AU2021106796A4 - Internet of things based intelligent irrigation system using cloud computing - Google Patents

Abstract

The Internet of Things Intelligent Irrigation System invention describes how to use cloud computing in Intelligent Irrigation Systems. An intelligent cloud data center, intelligent cloud service platform, Internet of Things terminal management controller, and an irrigation device are all included in the system. The intelligent irrigation cloud data center is connected to the Internet of Things terminal management controller by way of a wireless network, and the irrigation device is connected to the Internet of Things terminal management controller with the help of an irrigation controller device. The Invention is geared toward finding water-saving methods in agriculture and implementing them using IoT-enabled machine learning techniques. In this prototype, information that has been previously processed is being transferred from the cloud server to the farmer's mobile handset ahead of time. DRAWINGS SHEET 1 OF 1 User Information Crop Information WeatherAPI Viewdata RESET save sensor ML Results of reading Analyzer MLIDB Handler |DB Soil Soil oistre Ltemperature DHT sensor Usno

Description

DRAWINGS SHEET 1 OF 1

User Information Crop Information

WeatherAPI Viewdata RESET

save sensor ML Results of reading Analyzer MLIDB Handler |DB

Soil Soil oistre Ltemperature

DHT sensor Usno

TITLE OF INVENTION: INTERNET OF THINGS BASED INTELLIGENT IRRIGATION SYSTEM USING CLOUD COMPUTING FIELD OF INVENTION: COMPUTER SCIENCE BACKGROUND OF INVENTION

It requires a tremendous amount of water in the typical irrigation procedure, and as a result, water

is wasted. It is imperative that an intelligent irrigation system be developed to save water while

performing this menial chore. ML and the IoT bring with them enormous advantages when it

comes to designing intelligent systems that can accomplish this duty without the need for a lot of

human input. An IoT-enabled ML-trained recommendation system for effective water usage is

developed that does not need farmers to change their behavior. Crop fields are full of IoT sensors

that measure soil and environmental conditions. The data are sent to a cloud-based server where it

is analyzed using machine learning to generate recommendations for farmers. As part of this

process, a built-in feedback mechanism is integrated to increase system robustness and

responsiveness.

PRIOR ART OF WORK

("CN105573277A"): The invention discloses an Internet of Things intelligent irrigation system

based on cloud computing. The system comprises an intelligent irrigation cloud service platform,

an intelligent irrigation cloud data center, an Internet of Things terminal management controller,

and an irrigation device, the irrigation device and a sensor are both connected with the Internet of

Things terminal management controller, the Internet of Things terminal management controller is

connected with the intelligent irrigation cloud data center via a wireless network, a user logs in the

intelligent irrigation cloud service platform for obtaining service via the network, the intelligent irrigation cloud service platform is deployed in the intelligent irrigation cloud data center, and the intelligent irrigation cloud service platform provides service for the user. According to the system, the conception is novel, advanced cloud computing, the Internet of Things, big data, mobile application, and the artificial intelligence technology are employed, the system is simple, easy, and convenient, the timeliness is good, the networking is convenient, the reliability is high, the transmission rate is fast, and the advanced Internet of Things intelligent irrigation system based on cloud computing is provided for the application and promotion of the technologies of cloud computing and Internet of Things in the water conservancy industry.

("CN104914830A") : The invention provides an Internet of things-based intelligent agricultural

system. The Internet of things-based intelligent agricultural system includes a data acquisition and

control system, a bearing network system, an operation support and management system, an

application service system and an application display system; the application service system

includes an expert system, an agricultural greenhouse system, a storage logistics system and an

agricultural traceability system; and the data acquisition and control system includes a rain sensor,

a camera, a wind speed sensor, a temperature sensor and a soil moisture sensor. According to the

Internet of things-based intelligent agricultural system of the invention, intelligent agriculture is

mainly supported by modern informationized means; various kinds of data of a monitored object

can be acquired accurately and timely through various kinds of sensing acquisition devices such

as the environmental humidity sensor, the environmental temperature sensor and the camera;

relevant data can be stored in a business support and information management platform through

transmission networks such as a mobile Internet and a 4G wireless network, so that support can be

provided for relevant business application services; and therefore, monitoring and scientific

guidance for the whole process of agricultural production can be realized.

("CN202602714U") : The utility model discloses an intelligent greenhouse monitoring system

based on internet of things and a cloud computing technology. The system comprises an

information collection unit group, a monitoring center and a controlling unit group, wherein the

information collection unit group is used for transmitting collected greenhouse information to the

monitoring center, the monitoring center receives and analyses the greenhouse information and

transmits control signals to the controlling unit group, and the controlling unit group receives the

control signals and conducts corresponding control operations in accordance with the control

signals. According to the system, the internet of things and the cloud computing technology are

used, information such as air temperature and humidity, soil moisture content, soil temperature,

carbon dioxide concentration and illumination intensity can be obtained remotely in real time and

is transmitted to the monitoring center through networks such as general packet radio service

(GPRS), by the aid of crop growth model analysis, devices of the controlling unit group, such as a

dampening machine, a temperature-rising light-supplementing machine and a carbon dioxide

fertilizer machine can conduct corresponding operations through user remote control or automatic

control of a controlling center, and the environment in a greenhouse is suitable to crop growth.

("CN202486595U") : The utility model relates to an Internet of things intelligent environmental

monitoring system applied to agriculture. A monitoring host in a monitoring center is connected

with field data acquisition and control devices disposed in various agriculture greenhouses by

wireless data transceiver equipment. The wireless data transceiver equipment comprises a wireless

sensor network universal asynchronous receiver/transmitter (UART) data acquisition module. RS

485 port of the wireless sensor network UART data acquisition module is connected with the

monitoring host through electric cables. The field data acquisition and control devices are

respectively connected with a luminance sensor, a soil moisture sensor, a soil temperature sensor, a carbon dioxide sensor and an air temperature and humidity sensor through sensor signal electric cables. Data transmission of the Internet of things are achieved by wired acquisition and wireless transmission, and therefore construction cost and maintenance cost of the system are low, and the

Internet of things intelligent environmental monitoring system applied to the agriculture has good

application prospects in agriculture greenhouses environment monitoring and precision agriculture

management.

("CN105045321A"): The invention discloses an Intemet-of-things application design-based cloud

platform integrated management method. The method includes the following steps of: 1) data

acquisition: the growth information of crops in a greenhouse and environment parameter

information in the greenhouse are acquired; 2) data uploading: an Internet-of-things intelligent

controller stores the received the growth information of the crops in the greenhouse and

environment parameter information in the greenhouse and transmits the received information to a

monitoring information service cloud platform and terminal equipment through the Internet; and

(3) monitoring and control: the received the growth information of the crops in the greenhouse and

environment parameter information in the greenhouse are stored, and cloud computing and

drawing and plant maturation stage estimation are performed on the growth information of the

crops in the greenhouse and environment parameter information in the greenhouse, and the

Internet-of-things intelligent controller is controlled remotely through the Internet, so as to set

temperature, humidity and air quality parameters in the greenhouse and adjust soil parameter

indexes. Thus, with the method adopted, the growth cycle and growth environment of the crops in

the greenhouse can be regulated scientifically and automatically, and organic crops with rich

microelements can be planted.

("US20060116791Al") : An intelligent local irrigation system includes one or more sprinklers

and a controller coupled to the one or more sprinklers via a wired or wireless connection and

enabled to control the sprinklers thereby. A controller arrangement establishes connectivity with

an internet service portal which stores a profile of the local irrigation system and which obtains

information from internet-based resources. The internet service portal determines an irrigation

schedule based on the profile and on information obtained from the internet-based information

resources and provides the irrigation schedule to the controller arrangement for implementation.

SUMMARY OF THE INVENTION

The following presents a simplified summary of the invention in order to provide a basic

understanding of some aspects of the invention. This summary is not an extensive overview of the

present invention. It is not intended to identify the key/critical elements of the invention or to

delineate the scope of the invention. Its sole purpose is to present some concept of the invention

in a simplified form as a prelude to a more detailed description of the invention presented later.

The Invention discloses a Novel approach for Intelligence Agriculture

DETAILED DESCRIPTION

The following description is of exemplary embodiments only and is not intended to limit the scope,

applicability or configuration of the invention in any way. Rather, the following description

provides a convenient illustration for implementing exemplary embodiments of the invention.

Various changes to the described embodiments may be made in the function and arrangement of

the elements described without departing from the scope of the invention.

The details of each level are described below.

Crop Field Level: The first one is the crop field level where different sensors are deployed in the field. Various sensors like soil moisture (EC- 1258), soil temperature (DS18B20), air temperature (DHT11), and humidity (DHT11) are used to gather all these soil and environmental attributes. • Sensors will collect data and send them to the Arduino. • Then Arduino will forward the sensor data to the cloud server. • Using Microcontroller Device Sensor data are collected twice a day and forwarded for cloud storage. • To eliminate the inter-dependency among the used parameters, the Pearson correlation is computed. Hence average value is calculated and stored as the final reading for the particular day. • It is found that there is no strong correlation exists. • The Arduino is also connected to the motor pump through the breadboard and relay switch to turn on/off the pump for irrigation. the Arduino microcontroller is used because it requires low energy. Cloud Level: The second level is the cloud level where the cloud server is used to provide service to the user.

The sensors' data are stored in the database.

• The data are then feed to the ML-based model for analysis.

• This ML unit is the heart of this intelligent system, which has two sections.

• One is the regression model that is used to predict the soil and environmental parameters in advance. By doing so, it can be used effectively to improve the performance of the system.

• The parameters that are considered from forecasted weather data are the atmospheric pressure, precipitation, solar radiation, and wind speed.

• These predicted values are passed through a clustering model to reduce the predicted errors. The other ML-based model takes the results of the clustering model along with the forecasted weather data as input.

• This binary classification model categorizes the predicted samples into two predefined classes: irrigation required (Y) or not required (N).

• The results of these ML models are stored in the database for future actions.

• The last component of the cloud-based server is the handler used for coordination between the user and field units.

• Based on the suggestion of the ML model, the handler will send irrigation suggestions to the user via the Android application.

I

Based on the agricultural literature, the formula used for calculating the water requirement is discussed in Eq. (1).

EVo Cf %¼Wneed (1)

where, EVo= rate of evaporation Cf= crop factor Weed= amount of water needed.

User Level: The user interacts, at the user level with an Android application to enter the details about the farmer and crop.

Farmer credentials are used to authenticate the user through login operation.

In crop details, the farmer may have to provide the information by selecting the drop-down menus like a session, crop name, total crop days, date of sowing, and so on.

The farmer may get all relevant information about the crop and field. Thus, upon receiving the sensor data along with the irrigation suggestion on the Android application, the user may order the on/off command to the microcontroller. system gives feedback from the user for each suggestion by the handler.

If the farmer does not follow the recommendation, then feedback is sent to the server for updation. The system will be fine-tuned subsequently based on user feedback.

The microcontroller upon receiving the on/off command from the user performs the motor on/off operation for the supply of water to plants.

Thus, we have an automatic irrigation system, which can be used to increase the productivity of the crop by providing an optimal amount of water.

*Collected Sensor data Forecasted RTiontutdvaa procedurecnown a rite Regression weathr fial snstretraining that pi td model

of~ ~ ~ ~~~~~~rqie thTcrythrrdce utu aibe ACisa yp o herrciclluTraiing awokonaotto-papoc RT

RT constructed viaaprocedureknownas binary iterative splitting, which is arepetitiveprocess that partition thedata itowbranches,subbranches.

Each decision node in the treeassessesthevalue of several inputvariable's values.T heleafnodes of the RTcarythe predicted output variable.

AC AC is atype of hierarchical clustering that works on abottom-up approach A fundamental assumption in hierarchical ACis that the merge operation is monotonic. Each scrutiny begins in its own cluster, and clusters merged as one moves up the hierarchy. This clustering may improve the performance of classical regression by partitioning the sample training space into subspaces.

SVM SVM is a supervised ML model that works very well for many classification tasks

Once SVM is fed with sets of labeled training data for each class, they can be categorized into new samples.

For non-linear classification, it performs well with a limited number of labeled training data.

EDITORIAL NOTE 2021106796

There is 1 page of claims only.

Claims (1)

1. A Novel System for Intelligence Based Irrigation System