CN111964719A - Agricultural sensor crop accurate nutrition system and method based on artificial intelligence - Google Patents
Agricultural sensor crop accurate nutrition system and method based on artificial intelligence Download PDFInfo
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Abstract
The invention belongs to the technical field of intelligent agricultural management, and particularly relates to an agricultural sensor crop accurate nutrition system and method based on artificial intelligence. The system comprises a central workstation, an intelligent user side and a cloud system; the service range of the central workstation covers the whole agricultural planting area, agricultural sensors are adopted to collect agricultural information, and agricultural services including but not limited to data transmission and nutrition supply are completed; the intelligent user side is used for displaying information, acquiring abnormal seedling situation images and purchasing nutrition supply services; and the cloud system processes and analyzes the information. The invention provides a farming condition sampling means comprising multi-mode planting parameter acquisition and abnormal seedling condition image acquisition. In the two-stage work flow provided by the system, the analysis object is reduced from a large-scale agricultural planting area to a small-scale problem partition, so that the pertinence and the accuracy of the agricultural service scheme obtained by data analysis are gradually improved.
Description
Technical Field
The invention belongs to the technical field of intelligent agricultural management, and particularly relates to an agricultural sensor crop accurate nutrition system and method based on artificial intelligence.
Background
The monitoring of planting environment and crop growth condition is an important component of the agricultural condition monitoring work. Through real-time state monitoring and according to actual conditions adjustment strategy, solution problem, be favorable to realizing moisture, illumination and nutrient element's accurate supply, improve agricultural production efficiency and agricultural product quality to promote the green development of traditional agriculture.
As described in chinese patent nos. CN201621183257 and CN201910253674 in the prior art, agricultural sensors are now widely used for monitoring of planting environment and crop growth. In the prior art, a great number of agricultural sensors are distributed in planting areas such as farmlands and orchards, agricultural condition data such as humidity, illumination, soil moisture, soil nutrient elements and the like are collected in real time, and the collected data are compared with standard data which are prestored in a system background and reflect ideal growth states of crops and/or ideal environments suitable for crop growth, so that problems in agricultural production are found. On the basis, manpower and material resources are reasonably input, agricultural operation plans such as irrigation, light supply, nutrient supply and the like are timely adjusted, and the input of planting elements such as water, fertilizers and the like according to needs can be improved to a certain extent.
However, there is a limit to the actual agricultural condition determination based on the agricultural condition data collected by the sensor:
firstly, the formulation of the background standard data of the system is work with strong subjectivity and limited by the professional level of a formulator. The standard data is very likely to have limitations in aspects of specialty, accuracy and the like, and cannot provide effective reference; secondly, in the existing crop monitoring technology, although a technical scheme of performing partitioned collection and independent analysis of agricultural data by using a sensor exists, in view of extremely high complexity of agricultural production activities, diversified factors such as types, water content, organic matter content and temperature of soil can influence the measurement precision of the agricultural sensor, so that a system is difficult to obtain an accurate numerical value which truly reflects the environment of a planting partition and the growth condition of crops; in addition, compared with the collected data, the real growth state of the crops can more intuitively reflect whether the water, the illumination and the nutrient elements are applied reasonably. Therefore, the accuracy, the reasonability and the effectiveness of the method for establishing the agricultural operation scheme such as irrigation, illumination, nutrition supply and the like only by using the sensor data are difficult to ensure.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an agricultural sensor crop accurate nutrition system and method based on artificial intelligence.
In order to achieve the purpose, the specific scheme of the application is as follows:
an agricultural sensor crop accurate nutrition system based on artificial intelligence comprises a central workstation, an intelligent user side and a cloud system which is respectively connected with the central workstation and the intelligent user side through signals;
the service range of the central workstation covers the whole agricultural planting area, and agricultural services including but not limited to agricultural information acquisition, data transmission and nutrition supply are provided for the agricultural planting area; wherein the agricultural planting area comprises a plurality of sub-areas for which at least one user is responsible; the agricultural information acquisition is realized by a sensor group arranged in an agricultural planting area and is carried out overall data acquisition facing the agricultural planting area.
The intelligent user side is used for displaying information, shooting abnormal seedling situations and providing a channel for purchasing nutrition supply services for the user; the shooting of abnormal seedling situations is targeted data acquisition carried out in a subarea mode for problems with abnormal seedling situations.
The cloud system processes and analyzes information uploaded from the central workstation and the intelligent user side, generates an agricultural condition initial diagnosis report, a nutrition supply scheme and other agricultural operation suggestions, and realizes data management.
Furthermore, the central workstation, the intelligent user side and the cloud system are in signal connection through a wireless communication technology; still further, the wireless communication technology for realizing signal connection of the central workstation, the intelligent user side and the cloud system is selected from one or more of a 5G network, a 4G network, a WiFi network and a Bluetooth technology.
Furthermore, the area of the agricultural planting area is 3000-10000 mu, and the area of each planting subarea is 30-50 mu.
The central work station comprises an information acquisition center, a crop nutrition center and a data transmission center which is respectively connected with the information acquisition center and the crop nutrition center through signals;
the information acquisition center comprises sensor groups which are respectively arranged in each subarea; the sensor groups arranged in different subareas are respectively provided with unique corresponding numbers; the sensor group acquires the multi-mode planting parameters of the corresponding partitions according to a preset acquisition plan;
the data transmission center is used for uploading the multi-mode planting parameters to the cloud system, receiving signals which are sent by the cloud system and used for implementing nutrition supply service, and receiving a nutrition supply scheme formulated by the cloud system and a crop nutrition scheme customized by a user;
after receiving the signal of implementing the nutrition supply service sent by the cloud system, the crop nutrition center completes the nutrition supply service according to the nutrition supply scheme which is formulated by the cloud system and aims at the problem subarea and/or the crop nutrition scheme customized by the user.
Furthermore, the sensor groups in each subarea comprise a soil temperature and humidity sensor, an air temperature and humidity sensor, a soil pH value sensor and an illumination intensity sensor. The multi-mode planting parameters comprise soil temperature and humidity, air temperature and humidity, soil pH value and planting area illumination intensity.
Furthermore, the crop nutrition center comprises an intelligent nutrition allocation system and an automatic drip irrigation control system, and the nutrition supply service comprises the steps that the intelligent nutrition allocation system automatically allocates nutrient elements and the automatic drip irrigation control system controls the nutrient drip irrigation operation.
Furthermore, the intelligent user side comprises a display module, a shooting module, a mall module and a data transmission module which is respectively connected with the display module, the shooting module and the mall module through signals;
the data transmission module receives the agricultural condition initial diagnosis report, the nutrition supply scheme and other agricultural operation suggestions transmitted to the intelligent user side by the cloud system, transmits the agricultural condition initial diagnosis report, the nutrition supply scheme and the other agricultural operation suggestions to the display module, and displays information for the user;
aiming at abnormal seedling conditions in the problem subareas recorded by the agricultural condition initial diagnosis report, a user can adopt a shooting module to carry out on-site acquisition of abnormal seedling condition images in the problem subareas, and the abnormal seedling condition images are uploaded to a cloud system through a data transmission module to carry out subsequent intelligent accurate analysis;
the user purchases nutrition supply service through the mall module; wherein the nutritional provisioning service is based on: a nutrition supply scheme which is formulated by a cloud system and aims at problem subareas, and/or a crop nutrition scheme which is customized by a user in a mall module according to personal experience; and uploading the purchased payment information to the cloud system through the data transmission module.
Optionally, the smart client is selected from one or more of a smart phone, a tablet computer, and other terminal devices with functions of shooting, payment, display, and data transmission.
Furthermore, the cloud system comprises a multi-mode information analysis module, an accurate analysis module and an information management module which is respectively connected with the multi-mode information analysis module and the accurate analysis module through signals;
the multi-mode information analysis module analyzes abnormal seedling conditions in the agricultural planting area according to multi-mode planting parameters uploaded by the central workstation, the partition with the abnormal seedling conditions is defined as a problem partition, each abnormal seedling condition is respectively associated to the corresponding problem partition, a plurality of agricultural condition preliminary diagnosis reports aiming at different problem partitions are generated, and the plurality of agricultural condition preliminary diagnosis reports are output to the information management module. Wherein, the abnormal seedling condition comprises abnormal planting environment and abnormal crop growth parameters.
Furthermore, the multi-mode information analysis module prestores standard parameter values meeting ideal planting conditions and ideal growth conditions of crops; aiming at multi-mode planting parameters which are uploaded by a central workstation and collected from sensor groups with different numbers, a multi-mode information analysis module compares the multi-mode planting parameters with standard parameter values, respectively judges whether abnormal planting environment and abnormal crop growth parameters occur in partitions corresponding to different sensor groups, analyzes the specific type and the severity of abnormal conditions, defines the partitions with the abnormal conditions as problem partitions, and generates an initial diagnosis report of the agricultural conditions for different problem partitions.
The accurate analysis module comprises an artificial neural network model and is used for intelligently calculating abnormal seedling situation images of the problem partitions uploaded by the intelligent user side; in the training stage, the artificial neural network model realizes the training of the artificial neural network model through forward network calculation and reverse error propagation calculation; in the use stage of finishing training, the artificial neural network model calculates and accurately analyzes specific symptoms of abnormal seedling conditions through a forward network, provides a nutrition supply scheme aiming at problem subareas and other agricultural operation suggestions based on the specific symptoms, and outputs the suggestions to the information management module.
The information management module is used as a data interaction center of the cloud system, receives the multi-mode planting parameters uploaded by the central workstation, and receives the abnormal seedling situation images uploaded by the intelligent user side; and sending an agricultural condition initial diagnosis report, a nutrition supply scheme and other agricultural operation suggestions to the intelligent user side, and sending a signal for implementing the nutrition supply service to the central workstation after receiving the payment information of the intelligent user side.
The information management module prestores basic information of each subarea in the agricultural planting area. For the agricultural condition initial diagnosis report which is generated by the multi-mode analysis module and aims at each problem partition, the information management module acquires the user information of the problem partition according to the prestored partition basic information and sends the agricultural condition initial diagnosis report to the intelligent user side of the user corresponding to the problem partition.
Further, the basic information of the partition includes, but is not limited to, the type of the partitioned crop, the name of the user, the contact information of the user, and the like, and for a plurality of partitions for which the same user is responsible, the basic information of the partition also includes the partition number set by the user.
The invention also provides an implementation method of the agricultural sensor crop accurate nutrition system based on artificial intelligence, which comprises the following steps:
s1, collecting multi-mode planting parameters of corresponding partitions by the sensor groups respectively arranged in the partitions according to a preset collection plan, and uploading the multi-mode planting parameters to a cloud end system;
s3, analyzing abnormal seedling conditions in the agricultural planting area by the cloud system according to the multi-mode planting parameters, defining the partition with the abnormal seedling conditions as a problem partition, and respectively associating each abnormal seedling condition with the corresponding problem partition, thereby generating a plurality of agricultural condition preliminary diagnosis reports aiming at different problem partitions, and respectively sending the plurality of agricultural condition preliminary diagnosis reports to the intelligent user ends of the users of the corresponding problem partitions by the cloud system;
s4, the user checks abnormal seedling conditions in the problem partition recorded in the agricultural condition initial diagnosis report through the intelligent user side, then the intelligent user side is used for collecting abnormal seedling condition images in the problem partition on the spot and uploading the abnormal seedling condition images to the cloud system;
s5, the cloud system artificial intelligence model carries out intelligent operation on the abnormal seedling situation images of the problem subareas; in the training stage, the artificial neural network model realizes the training of the artificial neural network model through forward network calculation and reverse error propagation calculation; in the use stage of finishing training, the artificial neural network model calculates and accurately analyzes specific symptoms of abnormal seedling conditions through a forward network, provides a nutrition supply scheme aiming at problem partitions and other agricultural operation suggestions based on the specific symptoms, and sends the nutrition supply scheme and other agricultural operation suggestions to intelligent user ends of users of corresponding problem partitions.
Further, the implementation method of step S3 is:
and comparing the multi-mode planting parameters with pre-stored standard parameter values which accord with ideal planting conditions and ideal growth conditions of crops, respectively judging whether the partition corresponding to different sensor groups has abnormal planting environment and abnormal crop growth parameters, analyzing the specific type and severity of abnormal conditions, defining the abnormal partition as a problem partition, and generating a plurality of agricultural condition initial diagnosis reports aiming at different problem partitions.
Further, when the user selects to purchase the nutrition supply service, the implementation method of the agricultural sensor crop precision nutrition system based on artificial intelligence further comprises the following subsequent steps:
s6, the user purchases the nutrition supply service through the intelligent user side and finishes payment, and the cloud system sends a signal for implementing the nutrition supply service to the central workstation according to the payment information uploaded by the intelligent user side; and the crop nutrition center performs nutrient element allocation according to a nutrient supply scheme which is set by a cloud system and aims at problem subareas and/or a crop nutrition scheme customized by a user, and controls and finishes nutrient drip irrigation operation.
The application has the advantages that:
compared with the prior art, the agricultural sensor crop accurate nutrition system and method based on artificial intelligence, disclosed by the invention, realize information interconnection between the central workstation and the intelligent user side and the cloud system, and provide a flow agricultural condition sampling means from multi-modal planting parameter acquisition to abnormal seedling condition image acquisition. In the whole practical process of the system, the data acquisition range is reduced from an agricultural planting area to a problem subarea, so that the pertinence and the accuracy of the crop nutrition supply scheme obtained by data analysis are gradually improved.
Specifically, in two data acquisition-analysis stages related to the technical scheme of the application, sensor groups arranged in each partition are used for respectively carrying out indiscriminate integral acquisition on multi-mode planting parameters of the partition to which the sensor groups belong; the cloud system defines an area with abnormal agricultural condition as a problem partition based on the sensor group number and generates a corresponding agricultural condition initial diagnosis report; after the agricultural condition initial diagnosis report is obtained, the user shoots abnormal seedling condition images, and the specific data acquisition operation is carried out aiming at the problem subareas. As an analysis tool for abnormal seedling condition images with small acquisition range and high acquisition precision, the trained artificial intelligence model can reach the professional level of agricultural experts to a certain extent. Compared with the scheme that a user automatically adjusts the nutrition proportion according to planting experience in the prior art, the analysis result of the artificial intelligent model has high specialty and accuracy, so that the crop nutrient elements such as chemical fertilizers and the like are accurately applied, the utilization rate is improved, and meanwhile, the agricultural ecological environment is effectively protected.
In addition, through the agricultural service purchase channel that the intelligent user terminal provided, the user can implement the nutrition supply based on the scheme that the high in the clouds system made or was customized. The method disclosed by the invention runs through all links from problem analysis to problem solution in the accurate nutrition service of crops, and the application of intelligent agriculture is realized.
Drawings
Fig. 1 is a diagram of an example of distribution of system settings of an agricultural sensor crop precision nutrition system based on artificial intelligence according to an embodiment of the present invention.
Fig. 2 is a system structure diagram of an artificial intelligence-based agricultural sensor crop precision nutrition system according to an embodiment of the invention.
Detailed Description
Sensor technology has now become a widely used technical means for modern agricultural monitoring. The agricultural condition information is collected through the sensor, the problems are found in time, the blindness of manual operation can be reduced, and the traditional agricultural production mode mainly based on manpower is changed to the modern agricultural production mode mainly based on information technology. In addition, the artificial intelligence has excellent learning ability and strong data processing and analyzing ability, and the output highly-empirical and professional processing results can provide effective guidance basis for agricultural operation.
In the agricultural sensor crop accurate nutrition system and method based on artificial intelligence disclosed by the invention, the system obtains an agricultural condition initial diagnosis report based on sensor data analysis; the user goes to the problem subarea for carrying out on-site acquisition of abnormal seedling condition images according to the agricultural condition initial diagnosis report displayed by the intelligent user side; the cloud system artificial intelligence model analyzes and processes the abnormal seedling condition images, empirically judges the problems of abnormal planting environment, abnormal crop growth and the like, and intelligently formulates a nutrition supply scheme capable of improving the existing problems. The crop nutrition center automatically completes the nutrient element preparation and nutrient drip irrigation operation control according to the nutrient supply scheme.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
An agricultural sensor crop accurate nutrition system based on artificial intelligence comprises a central workstation 1, an intelligent user side 2 and a cloud system 3 which is respectively connected with the central workstation and the intelligent user side through signals;
the service range of the central workstation 1 covers the whole agricultural planting area S, and agricultural services including but not limited to agricultural information acquisition, data transmission and nutrition supply are provided for the agricultural planting area S; wherein the agricultural planting area S comprises a plurality of sections S1-Sn for which at least one user is responsible; the agricultural information collection is realized by a sensor group arranged in the agricultural planting area S and is carried out overall data collection facing the agricultural planting area S.
The intelligent user side 2 is used for displaying information, shooting abnormal seedling situations and providing a channel for purchasing nutrition supply services for users; the shooting of abnormal seedling situations is targeted data acquisition carried out in a subarea mode for problems with abnormal seedling situations.
The cloud system 3 processes and analyzes information uploaded from the central workstation 1 and the intelligent user side 2, generates an agricultural condition initial diagnosis report, a nutrition supply scheme and other agricultural operation suggestions, and realizes data management.
Further, the central workstation 1, the intelligent user side 2 and the cloud system 3 realize signal connection through a wireless communication technology; still further, the wireless communication technology for realizing signal connection of the central workstation 1, the intelligent user side 2 and the cloud system 3 is selected from one or more of a 5G network, a 4G network, a WiFi network and a bluetooth technology.
Furthermore, the area of the agricultural planting area S is 3000-10000 mu, and the area of each subarea S1-Sn is 30-50 mu.
The central workstation 1 comprises an information acquisition center 11, a crop nutrition center 12 and a data transmission center 13 which is respectively connected with the information acquisition center and the crop nutrition center through signals;
the information acquisition center 11 comprises sensor groups respectively arranged in the partitions S1-Sn; the sensor groups arranged in different partitions S1-Sn are respectively provided with unique corresponding numbers; the sensor group carries out multi-mode planting parameter acquisition of the corresponding partition S1-Sn according to a preset acquisition plan;
the data transmission center 13 is used for uploading the multi-modal planting parameters to the cloud system 3, receiving signals sent by the cloud system 3 for implementing the nutrition supply service, and receiving a nutrition supply scheme formulated by the cloud system 3 and a crop nutrition scheme customized by a user;
after receiving the signal for implementing the nutrition supply service sent by the cloud system 3, the crop nutrition center 12 completes the nutrition supply service according to the nutrition supply scheme for the problem partition, which is formulated by the cloud system 3, and/or the crop nutrition scheme customized by the user.
Furthermore, the sensor groups in each partition S1-Sn respectively comprise a soil temperature and humidity sensor, an air temperature and humidity sensor, a soil pH value sensor and an illumination intensity sensor. The multi-mode planting parameters comprise soil temperature and humidity, air temperature and humidity, soil pH value and planting area illumination intensity.
Further, the crop nutrition center 12 includes an intelligent nutrition blending system and an automatic drip irrigation control system; the nutrition supply service comprises that the intelligent nutrition allocation system automatically allocates the nutrition elements, and the automatic drip irrigation control system controls and completes the nutrition drip irrigation operation.
Furthermore, the intelligent user end 2 comprises a display module 21, a shooting module 22, a mall module 23 and a data transmission module 24 respectively connected with the display module, the shooting module 22 and the mall module through signals;
the data transmission module 24 receives the agricultural condition initial diagnosis report, the nutrition supply scheme and other agricultural operation suggestions transmitted to the intelligent user end 2 from the cloud system 3, transmits the agricultural condition initial diagnosis report, the nutrition supply scheme and the other agricultural operation suggestions to the display module 21, and displays information for the user;
aiming at abnormal seedling conditions in the problem subareas recorded by the agricultural condition initial diagnosis report, a user can adopt the shooting module 22 to collect abnormal seedling condition images in the problem subareas on site, and the abnormal seedling condition images are uploaded to the cloud system 3 through the data transmission module 24 to be subjected to subsequent intelligent accurate analysis;
the user purchases the nutrition supply service through the mall module 23; wherein the nutritional provisioning service is based on: a nutritional supply plan for problem zones formulated by the cloud system 3, and/or a crop nutritional plan customized by the user in the mall module 23 based on personal experience; the payment information after the purchase is uploaded to the cloud system through the data transmission module 24.
Alternatively, the smart client 2 is selected from one or more of a smart phone, a tablet computer, and other terminal devices having functions of shooting, payment, display, and data transmission.
Further, the cloud system 3 includes a multi-modal information analysis module 31, a precision analysis module 32, and an information management module 33 respectively connected to the multi-modal information analysis module and the precision analysis module.
The multi-modal information analysis module 31 analyzes abnormal seedling conditions occurring in the agricultural planting area S according to the multi-modal planting parameters uploaded by the central workstation 1, defines the partition in which the abnormal seedling conditions occur as a problem partition, associates each abnormal seedling condition with the corresponding problem partition, generates a plurality of agricultural condition preliminary diagnosis reports for different problem partitions, and outputs the plurality of agricultural condition preliminary diagnosis reports to the information management module 33. Wherein, the abnormal seedling condition comprises abnormal planting environment and abnormal crop growth parameters.
Furthermore, the multi-mode information analysis module 31 prestores standard parameter values meeting ideal planting conditions and ideal growth conditions of crops; aiming at the multi-mode planting parameters uploaded by the central workstation 1 and collected from the sensor groups with different numbers, the multi-mode information analysis module 31 compares the multi-mode planting parameters with standard parameter values, respectively judges whether the planting environment abnormality and the crop growth parameter abnormality occur in the partitions S1-Sn corresponding to different sensor groups, analyzes the specific type and the severity of the abnormal condition, defines the partition with the abnormal condition as a problem partition, and generates an initial diagnosis report of the agricultural condition aiming at different problem partitions.
The accurate analysis module 32 comprises an artificial neural network model, and is used for performing intelligent operation on the abnormal seedling condition images of the problem partitions uploaded by the intelligent user terminal 2; in the training stage, the artificial neural network model realizes the training of the artificial neural network model through forward network calculation and reverse error propagation calculation; in the use stage of the training completion, the artificial neural network model calculates and accurately analyzes specific symptoms of abnormal seedling conditions through the forward network, provides a nutrition supply scheme aiming at problem partitions and other agricultural operation suggestions based on the specific symptoms, and outputs the nutrition supply scheme and other agricultural operation suggestions to the information management module 33.
The information management module 33 serves as a data interaction center of the cloud system 3, receives the multi-mode planting parameters uploaded by the central workstation 1, and receives the abnormal seedling situation images uploaded by the intelligent user side 2; the agricultural condition initial diagnosis report, the nutrition supply scheme and other agricultural operation suggestions are sent to the intelligent user terminal 2, and after the payment information of the intelligent user terminal 2 is received, a signal for implementing the nutrition supply service is sent to the central workstation 1.
The information management module 33 prestores basic information of each partition S1-Sn in the agricultural planting area S. For the agricultural condition initial diagnosis report generated by the multi-modal analysis module 31 and for each problem partition, the information management module 33 obtains the user information of the problem partition according to the pre-stored partition basic information, and sends the agricultural condition initial diagnosis report to the intelligent user end 2 of the user corresponding to the problem partition.
Further, the basic information of the partition includes, but is not limited to, the type of the partitioned crop, the name of the user, the contact information of the user, and the like, and for a plurality of partitions for which the same user is responsible, the basic information of the partition also includes the partition number set by the user.
The invention also provides an implementation method of the agricultural sensor crop accurate nutrition system based on artificial intelligence, which comprises the following steps:
s1, respectively collecting multi-mode planting parameters of corresponding partitions by sensor groups arranged in the partitions S1-Sn according to a preset collection plan, and uploading the multi-mode planting parameters to the cloud system 3;
s3, the cloud system 3 analyzes abnormal seedling conditions occurring in the agricultural planting area S according to the multi-mode planting parameters, a partition where the abnormal seedling conditions occur is defined as a problem partition, each abnormal seedling condition is respectively associated to the corresponding problem partition, and therefore a plurality of agricultural condition primary diagnosis reports aiming at different problem partitions are generated, and the cloud system 3 respectively sends the plurality of agricultural condition primary diagnosis reports to the intelligent user end 2 of the user of the corresponding problem partition;
s4, the user checks abnormal seedling conditions in the problem partition recorded in the agricultural condition initial diagnosis report through the intelligent user side 2, then uses the intelligent user side 2 to collect the abnormal seedling condition images in the problem partition on the spot, and uploads the abnormal seedling condition images to the cloud system 3;
s5, the cloud system 3 artificial intelligence model carries out intelligent operation on the abnormal seedling situation images of the problem subareas; in the training stage, the artificial neural network model realizes the training of the artificial neural network model through forward network calculation and reverse error propagation calculation; in the use stage of the training completion, the artificial neural network model calculates and accurately analyzes specific symptoms of abnormal seedling conditions through a forward network, provides a nutrition supply scheme aiming at the problem partition and other agricultural operation suggestions based on the specific symptoms, and sends the nutrition supply scheme and other agricultural operation suggestions to the intelligent user side 2 of the user of the corresponding problem partition.
Further, the implementation method of step S3 is:
and comparing the multi-mode planting parameters with pre-stored standard parameter values which accord with ideal planting conditions and ideal growth conditions of crops, respectively judging whether the partition S1-Sn corresponding to different sensor groups has abnormal planting environment and abnormal crop growth parameters, analyzing the specific type and severity of abnormal conditions, defining the abnormal partition as a problem partition, and generating a plurality of agricultural condition initial diagnosis reports aiming at different problem partitions.
Further, when the user selects to purchase the nutrition supply service, the implementation method of the agricultural sensor crop precision nutrition system based on artificial intelligence further comprises the following subsequent steps:
s6, the user purchases the nutrition supply service through the intelligent user side 2 and completes payment, and the cloud system 3 sends a signal for implementing the nutrition supply service to the central workstation 1 according to the payment information uploaded by the intelligent user side 2; the crop nutrition center 12 performs nutrient element blending according to a nutrient supply scheme which is formulated by the cloud system 3 and is specific to the problem partition and/or a crop nutrition scheme which is customized by a user, and controls to complete nutrition drip irrigation operation.
The above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Those skilled in the art can easily conceive of changes or substitutions within the technical scope of the present disclosure, and all such changes or substitutions are intended to be included within the scope of the present disclosure. Therefore, the protection scope of the present invention shall be subject to the protection scope defined by the claims.
Claims (10)
1. The utility model provides an accurate nutrient system of agricultural sensor crop based on artificial intelligence which characterized in that: the system comprises a central workstation, an intelligent user side and a cloud system which is respectively connected with the central workstation and the intelligent user side through signals;
the service range of the central workstation covers the whole agricultural planting area, and agricultural services including but not limited to agricultural information acquisition, data transmission and nutrition supply are provided for the agricultural planting area; wherein the agricultural planting area comprises a plurality of sub-areas for which at least one user is responsible; the agricultural information acquisition is realized by a sensor group arranged in an agricultural planting area and is carried out overall data acquisition facing the agricultural planting area;
the intelligent user side is used for displaying information, shooting abnormal seedling situations and providing a channel for purchasing nutrition supply services for the user; shooting abnormal seedling situations is targeted data acquisition in a subarea mode for problems with abnormal seedling situations;
the cloud system processes and analyzes information uploaded from the central workstation and the intelligent user side, generates an agricultural condition initial diagnosis report, a nutrition supply scheme and other agricultural operation suggestions, and realizes data management.
2. The artificial intelligence based agricultural sensor crop precision nutrition system of claim 1, wherein: the central work station comprises an information acquisition center, a crop nutrition center and a data transmission center which is respectively connected with the information acquisition center and the crop nutrition center through signals;
the information acquisition center comprises sensor groups which are respectively arranged in each subarea; the sensor groups arranged in different subareas are respectively provided with unique corresponding numbers; the sensor group acquires the multi-mode planting parameters of the corresponding partitions according to a preset acquisition plan;
the data transmission center is used for uploading the multi-mode planting parameters to the cloud system, receiving signals which are sent by the cloud system and used for implementing nutrition supply service, and receiving a nutrition supply scheme formulated by the cloud system and a crop nutrition scheme customized by a user;
after receiving the signal of implementing the nutrition supply service sent by the cloud system, the crop nutrition center completes the nutrition supply service according to the nutrition supply scheme which is formulated by the cloud system and aims at the problem subarea and/or the crop nutrition scheme customized by the user.
3. The artificial intelligence based agricultural sensor crop precision nutrition system of claim 2, wherein:
the crop nutrition center comprises an intelligent nutrition allocation system and an automatic drip irrigation control system, and the nutrition supply service comprises that the intelligent nutrition allocation system automatically completes the allocation of nutrient elements and the automatic drip irrigation control system controls the completion of nutrition drip irrigation operation.
4. The artificial intelligence based agricultural sensor crop precision nutrition system of claim 1, wherein: the intelligent user side comprises a display module, a shooting module, a mall module and a data transmission module which is respectively connected with the display module, the shooting module and the mall module through signals;
the data transmission module receives the agricultural condition initial diagnosis report, the nutrition supply scheme and other agricultural operation suggestions transmitted to the intelligent user side by the cloud system, transmits the agricultural condition initial diagnosis report, the nutrition supply scheme and the other agricultural operation suggestions to the display module, and displays information for the user;
aiming at abnormal seedling conditions in the problem subareas recorded by the agricultural condition initial diagnosis report, a user can adopt a shooting module to carry out on-site acquisition of abnormal seedling condition images in the problem subareas, and the abnormal seedling condition images are uploaded to a cloud system through a data transmission module to carry out subsequent intelligent accurate analysis;
the user purchases nutrition supply service through the mall module; wherein the nutritional provisioning service is based on: a nutrition supply scheme which is formulated by a cloud system and aims at problem subareas, and/or a crop nutrition scheme which is customized by a user in a mall module according to personal experience; and uploading the purchased payment information to the cloud system through the data transmission module.
5. The artificial intelligence based agricultural sensor crop precision nutrition system of claim 1, wherein: the cloud system comprises a multi-mode information analysis module, an accurate analysis module and an information management module which is respectively connected with the multi-mode information analysis module and the accurate analysis module through signals;
the multi-mode information analysis module analyzes abnormal seedling conditions in the agricultural planting area according to multi-mode planting parameters uploaded by the central workstation, a partition with the abnormal seedling conditions is defined as a problem partition, each abnormal seedling condition is respectively associated to the corresponding problem partition, a plurality of agricultural condition preliminary diagnosis reports aiming at different problem partitions are generated, and the plurality of agricultural condition preliminary diagnosis reports are output to the information management module;
wherein the abnormal seedling conditions comprise abnormal planting environment and abnormal crop growth parameters;
the accurate analysis module comprises an artificial neural network model and is used for intelligently calculating abnormal seedling situation images of the problem partitions uploaded by the intelligent user side; in the training stage, the artificial neural network model realizes the training of the artificial neural network model through forward network calculation and reverse error propagation calculation; in the use stage of finishing training, the artificial neural network model calculates and accurately analyzes specific symptoms of abnormal seedling conditions through a forward network, provides a nutrition supply scheme aiming at problem subareas and other agricultural operation suggestions based on the specific symptoms, and outputs the nutrition supply scheme and other agricultural operation suggestions to the information management module;
the information management module is used as a data interaction center of the cloud system, receives the multi-mode planting parameters uploaded by the central workstation, and receives the abnormal seedling situation images uploaded by the intelligent user side; and sending an agricultural condition initial diagnosis report, a nutrition supply scheme and other agricultural operation suggestions to the intelligent user side, and sending a signal for implementing the nutrition supply service to the central workstation after receiving the payment information of the intelligent user side.
6. The artificial intelligence based agricultural sensor crop precision nutrition system of claim 5, wherein: the multi-mode information analysis module prestores standard parameter values which accord with ideal planting conditions and ideal growth conditions of crops; aiming at multi-mode planting parameters which are uploaded by a central workstation and collected from sensor groups with different numbers, a multi-mode information analysis module compares the multi-mode planting parameters with standard parameter values, respectively judges whether abnormal planting environment and abnormal crop growth parameters occur in partitions corresponding to different sensor groups, analyzes the specific type and the severity of abnormal conditions, defines the partitions with the abnormal conditions as problem partitions, and generates an initial diagnosis report of the agricultural conditions for different problem partitions.
7. The artificial intelligence based agricultural sensor crop precision nutrition system of claim 5, wherein: the information management module prestores basic information of each subarea in the agricultural planting area;
for the agricultural condition initial diagnosis report which is generated by the multi-mode analysis module and aims at each problem partition, the information management module acquires the user information of the problem partition according to the prestored partition basic information and sends the agricultural condition initial diagnosis report to the intelligent user side of the user corresponding to the problem partition.
8. A method of implementing an artificial intelligence based agricultural sensor crop precision nutrition system as claimed in claims 1-7, wherein the method comprises the steps of:
s1, collecting multi-mode planting parameters of corresponding partitions by the sensor groups respectively arranged in the partitions according to a preset collection plan, and uploading the multi-mode planting parameters to a cloud end system;
s3, analyzing abnormal seedling conditions in the agricultural planting area by the cloud system according to the multi-mode planting parameters, defining the partition with the abnormal seedling conditions as a problem partition, and respectively associating each abnormal seedling condition with the corresponding problem partition, thereby generating a plurality of agricultural condition preliminary diagnosis reports aiming at different problem partitions, and respectively sending the plurality of agricultural condition preliminary diagnosis reports to the intelligent user ends of the users of the corresponding problem partitions by the cloud system;
s4, the user checks abnormal seedling conditions in the problem partition recorded in the agricultural condition initial diagnosis report through the intelligent user side, then the intelligent user side is used for collecting abnormal seedling condition images in the problem partition on the spot and uploading the abnormal seedling condition images to the cloud system;
s5, the cloud system artificial intelligence model carries out intelligent operation on the abnormal seedling situation images of the problem subareas; in the training stage, the artificial neural network model realizes the training of the artificial neural network model through forward network calculation and reverse error propagation calculation; in the use stage of finishing training, the artificial neural network model calculates and accurately analyzes specific symptoms of abnormal seedling conditions through a forward network, provides a nutrition supply scheme aiming at problem partitions and other agricultural operation suggestions based on the specific symptoms, and sends the nutrition supply scheme and other agricultural operation suggestions to intelligent user ends of users of corresponding problem partitions.
9. The artificial intelligence based agricultural sensor crop precision nutrition system of claim 8, wherein: the implementation method of step S3 is:
and comparing the multi-mode planting parameters with pre-stored standard parameter values which accord with ideal planting conditions and ideal growth conditions of crops, respectively judging whether the partition corresponding to different sensor groups has abnormal planting environment and abnormal crop growth parameters, analyzing the specific type and severity of abnormal conditions, defining the abnormal partition as a problem partition, and generating a plurality of agricultural condition initial diagnosis reports aiming at different problem partitions.
10. The method for implementing an artificial intelligence based agricultural sensor crop precision nutrition system according to claim 8, wherein the method comprises the following steps: when the user selects to purchase the nutrition supply service, the implementation method of the agricultural sensor crop precision nutrition system based on artificial intelligence further comprises the following subsequent steps:
s6, the user purchases the nutrition supply service through the intelligent user side and finishes payment, and the cloud system sends a signal for implementing the nutrition supply service to the central workstation according to the payment information uploaded by the intelligent user side; and the crop nutrition center performs nutrient element allocation according to a nutrient supply scheme which is set by a cloud system and aims at problem subareas and/or a crop nutrition scheme customized by a user, and controls and finishes nutrient drip irrigation operation.
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