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Table of Contents
Cover
Title
Copyright
End User License Agreement
Contents
Foreword
Preface
List of Contributors
IoT and AI-based Smart Farm: Optimizing Crop Yield and Sustainability
Namrata Nishant Wasatkar1,*, Pranali Gajanan Chavhan1 and Vikas Kanifnath Kolekar1
INTRODUCTION
CHALLENGES AND ISSUES
PROCESS OF SMART FARMING
Predictive Analytics
Precision Farming
Autonomous Equipment
Image Processing
Blockchain Technology
Decision Support Systems
AUTONOMOUS EQUIPMENT FOR SMART FARMING
Autonomous Tractors
Drones
Robotic Harvesters
Autonomous Seeders
Autonomous Weeders
SENSORS IN SMART FARMS
Soil Sensors
Weather Sensors
Plant Sensors
Nutrient Sensors
GPS Sensors
BENEFITS OF SMART FARMING
Improved Efficiency
Increased Yields
Reduced Environmental Impact
Improved Quality and Safety
Increased Profitability
THE IMPACT OF CLIMATE ON SMART FARMING
CASE STUDY OF SMART FARMING USING IOT
HOW TO USE AI FOR OPTIMIZING AND PREDICTING YIELD
Data Collection and Analysis
Predictive Modeling
Machine Learning
Crop Monitoring
Precision Agriculture
Automated Irrigation Systems
Crop Monitoring
Livestock Monitoring
Automated Machinery
CASE STUDY -AUTOMATED IRRIGATION SYSTEMS
Water Conservation
Increased Crop Yield
Reduced Labor Costs
Improved Accuracy
Flexibility
CONCLUSION
REFERENCES
Impact of Automation, Artificial Intelligence and Deep Learning on Agriculture Crop Yield
Prabhakar Laxmanrao Ramteke1,*
INTRODUCTION
AI TECHNIQUES FOR PROBLEM SOLVING IN AGRICULTURE SECTOR
Fuzzy Logic
Artificial Neural Networks
Neuro- Fuzzy Logic
Expert System
OBSTACLES IN THE FIELD OF AGRICULTURE AND IN AI ADAPTATION
Consumer Inclinations
Lack of Labour
Environmental Accountability.
Tiny and Dispersed Landholdings
Seeds
Land Mechanization
Farm Automation or Smart Farming
REQUIREMENT OF ARTIFICIAL INTELLIGENCE IN THE AGRICULTURE SECTOR
Numerous Applications of AI &
other Technologies that can Boost Agriculture Yield
Development Driven by the IoT
Ingenious Agriculture
Advantages of Intelligent Farming
AGRICULTURE APPLICATIONS AND USE CASES
Climate Conditions Monitoring
Greenhouse Automation
Cattle Management and Monitoring
Precision Agriculture
Smart Farming Predictive Analytics
A SMART FARMING SOLUTION
IoT Hardware
Connectivity
Data Gathering Intervals
The Farming Sector's Data Integrity
Disease Detection
AUTOMATION TECHNIQUES FOR IRRIGATION AND RE-ASSISTING FARMER ABILITY
Using Drones and Robots to Automate Agriculture
Robots and Autonomous Machines
Robotic Weeding and Seeding
Automatic Irrigation
Automation of Harvest
AGRICULTURE AUTOMATION BENEFITS
The Agricultural Sector Satisfies Consumer Demand
The Industry's Labour Deficit is Becoming Better
Agriculture is Becoming More Environmental-friendly
MODERN AI-BASED PREDICTION MODEL APPLICATIONS IN AGRICULTURE RELATING TO SOIL, CROP, DISEASES, AND PEST MANAGEMENT
Soil Administration
Crop and Yield Management
Plant Disease Control
Weed Management
Pest Management
Monitoring and Storage Control Management for Agricultural Products
Manage Yield Prediction
SOLUTIONS FOR MONITORING SMART FARMING
Monitoring the State of Soil
Agriculture Weather Monitoring
Systems for Automating Greenhouses
System for Monitoring Crops
CONCLUDING REMARKS
ACKNOWLEDGEMENTS
REFERENCES
AIoT: Role of AI in IoT, Applications and Future Trends
Reena Thakur1,*, Prashant Panse2, Parul Bhanarkar1 and Pradnya Borkar3
INTRODUCTION
ROLE OF AI IN IOT.
VOICE ASSISTANTS
ROBOTS
SMART DEVICES
INDUSTRIAL IOT
APPLICATIONS
Impact of A IoT on Society
CONCLUSION
REFERENCES
The Role of Machine Intelligence in Agriculture: A Case Study
Prabhakar Laxmanrao Ramteke1,* and Ujwala Kshirsagar2,*
INTRODUCTION
Understanding Essential Agriculture Stages
Agriculture's Stages
CASE STUDIES
An IOT-based System for Crop Irrigation
Applications of Machine Learning Algorithms in High Precision Agriculture
Soil Characteristics and Weather Forecasting
MODELLING SOIL WATER BALANCE
DESIGN AND IMPLEMENTATION OF A SENSOR NETWORK-BASED SMART NODE
Smart-node Hardware
Acquisition Programme, Connectivity Architecture and Software
IN IRRIGATION MANAGEMENT DECISION SUPPORT SYSTEM: ANALYSIS AND APPLICATION
MACHINE LEARNING RECOMMENDED IRRIGATION METHODS
Cotton Centre Pivot Irrigation is Efficiently Scheduled and Controlled by a Mechanism based on Canopy Temperature
Intelligent Irrigation Monitoring with Thermal Imaging in Smart Agriculture with the Internet of Things
IRRIGATION SENSOR COUPLED TO AUTOMATIC WATERING SYSTEM
PREDICTION FOR CROP YIELD AND FERTILISER
CLASSIFICATION MODEL FOR RICE PLANT DISEASE DETECTION THAT IS OPTIMAL
Multi-Rotor Drone
Fixed-Wing Drone
Single-Rotor Helicopter Drone
FARMING USING ARTIFICIAL INTELLIGENCE
THE USE OF THE INTERNET OF THINGS AND CLOUD COMPUTING TO CREATE A CUSTOM AGRICULTURAL DRONE
Autonomous Quadcopter
On-Ground Sensor Nodes
Image Processing
Cloud Analytics and Data Storage
Frontend
INTERACTIVE CULTIVATION SENSING SYSTEM POWERED BY IOT
Use of Weather Forecasting
Using Drones to Assess Crop Health
Predictive Analytics and Precision Agriculture
A System Using AI that can Identify Pests
IMPACT OF ARTIFICIAL INTELLIGENCE ON AGRICULTURAL CROP YIELD.
The Internet of Things (IoT) Driven Development
The Development of Understanding via Images
Identifying Diseases
Determine the Crop's Readiness
Field Administration
Determining the Best Combination of Agronomic Goods
Crop Health Surveillance
Irrigation Automation Methods that Help Farmers
Precision Farming
APPLICATIONS OF AI TO AGRICULTURE
PRODUCT RECOMMENDATIONS USING AI: CASE STUDY
Solution Overview
Artificial Intelligence in Agriculture Sector: Case Study of Blue River Technology
CONCLUDING REMARKS
ACKNOWLEDGEMENTS
REFERENCES
Optimal Feature Selection and Prediction of Diabetes using Boruta- LASSO Techniques
Vijayshri Nitin Khedkar1,*, Sonali Mahendra Kothari1, Sina Patel1 and Saurabh Sathe2
INTRODUCTION
RELATED WORKS
DATASET USED
Handling Class Imbalance
RESEARCH APPROACH
FEATURE SELECTION METHODS
ReliefF
Boruta
Lasso
RESULT ANALYSIS
Feature Selection Results
Evaluation Metrics
Cross-Validation
Classification Method Results
Evaluation of Receiver Operating Characteristics (ROC)
DISCUSSION
CONCLUSION
FUTURE SCOPE
REFERENCES
Empowered Internet of Things for Sustainable Development Using Artificial Intelligence
Pranali Gajanan Chavhan1,*, Namrata Nishant Wasatkar1 and Gitanjali Rahul Shinde2
INTRODUCTION
Artificial Intelligence
Significance of Artificial Intelligence
Benefits of AI
Improving Sustainability in AI
IOT AND ITS SIGNIFICANCE
ROLE OF AI IN IOT
SUSTAINABLE SECURITY FOR THE IOT USING AI
A General Pseudo-code for a Sustainable Security Solution for IoT using AI
Process
DDoS (Distributed Denial of Service) Attacks
Types of Attack
Methods of Attack
Source of Attack
ENERGY MANAGEMENT USING AI
THE IMPACT OF THE IOT ON SUSTAINABLE WATER MANAGEMENT.
When and Where to Irrigate with the Right Amount of Water Using IoT
Smart Irrigation
Leak Detection
CLIMATE CONTROL SYSTEMS WITH AI
General Circulation Models (GCMs)
Earth System Models (ESMs)
AI-IOT USE CASES
Smart Home Automation
Intelligent Transportation Systems
Smart and Sustainable Transportation
Intelligent Traffic Management
Intelligent Transportation Systems (ITS)
Autonomous Vehicles
Predictive Maintenance
Ride-sharing and Carpooling
Smart Parking
Predictive Maintenance
Agricultural Monitoring
Healthcare Monitoring
Energy Management
FUTURE OF IOT IN SUPPORT OF SUSTAINABILITY
Smart Energy Management
Resource Conservation
Smart Transportation
Sustainable Agriculture
CONCLUSIONS
FUTURE SCOPE
REFERENCES
Digital Twin and Its Applications
Kiran Wani1,*, Nitin Khedekar1, Varad Vishwarupe1 and N. Pushyanth2
INRODUCTION
DIGITAL TWINS
AUGMENTED REALITY
Hardware for Augmented Reality
VISUALIZATION OF THE DIGITAL TWIN DATA
REAL-TIME MONITORING
DIGITAL TWINS USAGE &
APPLICATIONS
CONCLUSION
REFERENCES
Ontology Based Information Retrieval By Using Semantic Query
Rupali R. Deshmukh1,* and Anjali B. Raut2
INTRODUCTION
Historical Background
Growth of Information Retrieval
Ontology
MOTIVATION
LITERATURE REVIEW
ISSUES IN INFORMATION RETRIEVAL
AIM
PROPOSED RESEARCH METHODOLOGY
CONCLUSION
REFERENCES
Paradigm Shift of Online Education System Due to COVID-19 Pandemic: A Sentiment Analysis Using Machine Learning
Prajkta P. Chapke1,* and Anjali B. Raut1
INTRODUCTION
HISTORICAL BACKGROUND
Social Network Analysis
Impact of Social Networks
Positive Impact
Negative Impact
Characteristics of Social Networks
User-based
Interactive
Community-driven
Relationships.
Emotion Over Content.
Title
Copyright
End User License Agreement
Contents
Foreword
Preface
List of Contributors
IoT and AI-based Smart Farm: Optimizing Crop Yield and Sustainability
Namrata Nishant Wasatkar1,*, Pranali Gajanan Chavhan1 and Vikas Kanifnath Kolekar1
INTRODUCTION
CHALLENGES AND ISSUES
PROCESS OF SMART FARMING
Predictive Analytics
Precision Farming
Autonomous Equipment
Image Processing
Blockchain Technology
Decision Support Systems
AUTONOMOUS EQUIPMENT FOR SMART FARMING
Autonomous Tractors
Drones
Robotic Harvesters
Autonomous Seeders
Autonomous Weeders
SENSORS IN SMART FARMS
Soil Sensors
Weather Sensors
Plant Sensors
Nutrient Sensors
GPS Sensors
BENEFITS OF SMART FARMING
Improved Efficiency
Increased Yields
Reduced Environmental Impact
Improved Quality and Safety
Increased Profitability
THE IMPACT OF CLIMATE ON SMART FARMING
CASE STUDY OF SMART FARMING USING IOT
HOW TO USE AI FOR OPTIMIZING AND PREDICTING YIELD
Data Collection and Analysis
Predictive Modeling
Machine Learning
Crop Monitoring
Precision Agriculture
Automated Irrigation Systems
Crop Monitoring
Livestock Monitoring
Automated Machinery
CASE STUDY -AUTOMATED IRRIGATION SYSTEMS
Water Conservation
Increased Crop Yield
Reduced Labor Costs
Improved Accuracy
Flexibility
CONCLUSION
REFERENCES
Impact of Automation, Artificial Intelligence and Deep Learning on Agriculture Crop Yield
Prabhakar Laxmanrao Ramteke1,*
INTRODUCTION
AI TECHNIQUES FOR PROBLEM SOLVING IN AGRICULTURE SECTOR
Fuzzy Logic
Artificial Neural Networks
Neuro- Fuzzy Logic
Expert System
OBSTACLES IN THE FIELD OF AGRICULTURE AND IN AI ADAPTATION
Consumer Inclinations
Lack of Labour
Environmental Accountability.
Tiny and Dispersed Landholdings
Seeds
Land Mechanization
Farm Automation or Smart Farming
REQUIREMENT OF ARTIFICIAL INTELLIGENCE IN THE AGRICULTURE SECTOR
Numerous Applications of AI &
other Technologies that can Boost Agriculture Yield
Development Driven by the IoT
Ingenious Agriculture
Advantages of Intelligent Farming
AGRICULTURE APPLICATIONS AND USE CASES
Climate Conditions Monitoring
Greenhouse Automation
Cattle Management and Monitoring
Precision Agriculture
Smart Farming Predictive Analytics
A SMART FARMING SOLUTION
IoT Hardware
Connectivity
Data Gathering Intervals
The Farming Sector's Data Integrity
Disease Detection
AUTOMATION TECHNIQUES FOR IRRIGATION AND RE-ASSISTING FARMER ABILITY
Using Drones and Robots to Automate Agriculture
Robots and Autonomous Machines
Robotic Weeding and Seeding
Automatic Irrigation
Automation of Harvest
AGRICULTURE AUTOMATION BENEFITS
The Agricultural Sector Satisfies Consumer Demand
The Industry's Labour Deficit is Becoming Better
Agriculture is Becoming More Environmental-friendly
MODERN AI-BASED PREDICTION MODEL APPLICATIONS IN AGRICULTURE RELATING TO SOIL, CROP, DISEASES, AND PEST MANAGEMENT
Soil Administration
Crop and Yield Management
Plant Disease Control
Weed Management
Pest Management
Monitoring and Storage Control Management for Agricultural Products
Manage Yield Prediction
SOLUTIONS FOR MONITORING SMART FARMING
Monitoring the State of Soil
Agriculture Weather Monitoring
Systems for Automating Greenhouses
System for Monitoring Crops
CONCLUDING REMARKS
ACKNOWLEDGEMENTS
REFERENCES
AIoT: Role of AI in IoT, Applications and Future Trends
Reena Thakur1,*, Prashant Panse2, Parul Bhanarkar1 and Pradnya Borkar3
INTRODUCTION
ROLE OF AI IN IOT.
VOICE ASSISTANTS
ROBOTS
SMART DEVICES
INDUSTRIAL IOT
APPLICATIONS
Impact of A IoT on Society
CONCLUSION
REFERENCES
The Role of Machine Intelligence in Agriculture: A Case Study
Prabhakar Laxmanrao Ramteke1,* and Ujwala Kshirsagar2,*
INTRODUCTION
Understanding Essential Agriculture Stages
Agriculture's Stages
CASE STUDIES
An IOT-based System for Crop Irrigation
Applications of Machine Learning Algorithms in High Precision Agriculture
Soil Characteristics and Weather Forecasting
MODELLING SOIL WATER BALANCE
DESIGN AND IMPLEMENTATION OF A SENSOR NETWORK-BASED SMART NODE
Smart-node Hardware
Acquisition Programme, Connectivity Architecture and Software
IN IRRIGATION MANAGEMENT DECISION SUPPORT SYSTEM: ANALYSIS AND APPLICATION
MACHINE LEARNING RECOMMENDED IRRIGATION METHODS
Cotton Centre Pivot Irrigation is Efficiently Scheduled and Controlled by a Mechanism based on Canopy Temperature
Intelligent Irrigation Monitoring with Thermal Imaging in Smart Agriculture with the Internet of Things
IRRIGATION SENSOR COUPLED TO AUTOMATIC WATERING SYSTEM
PREDICTION FOR CROP YIELD AND FERTILISER
CLASSIFICATION MODEL FOR RICE PLANT DISEASE DETECTION THAT IS OPTIMAL
Multi-Rotor Drone
Fixed-Wing Drone
Single-Rotor Helicopter Drone
FARMING USING ARTIFICIAL INTELLIGENCE
THE USE OF THE INTERNET OF THINGS AND CLOUD COMPUTING TO CREATE A CUSTOM AGRICULTURAL DRONE
Autonomous Quadcopter
On-Ground Sensor Nodes
Image Processing
Cloud Analytics and Data Storage
Frontend
INTERACTIVE CULTIVATION SENSING SYSTEM POWERED BY IOT
Use of Weather Forecasting
Using Drones to Assess Crop Health
Predictive Analytics and Precision Agriculture
A System Using AI that can Identify Pests
IMPACT OF ARTIFICIAL INTELLIGENCE ON AGRICULTURAL CROP YIELD.
The Internet of Things (IoT) Driven Development
The Development of Understanding via Images
Identifying Diseases
Determine the Crop's Readiness
Field Administration
Determining the Best Combination of Agronomic Goods
Crop Health Surveillance
Irrigation Automation Methods that Help Farmers
Precision Farming
APPLICATIONS OF AI TO AGRICULTURE
PRODUCT RECOMMENDATIONS USING AI: CASE STUDY
Solution Overview
Artificial Intelligence in Agriculture Sector: Case Study of Blue River Technology
CONCLUDING REMARKS
ACKNOWLEDGEMENTS
REFERENCES
Optimal Feature Selection and Prediction of Diabetes using Boruta- LASSO Techniques
Vijayshri Nitin Khedkar1,*, Sonali Mahendra Kothari1, Sina Patel1 and Saurabh Sathe2
INTRODUCTION
RELATED WORKS
DATASET USED
Handling Class Imbalance
RESEARCH APPROACH
FEATURE SELECTION METHODS
ReliefF
Boruta
Lasso
RESULT ANALYSIS
Feature Selection Results
Evaluation Metrics
Cross-Validation
Classification Method Results
Evaluation of Receiver Operating Characteristics (ROC)
DISCUSSION
CONCLUSION
FUTURE SCOPE
REFERENCES
Empowered Internet of Things for Sustainable Development Using Artificial Intelligence
Pranali Gajanan Chavhan1,*, Namrata Nishant Wasatkar1 and Gitanjali Rahul Shinde2
INTRODUCTION
Artificial Intelligence
Significance of Artificial Intelligence
Benefits of AI
Improving Sustainability in AI
IOT AND ITS SIGNIFICANCE
ROLE OF AI IN IOT
SUSTAINABLE SECURITY FOR THE IOT USING AI
A General Pseudo-code for a Sustainable Security Solution for IoT using AI
Process
DDoS (Distributed Denial of Service) Attacks
Types of Attack
Methods of Attack
Source of Attack
ENERGY MANAGEMENT USING AI
THE IMPACT OF THE IOT ON SUSTAINABLE WATER MANAGEMENT.
When and Where to Irrigate with the Right Amount of Water Using IoT
Smart Irrigation
Leak Detection
CLIMATE CONTROL SYSTEMS WITH AI
General Circulation Models (GCMs)
Earth System Models (ESMs)
AI-IOT USE CASES
Smart Home Automation
Intelligent Transportation Systems
Smart and Sustainable Transportation
Intelligent Traffic Management
Intelligent Transportation Systems (ITS)
Autonomous Vehicles
Predictive Maintenance
Ride-sharing and Carpooling
Smart Parking
Predictive Maintenance
Agricultural Monitoring
Healthcare Monitoring
Energy Management
FUTURE OF IOT IN SUPPORT OF SUSTAINABILITY
Smart Energy Management
Resource Conservation
Smart Transportation
Sustainable Agriculture
CONCLUSIONS
FUTURE SCOPE
REFERENCES
Digital Twin and Its Applications
Kiran Wani1,*, Nitin Khedekar1, Varad Vishwarupe1 and N. Pushyanth2
INRODUCTION
DIGITAL TWINS
AUGMENTED REALITY
Hardware for Augmented Reality
VISUALIZATION OF THE DIGITAL TWIN DATA
REAL-TIME MONITORING
DIGITAL TWINS USAGE &
APPLICATIONS
CONCLUSION
REFERENCES
Ontology Based Information Retrieval By Using Semantic Query
Rupali R. Deshmukh1,* and Anjali B. Raut2
INTRODUCTION
Historical Background
Growth of Information Retrieval
Ontology
MOTIVATION
LITERATURE REVIEW
ISSUES IN INFORMATION RETRIEVAL
AIM
PROPOSED RESEARCH METHODOLOGY
CONCLUSION
REFERENCES
Paradigm Shift of Online Education System Due to COVID-19 Pandemic: A Sentiment Analysis Using Machine Learning
Prajkta P. Chapke1,* and Anjali B. Raut1
INTRODUCTION
HISTORICAL BACKGROUND
Social Network Analysis
Impact of Social Networks
Positive Impact
Negative Impact
Characteristics of Social Networks
User-based
Interactive
Community-driven
Relationships.
Emotion Over Content.