IOT BASED AUTOMATED SOLAR IRREGATION SYSTEM USING AI

Abstract

IOT BASED AUTOMATED SOLAR IRRIGATION SYSTEM USING Al ABSTRACT Artificial intelligence and sensor technology have become indispensable in the agriculture sector in recent years. In the present day, agricultural science diligence is, in fact, more thorough, accurate, data-driven, and energetic than ever before, regardless of any insights that may exist on agricultural processes. Since the advent of Internet of Things (IoT)-based technologies, nearly every industry has undergone change. Examples of these include "smart agriculture or precision agriculture," as well as smart homes, smart grids, smart cities, and smart health. The agriculture sector will gain new advantages from the application of machine learning through IoT data analytics, helping to meet the world's expanding food needs. This will lead to an increase in crop field productivity in terms of both quantity and quality. With a few notable exceptions, these groundbreaking findings are upending traditional agricultural methods and creating exciting new opportunities. The degree of humidity on the soil and typical farming procedures make it impossible to assess plant development on a regular basis. An excessively large crop field makes it impossible to inspect every area, which causes plant water shortages. On the other hand, sending more water to the same field will result in excessive water waste. Power was squandered running the motor as well. As a result, farmers experience losses and are unable to get consistent outcomes. Humidity sensors, artificial intelligence, and the Internet of Things allow us to easily automate solar-powered irrigation systems. A solar irrigation system is installed in agricultural fields under this technology. It is managed by the control model based on Al. The control model, which has already been trained with machine learning methods and has the reference humidity for each crop, receives the measured value from humidity sensors. The watering system will be controlled by the field's humidity level.

Specification

FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
And
THE PATENTS RULES 2003
COMPLETE SPECIFICATION
(See Section 10; Rule 13)

ARTIFICIAL INTELLIGENCE
FOR
IOT BASED AUTOMATED SOLAR IRRIGATION SYSTEM USING Al
Dr.N.Gopinath
Having address at
SRM Institute of Science & Technology,
SRM Nagar, Kattankulathur - 603 203,
Tamil Nadu.
India.

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED:

1. DESCRIPTION

Field of the Invention
The present invention is related to the field of Internet of Things with Artificial Intelligence.

Detailed Description of the invention
Artificial intelligence and sensor technology have become indispensable in the agriculture sector in recent years. In the present day, agricultural science diligence is, in fact, more thorough, accurate, data-driven, and energetic than ever before, regardless of any insights that may exist on agricultural processes. Since the advent of Internet of Things (IoT)-based technologies, nearly every industry has undergone change. Examples of these include "smart agriculture or precision agriculture," as well as smart homes, smart grids, smart cities, and smart health. The agriculture sector will gain new advantages from the application of machine learning through IoT data analytics, helping to meet the world's expanding food needs. This will lead to an increase in crop field productivity in terms of both quantity and quality. With a few notable exceptions, these groundbreaking findings are upending traditional agricultural methods and creating exciting new opportunities. The degree of humidity on the soil and typical farming procedures make it impossible to assess plant development on a regular basis. An excessively large crop field makes it impossible to inspect every area, which causes plant water shortages. On the other hand, sending more water to the same field will result in excessive water waste. Power was squandered running the motor as well. As a result, farmers experience losses and are unable to get consistent outcomes. Humidity sensors, artificial intelligence, and the Internet of Things allow us to easily automate solar-powered irrigation systems. A solar irrigation system is installed in agricultural fields under this technology. It is managed by the control model based on Al. The control model, which has already been trained with machine learning methods and has the reference humidity for each crop, receives the measured value from humidity sensors. The watering system will be controlled by the field's humidity level.

WORKING OF IoT
Sensors and actuators are devices, which help in interacting with the physical environment. The data collected by the sensors has to be stored and processed intelligently in order to derive useful inferences from it. Note that we broadly define
the term sensor; a mobile phone or even a microwave oven can count as a sensor as long as it provides inputs about its current state (internal state + environment). An actuator is a device that is used to effect a change in the environment such as the temperature controller of an air conditioner.

ARCHITECTURE OF IoT
The 4 Stage IoT architecture consists of
1. Sensors and actuators
2. Internet getaways and Data Acquisition Systems
3. Edge IT
4. Data center and cloud.

WORKING OF ARTIFICIAL INTELLIGENCE

The definition of Al is "automation based on associations." Two fundamental shifts in computing
that go beyond traditional educational technologies occur when computers automate reasoning based on associations in data (or associations inferred from expert knowledge): (1) from capturing data to detecting patterns in data and (2) from providing access to instructional
resources to automating decisions about instruction and other educational processes. The range of tasks that can be assigned to a computer system has expanded with the ability to identify
patterns and automate judgments. The process of creating an Al system could result in unfair
decision-making and biased pattern recognition. As a result, the use of Al systems in education
must be regulated. This study outlines opportunities for utilizing Al to enhance education, acknowledges potential difficulties, and formulates suggestions to direct future policy
development.

DEEP LEARNING
Deep learning is a part of machine learning. Contrary to traditional machine learning algorithms, many of which have a finite ability to learn regardless of the quantity of data they get, deep learning systems can perform better with access to more data, which is the machine equivalent of more experience. Once they have accumulated enough experience through deep learning, machines can be trained to perform specific tasks like driving a car, identifying weeds in a field of crops, diagnosing diseases, looking for weaknesses in machinery, and other duties. Deep learning networks learn by spotting intricate patterns in the material they analyse. In order to describe the data, the networks can achieve various levels of abstraction by building
computational models that are composed of several processing layers.

For example, a convolutional neural network, a sort of deep learning model, can be trained using
numerous (hundreds of thousands or millions) of images, such as ones of cats. This particular
neural network frequently gathers data from the pixels in the images it collects. With sets like
claws, ears, and eyes indicating the presence of a cat in a photo, it has the capacity to classify
sets of pixels that are typical of cat characteristics. There are significant differences between deep learning and conventional machine learning. In order to recognize the traits of a cat in this scenario, a domain expert would have to put a lot of effort into creating a standard machine learning system. With deep learning, all that is necessary to teach the system the characteristics of a cat is to feed it a very large number of images of cats. For many tasks, such as computer vision, speech recognition (also known as natural language processing), machine translation, and robotics, deep learning systems outperform typical machine learning systems by a large margin. This does not imply that creating regular machine learning systems is easier than creating deep learning systems.

ARCHITECTURE OF DEEP LEARNING

Deep learning is a subset of machine learning, which is a subset of artificial intelligence.
Generally speaking, "artificial intelligence" refers to techniques that enable computers to mimic human behavior. Machine learning, a group of algorithms that have been taught on data, enables
all of this. Deep learning is just one type of machine learning that models the structure of the
human brain.

Using a predetermined logical structure, deep learning algorithms continuously examine data in
an effort to come to conclusions that are akin to those made by people. Neural networks, a multilayered structure of algorithms, are used by deep learning to do this.

The architecture of the neural network was inspired by the structure of the human brain. Similar to how human brains automatically recognize patterns and group different types of information, neural networks may be trained to do the same. Using the distinct neural network layers as a form of filter that goes from coarse to fine increases the likelihood of recognizing and generating an appropriate outcome. The human brain acts in a similar manner. Every time we receive new knowledge, the brain tries to draw parallels with familiar objects. The same concept is also used by deep neural networks. Neural networks can be used for a range of tasks, such as grouping,
classification, and regression.

DESCRIPTION OF DRAWINGS
FIG 1: ARCHITECTURE OF SENSORS AND ACTUATORS
FIG 2: ARCHITECTURE OF IOT
FIG 3: ARCHITECTURE OF DEEP LEARING
FIG 4: ARCHITECTURE OF NEURAL NETWORK
FIG 5: ARCHITECTURE OF IOT BASED AUTOMATED SOLAR IRRIGATION SYSTEM
USING Al

FIG 5: ARCHITECTURE OF IOT BASED AUTOMATED SOLAR IRRIGATION SYSTEM

CLAIMS
a. IoT based automated solar irrigation system using Al uses high speed network connections to communicate between the smart farm and the control unit. Here, a person does not require carrying any physical gadgets.

b. IoT based automated solar irrigation system using Al uses deep learning and machine learning methods, also it includes Humidity Sensors, Solar Panels, Irrigation tools and communicating module with high speed network.

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