production environment related prediction analysis model, the model output results of the planting adaptability model are the planting suitability degree of the citrus of the multiple varieties, the model output results of the citrus growing model are the harvest time and the yield of the citrus of the multiple varieties, the model output results of the price prediction model are the predicted price of the citrus of the multiple varieties, the model output results of the production environment related prediction analysis model are the optimal picking time and the required farming operation of the citrus of the multiple varieties, and therefore planting and recommending can be performed for a peasant variety by combining the planting suitability degree, the harvest time, the yield, the optimal picking time and the required farming operation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic implementation flow chart of a citrus variety planting recommendation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a citrus variety planting recommendation device according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a terminal device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be protected herein.
In order to illustrate the technical solution of the present application, the following description is made by specific examples.
Fig. 1 shows a schematic implementation flow chart of a citrus variety planting recommendation method provided in an embodiment of the present application, where the method may be applied to a terminal device, and may be applicable to a situation where reliability of a planting recommendation result needs to be improved. The terminal device may refer to intelligent devices such as a mobile phone, a computer, a tablet computer, and the like, which is not limited in this application.
Specifically, the method for recommending the planting of citrus varieties may include the following steps S101 to S103.
Step S101, planting data are acquired.
The planting information may include a planting position where citrus planting is performed, production environment information related to a harvest time and a price, and the like. The terminal equipment can acquire planting data input by farmers through touch screen operation, willingness to input and the like, and can also determine the planting information based on the positioning information of the terminal equipment.
Step S102, determining output results of the multiple models according to the planting information and the multiple models.
In embodiments of the present application, the plurality of models may include a planting adaptability model, a citrus growth model, a price prediction model, and a production environment-related predictive analysis model. Specifically, the terminal device may acquire historical planting data; and training to obtain a plurality of models according to the historical planting data. By way of example, the historical planting data may be that of high quality citrus of each variety that has been cultivated for 5-10 years.
The planting adaptability model can be used for analyzing the planting suitability, and the output result of the model can be the planting suitability of citrus of a plurality of varieties. From the planting data, the terminal device may determine key ecological factors including, but not limited to, illumination of the planting location, annual average temperature, precipitation, altitude, soil pH, soil organic matter, soil, and the like. And inputting the key ecological factors into the planting adaptability model to obtain the planting suitability degree of citrus of a plurality of varieties.
Specifically, the terminal equipment can acquire historical planting data of a high-quality fruit area cultivated for 5-10 years, determine key ecological factors related to the growth and development of fruit trees, the fruit quality and the yield, perform a multi-factor comprehensive weighted evaluation method on the key factors and the like, adopt a scoring model to evaluate and evaluate, rank the ecological fitness of different tree species in a planning area according to scoring, take the break points of grading standards as indexes, input each key factor into a model for training, and obtain an ecological adaptability evaluation decision model; and evaluating whether the decision-making planting area is suitable for introducing and planting the fruit tree or not by using the model.
The citrus growth model can be used to predict the growth period of each variety of citrus, and the model output can be the harvest period and yield of multiple varieties of citrus. The terminal device may determine, based on the planting information, a growth indicator for each of the plurality of varieties of citrus, where the growth indicator may include, but is not limited to, a growth period, a crown size, a chlorophyll content, a flowering capacity, a fruit capacity of each of the plurality of varieties of citrus. And inputting the growth index of the citrus of each of the multiple varieties into a citrus growth model of the corresponding variety to obtain the harvest time and the yield of the citrus of the corresponding variety.
Specifically, the terminal equipment can obtain various growth indexes, and combines environmental factors of planting positions, such as soil fertility, pest and disease damage occurrence condition, environmental climate factors and the like, so as to screen key growth indexes. And (3) comparing and analyzing the original data sets of different varieties in the citrus growing period or the data sets after data conversion to establish citrus growth models of different citrus varieties. The conversion modes can comprise normalization, normalized logarithmic conversion, sigmoid conversion, box-cox conversion and the like, and the data analysis method comprises various modes such as correlation analysis, multiple regression analysis, principal component analysis, an exponential smoothing method, a Downhill-Simplex algorithm and the like. And (3) correlating models in different growth periods, adopting an interval assignment method, namely combining contemporaneous environmental growth factors for comparison, giving different scores according to the matching degree of the growth factors, multiplying weights of the factors in the whole model, and calculating a matrix to establish citrus growth models of different citrus varieties. The citrus growth model can predict and correct the citrus growth condition of the planting position in real time, so that the harvest time and the yield can be accurately predicted and judged.
The price prediction model can be used for predicting the price of the citrus, and the model output result can be the predicted price of the citrus of a plurality of varieties. The terminal device can determine the price factor of the citrus of each variety in the varieties according to the planting information, and input the price factor of the citrus of each variety in the varieties into the price prediction model of the corresponding variety to obtain the predicted price of the citrus of the corresponding variety.
Specifically, the terminal equipment can acquire the prices and related factors of each variety within 5-10 years, screen out key factors through correlation analysis, principal component analysis, regression analysis and other modes, calculate the weight of the key factors, and establish price prediction models of different varieties of oranges through an exponential smoothing method, an integrated learning method, an optimized BP neural network, a KNN algorithm and other modes, so that price trend judgment and real-time prediction of the different varieties of oranges are realized.
The production environment related prediction analysis model can be used for analyzing the production environment of the citrus, and the output result of the model can be the optimal picking time of the citrus of a plurality of varieties and the required agronomic operation. The terminal equipment can determine the price fluctuation condition factors and the harvest time condition factors of each variety in the varieties according to the planting information, and input the price fluctuation condition factors and the harvest time condition factors into a production environment related prediction analysis model to obtain the optimal picking time and the required agronomic operation of the citrus of the varieties.
Specifically, the terminal device can obtain environmental factor data related to citrus planting and price for 5-10 years, screen out key factors by means of correlation analysis, principal component analysis, regression analysis and the like, and calculate the weight of the key factors. Price prediction models of different varieties of oranges are established through various modes such as an integrated learning mode, an optimized BP neural network mode, a KNN mode and an LSTM mode, an environment analysis prediction model is established, prediction analysis is conducted on conditions affecting orange harvesting and price fluctuation, therefore optimal picking time is calculated, whether corresponding farming operations are needed to be conducted for price adjustment picking periods or not is judged, and maximum benefits are guaranteed to users. Preferably, the price prediction model may also output the required storage measures. The agricultural operation advice is advice given by referring to historical experience and citrus soil nutrient standard proper value, citrus leaf nutrient content standard proper value, citrus humiture proper value, disaster standard value and citrus pest control standard in the whole growth period (young sprout period, bud bloom period, fruit development period and fruit maturity period) of citrus. For example, if the proper soil nutrient content value is not reached, a fertilization operation is suggested; if the proper nutrient element content value of the citrus leaves is not reached, suggesting to carry out fertilization operation; if the plant diseases and insect pests reach the control standard, the plant diseases and insect pests control operation is recommended, the damage of the plant diseases and insect pests to fruits is reduced, and the quality is reduced. The optimal picking time is predicted by analysis and storage measures are taken, so that low price can be avoided, farmers can sell the picking time when the price is high, and high yield and harvest are achieved.
And step S103, fusing output results of the multiple models to obtain recommendation results of planting recommendation of the multiple varieties.
Specifically, the terminal device may obtain the weight of the output result of each of the plurality of models, perform weighting processing on the output result of each of the plurality of models by using the corresponding weight, and perform serial connection on the weighted results to obtain the recommended result.
In order to improve the fusion effect, the user can be checked whether the recommendation result is evaluated by a dynamic parameter mechanism, the recommendation result is consistent with the prediction of the system, a weighted model is generated, and the effect is greatly improved by dynamically adjusting the weight. And then, using a cross fusion algorithm to insert results of different recommendation models into the recommendation results so as to ensure the diversity of the results, and inserting the results into the recommendation results for display, so that the recommendation method is suitable for recommendation scenes capable of displaying more recommendation results at the same time, and meets the requirement of farmers for selecting the recommendation results. The four models can be connected in series by utilizing a waterfall fusion method, and each prediction algorithm is equivalent to a filter, so that a result set with small quantity and high quality is finally obtained.
In some embodiments, in order to verify the reliability of the model and verify whether the data fed back by the farmer matches the predictions, each prediction algorithm is predicted again to obtain the prediction results of different algorithms. The terminal device can train a second-layer prediction algorithm to predict the prediction result for the second time and generate a final recommendation result. Specifically, the terminal device may train the output results of the multiple models with three different algorithms to obtain three different prediction results, then perform data processing and training data to obtain an optimal training model, obtain an evaluation model (merging model), and finally output the user requirements by the model.
In the embodiment of the application, the output results of the multiple models are determined according to the planting information and the multiple models, and the output results of the multiple models are fused to obtain the recommended results for planting and recommending the multiple varieties, wherein the multiple models comprise a planting adaptability model, a citrus growing model, a price prediction model and a production environment related prediction analysis model, the model output results of the planting adaptability model are the planting suitability degree of the citrus of the multiple varieties, the model output results of the citrus growing model are the harvest time and the yield of the citrus of the multiple varieties, the model output results of the price prediction model are the predicted price of the citrus of the multiple varieties, the model output results of the production environment related prediction analysis model are the optimal picking time and the required farming operation of the citrus of the multiple varieties, and therefore planting and recommending can be performed for a peasant variety by combining the planting suitability degree, the harvest time, the yield, the optimal picking time and the required farming operation.
Based on the embodiment of the application, farmers can select the best citrus varieties, change the current situation that citrus growers blindly follow the wind when selecting varieties, simultaneously predict and evaluate the varieties planting cost, difficulty, yield, planting risk and other multidimensional degree according to the characteristics of different varieties of citrus, and provide planting technical guidance and risk assessment reports for the citrus growers in advance, and provide reliable analysis data support. And, introduce the production environment information, correct the orange growth state in real time, thus obtain harvest time and optimum harvest time. In addition, by predicting short term citrus prices and fluctuations from the historical and current sales prices of citrus, the benefits of farmer citrus planting can be maximized. Therefore, comprehensive planting, benefit, yield, price and variety multiple analysis before, during and after birth are comprehensively carried out, and more reliable recommended results are provided for farmers.
It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order according to the present application.
Fig. 2 is a schematic structural diagram of a citrus variety planting recommendation device 200 according to an embodiment of the present application, where the citrus variety planting recommendation device 200 is configured on a terminal device.
Specifically, the citrus variety planting recommendation device 200 may include:
an acquisition unit 201 for acquiring planting data including a planting position at which citrus planting is performed, production environment information related to a harvest time and a price;
a determining unit 202, configured to determine output results of the multiple models according to the planting information and the multiple models, where the multiple models include a planting adaptability model, a citrus growing model, a price prediction model, and a production environment related prediction analysis model, and the model output result of the planting adaptability model is a planting suitability degree of citrus of multiple varieties, the model output result of the citrus growing model is a harvest time and yield of citrus of the multiple varieties, the model output result of the price prediction model is a predicted price of citrus of the multiple varieties, and the model output result of the production environment related prediction analysis model is an optimal picking time and a required agronomic operation of citrus of the multiple varieties;
and the fusion unit 203 is configured to fuse the output results of the multiple models to obtain a recommendation result for performing planting recommendation on the multiple varieties.
In some embodiments of the present application, the above-mentioned fusing unit 203 may be specifically used for: obtaining the weight of the output result of each model in the plurality of models; and respectively weighting the output result of each model in the plurality of models by using corresponding weights, and connecting the weighted results in series to obtain the recommended result.
In some embodiments of the present application, the determining unit 202 may be specifically configured to: determining key ecological factors according to the planting data, wherein the key ecological factors comprise illumination, annual average temperature, precipitation, altitude, soil pH value, soil organic matters and soil of the planting position; and inputting the key ecological factors into the planting adaptability model to obtain the planting suitability degree of the citrus of the multiple varieties.
In some embodiments of the present application, the determining unit 202 may be specifically configured to: determining the growth index of the citrus of each of the varieties according to the planting information, wherein the growth index comprises the growth period, crown width, chlorophyll content, flowering quantity and fruit quantity of the citrus of each of the varieties; and inputting the growth index of the citrus of each variety in the plurality of varieties into the citrus growth model of the corresponding variety to obtain the harvest time and the yield of the citrus of the corresponding variety.
In some embodiments of the present application, the determining unit 202 may be specifically configured to: determining a price factor of citrus of each of the plurality of varieties according to the planting information; and inputting the price factor of the citrus of each variety in the plurality of varieties into the price prediction model of the corresponding variety to obtain the predicted price of the citrus of the corresponding variety.
In some embodiments of the present application, the determining unit 202 may be specifically configured to: determining price fluctuation condition factors and harvest time condition factors of each variety in the plurality of varieties according to the planting information; inputting the price fluctuation condition factors and the harvest time condition factors into the production environment related prediction analysis model to obtain the optimal picking time and the required agronomic operations of the citrus of the plurality of varieties.
In some embodiments of the present application, the citrus variety planting recommendation device 200 may further include a training unit for: acquiring historical planting data; and training according to the historical planting data to obtain the multiple models.
It should be noted that, for convenience and brevity of description, the specific working process of the citrus fruit planting recommendation device 200 may refer to the corresponding process of the method described in fig. 1, and will not be described herein again.
Fig. 3 is a schematic diagram of a terminal device according to an embodiment of the present application. The terminal device 3 may include: a processor 30, a memory 31 and a computer program 32 stored in said memory 31 and executable on said processor 30, for example a planting recommendation program for citrus varieties. The processor 30, when executing the computer program 32, implements the steps of the above-described embodiments of the planting recommendation method for each citrus variety, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor 30 may implement the functions of the modules/units in the above-described device embodiments when executing the computer program 32, such as the acquisition unit 201, the determination unit 202, and the fusion unit 203 shown in fig. 2.
The computer program may be divided into one or more modules/units, which are stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device.
For example, the computer program may be split into: the device comprises an acquisition unit, a determination unit and a fusion unit.
The specific functions of each unit are as follows: the citrus planting system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring planting data, and the planting information comprises planting positions for citrus planting and production environment information related to harvest time and price; a determining unit, configured to determine output results of the multiple models according to the planting information and the multiple models, where the multiple models include a planting adaptability model, a citrus growing model, a price prediction model, and a production environment related prediction analysis model, and the model output result of the planting adaptability model is a planting suitability degree of citrus of multiple varieties, the model output result of the citrus growing model is a harvest time and yield of citrus of the multiple varieties, the model output result of the price prediction model is a predicted price of citrus of the multiple varieties, and the model output result of the production environment related prediction analysis model is an optimal picking time and a required agronomic operation of citrus of the multiple varieties; and the fusion unit is used for fusing the output results of the models to obtain a recommendation result for planting recommendation of the varieties.
The terminal device may include, but is not limited to, a processor 30, a memory 31. It will be appreciated by those skilled in the art that fig. 3 is merely an example of a terminal device and is not limiting of the terminal device, and may include more or fewer components than shown, or may combine some components, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The processor 30 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal device, such as a hard disk or a memory of the terminal device. The memory 31 may also be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal device. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device. The memory 31 is used for storing the computer program as well as other programs and data required by the terminal device. The memory 31 may also be used for temporarily storing data that has been output or is to be output.
It should be noted that, for convenience and brevity of description, the structure of the above terminal device may also refer to a specific description of the structure in the method embodiment, which is not repeated herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A citrus variety planting recommendation method, comprising:
obtaining planting data, wherein the planting information comprises planting positions for citrus planting and production environment information related to harvest time and price;
determining output results of the multiple models according to the planting information and the multiple models, wherein the multiple models comprise a planting adaptability model, a citrus growing model, a price prediction model and a production environment related prediction analysis model, the model output results of the planting adaptability model are the planting suitability of citrus of multiple varieties, the model output results of the citrus growing model are the harvest time and the yield of citrus of the multiple varieties, the model output results of the price prediction model are the predicted price of citrus of the multiple varieties, and the model output results of the production environment related prediction analysis model are the optimal picking time and the required agronomic operation of citrus of the multiple varieties;
and fusing the output results of the multiple models to obtain a recommendation result of planting recommendation of the multiple varieties.
2. The citrus variety planting recommendation method as claimed in claim 1, wherein the fusing of the output results of the plurality of models to obtain a recommendation result for planting recommendation of the plurality of varieties includes:
obtaining the weight of the output result of each model in the plurality of models;
and respectively weighting the output result of each model in the plurality of models by using corresponding weights, and connecting the weighted results in series to obtain the recommended result.
3. A citrus variety planting recommendation method as claimed in claim 1, wherein said determining output results of said plurality of models based on said planting information and a plurality of models comprises:
determining key ecological factors according to the planting data, wherein the key ecological factors comprise illumination, annual average temperature, precipitation, altitude, soil pH value, soil organic matters and soil of the planting position;
and inputting the key ecological factors into the planting adaptability model to obtain the planting suitability degree of the citrus of the multiple varieties.
4. A citrus variety planting recommendation method as claimed in claim 1, wherein said determining output results of said plurality of models based on said planting information and a plurality of models comprises:
determining the growth index of the citrus of each of the varieties according to the planting information, wherein the growth index comprises the growth period, crown width, chlorophyll content, flowering quantity and fruit quantity of the citrus of each of the varieties;
and inputting the growth index of the citrus of each variety in the plurality of varieties into the citrus growth model of the corresponding variety to obtain the harvest time and the yield of the citrus of the corresponding variety.
5. A citrus variety planting recommendation method as claimed in claim 1, wherein said determining output results of said plurality of models based on said planting information and a plurality of models comprises:
determining a price factor of citrus of each of the plurality of varieties according to the planting information;
and inputting the price factor of the citrus of each variety in the plurality of varieties into the price prediction model of the corresponding variety to obtain the predicted price of the citrus of the corresponding variety.
6. A citrus variety planting recommendation method as claimed in claim 1, wherein said determining output results of said plurality of models based on said planting information and a plurality of models comprises:
determining price fluctuation condition factors and harvest time condition factors of each variety in the plurality of varieties according to the planting information;
inputting the price fluctuation condition factors and the harvest time condition factors into the production environment related prediction analysis model to obtain the optimal picking time and the required agronomic operations of the citrus of the plurality of varieties.
7. A citrus variety planting recommendation method according to any of claims 1 to 6, including, prior to said determining output results of said plurality of models from said planting information and a plurality of models:
acquiring historical planting data;
and training according to the historical planting data to obtain the multiple models.
8. A citrus variety planting recommendation device, comprising:
the citrus planting system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring planting data, and the planting information comprises planting positions for citrus planting and production environment information related to harvest time and price;
a determining unit, configured to determine output results of the multiple models according to the planting information and the multiple models, where the multiple models include a planting adaptability model, a citrus growing model, a price prediction model, and a production environment related prediction analysis model, and the model output result of the planting adaptability model is a planting suitability degree of citrus of multiple varieties, the model output result of the citrus growing model is a harvest time and yield of citrus of the multiple varieties, the model output result of the price prediction model is a predicted price of citrus of the multiple varieties, and the model output result of the production environment related prediction analysis model is an optimal picking time and a required agronomic operation of citrus of the multiple varieties;
and the fusion unit is used for fusing the output results of the models to obtain a recommendation result for planting recommendation of the varieties.
9. Terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method for recommended planting of citrus varieties according to any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of a method for recommended planting of citrus varieties according to any of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117056599A (en) * | 2023-08-12 | 2023-11-14 | 布瑞克(苏州)农业互联网股份有限公司 | Crop planting recommendation method and system |
| CN117973701A (en) * | 2024-03-29 | 2024-05-03 | 广东海洋大学 | Agricultural service management method and system based on big data |
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2022
- 2022-12-29 CN CN202211707433.1A patent/CN116186392A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117056599A (en) * | 2023-08-12 | 2023-11-14 | 布瑞克(苏州)农业互联网股份有限公司 | Crop planting recommendation method and system |
| CN117973701A (en) * | 2024-03-29 | 2024-05-03 | 广东海洋大学 | Agricultural service management method and system based on big data |
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