CN111223003A

CN111223003A - Production area-oriented planting decision service system and method

Info

Publication number: CN111223003A
Application number: CN202010172862.8A
Authority: CN
Inventors: 洪波; 杭兴; 张锋
Original assignee: Shaanxi Nongjin Information Technology Service Co Ltd
Current assignee: Shaanxi Nongjin Information Technology Service Co Ltd
Priority date: 2020-03-13
Filing date: 2020-03-13
Publication date: 2020-06-02

Abstract

The invention relates to a planting decision service system and a planting decision service method for a production area, which comprise the following steps: a crop development model based on the effective accumulated temperature; sensors deployed on the basis of the division of cultivars, cultivation modes and geography of production areas: real-time and historical meteorological data are introduced to monitor the growth and development of crops, and development nodes are automatically and intelligently identified so as to master the accurate phenological period nodes of the crops; production environment, water and fertilizer index database: controlling and recommending to issue production parameters through a universal data interface; cloud service system and APP: the system comprises a crop index library, production area environment data, meteorological lattice point data, a crop-accumulated temperature-development basic model, feature identification, meteorological data service, process pushing service and equipment control service. The method has the advantages that the definition of the phenological period is more intelligent and accurate, the planting plan, the planting process definition and the equipment intelligent control of crops from planting to harvesting are realized, and meanwhile, the method is wide in service range, personalized and low in cost.

Description

Production area-oriented planting decision service system and method

Technical Field

The invention belongs to the technical field of agricultural technical services, and particularly relates to a planting decision service system and method for a production area.

Background

In China, the economic planting is mainly performed by medium and small-scale seeds, and the production areas are centralized. The production area usually consists of one or several counties with similar climate and soil condition, one or several similar cash crops are planted intensively, and the planting modes of the cash crops are diversified (open field, greenhouse, sunlight greenhouse and the like). A similar problem is faced by a large number of growers in a producing area to manage their own land in a decentralized manner. In recent years, with the rapid development of information network technology, the production area generally covers the network, and the field is connected with electric power. Therefore, individual advanced farmers have started to install and deploy production tools such as temperature and humidity control, automatic water-saving equipment and the like to promote mass production and save labor. However, in actual use, ordinary farmers cannot accurately adjust the operation of equipment such as water fertilizer, temperature and humidity control and the like according to the growth stage and growth vigor of crops, and only operate the equipment according to experience, so that the use of the equipment is greatly limited, and even the equipment does not play a role.

Because the producing area is large, in order to effectively help the grower, the most reasonable mode is to monitor the growth vigor of the crops through an intelligent and information system and provide production suggestions for different plots. Meanwhile, from the perspective of a single farmer or a single park, it is uneconomical and difficult to develop such information systems, and the planting commonality characteristics of the producing area provide a basis and feasibility for developing such systems.

Therefore, how to build a service system capable of covering a production area and making an intelligent decision to generate production decision information, help growers to produce in a quality and labor-saving manner is one of the problems to be solved urgently in the industry.

A planting technology service system and method for small and medium-sized planters is described in patent application No. 201710154721.1, and the service system includes a server side and a user side. The server side comprises a user interaction module, a planting plan module, a weather data module, a user data module and a data interface; the user side comprises a user interaction module, a planting data pushing module, a communication module and a user data module. The system defines the growth nodes of crops, including the growth nodes in the stages of germination, flowering, fruiting, leaf fall, dormancy and the like, forms a planting plan of the crops, and pushes pictures and texts for the planting processes of daily farming operation, irrigation and fertilization, pest control, weather early warning and the like.

Since the growth node of the crop is not a constant as an important basis for adjusting the production measures, and may have a certain difference according to the year or the climate condition of the environment, the application patent has the following disadvantages: (1) a basic index model of crop growth and development is not established; (2) an effective growing node acquisition means is not established, and only the user is relied on to carry out autonomous judgment; (3) the method does not effectively combine field sensor data, and does not provide quantitative index data and interfaces for water, fertilizer and temperature and humidity control in each stage of a planting plan; (4) only weather forecast is introduced in the aspect of weather data, and real-time and historical monitoring data related to crop growth and development are not introduced; (5) in the planting process pushing link, the planting process is arranged and deployed at a client (APP), which means that a user needs to obtain all process information and data from the client to make a decision to push, which reduces the reliability and accuracy of the system.

Therefore, the present patent improves upon the related art and forms a unique system and method based on the' 201710154721.1 patent.

Disclosure of Invention

The invention aims to solve the problems and provides a planting decision service system and a planting decision service method for a production area, which have more intelligent and accurate definition of a phenological period, realize the planting plan, the planting process definition and the equipment intelligent control of crops from planting to harvesting, and have the advantages of wide service range, individuation and low cost.

In order to achieve the purpose, the invention provides the following technical scheme:

a planting decision service system for a producing area, comprising:

a crop development model based on the effective accumulated temperature;

sensors deployed on the basis of the division of cultivars, cultivation modes and geography of production areas: real-time and historical meteorological data are introduced to monitor the growth and development of crops, and development nodes are automatically and intelligently identified so as to master the accurate phenological period nodes of the crops;

production environment, water and fertilizer index database: controlling and recommending to issue production parameters through a universal data interface;

cloud service system and APP: the system comprises a crop index library, production area environment data, meteorological lattice point data, a crop-accumulated temperature-development basic model, feature identification, meteorological data service, process pushing service and equipment control service.

Furthermore, the crop-accumulated temperature-development basic model is mainly used for monitoring growth and development nodes, and the growth and development of crops are expressed by a formula as follows:

in the growth and development formula of the crops, DSI (s, k) represents the development index of the kth day at the s stage, ATDi represents the effective accumulated temperature of the ith day at the s stage, and ACTem_s-1The accumulated effective accumulated temperature of the previous s stages.

A working method of a planting decision service system facing a production area comprises the following steps:

s1: establishing a basic crop-accumulated temperature-development model to quantitatively monitor the crop growth and development nodes and provide a basic basis for decision making;

s2: acquiring meteorological lattice point data through a meteorological information sharing platform interface, calculating real-time temperature and accumulated temperature conditions of different plots by combining the longitude and latitude positions of the plots, performing meteorological disaster early warning on low-temperature freezing damage and heat damage, and predicting the growth and development conditions of crops according to a model;

s3: building a crop index library at a server side, and providing different planting process pushing services and equipment control services for crops according to different stages of crop growth and development and weather data;

s4: a data service system is constructed, and the accurate farming service of the universe is realized by adopting sensor data: according to the geographical position condition, the collected real-time data and the meteorological lattice data are subjected to fusion calculation to manufacture data guidance products aiming at different fields and different cultivation modes;

s5: building a disease and pest prediction model by building a model relation between the environmental data and main crop diseases, and early warning the crop disease trend of the plot;

s6: a crop growth node monitoring system is constructed to build a crop index library, and equipment is controlled on the basis of integrating meteorological grid point data and sensor data to provide decision-making service;

s7: and calculating and pushing a planting plan, a planting process and equipment control parameters at the server side, and calling a calculation result by the APP side for displaying.

Further, the working process of the crop-accumulated temperature-development model is as follows:

1) calculating accumulated temperature;

2) predicting a phenological period by the accumulated temperature model;

3) confirming the phenological period: entering the next step if no error is confirmed, otherwise, returning to the step 2) by regressing the optimization model;

4) and adjusting the process parameters.

Further, the phenological period confirmation method in the step 3) is as follows: and the video or the image uploaded by the user is used as a confirmation basis for the crop to enter the next growth stage.

Further, in S1, a typical sign of each stage is defined on the morphology of growth and development of different crops, a linear relationship between growth and development indicators and accumulated temperature and growth check points is established, and the model is optimized through machine learning.

Further, in S4, when the data service system is constructed, in a production area without field monitoring equipment, a method of sparsely arranging sensors is adopted to enhance system capacity.

Further, in S6, the specific method for controlling the device is as follows: the automatic control of the air release machine and the curtain rolling machine device according to the environment temperature and humidity, the automatic control of the light supplement lamp device according to the crop growth situation and the control of the water and fertilizer integrated machine device according to the water and fertilizer plan of the crop plot.

Further, the decision service in S6 includes: the method comprises the following steps of planting process pushing, greenhouse ventilation temperature control, water and fertilizer plan pushing, phenological period monitoring, pest and disease early warning and weather disaster early warning.

Compared with the prior art, the invention has the beneficial effects that:

compared with a planting technology service system and a planting technology service method for medium and small planters, the planting decision service system has the advantages that the phenological period definition is more intelligent and accurate, and planting plan, planting process definition and equipment intelligent control of crops from planting to harvesting are realized on the basis. In addition, the invention is not a single-point service system which cannot be popularized, but a system which can cover county-level large-area production areas and can provide personalized services for different crop varieties, thereby effectively reducing the popularization cost.

According to the system and the method for the planting decision of the producing area, which are described by the patent, an accurate production service system facing a large number of growers in the producing area can be constructed, the waste of repeated construction of the production area service system can be avoided, and the problem that a single main body (peasant household and garden) cannot construct an information system is solved. After the device is put into use, the device not only can serve production and assist the production area to improve the production quality, but also can provide technical support for commercial operation and equipment popularization of a digital system in the production area.

Drawings

In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings needed to be used in the description of the embodiment will be briefly introduced below, it is obvious that the drawings in the following description are only for more clearly illustrating the embodiment of the present invention or the technical solution in the prior art, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic diagram of a cloud service system according to the present invention;

FIG. 2 is a flow chart of the operation of the crop-temperature accumulation-development model of the present invention;

FIG. 3 is a flow chart of the planting process push service and equipment control service of the present invention;

FIG. 4 is a flow chart of a data service system of the present invention;

FIG. 5 is a schematic diagram of a decision service system according to the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood and implemented by those skilled in the art, the present invention is further described with reference to the following specific examples, which are provided for illustration only and are not intended to limit the present invention.

As shown in fig. 1, a planting decision service system for a producing area, 1) the system first establishes a crop development model based on an effective accumulated temperature; 2) secondly, dividing the cultivated species, cultivation mode and geography of a production area into basic deployment sensors, introducing real-time and historical meteorological data to monitor the growth and development of crops, and automatically and intelligently identifying development nodes to master accurate phenological period nodes of the crops; 3) establishing a production environment and water and fertilizer index database according to different stages, and controlling and recommending to issue production parameters through a universal data interface; 4) and finally, the task allocation and communication modes of the cloud service system, the APP and the control system are improved to optimize the system. Fig. 1 is a schematic diagram of the cloud service system.

1. Crop-accumulated temperature-development-model

The growth and development of crops are related to factors such as temperature, humidity, water and fertilizer, illumination and the like, but the physiological development of the crops is mainly strongly related to the temperature, and the management modes of the crops in different physiological periods are greatly different. The model is mainly used for monitoring growth and development nodes (phenological stages), so that a basic crop-accumulated temperature-development model is established to quantitatively monitor the growth and development nodes of crops, and a basic basis is provided for decision making. As the annual soil temperature and the air temperature in the same region have linear correlation, the appropriate effective accumulated temperature (the accumulated temperature quantity greater than the growth development threshold value) is selected for different crops to represent the growth and development of the crops, and the accuracy of the model is continuously optimized through machine learning (regression algorithm). Wherein the growth and development of the crops are represented as:

DSI (s, k) represents the developmental index of the kth day at the s stage, ATDi represents the effective accumulated temperature of the ith day at the s stage, ACTem_s-1The accumulated effective accumulated temperature of the previous s stages. On the basis, typical signs (such as germination, leaf expansion, early flowering, fruit bearing and the like) of each stage are defined on the forms of growth and development of different crops, the linear relation between growth and development indexes and accumulated temperature and growth check points is established, and model indexes are optimized through machine learning (clustering analysis algorithm). The flow is shown in fig. 2.

Example (b):

taking an open-field apple as an example, assuming that the effective accumulated temperature index from the germination stage to the initial flowering stage is 1000, the service object is a Luochuan apple producing area grower. Through calculation of historical temperature data in the early stage, the time of the No. 1 plot entering the initial flowering stage is 3 months and 28 days. In practice, the initial flowering phase confirmed by the APP user or identified by the camera is actually 3-29 days, and the temperature accumulation index of the phenological period can be optimized through weight through learning comparison of the temperature data of the current-year plot. Similarly, if 1 ten thousand plots are confirmed by the user or the sensor to be the actual date of the first bloom, the objective period indicators can be optimized by methods such as cluster analysis and the like, and the situation approaches to the real situation infinitely. The same is true for other climatic stages of the crop, and so on.

Feature recognition confirms the exact date of the crop entering the next growth stage by means of image recognition (user upload or video capture) by defining the typical features of a certain crop entering the next stage.

2. Aiming at the practical situation that the geographical positions of the production areas are close and the cultivated crops are close, firstly, 5 multiplied by 5km is collected through a meteorological information sharing platform interface²And calculating real-time temperature and accumulated temperature conditions of different plots according to the weather grid point data of the resolution ratio and the longitude and latitude positions of the plots, performing weather disaster early warning on low-temperature freezing injury and heat injury, and predicting the growth and development conditions of crops according to the model. Meanwhile, sparse sensors (video and temperature) are deployed or existing sensors are connected to monitor the development of crops.

3. A crop index library is built at a server end, different planting process pushing services and equipment control services are provided for crops at different stages of crop growth and development and weather data, and the specific flow is shown in figure 3.

4. A certain number of field sensors are usually deployed in the existing production area, and generally comprise temperature, humidity, precipitation, illumination and CO₂Concentration, soil pH value and the like, but because the support of the algorithm and the model is weak, direct service cannot be provided for planting, and meanwhile, the data of the algorithm and the model cannot be used for processing service products facing to a production area. In fact, the production area of economic planting is mainly vegetables and fruit trees, wherein the cultivation mode can be divided into 3 types of open field cultivation, greenhouse (arched shed) cultivation and sunlight greenhouse cultivation. On the basis of mastering geographical positions (three-dimensional coordinates) of the fields and crop types, a data service system can be constructed, the accurate agricultural service of the universe is realized by adopting the existing sensor data, namely, the acquired real-time data and the meteorological lattice data are fused and calculated according to the geographical position condition, and data guidance products aiming at different fields and different cultivation modes are manufactured. In a production area without field monitoring equipment, a method of sparsely arranging sensors can be adopted to enhance the capacity of the system. The specific flow is shown in fig. 4.

The specific way of fusion calculation is as follows:

1) firstly, weather grid point data (temperature) is used for helping cultivation growers to accurately plan stubble periods, and crop production plans including phenological period forecast, and farming guidance of various phenological periods and the like are provided for crops cultivated in open field;

2) early warnings in different stages, such as low-temperature freeze injury, high temperature, waterlogging and the like, are provided for growers in different cultivation modes and planting varieties in a production area through meteorological data;

3) because the grid point meteorological data of the field is calculated by an interpolation method according to the longitude and latitude of the field through the meteorological station data, errors exist, and the method can be used for calculating the phenological period of a large area and pushing the agricultural process. But for the water and fertilizer control, the environmental control of facility production is insufficient;

4) the sensor deployed in the field is used for supplementing the deficiency of meteorological data, mainly aiming at the water and fertilizer control of open crops and the environmental control of production crops;

5) through the comprehensive application of meteorological data and monitoring data, the method provides integral and different levels of accurate planting data service for the production area, and can supplement and correct each other from planting plans, farming techniques, disease and pest management, early warning information to water and fertilizer control, environment control and the like, thereby reducing data errors.

5. On the basis of an insect and disease process library of the patent 201710154721.1, an insect and disease prediction model is constructed by constructing a model relation between environmental data (temperature and humidity) and main diseases of crops, early warning is carried out on the crop disease trend of a plot, and a water and fertilizer plan of the plot is generated according to meteorological lattice point data and sensor data by combining a water and fertilizer model library of the patent 201710154721.1.

6. On the basis of 201710154721.1 patent, this patent constructs the crop index storehouse through constructing crop growth node monitoring system, on the basis of fusing meteorological lattice point data and sensor data, according to environment humiture automatic control air release machine and roll up curtain machine equipment, according to the growth situation automatic control light filling lamp equipment of crop, according to the liquid manure plan control liquid manure all-in-one equipment of crop plot. Therefore, the system not only can provide planting technology pushing service for growers, but also can provide equipment control service for production equipment, and jointly form production decision service of a production area. The above devices are all the prior art, and the services supported by the decision service system are shown in fig. 5.

7. On the basis of 201710154721.1 patent, this patent optimization calculation structure through plant plan, planting technology and the equipment control parameter of calculating the propelling movement at the server end, and the APP end only needs to call the calculation result and demonstrates, and this frame that can exert the server end and calculation power, data advantage have alleviateed the pressure of client, have promoted the reliability of system.

Compared with a planting technology service system and a planting technology service method for medium and small-sized planters, the system and the method have the advantages that the phenological period definition is more intelligent and accurate, and planting plan, planting process definition and equipment intelligent control of crops from planting to harvesting are realized on the basis. In addition, the system is not a single-point service system which cannot be popularized, but a system which can cover county-level large-area production areas and can provide personalized services for different crop varieties, so that the popularization cost is effectively reduced.

The 201710154721.1 patent is disclosed, and reference is made to the details of the patent application, which are not detailed herein.

The details of the present invention not described in detail are prior art.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A planting decision service system for a producing area, comprising:

a crop development model based on the effective accumulated temperature;

2. The planting decision service system for the growing area according to claim 1, wherein the crop-temperature accumulation-development basic model is mainly used for monitoring growth and development nodes, and the growth and development of crops are formulated as follows:

3. the growing area oriented planting decision service system of claim 2, wherein DSI (s, k) in the growing development formula of the crop represents a development index of kth day at s stage, ATDi represents an effective accumulated temperature of ith day at s stage, ACTem_s-1The accumulated effective accumulated temperature of the previous s stages.

4. A working method of a planting decision service system facing a production area is characterized by comprising the following steps:

5. The working method of the planting decision service system for the producing area according to claim 4, wherein the working process of the crop-temperature accumulation-development model is as follows:

1) calculating accumulated temperature;

2) predicting a phenological period by the accumulated temperature model;

4) and adjusting the process parameters.

6. The operating method of a planting decision service system facing a producing area according to claim 5, wherein the phenological period confirmation method in the step 3) is: and the video or the image uploaded by the user is used as a confirmation basis for the crop to enter the next growth stage.

7. The operating method of a planting decision service system for a producing area according to claim 4, wherein in the step S1, typical signs of each stage are defined on the forms of different crop growth and development, linear relations between growth and development indexes and accumulated temperature and growth check points are established, and models are optimized through machine learning.

8. The operating method of a planting decision service system facing a producing area according to claim 4, wherein in the step S4, when the data service system is constructed, in the producing area without field monitoring equipment, a method of sparsely arranging sensors is adopted to enhance system capacity.

9. The operating method of a planting decision service system for a producing area according to claim 4, wherein in the step S6, the specific method for controlling the equipment is as follows: the automatic control of the air release machine and the curtain rolling machine device according to the environment temperature and humidity, the automatic control of the light supplement lamp device according to the crop growth situation and the control of the water and fertilizer integrated machine device according to the water and fertilizer plan of the crop plot.

10. The operating method of a planting decision service system for a producing area according to claim 4, wherein the decision service in S6 comprises: the method comprises the following steps of planting process pushing, greenhouse ventilation temperature control, water and fertilizer plan pushing, phenological period monitoring, pest and disease early warning and weather disaster early warning.