Preface
Page: ii-ii (1)
Author: Sonali Mahendra Kothari, Vijayshri Nitin Khedkar, Ujwala Kshirsagar and Gitanjali Rahul Shinde
DOI: 10.2174/9789815136449123010002
IoT and AI-based Smart Farm: Optimizing Crop Yield and Sustainability
Page: 1-17 (17)
Author: Namrata Nishant Wasatkar*, Pranali Gajanan Chavhan and Vikas Kanifnath Kolekar
DOI: 10.2174/9789815136449123010004
PDF Price: $15
Abstract
The “Smart Farm” project is an IoT-based agriculture project aimed at
optimizing crop yield and promoting sustainability in farming practices. By integrating
various IoT devices and sensors, the project aims to improve the efficiency of farming
operations, reduce waste, and enhance the quality and quantity of crop yields. The
project focuses on using IoT technology to monitor and control various aspects of
farming, including soil moisture, temperature, humidity, crop health, and livestock
behaviour. By leveraging data from these sensors and devices, farmers can make more
informed decisions about irrigation, pest control, and crop management, leading to
increased productivity and sustainability. Overall, the Smart Farm project seeks to
transform traditional farming practices into a more efficient, data-driven, and
sustainable model that benefits both farmers and the environment.
Impact of Automation, Artificial Intelligence and Deep Learning on Agriculture Crop Yield
Page: 18-41 (24)
Author: Prabhakar Laxmanrao Ramteke*
DOI: 10.2174/9789815136449123010005
PDF Price: $15
Abstract
Every nation is concerned about the growing problem of agriculture
automation. It is challenging to supply the food needs of the existing population due to
rising numbers, frequent climate change, and scarce resources. Farmers are forced to
wreak havoc on the land by applying dangerous pesticides more often since their old
techniques cannot keep up with the growing demand. As a result, agricultural practices
are significantly impacted, and the land gradually loses its fertility and becomes
unproductive. The agriculture sector can benefit from technology like Artificial
Intelligence (AI), deep learning, the Internet of Things (IoT), embedded systems, and
automation. Artificial neural networks, the Internet of Things, fuzzy logic, machine
learning, and other technologies may all be used to automate agricultural systems.
Artificial intelligence technology is advancing quickly, and as a result, its employment
is in a wide range of fields. Utilizing clever technologies, the agricultural industry has
become able to regulate the field environment that is essential to the care of every
plant. A suitable atmosphere and appropriate irrigation are provided by the plant's
identification and suitable circumstances. In order to increase agriculture yields, it has
become important to manage crops in controlled settings like greenhouses that can
enhance the output. This chapter focuses on the use of artificial intelligence and IoT
technology to improve the productivity of agricultural enterprises. AI technologies
might help farmers overcome problems like weeds, pests, and climatic variability that
lower output. Numerous uses of AI are now being deployed, such as automatic
machine changes for weather forecasting and pest detection. The goal of implementing
AI and IoT is to increase the possibility of producing healthy crops by recognizing
damaged crops and crop yield growth.
AIoT: Role of AI in IoT, Applications and Future Trends
Page: 42-53 (12)
Author: Reena Thakur*, Prashant Panse, Parul Bhanarkar and Pradnya Borkar
DOI: 10.2174/9789815136449123010006
PDF Price: $15
Abstract
Technology such as the Internet of Things (IoT), big data, cloud computing,
fog computing, edge computing, and blockchain can be a perilous factor when it comes
to encouraging the integration of new technologies. Artificial Intelligence (AI) is an
integral part of agricultural and industrial development. This chapter describes the
extensive evolution of AI and IoT. Researchers and practitioners will find this chapter
essential for understanding AI and IoT, along with models, current status, future trends,
and industrial development. There are a number of issues with AI, but overall, it is
considered to be an advanced and revolutionary assistant in a wide range of fields. The
purpose of this chapter is to present a comprehensive study on AIoT that explains the
convergence of AI and IoT. Herein, we summarize some innovative AIoT applications
that are likely too intense for our world.
The Role of Machine Intelligence in Agriculture: A Case Study
Page: 54-79 (26)
Author: Prabhakar Laxmanrao Ramteke* and Ujwala Kshirsagar*
DOI: 10.2174/9789815136449123010007
PDF Price: $15
Abstract
India's GDP is heavily reliant on agricultural products and business
management. Therefore, it is crucial for the agriculture industry to comprehend the
most common uses of artificial intelligence (AI) through case studies. To increase its
production, this industry must overcome a number of obstacles, such as soil treatment,
plant disease and pest effects, crop management, farmers' innovative methods, and the
use of technology. The major ideas behind AI in agriculture are its adaptability,
excellence, accuracy, and economy. It is critical to examine AI applications for
managing soil, crops, and the environment, and plant or leaf diseases. Food security
continues to be seriously threatened by deforestation and poor soil conditions, both of
which harm the economy. The application's advantages, constraints, and methods for
employing expert systems to increase productivity are all given particular attention.
Businesses are utilizing robots and automation to assist farmers in developing more
effective weed control strategies for their crops. See & Spray, a robot created by Blue
River Technology, is said to use computer vision to monitor and accurately spray
weeds on cotton plants. Crop and Soil Monitoring - Businesses are using deep learning
and computer vision algorithms to interpret data taken by drones and/or software-based
technologies to monitor the health of crops and soil. Crop sustainability and weather
forecasting are accomplished via satellite systems. A Colorado-based startup employs
satellites and machine learning algorithms to examine agricultural sustainability,
forecast weather, and assess farms for the presence of diseases and pests. Utilizing
predictive analytics, machine learning models are being created to monitor and forecast
various environmental factors, such as weather variations. Drones and computer vision
are used for crop analysis, while machine learning is used for identifying soil flaws.
Optimal Feature Selection and Prediction of Diabetes using Boruta- LASSO Techniques
Page: 80-95 (16)
Author: Vijayshri Nitin Khedkar*, Sonali Mahendra Kothari, Sina Patel and Saurabh Sathe
DOI: 10.2174/9789815136449123010008
PDF Price: $15
Abstract
Diabetes prediction is an ongoing research problem. The sooner diabetes is
detected in a human, the sooner lives and medical resources can be saved. Predicting
diabetes as early as possible with easy to measures parameters with optimal accuracy is
an ongoing problem. When dealing with large data, feature selection plays an important
role. It not only reduces the computational cost but also increases the performance of a
model. This study ensemble three different types of feature selection techniques: filter,
wrapper and embedded. Ensembling Boruta and LASSO features give optimal results.
Also, effectively handling class imbalance leads to better results.
Empowered Internet of Things for Sustainable Development Using Artificial Intelligence
Page: 96-119 (24)
Author: Pranali Gajanan Chavhan*, Namrata Nishant Wasatkar and Gitanjali Rahul Shinde
DOI: 10.2174/9789815136449123010009
PDF Price: $15
Abstract
Everyone has been utilizing the Internet of Things in current years (IoT).
Therefore, as the IoT has grown enormously, so too have concerns about Artificial
Intelligence. Artificial intelligence (AI) is at the forefront of ubiquitous computing and
is utilized to create intricate algorithms to safeguard networks and systems, including
IoT devices. But to carry out cyber security assaults, hackers have learned how to make
use of AI, and they have even started to deploy antagonistic AI. The goal of this
research work is to comprehensively present and summarize the pertinent literature in
these fields. It does this by compiling data from a few other surveys and research
papers regarding sustainable development using AI, IoT, and attacks with and against
AI. It also shows the relationship between IoT and AI.
Digital Twin and Its Applications
Page: 120-134 (15)
Author: Kiran Wani*, Nitin Khedekar, Varad Vishwarupe and N. Pushyanth
DOI: 10.2174/9789815136449123010010
PDF Price: $15
Abstract
Digital twin technology is an important part of the industry 4.0 revolution.
Digital twins is a concept of integrating different smart technologies including
integration of data and digitization. The vision of the digital twin technology is based
on the philosophy that any component, assembly, system, process, product, or even
environment can be replicated in terms of form, functionality and several other
parameters in a digital way throughout different phases of its lifecycle. The Digital
Twin concept is based on the highly growing information and communication
technologies alongside conventional methods for getting better interconnection and
integration amongst all the entities involved in a particular phase of the product
lifecycle. The Internet of things (IOT) and artificial intelligence (AI) are crucial parts of
the Industry 4.0 revolution. IOT proposes to embed electronics, sensors, network
connectivity and different software platforms with products. Better integration between
the physical and digital world is achieved by a strong network infrastructure that
facilitates remote sensing, monitoring and even controlling of connected systems. This
technology provides users with many benefits such as increased efficiency, reduced
downtime, improvement in precision and accuracy along with cost saving.
This chapter includes an overview of digital twin-enabling technologies and their
applications. The applications range from Artificial Intelligence, Internet of Things
(IoT) to manufacturing.
Ontology Based Information Retrieval By Using Semantic Query
Page: 135-149 (15)
Author: Rupali R. Deshmukh* and Anjali B. Raut
DOI: 10.2174/9789815136449123010011
PDF Price: $15
Abstract
The volume of data is increasing quickly in the modern day. Effective
information retrieval techniques are needed to extract important facts from such a large
collection of information. As a result, retrieval of information is the process of
gathering valid data from a variety of sources. The majority of the time, information is
retrieved from the internet using search queries. The aim of this research is to explore
various issues existing in information retrieval techniques and to propose new
techniques to overcome existing challenges in the field of Information retrieval.
Modern information retrieval methods have been examined, and it was discovered that
they do not take semantic keyword knowledge into account when returning results. The
semantic web is a development of the internet that enables computers to comprehend
human inquiries in terms of their intent and produce pertinent responses.
This research mainly focuses on Ontology-Based Information Retrieval which can
support semantic similarity and retain the view of an approximate search in a document
repository using machine learning techniques. Further, this research works explores an
adaptive update model for retrieving the information and proposes a semantic search
model for the given user query. The objective of ontology-based semantic web
information search is to increase the accuracy, precision and recall of user queries.
Paradigm Shift of Online Education System Due to COVID-19 Pandemic: A Sentiment Analysis Using Machine Learning
Page: 150-166 (17)
Author: Prajkta P. Chapke* and Anjali B. Raut
DOI: 10.2174/9789815136449123010012
PDF Price: $15
Abstract
The COVID-19 epidemic has completely altered the environment and every
aspect of every individual. The most affected part is the education system and the
stakeholders associated with it. Organizations are currently being forced to adapt and
alter their strategies in response to the new situation created by the COVID-19
epidemic. The proposed study gathers tweets on online schooling from social media
sites like Twitter and Facebook comments in order to conduct a thorough sentiment
analysis (SA) during the epidemic. The current study utilizes techniques for natural
language processing (NLP) and machine learning (ML) to extract subjective data,
establish polarity, and identify how people felt about the educational system prior to
and following the COVID-19 crisis. The first step in the proposed study is to retrieve
tweets using Twitter APIs before they are ready for rigorous preprocessing. One
filtering method is Information Gain (IG). We will identify and examine the latent
causes of the unpleasant feelings. We'll look at the machine-learning classification
algorithm at the end. The proposed model will analyse the perceptions of people about
the online educational system during COVID-19
Image Processing for Autonomous Vehicle Based on Deep Learning
Page: 167-185 (19)
Author: Tanvi Raut, Ishan Sarode, Riddhi Mirajkar* and Ruchi Doshi
DOI: 10.2174/9789815136449123010013
PDF Price: $15
Abstract
The automation industry is rapidly growing and coming up with new and
improved techniques for reducing time and efforts. One such example is the
autonomous cars which are said to be the future of the automobile industry since they
would be driver less, very efficient and relieve the stress of daily commuting [1].
Advances in technology using the AI and deep learning techniques help in improving
the safety of the passengers and also in minimizing the efforts of the driver. For the
study of autonomous vehicles, a lot of data needs to be collected, some of which
include warning signals, speed limits, obstacles, collision avoidance, etc. This paper
shows how IoT devices i.e. cameras and LiDAR sensors help in data collection, how
deep learning is a solution, and how image recognition methods that use deep learning
can help in object or any obstacle detection. An image processing algorithm based on
deep learning is proposed in which the image perception can be made by an optical
camera communication technique that can be used for collecting the data. Hence it will
highlight how deep learning is used in the field of image processing or image
recognition.
Applications of AI and IoT for Smart Cities
Page: 186-202 (17)
Author: A. Kannammal* and S. Chandia
DOI: 10.2174/9789815136449123010014
PDF Price: $15
Abstract
Due to the rapid increase in urban population, the today’s life of every
citizen undergoes drastic changes. For the betterment of human life, Government of
India had decided and announced the development of smart cities i.e the cities to be
developed with all modern facilities in which people can use the internet for their daily
activities. Smart city development would heavily depend on the Internet of Things
(IOT) which combines three important aspects of Internet such as people-people
interaction, people-objects interaction, and objects-objects interaction. Artificial
Intelligence (AI) is another technology that provides the city equipped with efficient
systems for security, safety, parking, etc.
The applications of AI enable IoT in smart cities that are discussed in this chapter such
as Smart Home, Smart Healthcare, Smart Water, Smart Grid and Energy, Smart
Transport, and Real Estate investment. Section 12.1 gives an introduction about the
chapter that includes an introduction to Smart City, IoT and integration of AI and IoT.
Section 12.2 discusses the potential use cases of AI and IoT in smart cities. Smart
Home use case that includes smart management of equipment and human activity
recognition is discussed. It also discusses Smart Healthcare which includes the fitness-tracking system, glucose-level monitoring system, body-temperature monitoring
system, stress detection system, and oxygen-saturation monitoring system. Smart
Infrastructure is also discussed which includes Smart Water, Smart Grid and Energy,
and structural health monitoring. Smart Transport is also discussed in this chapter
which includes the Vehicle Infrastructure Pedestrian (VIP), Smart Parking System
(SPS) and Automated Incident Detection system (AID). The final section concludes the
chapter by discussing the challenges in the smart city environment and future
enhancements.
Analysis of RGB Depth Sensors on Fashion Dataset for Virtual Trial Room Implementation
Page: 203-220 (18)
Author: Sonali Mahendra Kothari*, Vijayshri Nitin Khedkar, Rahul Jadhav and Madhumita Bawiskar
DOI: 10.2174/9789815136449123010015
PDF Price: $15
Abstract
This paper presents a Virtual Trial Room software using Augmented Reality
which allows the user to wear clothes virtually by superimposing 3d clothes over the
user. These sensors are valued particularly for robotics or computer vision applications
because of their low cost and their ability to measure distances at a high frame rate. In
November 2010, the Kinect v1 (Microsoft) release encouraged the use of Red Green
Blue (RGB)-D cameras, and in July 2014, a second version of the sensor was launched.
Because high-frequency point nuclei can be obtained from an observed picture, users
can imagine employing these sensors to fulfill 3D acquisition requirements. However,
certain issues such as the adequacy and accuracy of RGB-D cameras in close-range 3D
modeling have to be addressed owing to the technology involved. The quality of the
data obtained therefore constitutes an important dimension. In this study, the usage of
the current sensor Kinect v2 is explored in the three-dimensional reconstruction of tiny
objects. The advantages and problems of Kinect v2 are addressed in the first section
and then photogrammetry versions are presented after an accurate evaluation of the
generated models.
Subject Index
Page: 221-225 (5)
Author: Sonali Mahendra Kothari, Vijayshri Nitin Khedkar, Ujwala Kshirsagar and Gitanjali Rahul Shinde
DOI: 10.2174/9789815136449123010016
Introduction
Stay informed about recent trends and groundbreaking research driving innovation in the AI-IoT landscape. AI, a simulated form of natural intelligence within machines, has revolutionized various industries, simplifying daily tasks for end-users. This book serves as a handy reference, offering insights into the latest research and applications where AI and IoT intersect. The book includes 12 edited chapters that provide a comprehensive exploration of the synergies between AI and IoT. The contributors attempt to address engineering opportunities and challenges in different fields. Key Topics: AI and IoT in Smart Farming: Explore how these technologies enhance crop yield and sustainability, revolutionizing agricultural practices. AIoT (Artificial Intelligence of Things): Understand the amalgamation of AI and IoT and its applications, particularly focusing on smart cities and agriculture. Smart Healthcare and Predictive Disease Analysis: Uncover the crucial role of AI and IoT in early disease prediction and improving healthcare outcomes. Applications of AI in Various Sectors: Explore how AI contributes to sustainable development, sentiment analysis, education, autonomous vehicles, fashion, virtual trial rooms, and more. Each chapter has structured sections with summaries and reference lists, making it an invaluable resource for researchers, professionals, and enthusiasts keen on understanding the potential and impact of these technologies in today's rapidly evolving world.