AU2021105327A4 - A computer implemented and IoT based method for increasing crop production using machine learning model - Google Patents
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Abstract
A computer implemented and loT based method for increasing crop
production using machine learning model
The present invention relates to the field of Internet of Things (loT) and agricultural crops.
More specifically, the present invention relates to the field of increasing crop production
using loT sensors and machine learning model. In today's world, with the advancement in
technology, increasing farming yield with the use of the current technology will boost the
growth of a nation as well as individual farmers. The present system is a combination of
various loT sensors, central server, database, client devices and expert devices. The
agricultural field contains various kind of sensors e.g. soil moisture sensors, environmental
humidity sensors, rain sensor, temperature sensor, air-flow sensor, a camera unit and soil
nutrients determination sensor. These sensors are operated by solar powered battery.
These sensors use ZigBee chip integrated wireless sensors and data collecting modules and
communicates to server through wireless communicating modules. The sever collects data
at predetermined intervals and analyses the said data. The server machine utilizes the data
from the database and database is equipped with initial data about agricultural aspects.
Further, the server machine is trained on the data from the database through deep learning
algorithm based on machine learning model (MLM) for making an artificial intelligent model
to suggest the individual farmers regarding growth of the current agricultural crop. The
captured/collected data from various loT sensor modules is analysed by the analyser
module and alarm the individual farmers regarding their individual agricultural fields based
on the results of the analyser and individual farmers act accordingly. The individual farmers
are already linked with their corresponding agricultural field based on the registration done
on the central server machine. Further, the said system can remotely, by the mobile
application, start the irrigation system installed in the agricultural field using the solenoid
valve as there is a need to irrigate the field and determined by the machine learning model.
The proposed system also provides the feature of providing expert advice in need. If the
server alarms any abnormal situation to an individual farmer about crop and suggest for an
expert advice. The server then sends the query or abnormal situation to the farming expert
if requested by an individual along with the images of the agricultural field taken by camera
sensor to aid in providing suitable advice. The suggested advice by the farming expert is
then forwarded to the client device by the server to act accordingly. The alarms and advice
may be provided to the client device as per the language selected by the individual for
communication and the server machine is equipped enough to translate and provide the
alarms and advice to the individual farmer in their selected language. Thus, the present
system automatically increases crop production using current technology and farmers will
get benefited of the technology.
1
Training the server with the machine learning model using
large database and test cases of agricultural aspects (101)
captures data from various loT sensors at predetermined
interval and transmit to central server through
communication module (102)
Server analyses the collected data with the database stored
using adopted machine learning model (MLM) (103)
Generating and sending an alarm to client application of a
specified user along with request for expert advice if
needed(104)
starting remotely, by the client application,
irrigation system installed in field using solenoid
valve as determined by the machine learning
model and notifies to the farmer (105)
Figure 1 - Flow-diagram of the method for increasing crop production using loT sensors and
machine learning model in accordance with present invention
1
Description
Training the server with the machine learning model using large database and test cases of agricultural aspects (101)
captures data from various loT sensors at predetermined interval and transmit to central server through communication module (102)
Server analyses the collected data with the database stored using adopted machine learning model (MLM) (103)
Generating and sending an alarm to client application of a specified user along with request for expert advice if needed(104)
starting remotely, by the client application, irrigation system installed in field using solenoid valve as determined by the machine learning model and notifies to the farmer (105)
Figure 1 - Flow-diagram of the method for increasing crop production using loT sensors and machine learning model in accordance with present invention
A computer implemented and loT based method for increasing crop production using machine learning model
[0001] The present invention relates to the field of Internet of Things (loT) and agricultural crops. The field of the invention is to use the current technology i.e., Internet of Things (loT) and machine learning models to increase crop production and maintain proper growth of the agricultural crops to aid the farmers.
[0002] More specifically, the present invention relates to the field of automatically monitoring and increasing crop production with the use of today's most advanced technology i.e., Internet of Things (loT) and machine learning technique for suitable and maximum growth of agricultural by products while using minimum and only required expenses in the field.
[0003] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in-and-of-themselves may also be inventions.
[0004] The agriculture sector or farmers are one of the pillars of any country for development and growth of the nation. Agriculture has been an important aspect for human existence for so many years. The financial and economic growth of any country depends on the growth of the farmers and agricultural yields. The farmers or the persons involved in farming or agricultural activities do not have adequate knowledge and has to pay their whole time in the agricultural fields for proper growth of their crops. The farmers do not have enough knowledge about field's soil properties and minerals deficiency. They are just using the chemical fertilizers without any knowledge of which and what quantity need to be used for proper growth of the particular crop. Further, the farmers do not have proper knowledge about irrigation, environmental predictions, crops disease and suitable crop for particular soil. In today's world, with the advancement in technology, monitoring and improving/increasing farming yield with the use of the advanced technology will increase the economic or financial growth of a nation as well as individual farmers that will lead to a prosperous nation. The use of these technologies can help/aid the farmers providing adequate information and monitor as well as increase the crops and agricultural yields to provide required action about the current crop in the agricultural fields. There are various technologies exist in the said field that can help farmers with the use of the technology to aid them which are as follows.
[0005] US2018014455 Al - A control system for sensing soil moisture on-the go while planting and adjust planting depth, accordingly. The present invention provides a planter for sensing soil moisture and seed to soil contact on-the-go and adjust planting depth and row unit down pressure, accordingly. Yet another object, feature, or advantage is to provide a commercially viable, easy to use, reliable, on-the-go soil moisture and seed to soil contact sensor.
[0006] US10721859 B2 - A machinery includes an automated crop management motorized vehicle having an intelligent, modularized image sensor (e.g. camera or video) system that is portable to other crop management vehicles such as a combine, planter or a tillage machine. The image sensor system includes a framework having a bank of procedures for monitoring and control of navigation, spray application, weeding, seeding, machine configuration, in real time as the machines go through a crop field throughout a crop cycle. One example implementation includes electronic circuits, with more than one set mounted on a platform that facilitates moving the setup to other agricultural machines. The framework captures, preserves and corrects the captured images for real time analysis and response, and for spray management to improve crop yield that is correlated with the machine settings and crop management practices.
[0007] CN203241793 U - An agriculture production monitoring and management system based on internet of things comprises the following elements: an information acquisition and network communication module comprising an information acquisition module, at least one wireless sensing collector and a ZigBee/GPRS gateway or a ZigBee/3G gateway, and the information acquisition module comprises agriculture environment information sensors, an IP camera and a RFID wireless radio frequency identification device; a function execution module; and a comprehensive information control management system comprising a monitoring center and a cloud server. The agriculture production monitoring and management system based on internet of things can carry out digital design, visualized expression and intelligent control in agriculture production, decision management and operation circulation fields, can construct different operation platform according to different needs, and realizes information acquisition and data sharing of the agricultural products in a circulation process.
[0008] US8643495 B2 - A farm greenhouse monitor and alarm management system based on the Internet of things with real-time monitoring environmental parameters, which is aimed at monitoring and managing the growth of crops in the farm greenhouse, includes mobile inspection devices, data acquisition units, data receiving devices, REID devices and data storage servers. The system can automatically collect greenhouse environmental parameters such as air temperature, air humidity, illumination, soil temperature and soil moisture, etc. and also can automatically judge the critical value of every parameter and alarm.
[0009] CN104881017 B - A kind of crop growth supervisory systems based on
the Big Dipper, including : Image collecting device, image processing
apparatus, detection means, computer controlling center, positioner and crops supervision control centre, the computer controlling center is connected respectively with described image harvester, described image processing unit, the detection means, the positioner, crops supervision control centre, for controlling the positioning of the positioner. The beneficial effect of crop growth supervisory systems provided by the invention based on the Big Dipper is : Being capable of parameter data values of the growing state of comprehensive monitoring crops and the growing environment of crops in real time, and carry out intelligent management crops by the parameter data values, due to its intelligentized management system so that proportion of crop planting personnel can save substantial amounts of work workload and working time.
[0010] CN104852989 B - A reading intelligent agriculture monitoring system based on Internet of Things that the present invention relates to a kind of, including central processing unit are connected with intelligence sensor, intelligent controlling device, real-time graphic and video monitoring equipment, transmission device, alarm system and terminal on the central
processing unit ; The central processing unit is connect with intelligence
sensor, intelligent controlling device, real-time graphic and video monitoring equipment, transmission device, alarm system and terminal by wireless
network control system ; The reading intelligent agriculture monitoring
system is powered by solar power supply unit.
[0011] All the above prior arts provide the management of the crops in agricultural fields using Internet of Things. But none of the arts provide complete and automatic process for accelerating growth of crops in agricultural fields. Further, none of the cited prior art uses technology that can provide or build a database that can helps farmers provide information/knowledge about accelerating growth of the agricultural crops in fields. None of the prior art provides solution or technology in case crops is having diseases and fulfils the requirements of providing solution to that crop disease on the go while farmers do not go anywhere and get solution for the disease using mobile application only. Thus, there is a need to automate the above said process.
[0012]Therefore, there is a need in the state of the art for a method and system for automating the process of agriculture and increase crop production through various machine learning algorithms and Internet of Things (loT) technology. Further, the proposed system is also equipped enough to provide the information/knowledge about crop disease and gives solution/advice for crop diseased using mobile technology and advice the suitable action accordingly.
[0013] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markus groups used in the appended claims.
[0014] As used in the description herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural reference unless the context clearly dictate otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.
[0015] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.
[0016] The use of any and all examples, or exemplary language (e.g. "such as") provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[0017] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.
[0018] Before the present systems and methods, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to a computer implemented method and loT based system for increasing crop production using machine learning model and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[0019] The present invention mainly solves the technical problems existing in the prior art. In response to these problems, the present invention discloses a computer implemented and loT based method for increasing crop production using machine learning model. The objective of the invention is to provide a complete and automated process for increasing crop production in the agricultural fields using current technology of machine learning models and Internet of Things (loT) so that the farmers and agricultural field may get benefitted of the technology.
[0020] The object of the present invention is to increase the crop production based on loT (internet of Things) and machine learning models (MLM) and providing crop disease resolution using the same by providing a database adequate enough to resolve the disease related query and build using machine learning model. The proposed method and system is fully automated and provides a complete system where various Internet of Things (loT) sensors captures data in real-time (i.e. dynamically) related to soil, crops and atmosphere/environment in the agricultural fields and central server analyses the captured data using adopted machine learning model and notifies the client via client application (i.e. farmers) regarding the results of the analysis. The database is self-learning database that modifies itself based on the machine learning models and previous stored data and analysis results of the model. This makes the said system artificial intelligent and provide the better results to the users. Further, in case of any crop disease, the said system notifies the client application and advice to the expect device if requested by the individual for resolution of the same using the client application itself. The proposed system is also multilingual system that can provide or notifies the client application in their chosen language. Further, the said system can remotely, by the mobile application, start the irrigation system installed in the agricultural field using the solenoid valve as there is a need to irrigate the field and determined by the machine learning model. The said system can be accessed via mobile application or client application in various languages
[0021] One aspect of the present invention relates to a method for increasing crop production based on loT (internet of Things) and machine learning models (MLN). The proposed method is completely automatic and captures the data related to the fields and crops automatically and provide complete solution for increasing crop production. The proposed method comprises various loT sensors, central server, database, client devices and expert devices. The agricultural field contains various kind of loT sensors e.g. soil moisture sensors, environmental humidity sensors, rain sensor, temperature sensor, air-flow sensor, a camera unit and soil nutrients determination sensor. These loT sensors are operated by self-recharged solar powered battery. These loT sensors use ZigBee chip integrated wireless sensors and data collecting modules and communicates to central server through wireless communicating transmitting/receiving modules. The central sever collects/captures data at predetermined intervals and analyse the captured data. The server machine utilizes the data from the database and database is equipped with initial data and test cases about agricultural, crops and soil aspects. Further, the central server machine is trained on the data from the database through machine learning algorithm for making an artificial intelligent model to suggest the individual farmers regarding proper growth of the current agricultural crop. The collected data from various sensor modules is analysed by the analyser module and alarm the individual farmers regarding their individual agricultural fields/crops based on the results of the analyser and individual farmers act accordingly. The individual farmers are already linked with their corresponding agricultural field based on the registration done on the server machine. The proposed method also provides the feature of providing expert advice in need and the server machine also learn itself by time and analyzes the crop disease itself and suggest/advices the medicines required to be used for getting rid of the crop disease. If the server alarms any abnormal situation to an individual farmer about crop and suggest for an expert advice. The server then sends the query or abnormal situation to the farming expert using expert advice if requested by an individual along with the images of the agricultural field taken by camera sensor to aid in providing suitable advice. The suggested advice by the farming expert is then forwarded to the client device by the server to act accordingly. The alarms and advice may be provided to the client device as per the language selected by the individual for communication and the central server machine is equipped enough to translate and provide the alarms and advice to the individual farmer in their selected language. Further, the said system can remotely, by the mobile application, start the irrigation system installed in the agricultural field using the solenoid valve as there is a need to irrigate the field and determined by the machine learning model. The said service may be paid service. Further, the database and server machine trained using the suggestion received from the expert device for future use.
[0022] Another aspect of the present disclosure relates to a a system for increasing crop production based on loT (internet of Things) and machine learning models (MLM). The proposed system comprising of various loT sensors, central server, database, client devices and expert devices. The agricultural field contains various kind of sensors e.g. soil moisture sensors, environmental humidity sensors, rain sensor, temperature sensor, air-flow sensor, a camera unit and soil nutrients determination sensor. These sensors are operated by solar powered battery. These sensors use ZigBee chip integrated wireless sensors and data collecting modules and communicates to server through wireless communicating modules. The sever collects data at predetermined intervals and analyses the said data. The server machine utilizes the data from the database and database is equipped with initial data about agricultural aspects. Further, the central server machine is trained on the data from the database through machine learning algorithm for making an artificial intelligent model to suggest the individual farmers regarding growth of the current agricultural crop. The collected/captured data from various loT sensor modules is analysed by the analyser module and alarm the individual farmers regarding their individual agricultural fields based on the results of the analyser and individual farmers act accordingly. The individual farmers are already linked with their corresponding agricultural field based on the registration done on the central server machine. The proposed system also provides the feature of providing expert advice in need. If the server alarms any abnormal situation to an individual farmer about crop and suggest for an expert advice. The server then sends the query or abnormal situation to the farming expert if requested by an individual along with the images of the agricultural field taken by camera sensor to aid in providing suitable advice. The suggested advice by the farming expert is then forwarded to the client device by the server to act accordingly. The alarms and advice may be provided to the client device as per the language selected by the individual for communication and the server machine is equipped enough to translate and provide the alarms and advice to the individual farmer in their selected language. Further, the said system can remotely, by the mobile application, start the irrigation system installed in the agricultural field using the solenoid valve as there is a need to irrigate the field and determined by the machine learning model. The said service may be paid service. Further, the database and server machine trained using the suggestion received from the expert device for future use.
[0023] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
[0024] The primary objective of the present invention is to provide a method and system for increasing crop production based on loT (internet of Things) and machine learning models (MLM). The prime objective of the proposed invention is to make an automated system to provide timely and correct suggestions and alarm to the users. One objective of the invention is to make system and interface user-friendly and provide the alarms and suggestion/advice in their comfortable/chosen language. One more objective of the invention is to prepare a database that can automatically provide the resolution to the crop disease without going anywhere even on farm. One more objective of the invention is to make a database that makes the said system artificial intelligent using machine learning model.
[0025] To clarify various aspects of some example embodiments of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It is appreciated that these drawings depict only illustrated embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
[0026] In order that the advantages of the present invention will be easily understood, a detail description of the invention is discussed below in conjunction with the appended drawings, which, however, should not be considered to limit the scope of the invention to the accompanying drawings, in which:
[0027] Figure 1 show a Flow diagram for increasing crop production based on loT (internet of Things) and machine learning models (MLM).
[0028] Figure 2 shows the prosed system architecture with the embodiments of the system.
[0029] The present invention is related to a method and system for automatically monitoring and alarming the farmers regarding abnormal situation in current crops of the agricultural field based on convolutional neural network and providing the resolution for the crop disease on the go without going anywhere.
[0030] Figure 1 show a flow-diagram for increasing crop production based on loT (internet of Things) and machine learning models (MLM).
[0031] Although the present disclosure has been described with the purpose of a method and system for increasing crop production based on loT (internet of Things) and machine learning models (MLM), it should be appreciated that the same has been done merely to illustrate the invention in an exemplary manner and to highlight any other purpose or function for which explained structures or configurations could be used and is covered within the scope of the present disclosure.
[0032] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words and other forms thereof are intended to be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary systems and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
[0033] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0034] Figure 1 show the steps involved in performing the method for automatically increasing crop production based on loT (internet of Things) and machine learning models (MLM). The proposed method comprising of data processing taken from various loT sensors installed at the agricultural field. At step 101, the database or system is first trained using machine learning model on the data and test cases about agricultural aspects which helps in making the proposed system Al enable system for taking faster and accurate decision/analysis. At step 102, the central server collects data taken from various loT sensors for particular agricultural field at predetermined interval. The predetermined interval is determined by the server machine and can be updated by the system itself based on the need. At step 103, the proposed system analyses the collected data at step 102 with the stored databased using adopted machine learning model. At final stage of 104, the system alarms the client via client application installed in the client device about the abnormal condition or suggestion/advice according to the analysis results. Further, the said system can remotely, by the mobile application, start the irrigation system installed in the agricultural field using the solenoid valve as there is a need to irrigate the field and determined by the machine learning model at step 105. Thus, the proposed system is automatic and intelligent enough to alert the user and provide the suggestion for proper growth of the crops in the agricultural fields.
[0035]The steps of the method characterized in that the said method does not involve only one agricultural field or client application. The proposed system is well equipped to involve n number of user and agricultural fields. The proposed method and system can monitor any number of agricultural field and provide the accurate results as the system is Al enabled artificial intelligent system that is based on machine learning model and Internet of Things model. This will drastically improve the quantity and quality of the crops and saves the time and money of the farmers.
[0036] Figure 2 shows the proposed system architecture according to the proposed invention. The proposed system comprises of communication network (1) that connect the various embodiments of the system. The system comprises various loT sensors (2) i.e. soil moisture sensors, environmental humidity sensors, rain sensor, temperature sensor, air-flow sensor, a camera unit and soil nutrients determination sensor having designated feature that are installed on the agricultural fields. These sensors are operated by self recharged solar powered battery. These sensors use ZigBee chip integrated wireless sensors and data collecting modules and communicates to server through wireless communicating modules. The Al enabled server (3) is connected with the database (4) that makes the said system intelligent and knowledge rich which communicates to the other embodiments of the system through communication network. The client application (5) installed on the client device provide the alarms and other information to the client/user of the system. The expert advice application (6) installed in the expert device helps the expert person to receive the query and submit the resolution to the system based on the photographs taken by the camera sensor. All these embodiments of the communicates each other through the communication network which can be a wired communication network or a wireless communication network.
[0037] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
[0038] Although implementations for invention have been described in a language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for the invention.
Claims (5)
1. A computer implemented method for automatically increasing crop production using Internet of Things (loT) sensors and machine learning model (MLM), the method comprising: training the central server (203) with the machine learning model using large database (4) and test cases of agricultural aspects (101); capture/collect data (102) from various loT sensors at predetermined interval and transmitting the captured data to central server through communication module; central Server (2033) analyses (103) the collected data with the database stored using machine learning model (103); Generating and sending an alarm to client application (5) of a specified user along with request for expert advice if needed (104); starting remotely, by the client application, irrigation system installed in field using solenoid valve as determined by the machine learning model and notifies to the farmer (105).
2. The method as claimed in claim 1, further comprising: Client application (5) sends request for expert advice in case of crop disease to the server (3); Server (3) sends the received request to the expert device application (6) along with photographs of the crop taken by camera sensor; Server (3) receives the advice/resolution along with the details from the expert device and makes the system Al enabled using CNN algorithm; Server sends the advice/resolution along with the details to client application in their chosen language.
3. The method as claimed in claim 1, wherein sensors use ZigBee chip integrated wireless sensors and data collecting modules and communicates to server through wireless communicating modules.
4. The method as claimed in claim 1, wherein the communication network may be wired or wireless network.
5. A system for automatically increasing crop production using Internet of Things (loT) sensors and machine learning model (MLM), wherein the system is performed by a computing unit or central server for accessing system, wherein the computing unit comprises a processor, a memory, input/output device and communication unit, the system comprising: a communication network (201) to transmit/receive data from other embodiments of the system; database (202) to store data related to the touches, test cases and initial database related to touches; various loT sensors (204) to capture data related to soil, crop and environment;
a central server (203) to analyze and process received data for performing the steps of:
training the server with the machine learning model using large database and test cases of agricultural aspects (101); capture/collect data (102) from various loT sensors at predetermined interval and transmitting the captured data to central server through communication module; central Server analyses (103) the collected data with the database stored using machine learning model (103); Generating and sending an alarm to client application of a specified user along with request for expert advice if needed (104) starting remotely, by the client application, irrigation system installed in field using solenoid valve as determined by the machine learning model and notifies to the farmer (105).
Training the server with the machine learning model using large database and test cases of agricultural aspects (101) 2021105327
captures data from various IoT sensors at predetermined interval and transmit to central server through communication module (102)
Server analyses the collected data with the database stored using adopted machine learning model (MLM) (103)
Generating and sending an alarm to client application of a specified user along with request for expert advice if needed (104)
starting remotely, by the client application, irrigation system installed in field using solenoid valve as determined by the machine learning model and notifies to the farmer (105)
Figure 1 – Flow-diagram of the method for increasing crop production using IoT sensors and machine learning model in accordance with present invention
Databases (102) Various IoT sensors (104) 2021105327
Central server (103) Communication network (101) Client device having client application
Expert device
figure 2: Block diagram of increasing crop production using IoT sensors and machine learning model in accordance with the present invention
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117176513A (en) * | 2023-10-31 | 2023-12-05 | 湖南承希科技有限公司 | Internet of things data acquisition gateway equipment based on 5G-R technology |
| CN117540372A (en) * | 2023-11-22 | 2024-02-09 | 西藏朗杰信息科技有限公司 | Database intrusion detection and response system for intelligent learning |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117176513A (en) * | 2023-10-31 | 2023-12-05 | 湖南承希科技有限公司 | Internet of things data acquisition gateway equipment based on 5G-R technology |
| CN117176513B (en) * | 2023-10-31 | 2024-01-12 | 湖南承希科技有限公司 | Internet of things data acquisition gateway equipment based on 5G-R technology |
| CN117540372A (en) * | 2023-11-22 | 2024-02-09 | 西藏朗杰信息科技有限公司 | Database intrusion detection and response system for intelligent learning |
| CN117540372B (en) * | 2023-11-22 | 2024-05-14 | 西藏朗杰信息科技有限公司 | Database intrusion detection and response system for intelligent learning |
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