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Submit a ProposalSmart Agritech
 | Robotics, AI, and Internet of Things (IoT) in Agriculture Edited by Santosh Kumar Srivastava, Durgesh Srivastava, Korhan Cengiz, and Pramod Gaur Copyright: 2024 | Status: Published ISBN: 9781394302959 | Hardcover | 584 pages Price: $225 USD  |
One Line Description
The main goal of Smart Agritech: Robotics, AI, and the Internet of Things (IoT) in Agriculture is to explore how emerging technologies such as robotics, artificial intelligence (AI), and IoT can be leveraged to improve efficiency, sustainability, and productivity in agriculture.
Audience
Students, teachers, researchers, industry personnel, researchers, academicians, and other industry professionals, scientists, and engineers
Students, teachers, researchers, industry personnel, researchers, academicians, and other industry professionals, scientists, and engineers
Description
Agriculture has always been a vital sector of the global economy, providing food and raw materials for industries and households. However, with the growing population, changing climate conditions, and limited resources, the agriculture sector is facing numerous challenges. To address these challenges, farmers and agricultural companies are turning to advanced technologies such as Robotics, Artificial Intelligence (AI), and the Internet of Things (IoT).
This exciting new volume provides a comprehensive overview of the latest technological advances in agriculture, focusing on the use of these three cutting-edge technologies. The book will explore the potential benefits of these technologies in improving agricultural efficiency, productivity, and sustainability.
Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.
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Agriculture has always been a vital sector of the global economy, providing food and raw materials for industries and households. However, with the growing population, changing climate conditions, and limited resources, the agriculture sector is facing numerous challenges. To address these challenges, farmers and agricultural companies are turning to advanced technologies such as Robotics, Artificial Intelligence (AI), and the Internet of Things (IoT).
This exciting new volume provides a comprehensive overview of the latest technological advances in agriculture, focusing on the use of these three cutting-edge technologies. The book will explore the potential benefits of these technologies in improving agricultural efficiency, productivity, and sustainability.
Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.
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Author / Editor Details
Santosh Kumar Srivastava, PhD, has been an assistant professor in the Department of Computer Science and Engineering, School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, India, since 2022. He completed his PhD in Computer Science and Engineering in 2021 from Shri JJT University, Jhunjhunu, Rajasthan, India. He has 20 years of IT administration, research and academic experience and has published more than 15 papers in reputed scientific journals and conferences. He also has five patents to his credit and is a member of IEEE, ACM, and other professional societies.
Santosh Kumar Srivastava, PhD, has been an assistant professor in the Department of Computer Science and Engineering, School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, India, since 2022. He completed his PhD in Computer Science and Engineering in 2021 from Shri JJT University, Jhunjhunu, Rajasthan, India. He has 20 years of IT administration, research and academic experience and has published more than 15 papers in reputed scientific journals and conferences. He also has five patents to his credit and is a member of IEEE, ACM, and other professional societies.
Durgesh Srivastava, PhD, is an associate professor at Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India. He received his PhD degree in computer science and engineering from IKG Punjab Technical University, Jalandhar, Punjab, India in 2020. He has over 14 years of research and academic experience and has published more than 30 papers in reputed scientific journals and conferences. He has published several patents, and one book.
Korhan Cengiz, PhD, is an associate professor in the department of Computer Engineering, Istinye University, Istanbul, Turkey. He earned his PhD in electronics engineering from Kadir Has University, Turkey in 2016. He has published more than 40 articles in scientific journals, he has five international patents, published more than ten book chapters, and one book. He has edited over 20 books and is the Associate Editor of IEEE Transactions on Intelligent Transportation Systems, IEEE Potentials Magazine, and holds editor positions with other reputed scientific publishers. He has presented more than 40 keynote talks in reputed conferences, and he has earned numerous awards for his research.
Pramod Gaur, PhD, is an assistant professor of computer science in the Department of Computer Science, BITS Pilani Dubai Campus, Dubai, UAE. He received his PhD from Ulster University, U.K., in 2018. Previously, he also worked as Assistant Professor at the LNMIIT, Jaipur and Postdoctoral RA in Neuro-Imaging Technology with the Intelligent Systems Research Centre, Ulster University. His research interests include brain–computer interface, the analysis of non-stationary signals, and machine learning.
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Table of Contents
Preface
1. Introduction to Smart Farming: Definition, Importance and Trends
Manoj Kumar Mahto, Santosh Kumar Srivastava and Basant Sah
1.1 Introduction
1.2 Smart Farming
1.3 Internet of Things
1.3.1 Fundamentals of IoT Applications in Smart Farming
1.4 Technologies Used in Smart Farming
1.4.1 Global Positioning System (GPS)
1.4.2 Sensor Technologies
1.4.3 Variable-Rate of Technology (VRT) and Grid Soil Sampling
1.4.4 Geographic Information System (GIS)
1.4.5 Crop Management
1.4.6 Soil and Plant Sensors
1.4.7 Rate Controllers
1.4.8 Precision Irrigation in Pressurized Systems
1.4.9 Yield Monitor
1.4.10 Software
1.5 Importance of Smart Farming
1.5.1 Soil Mapping and Plant Monitoring
1.5.2 Irrigation
1.5.3 Site-Specific Nutrient Management
1.5.4 Crop Pest and Disease Management
1.5.5 Yield Monitoring and Forecasting
1.5.6 Enhanced Production Rates
1.5.7 Water Conservation
1.5.8 Real-Time Data and Insights
1.5.9 Reduction in Operation Costs
1.5.10 High-Quality Production
1.5.11 Accurate Farm and Yield Evaluation
1.5.12 Improved Livestock Production
1.5.13 Reduced Environmental Footprint Impact
1.5.14 Remote Monitoring for Easy Management
1.5.15 Expensive Asset Monitoring
1.6 Role of IoT in Advanced Farming Practices
1.6.1 Greenhouse Farming and Protected Cultivation
1.6.2 Hydroponics
1.6.3 Vertical Farming
1.6.4 Phenotyping
1.7 Trends of Smart Farming
1.7.1 Precision Agriculture
1.7.2 Automation
1.7.3 Internet of Things (IoT)
1.7.4 Big Data Analytics
1.7.5 Vertical Farming
1.7.6 Aquaponics
References
2. Overview of Robotics in Agriculture: Types and Applications
Praveen Kantha, Durgesh Srivastava, Santosh Kumar Srivastava, Sunil Kr. Maakar and Basant Sah
2.1 Introduction
2.2 Background
2.2.1 Historical Perspective of Agriculture and Technology
2.2.2 Overview of the Current State of Agriculture and Technology
2.2.3 Benefits and Challenges of Robotics in Agriculture
2.3 Types of Robotics in Agriculture
2.4 Applications of Robotics in Agriculture
2.5 Advantages of Robotics in Agriculture
2.6 Limitations of Robotics in Agriculture
2.7 Future of Robotics in Agriculture
2.8 Case Studies and Examples
2.9 Implications and Recommendations
2.10 Conclusion
References
3. Digital Farming: The New Era of Agriculture-Opportunities and Challenges
Anju Khandelwal and Avanish Kumar
3.1 Introduction
3.1.1 Digital Farming
3.1.2 Automated Farming
3.2 Tools and Methods Used in Digital Farming
3.2.1 GPS
3.2.2 GIS
3.2.3 Grid Sampling
3.2.4 Variable Rate Technology
3.2.5 Yield Monitors
3.2.6 Remote Sensors
3.2.7 Auto-Guidance System
3.3 Pandemic Effects on Traditional Farming
3.4 New Scope at Digital Farming
3.5 Challenges or Difficulties for Digital Farming
3.6 Future Scope and Benefits
References
4. Challenges and Barriers to Smart Farming Adaptation: A Technical, Economic, and Social Perspective
Shivam Tiwari, Barkha Bhardwaj, Deepak Arora and Sabita Khatri
4.1 Introduction
4.1.1 Definition and Importance of Smart Farming
4.1.2 Objectives and Scope of the Chapter
4.2 Technical Challenges in Smart Farming Adaptation
4.2.1 Overview of Smart Farming Technologies
4.2.1.1 Precision Agriculture
4.2.1.2 Drones
4.2.1.3 Robotics
4.2.1.4 Livestock Management
4.2.1.5 Aquaculture
4.2.1.6 Data Analytics
4.2.2 Technical Challenges in Data Management and Connectivity
4.2.2.1 Data Management
4.2.2.2 Connectivity
4.2.2.3 Integration
4.2.2.4 Data Standards
4.2.3 Role of Sensors, Internet of Things, and AI in Smart Farming
4.2.3.1 Sensors
4.2.3.2 IoT
4.2.3.3 AI
4.2.3.4 Integration
4.3 Economic Barriers to Smart Farming Implementation
4.3.1 Cost of Implementing Smart Farming Technologies
4.3.1.1 Hardware Costs
4.3.1.2 Data Management Costs
4.3.1.3 Training and Support Costs
4.3.1.4 Infrastructure Costs
4.3.1.5 Financing Costs
4.3.2 Financing and Investment Challenges
4.3.3 Economic Benefits and Returns on Investment
4.4 Social Obstacles to Smart Farming Adoption
4.4.1 Lack of Knowledge and Understanding Among Farmers
4.4.2 Behavioral and Cultural Barriers to Change
4.4.3 Role of Education and Awareness Raising
4.5 Environmental Considerations in Smart Farming
4.5.1 Sustainable Agricultural Practices and Smart Farming
4.5.2 Environmental Benefits and Concerns
4.5.3 Climate Change Mitigation and Adaptation
4.5.3.1 Mitigating Climate Change
4.5.3.2 Adapting to Climate Change
4.6 Future Prospects for Smart Farming
4.6.1 Emerging Trends and Innovations in Smart Farming
4.6.2 Prospects for Overcoming Challenges and Barriers
4.6.3 Future Outlook for Sustainable Agriculture
4.7 Conclusion and Recommendations
4.7.1 Summary of Key Points
4.7.2 Policy Recommendations for Promoting Smart Farming
4.7.3 Conclusion and Future Directions for Research
References
5. Sustainable Development in Agriculture: Soil Management
Shivani Dubey, Rishav Yadav, Vikas Singhal and Anoop Dixit
5.1 Introduction
5.2 Reviewing the Need for Global Food Production to be Upgraded
5.2.1 Growing Population and Changing Dietary Preferences
5.2.2 Climate Change and Resource Limitations
5.2.3 Environmental Sustainability and Biodiversity Conservation
5.2.4 Food Loss and Waste
5.2.5 Technological Advancements and Digitalization
5.2.6 Socio-Economic Equity and Rural Development
5.2.7 Policy Interventions and Governance
5.3 Soil Quality and Its Impact
5.4 Emerging Technologies
5.4.1 AI Revolution
5.4.1.1 Precision Farming and Decision Support Systems
5.4.1.2 Crop Monitoring and Disease Detection
5.4.1.3 Autonomous Farming and Robotics
5.4.1.4 Supply Chain Optimization
5.4.1.5 Data-Driven Farm Management
5.4.2 Computer Vision
5.4.2.1 Crop Monitoring and Disease Detection
5.4.2.2 Weed Identification and Management
5.4.2.3 Yield Estimation and Quality Assessment
5.4.2.4 Smart Irrigation and Resource Management
5.4.2.5 Plant Breeding and Genetic Improvement
5.4.2.6 Farm Automation and Robotics
5.4.2.7 Decision Support Systems
5.5 Introduction and Theory of IoT
5.5.1 Perception Layer
5.5.2 Network Layer
5.5.3 Middleware Layer
5.5.4 Service Layer
5.5.5 Analytics Layer
5.5.6 End-User Layer
5.6 Several Sensors and How They are Used in Agriculture
5.6.1 Level Sensors
5.6.1.1 Continuous Measurements
5.6.1.2 Point-Level Measurements
5.6.2 Temperature Sensors
5.6.3 Proximity Sensors
5.6.4 Infrared Sensors
5.6.5 Touch Sensors
5.7 Centralized Agriculture System
5.8 Conclusion
References
6. Concepts of Robotics, AI, and Internet of Things (IoT) in Agriculture
Devesh Kumar Bandil and Pankaj Rahi
6.1 Introduction
6.1.1 The Implantation of Information and Commutation Technology in Agriculture Sector
6.2 General Challenges Faced in the Agriculture Industry
6.2.1 Growing Demand for Food
6.2.2 Limited Resources
6.2.3 Climate Change
6.2.4 Labor Shortages
6.2.5 Food Demand
6.2.6 Specific Challenges in the Deployment of Technology of Robotics, AI, and IoT in Agriculture
6.3 Role of Robotics, AI, and IoT in Agriculture
6.4 Benefits of Robotic and AI in Improving Agriculture
6.4.1 Increased Efficiency
6.4.2 Improved Precision
6.4.3 Reduced Labor Costs
6.4.4 Sustainability
6.5 Collecting Data and Performing Analyses With the Help of the Internet of Things
6.6 Relationships of IoT and AI Strengthen the Agriculture Sector
6.7 Benefits of IoT in Agriculture
6.8 The Role of AI in Tomorrow’s Farming
6.8.1 How Artificial Intelligence (AI) Can Help the Farming Industry
6.8.2 Selecting Seeds and Plants With the Help of AI
6.8.3 Use of Artificial Intelligence in Farming
6.8.4 A Predictive Model for Yield Using Artificial Intelligence
6.8.5 Implementing AI for Weed and Pest Management
6.8.6 AI-Powered Inventory Management and Sales Promotion
6.9 Robots in Agriculture—Perceptions and Pros, Cons
6.9.1 Farming Equipment With Automated Functions and the Positive Effects
6.10 Complications Associated With Robots Used in Agriculture
6.10.1 Difficulties and Limitations to be Conquered
6.10.2 Successful Applications and Other Case Studies
6.10.3 Societal and Ethical Implications
6.11 Conclusion
References
7. Data Analytics in Agriculture: Predictive Models and Real-Time Decision-Making
Raman Kumar, Harpreet Kaur Channi and Harish Kumar Banga
7.1 Introduction
7.2 Data Collection and Management in Agriculture
7.2.1 Soil Sensors
7.2.2 Weather Stations
7.2.3 Drones
7.2.4 Satellite Imagery
7.2.5 Farm Machinery
7.2.6 Pest and Disease Monitoring Systems
7.2.7 Market Data
7.2.8 Crop Records
7.2.9 Livestock Records
7.3 Challenges in Collecting and Managing Agricultural Data
7.4 Strategies for Effective Data Collection and Management
7.5 Predictive Models in Agriculture
7.5.1 Types of Predictive Models
7.6 Applications of Predictive Models in Agriculture
7.7 Real-Time Decision-Making in Agriculture
7.7.1 Importance of Real-Time Decision-Making in Agriculture
7.7.2 Tools and Technologies for Real-Time Decision Making
7.8 Integration of Predictive Models and Real-Time Decision Making in Agriculture
7.8.1 Benefits of Integrating Predictive Models and Real-Time Decision-Making
7.8.2 Challenges of Integration
7.8.3 Strategies for Successful Integration
7.9 Future of Data Analytics in Agriculture
7.9.1 Emerging Trends and Technologies
7.9.2 Potential Applications of Data Analytics in Agriculture
7.9.3 Implications for Agriculture Industry and Society
7.10 Concluding Remarks
7.11 Future Directions for Research and Practice
References
8. Examining the Role of IoT and AI in Revolutionizing Agriculture: A Smart Farming Approach
Santosh Kumar Srivastava, Manoj Kumar Mahto, Sheo Kumar, Deepak Kumar Verma and Hare Ram Singh
8.1 Introduction
8.2 IoT in Agriculture
8.2.1 Soil Moisture
8.2.2 Temperature and Humidity
8.2.3 Light Intensity
8.2.4 Weather Conditions
8.2.5 Pest and Disease Detection
8.2.6 Livestock Monitoring
8.3 Major Advantages of Using IoT in Agriculture
8.4 Major Disadvantages of Using IoT in Agriculture
8.5 AI in Agriculture
8.5.1 Equipment Used for AI in Agriculture
8.6 Benefits of Drones in Agriculture
8.6.1 Precision Farming
8.6.2 Crop Management
8.6.3 Disease and Pest Detection
8.6.4 Weed Management
8.7 Main Part of AI in Agriculture
8.8 Effects of AI in Agriculture
8.9 Combination of AI and IoT
8.10 Automatic Irrigation
8.11 Crop Health Monitoring
8.12 Supply Yields to the Demanded Areas
8.13 Conclusion
References
9. Smart AgriTech: Sensors & Networks
Bikram Kar and Amit Kumar
9.1 Introduction
9.2 IoT in Agriculture
9.2.1 Benefits of IoT in the Agricultural Sector
9.2.2 Limitations and Challenges of IoT in Agricultural Farming
9.3 Utilization of Sensor-Based IoT Devices in the Agricultural Sector
9.3.1 Precision Farming
9.3.2 Greenhouse Monitoring
9.3.3 Livestock Monitoring
9.3.4 Agricultural Solutions by Using Smartphones
9.3.5 IoT Devices and Sensors in Agriculture
9.4 Networks for IoT in Agriculture
9.4.1 Wireless Networks
9.4.2 Cellular Networks
9.4.3 Satellite Networks
9.4.4 Network-Related Issues
9.4.5 New Network Technologies
9.5 Communication Protocols Used in Agriculture
9.6 Case Studies and Examples of IoT Applications in Agriculture
9.7 The Future of IoT in Agriculture
9.7.1 Emerging Technologies
9.7.2 Ethical and Societal Implications
9.8 Conclusion
References
10. Internet of Things in Agriculture: Sensor, Network and Communication Protocol
Rajeev Kumar, Surinder Singh and Prem Prakash
10.1 Introduction
10.2 IoT in Agriculture
10.3 Physical Design of IoT Architecture
10.3.1 IoT Devices
10.3.2 Connectivity
10.3.3 Gateways
10.3.4 Network Infrastructure
10.4 IoT Network Levels and Stages
10.5 Characteristics of IoT
10.6 IoT Enabling Technology
10.6.1 Wireless Communication
10.6.2 Cloud Computing
10.6.3 Embedded Systems
10.6.4 Edge Computing
10.6.5 Artificial Intelligence and Machine Learning
10.6.6 Blockchain Technology
10.6.7 Remote Sensing Technology
10.7 IoT Challenges
10.8 IoT Communication Protocol
10.8.1 MQTT Protocol
10.8.2 CoAP Protocol
10.8.3 HTTP (Hypertext Transfer Protocol) for IOT
10.8.4 Advanced Message Queuing Protocol (AMQP)
10.8.5 Extensible Messaging and Presence Protocol (XMPP)
10.9 Sensors for IoT-Based Smart Agriculture
10.9.1 Soil Moisture Sensor
10.9.1.1 Resistive-Type Sensor
10.9.1.2 Capacitive-Type Soil Moisture Sensor
10.9.1.3 Tensiometer
10.9.2 pH Sensor
10.9.2.1 Water pH Sensor
10.9.2.2 Soil pH Sensor
10.9.3 Soil Nutrient Sensor
10.9.3.1 Soil Electrical Conductivity and Salinity Sensor
10.9.3.2 Ion-Selective Electrode Sensor
10.9.3.3 Optical Soil Nutrient Sensors
10.9.4 Temperature and Humidity Sensors
10.9.4.1 DHT11 Sensor
10.9.4.2 BME280 Sensor Module
10.9.4.3 LM35 Sensor Module
10.9.5 Leaf Wetness Sensor
10.9.6 Gas/Smoke Sensors
10.9.7 Light-Dependent Resistor (LDR)
10.9.8 Rain Sensor
10.9.9 MEMS Microphone Sensor
10.9.10 Ultrasonic Sensor
10.9.11 PIR Motion Sensor
10.10 Sensor Interfacing and Control
10.10.1 Feature of Arduino UNO Board
10.10.2 Interfacing for Soil Moisture Sensor
10.10.3 Interfacing for Motion Sensor
10.10.4 Interfacing for LDR Sensor
10.10.5 Interfacing for Ultrasonic Sensor
10.10.6 Interfacing for Smoke Sensor
10.10.7 Interfacing for TMP36 Temperature Sensor
10.11 Conclusion
References
11. An Irrigation System: Design, Implementation, and Benefits
Shilpa Mahajan
11.1 Introduction
11.2 Smart Irrigation Composition
11.3 Advantages of Intelligent Irrigation Systems
11.4 Irrigation Methods
11.5 Review on Smart Irrigation Systems
11.6 Smart-Based Irrigation Techniques for Urban and Rural Areas
11.7 Barriers of Smart Irrigation Systems 3
11.8 Conclusion
References
12. Implementation of Smart Irrigation Systems
Jabar H. Yousif , Ghassan Al-Kindi, Ahmad. K. Kayed and Durgesh K. Srivastava
12.1 Introduction
12.2 Literature Survey
12.3 Irrigation System Design
12.4 Irrigation System Implementation Benefits
12.4.1 Sensor Types
12.4.2 Machine Learning Methods
12.4.3 IoT Methods
12.4.4 Land Types
12.5 Irrigation System Benefits
12.6 Results and Discussion
12.7 Conclusion
References
13. Smart Plant-Based Wastewater Treatment for Agricultural Irrigation Systems
Rituparna Saha and Amit Biswas
13.1 Introduction
13.1.1 Importance of Wastewater Treatment
13.1.2 Steps of Conventional Wastewater Treatment
13.1.3 Aquatic Plant-Based Wastewater Treatment
13.1.4 Objectives and Scopes
13.2 Water Quality Parameters for Agricultural Irrigation
13.2.1 Physical Parameters
13.2.2 Chemical Parameters
13.2.3 Biological Parameters
13.2.4 Treated Wastewater Suitability for Agricultural Irrigation Application
13.3 Wetland Method for Wastewater Treatment
13.3.1 Working Principle of Wetland for Wastewater Treatment
13.3.2 Types of Wetland
13.3.2.1 Natural Wetland
13.3.2.2 Constructed Wetland
13.3.2.3 Hybrid Wetland
13.3.3 Aquatic Plants for Wetland Treatment
13.3.3.1 Reeds
13.3.3.2 Cattails
13.3.3.3 Water Hyacinth
13.3.3.4 Duckweed
13.3.3.5 Water Willow
13.4 Importance of Artificial Intelligence in Wastewater Treatment
13.4.1 Different Artificial Intelligence Models
13.4.1.1 Regression Model
13.4.1.2 Support Vector Machine (SVM)
13.4.1.3 Artificial Neural Network (ANN)
13.4.1.4 Deep Learning Model
13.4.2 Application of Artificial Intelligence for Wastewater Treatment
13.4.2.1 Challenges of Aquatic Plant-Based Wastewater Treatment
13.4.2.2 Application of Artificial Intelligence
13.5 Economic Feasibility of Smart Plant-Based Treatment for Agricultural Irrigation
13.5.1 Cost Analysis of Smart Treatment System for Irrigation
13.5.1.1 Initial Investment
13.5.1.2 Operational Cost
13.5.1.3 Maintenance Cost
13.5.2 Comparison of Smart System Cost With Conventional Method
13.5.3 Economic Viability of Smart System Implementation in Rural Agriculture
13.6 Challenges Associated with Artificial Intelligence Application in Wastewater Treatment
13.6.1 Social, Technical, and Regulatory Challenges
13.6.1.1 Quality and Availability of Data
13.6.1.2 Compatibility and Integration of Data
13.6.1.3 Model Development and Validation
13.6.1.4 Explainability and Interpretability
13.6.1.5 Regulatory and Ethical Considerations
13.6.2 Strategies and Solutions for Addressing Challenges
13.6.2.1 Quality and Availability of Data
13.6.2.2 Pre-Processing and Integration of Data
13.6.2.3 Model Development, Training, and Validation
13.7 Conclusion
References
14. Drones in Agriculture: Mapping, Monitoring, and Decision-Making
Umangkumar B. Zalavadiya
14.1 Introduction
14.2 Opportunities
14.3 Drone Mapping for Transforming Agriculture
14.4 Significance of Drone Monitoring in Precision Agriculture
14.4.1 Crop Health Assessment
14.4.2 Irrigation Management
14.4.3 Livestock Monitoring
14.4.4 Weather Monitoring
14.5 Leveraging Drone for Real-Time Decision‑Making in Agriculture
14.6 Common Usages of Agricultural Drones
14.6.1 Soil and Field Analysis
14.6.2 Seed Planting
14.6.3 Crop Spraying
14.6.4 Crop Scouting
14.6.5 Land Management
14.6.6 Tracing Plantation Lines
14.6.7 Biomass Estimation
14.6.8 Integrated GIS Mapping
14.7 Challenges
14.8 Summary and Scope
14.9 Conclusion
References
15. Investigation of Banana Plant Disease Detection Using Transfer Learning
R. Karthickmanoj and S. Aasha Nandhini
15.1 Introduction
15.2 Literature Survey
15.3 Transfer Learning
15.4 Methodology
15.5 Result and Discussion
15.6 Conclusion
References
16. Exploring Dataset for Apple Leaf Disease Detection: A Focus on Horticulture
Anupam Bonkra, Sunil Pathak and Amandeep Kaur
16.1 Introduction
16.1.1 Segmentation and Machine Learning Techniques for Detection of Apple Foliage Disease
16.1.2 Deep Learning Method for Apple Leaf Disease Detection
16.1.3 Contribution
16.2 Literature Review
16.3 Apple Leaf Disease Detection Mechanism
16.4 Significance of Dataset
16.5 Discussion
16.6 Conclusion
References
17. Optimizing Crop Yield Prediction Using Machine Learning Algorithms
Rejuwan Shamim and Trapty Agarwal
17.1 Introduction
17.1.1 Importance of Accurate Crop Yield Prediction
17.1.2 Role of Machine Learning Algorithms in Optimizing Predictions
17.2 Related Work
17.2.1 Limitations and Challenges of Traditional Approaches
17.2.2 Previous Studies Utilize Machine Learning Algorithms for Crop Yield Prediction
17.3 Methodology
17.3.1 Data Collection and Preprocessing
17.3.2 Selection of Machine Learning Algorithms
17.3.3 Feature Engineering and Selection
17.3.3.1 Feature Engineering
17.3.3.2 Feature Selection
17.3.4 Model Training and Evaluation
17.4 Machine Learning Algorithms for Crop Yield Prediction
17.4.1 Regression Algorithms (e.g., Linear Regression, Decision Trees)
17.4.1.1 Linear Regression
17.4.1.2 Decision Trees
17.4.2 Ensemble Methods (e.g., Random Forests, Gradient Boosting)
17.4.2.1 Random Forests
17.4.2.2 Gradient Boosting
17.4.3 Deep Learning Models (e.g., Neural Networks, Convolutional Neural Networks)
17.4.3.1 Neural Networks
17.4.3.2 Convolutional Neural Networks (CNNs)
17.4.4 Support Vector Machines (SVM)
17.4.5 Other Relevant Algorithms
17.4.5.1 K-Nearest Neighbors (KNN)
17.4.5.2 Naive Bayes
17.4.5.3 Ensemble Methods
17.5 Experimental Results and Analysis
17.5.1 Description of the Dataset
17.5.2 Performance Evaluation Metrics
17.5.3 Comparative Analysis of Different Machine Learning Algorithms
17.5.4 Discussion of Results and Insights Gained
17.6 Optimization Techniques
17.6.1 Feature Selection and Dimensionality Reduction
17.6.2 Hyperparameter Tuning
17.6.3 Ensemble Methods for Improving Predictions
17.6.4 Transfer Learning and Domain Adaptation
17.7 Challenges and Future Directions
17.7.1 Explainability and Interpretability of the Models
17.8 Conclusion
17.8.1 Importance of Machine Learning in Optimizing Crop Yield Prediction
17.8.2 Applications
References
18. Analyzing Smart Farming Technologies: A Study on Indian Farmers’ Adoption Trends
Nilesh Tejrao Kate, Upasana Acharya and Chandraprabha Vaidya
18.1 Introduction
18.2 Research Methodology
18.3 Objectives
18.4 Literature Review
18.5 Data Analysis
18.6 Key Findings
18.7 Discussions and Recommendations
18.8 Conclusion
References
19. Leveraging Linear Regression Model to Address Food Insecurity in the United States: A Smart Agritech Approach
Anoushka Tomar, Shivani Dubey, Vikas Singhal and Ajay Kumar Sahu
19.1 Introduction
19.2 Literature Review
19.3 Data Sources
19.4 Preprocessing
19.5 Data Dictionary
19.6 Exploratory Data Analysis
19.6.1 Question 1: In What Ways Have Metrics Like Unemployment, Houselessness, and Food Insecurity Rates Evolved Over Time?
19.6.2 Question 2: What is the Geographic Variability in Unemployment, Race, and Food Insecurity Rates?
19.6.3 Question 3: How are Factors Such as Rent Prices, Unemployment, Houselessness, and Race Related to the Food Insecurity Rate?
19.6.4 Conclusion for Exploratory Data Analysis
19.7 Feature Engineering
19.7.1 Feature Selection and Modeling
19.8 Predictions
19.9 Conclusion
References
About the Editors
Index
Back to Top
Preface
1. Introduction to Smart Farming: Definition, Importance and Trends
Manoj Kumar Mahto, Santosh Kumar Srivastava and Basant Sah
1.1 Introduction
1.2 Smart Farming
1.3 Internet of Things
1.3.1 Fundamentals of IoT Applications in Smart Farming
1.4 Technologies Used in Smart Farming
1.4.1 Global Positioning System (GPS)
1.4.2 Sensor Technologies
1.4.3 Variable-Rate of Technology (VRT) and Grid Soil Sampling
1.4.4 Geographic Information System (GIS)
1.4.5 Crop Management
1.4.6 Soil and Plant Sensors
1.4.7 Rate Controllers
1.4.8 Precision Irrigation in Pressurized Systems
1.4.9 Yield Monitor
1.4.10 Software
1.5 Importance of Smart Farming
1.5.1 Soil Mapping and Plant Monitoring
1.5.2 Irrigation
1.5.3 Site-Specific Nutrient Management
1.5.4 Crop Pest and Disease Management
1.5.5 Yield Monitoring and Forecasting
1.5.6 Enhanced Production Rates
1.5.7 Water Conservation
1.5.8 Real-Time Data and Insights
1.5.9 Reduction in Operation Costs
1.5.10 High-Quality Production
1.5.11 Accurate Farm and Yield Evaluation
1.5.12 Improved Livestock Production
1.5.13 Reduced Environmental Footprint Impact
1.5.14 Remote Monitoring for Easy Management
1.5.15 Expensive Asset Monitoring
1.6 Role of IoT in Advanced Farming Practices
1.6.1 Greenhouse Farming and Protected Cultivation
1.6.2 Hydroponics
1.6.3 Vertical Farming
1.6.4 Phenotyping
1.7 Trends of Smart Farming
1.7.1 Precision Agriculture
1.7.2 Automation
1.7.3 Internet of Things (IoT)
1.7.4 Big Data Analytics
1.7.5 Vertical Farming
1.7.6 Aquaponics
References
2. Overview of Robotics in Agriculture: Types and Applications
Praveen Kantha, Durgesh Srivastava, Santosh Kumar Srivastava, Sunil Kr. Maakar and Basant Sah
2.1 Introduction
2.2 Background
2.2.1 Historical Perspective of Agriculture and Technology
2.2.2 Overview of the Current State of Agriculture and Technology
2.2.3 Benefits and Challenges of Robotics in Agriculture
2.3 Types of Robotics in Agriculture
2.4 Applications of Robotics in Agriculture
2.5 Advantages of Robotics in Agriculture
2.6 Limitations of Robotics in Agriculture
2.7 Future of Robotics in Agriculture
2.8 Case Studies and Examples
2.9 Implications and Recommendations
2.10 Conclusion
References
3. Digital Farming: The New Era of Agriculture-Opportunities and Challenges
Anju Khandelwal and Avanish Kumar
3.1 Introduction
3.1.1 Digital Farming
3.1.2 Automated Farming
3.2 Tools and Methods Used in Digital Farming
3.2.1 GPS
3.2.2 GIS
3.2.3 Grid Sampling
3.2.4 Variable Rate Technology
3.2.5 Yield Monitors
3.2.6 Remote Sensors
3.2.7 Auto-Guidance System
3.3 Pandemic Effects on Traditional Farming
3.4 New Scope at Digital Farming
3.5 Challenges or Difficulties for Digital Farming
3.6 Future Scope and Benefits
References
4. Challenges and Barriers to Smart Farming Adaptation: A Technical, Economic, and Social Perspective
Shivam Tiwari, Barkha Bhardwaj, Deepak Arora and Sabita Khatri
4.1 Introduction
4.1.1 Definition and Importance of Smart Farming
4.1.2 Objectives and Scope of the Chapter
4.2 Technical Challenges in Smart Farming Adaptation
4.2.1 Overview of Smart Farming Technologies
4.2.1.1 Precision Agriculture
4.2.1.2 Drones
4.2.1.3 Robotics
4.2.1.4 Livestock Management
4.2.1.5 Aquaculture
4.2.1.6 Data Analytics
4.2.2 Technical Challenges in Data Management and Connectivity
4.2.2.1 Data Management
4.2.2.2 Connectivity
4.2.2.3 Integration
4.2.2.4 Data Standards
4.2.3 Role of Sensors, Internet of Things, and AI in Smart Farming
4.2.3.1 Sensors
4.2.3.2 IoT
4.2.3.3 AI
4.2.3.4 Integration
4.3 Economic Barriers to Smart Farming Implementation
4.3.1 Cost of Implementing Smart Farming Technologies
4.3.1.1 Hardware Costs
4.3.1.2 Data Management Costs
4.3.1.3 Training and Support Costs
4.3.1.4 Infrastructure Costs
4.3.1.5 Financing Costs
4.3.2 Financing and Investment Challenges
4.3.3 Economic Benefits and Returns on Investment
4.4 Social Obstacles to Smart Farming Adoption
4.4.1 Lack of Knowledge and Understanding Among Farmers
4.4.2 Behavioral and Cultural Barriers to Change
4.4.3 Role of Education and Awareness Raising
4.5 Environmental Considerations in Smart Farming
4.5.1 Sustainable Agricultural Practices and Smart Farming
4.5.2 Environmental Benefits and Concerns
4.5.3 Climate Change Mitigation and Adaptation
4.5.3.1 Mitigating Climate Change
4.5.3.2 Adapting to Climate Change
4.6 Future Prospects for Smart Farming
4.6.1 Emerging Trends and Innovations in Smart Farming
4.6.2 Prospects for Overcoming Challenges and Barriers
4.6.3 Future Outlook for Sustainable Agriculture
4.7 Conclusion and Recommendations
4.7.1 Summary of Key Points
4.7.2 Policy Recommendations for Promoting Smart Farming
4.7.3 Conclusion and Future Directions for Research
References
5. Sustainable Development in Agriculture: Soil Management
Shivani Dubey, Rishav Yadav, Vikas Singhal and Anoop Dixit
5.1 Introduction
5.2 Reviewing the Need for Global Food Production to be Upgraded
5.2.1 Growing Population and Changing Dietary Preferences
5.2.2 Climate Change and Resource Limitations
5.2.3 Environmental Sustainability and Biodiversity Conservation
5.2.4 Food Loss and Waste
5.2.5 Technological Advancements and Digitalization
5.2.6 Socio-Economic Equity and Rural Development
5.2.7 Policy Interventions and Governance
5.3 Soil Quality and Its Impact
5.4 Emerging Technologies
5.4.1 AI Revolution
5.4.1.1 Precision Farming and Decision Support Systems
5.4.1.2 Crop Monitoring and Disease Detection
5.4.1.3 Autonomous Farming and Robotics
5.4.1.4 Supply Chain Optimization
5.4.1.5 Data-Driven Farm Management
5.4.2 Computer Vision
5.4.2.1 Crop Monitoring and Disease Detection
5.4.2.2 Weed Identification and Management
5.4.2.3 Yield Estimation and Quality Assessment
5.4.2.4 Smart Irrigation and Resource Management
5.4.2.5 Plant Breeding and Genetic Improvement
5.4.2.6 Farm Automation and Robotics
5.4.2.7 Decision Support Systems
5.5 Introduction and Theory of IoT
5.5.1 Perception Layer
5.5.2 Network Layer
5.5.3 Middleware Layer
5.5.4 Service Layer
5.5.5 Analytics Layer
5.5.6 End-User Layer
5.6 Several Sensors and How They are Used in Agriculture
5.6.1 Level Sensors
5.6.1.1 Continuous Measurements
5.6.1.2 Point-Level Measurements
5.6.2 Temperature Sensors
5.6.3 Proximity Sensors
5.6.4 Infrared Sensors
5.6.5 Touch Sensors
5.7 Centralized Agriculture System
5.8 Conclusion
References
6. Concepts of Robotics, AI, and Internet of Things (IoT) in Agriculture
Devesh Kumar Bandil and Pankaj Rahi
6.1 Introduction
6.1.1 The Implantation of Information and Commutation Technology in Agriculture Sector
6.2 General Challenges Faced in the Agriculture Industry
6.2.1 Growing Demand for Food
6.2.2 Limited Resources
6.2.3 Climate Change
6.2.4 Labor Shortages
6.2.5 Food Demand
6.2.6 Specific Challenges in the Deployment of Technology of Robotics, AI, and IoT in Agriculture
6.3 Role of Robotics, AI, and IoT in Agriculture
6.4 Benefits of Robotic and AI in Improving Agriculture
6.4.1 Increased Efficiency
6.4.2 Improved Precision
6.4.3 Reduced Labor Costs
6.4.4 Sustainability
6.5 Collecting Data and Performing Analyses With the Help of the Internet of Things
6.6 Relationships of IoT and AI Strengthen the Agriculture Sector
6.7 Benefits of IoT in Agriculture
6.8 The Role of AI in Tomorrow’s Farming
6.8.1 How Artificial Intelligence (AI) Can Help the Farming Industry
6.8.2 Selecting Seeds and Plants With the Help of AI
6.8.3 Use of Artificial Intelligence in Farming
6.8.4 A Predictive Model for Yield Using Artificial Intelligence
6.8.5 Implementing AI for Weed and Pest Management
6.8.6 AI-Powered Inventory Management and Sales Promotion
6.9 Robots in Agriculture—Perceptions and Pros, Cons
6.9.1 Farming Equipment With Automated Functions and the Positive Effects
6.10 Complications Associated With Robots Used in Agriculture
6.10.1 Difficulties and Limitations to be Conquered
6.10.2 Successful Applications and Other Case Studies
6.10.3 Societal and Ethical Implications
6.11 Conclusion
References
7. Data Analytics in Agriculture: Predictive Models and Real-Time Decision-Making
Raman Kumar, Harpreet Kaur Channi and Harish Kumar Banga
7.1 Introduction
7.2 Data Collection and Management in Agriculture
7.2.1 Soil Sensors
7.2.2 Weather Stations
7.2.3 Drones
7.2.4 Satellite Imagery
7.2.5 Farm Machinery
7.2.6 Pest and Disease Monitoring Systems
7.2.7 Market Data
7.2.8 Crop Records
7.2.9 Livestock Records
7.3 Challenges in Collecting and Managing Agricultural Data
7.4 Strategies for Effective Data Collection and Management
7.5 Predictive Models in Agriculture
7.5.1 Types of Predictive Models
7.6 Applications of Predictive Models in Agriculture
7.7 Real-Time Decision-Making in Agriculture
7.7.1 Importance of Real-Time Decision-Making in Agriculture
7.7.2 Tools and Technologies for Real-Time Decision Making
7.8 Integration of Predictive Models and Real-Time Decision Making in Agriculture
7.8.1 Benefits of Integrating Predictive Models and Real-Time Decision-Making
7.8.2 Challenges of Integration
7.8.3 Strategies for Successful Integration
7.9 Future of Data Analytics in Agriculture
7.9.1 Emerging Trends and Technologies
7.9.2 Potential Applications of Data Analytics in Agriculture
7.9.3 Implications for Agriculture Industry and Society
7.10 Concluding Remarks
7.11 Future Directions for Research and Practice
References
8. Examining the Role of IoT and AI in Revolutionizing Agriculture: A Smart Farming Approach
Santosh Kumar Srivastava, Manoj Kumar Mahto, Sheo Kumar, Deepak Kumar Verma and Hare Ram Singh
8.1 Introduction
8.2 IoT in Agriculture
8.2.1 Soil Moisture
8.2.2 Temperature and Humidity
8.2.3 Light Intensity
8.2.4 Weather Conditions
8.2.5 Pest and Disease Detection
8.2.6 Livestock Monitoring
8.3 Major Advantages of Using IoT in Agriculture
8.4 Major Disadvantages of Using IoT in Agriculture
8.5 AI in Agriculture
8.5.1 Equipment Used for AI in Agriculture
8.6 Benefits of Drones in Agriculture
8.6.1 Precision Farming
8.6.2 Crop Management
8.6.3 Disease and Pest Detection
8.6.4 Weed Management
8.7 Main Part of AI in Agriculture
8.8 Effects of AI in Agriculture
8.9 Combination of AI and IoT
8.10 Automatic Irrigation
8.11 Crop Health Monitoring
8.12 Supply Yields to the Demanded Areas
8.13 Conclusion
References
9. Smart AgriTech: Sensors & Networks
Bikram Kar and Amit Kumar
9.1 Introduction
9.2 IoT in Agriculture
9.2.1 Benefits of IoT in the Agricultural Sector
9.2.2 Limitations and Challenges of IoT in Agricultural Farming
9.3 Utilization of Sensor-Based IoT Devices in the Agricultural Sector
9.3.1 Precision Farming
9.3.2 Greenhouse Monitoring
9.3.3 Livestock Monitoring
9.3.4 Agricultural Solutions by Using Smartphones
9.3.5 IoT Devices and Sensors in Agriculture
9.4 Networks for IoT in Agriculture
9.4.1 Wireless Networks
9.4.2 Cellular Networks
9.4.3 Satellite Networks
9.4.4 Network-Related Issues
9.4.5 New Network Technologies
9.5 Communication Protocols Used in Agriculture
9.6 Case Studies and Examples of IoT Applications in Agriculture
9.7 The Future of IoT in Agriculture
9.7.1 Emerging Technologies
9.7.2 Ethical and Societal Implications
9.8 Conclusion
References
10. Internet of Things in Agriculture: Sensor, Network and Communication Protocol
Rajeev Kumar, Surinder Singh and Prem Prakash
10.1 Introduction
10.2 IoT in Agriculture
10.3 Physical Design of IoT Architecture
10.3.1 IoT Devices
10.3.2 Connectivity
10.3.3 Gateways
10.3.4 Network Infrastructure
10.4 IoT Network Levels and Stages
10.5 Characteristics of IoT
10.6 IoT Enabling Technology
10.6.1 Wireless Communication
10.6.2 Cloud Computing
10.6.3 Embedded Systems
10.6.4 Edge Computing
10.6.5 Artificial Intelligence and Machine Learning
10.6.6 Blockchain Technology
10.6.7 Remote Sensing Technology
10.7 IoT Challenges
10.8 IoT Communication Protocol
10.8.1 MQTT Protocol
10.8.2 CoAP Protocol
10.8.3 HTTP (Hypertext Transfer Protocol) for IOT
10.8.4 Advanced Message Queuing Protocol (AMQP)
10.8.5 Extensible Messaging and Presence Protocol (XMPP)
10.9 Sensors for IoT-Based Smart Agriculture
10.9.1 Soil Moisture Sensor
10.9.1.1 Resistive-Type Sensor
10.9.1.2 Capacitive-Type Soil Moisture Sensor
10.9.1.3 Tensiometer
10.9.2 pH Sensor
10.9.2.1 Water pH Sensor
10.9.2.2 Soil pH Sensor
10.9.3 Soil Nutrient Sensor
10.9.3.1 Soil Electrical Conductivity and Salinity Sensor
10.9.3.2 Ion-Selective Electrode Sensor
10.9.3.3 Optical Soil Nutrient Sensors
10.9.4 Temperature and Humidity Sensors
10.9.4.1 DHT11 Sensor
10.9.4.2 BME280 Sensor Module
10.9.4.3 LM35 Sensor Module
10.9.5 Leaf Wetness Sensor
10.9.6 Gas/Smoke Sensors
10.9.7 Light-Dependent Resistor (LDR)
10.9.8 Rain Sensor
10.9.9 MEMS Microphone Sensor
10.9.10 Ultrasonic Sensor
10.9.11 PIR Motion Sensor
10.10 Sensor Interfacing and Control
10.10.1 Feature of Arduino UNO Board
10.10.2 Interfacing for Soil Moisture Sensor
10.10.3 Interfacing for Motion Sensor
10.10.4 Interfacing for LDR Sensor
10.10.5 Interfacing for Ultrasonic Sensor
10.10.6 Interfacing for Smoke Sensor
10.10.7 Interfacing for TMP36 Temperature Sensor
10.11 Conclusion
References
11. An Irrigation System: Design, Implementation, and Benefits
Shilpa Mahajan
11.1 Introduction
11.2 Smart Irrigation Composition
11.3 Advantages of Intelligent Irrigation Systems
11.4 Irrigation Methods
11.5 Review on Smart Irrigation Systems
11.6 Smart-Based Irrigation Techniques for Urban and Rural Areas
11.7 Barriers of Smart Irrigation Systems 3
11.8 Conclusion
References
12. Implementation of Smart Irrigation Systems
Jabar H. Yousif , Ghassan Al-Kindi, Ahmad. K. Kayed and Durgesh K. Srivastava
12.1 Introduction
12.2 Literature Survey
12.3 Irrigation System Design
12.4 Irrigation System Implementation Benefits
12.4.1 Sensor Types
12.4.2 Machine Learning Methods
12.4.3 IoT Methods
12.4.4 Land Types
12.5 Irrigation System Benefits
12.6 Results and Discussion
12.7 Conclusion
References
13. Smart Plant-Based Wastewater Treatment for Agricultural Irrigation Systems
Rituparna Saha and Amit Biswas
13.1 Introduction
13.1.1 Importance of Wastewater Treatment
13.1.2 Steps of Conventional Wastewater Treatment
13.1.3 Aquatic Plant-Based Wastewater Treatment
13.1.4 Objectives and Scopes
13.2 Water Quality Parameters for Agricultural Irrigation
13.2.1 Physical Parameters
13.2.2 Chemical Parameters
13.2.3 Biological Parameters
13.2.4 Treated Wastewater Suitability for Agricultural Irrigation Application
13.3 Wetland Method for Wastewater Treatment
13.3.1 Working Principle of Wetland for Wastewater Treatment
13.3.2 Types of Wetland
13.3.2.1 Natural Wetland
13.3.2.2 Constructed Wetland
13.3.2.3 Hybrid Wetland
13.3.3 Aquatic Plants for Wetland Treatment
13.3.3.1 Reeds
13.3.3.2 Cattails
13.3.3.3 Water Hyacinth
13.3.3.4 Duckweed
13.3.3.5 Water Willow
13.4 Importance of Artificial Intelligence in Wastewater Treatment
13.4.1 Different Artificial Intelligence Models
13.4.1.1 Regression Model
13.4.1.2 Support Vector Machine (SVM)
13.4.1.3 Artificial Neural Network (ANN)
13.4.1.4 Deep Learning Model
13.4.2 Application of Artificial Intelligence for Wastewater Treatment
13.4.2.1 Challenges of Aquatic Plant-Based Wastewater Treatment
13.4.2.2 Application of Artificial Intelligence
13.5 Economic Feasibility of Smart Plant-Based Treatment for Agricultural Irrigation
13.5.1 Cost Analysis of Smart Treatment System for Irrigation
13.5.1.1 Initial Investment
13.5.1.2 Operational Cost
13.5.1.3 Maintenance Cost
13.5.2 Comparison of Smart System Cost With Conventional Method
13.5.3 Economic Viability of Smart System Implementation in Rural Agriculture
13.6 Challenges Associated with Artificial Intelligence Application in Wastewater Treatment
13.6.1 Social, Technical, and Regulatory Challenges
13.6.1.1 Quality and Availability of Data
13.6.1.2 Compatibility and Integration of Data
13.6.1.3 Model Development and Validation
13.6.1.4 Explainability and Interpretability
13.6.1.5 Regulatory and Ethical Considerations
13.6.2 Strategies and Solutions for Addressing Challenges
13.6.2.1 Quality and Availability of Data
13.6.2.2 Pre-Processing and Integration of Data
13.6.2.3 Model Development, Training, and Validation
13.7 Conclusion
References
14. Drones in Agriculture: Mapping, Monitoring, and Decision-Making
Umangkumar B. Zalavadiya
14.1 Introduction
14.2 Opportunities
14.3 Drone Mapping for Transforming Agriculture
14.4 Significance of Drone Monitoring in Precision Agriculture
14.4.1 Crop Health Assessment
14.4.2 Irrigation Management
14.4.3 Livestock Monitoring
14.4.4 Weather Monitoring
14.5 Leveraging Drone for Real-Time Decision‑Making in Agriculture
14.6 Common Usages of Agricultural Drones
14.6.1 Soil and Field Analysis
14.6.2 Seed Planting
14.6.3 Crop Spraying
14.6.4 Crop Scouting
14.6.5 Land Management
14.6.6 Tracing Plantation Lines
14.6.7 Biomass Estimation
14.6.8 Integrated GIS Mapping
14.7 Challenges
14.8 Summary and Scope
14.9 Conclusion
References
15. Investigation of Banana Plant Disease Detection Using Transfer Learning
R. Karthickmanoj and S. Aasha Nandhini
15.1 Introduction
15.2 Literature Survey
15.3 Transfer Learning
15.4 Methodology
15.5 Result and Discussion
15.6 Conclusion
References
16. Exploring Dataset for Apple Leaf Disease Detection: A Focus on Horticulture
Anupam Bonkra, Sunil Pathak and Amandeep Kaur
16.1 Introduction
16.1.1 Segmentation and Machine Learning Techniques for Detection of Apple Foliage Disease
16.1.2 Deep Learning Method for Apple Leaf Disease Detection
16.1.3 Contribution
16.2 Literature Review
16.3 Apple Leaf Disease Detection Mechanism
16.4 Significance of Dataset
16.5 Discussion
16.6 Conclusion
References
17. Optimizing Crop Yield Prediction Using Machine Learning Algorithms
Rejuwan Shamim and Trapty Agarwal
17.1 Introduction
17.1.1 Importance of Accurate Crop Yield Prediction
17.1.2 Role of Machine Learning Algorithms in Optimizing Predictions
17.2 Related Work
17.2.1 Limitations and Challenges of Traditional Approaches
17.2.2 Previous Studies Utilize Machine Learning Algorithms for Crop Yield Prediction
17.3 Methodology
17.3.1 Data Collection and Preprocessing
17.3.2 Selection of Machine Learning Algorithms
17.3.3 Feature Engineering and Selection
17.3.3.1 Feature Engineering
17.3.3.2 Feature Selection
17.3.4 Model Training and Evaluation
17.4 Machine Learning Algorithms for Crop Yield Prediction
17.4.1 Regression Algorithms (e.g., Linear Regression, Decision Trees)
17.4.1.1 Linear Regression
17.4.1.2 Decision Trees
17.4.2 Ensemble Methods (e.g., Random Forests, Gradient Boosting)
17.4.2.1 Random Forests
17.4.2.2 Gradient Boosting
17.4.3 Deep Learning Models (e.g., Neural Networks, Convolutional Neural Networks)
17.4.3.1 Neural Networks
17.4.3.2 Convolutional Neural Networks (CNNs)
17.4.4 Support Vector Machines (SVM)
17.4.5 Other Relevant Algorithms
17.4.5.1 K-Nearest Neighbors (KNN)
17.4.5.2 Naive Bayes
17.4.5.3 Ensemble Methods
17.5 Experimental Results and Analysis
17.5.1 Description of the Dataset
17.5.2 Performance Evaluation Metrics
17.5.3 Comparative Analysis of Different Machine Learning Algorithms
17.5.4 Discussion of Results and Insights Gained
17.6 Optimization Techniques
17.6.1 Feature Selection and Dimensionality Reduction
17.6.2 Hyperparameter Tuning
17.6.3 Ensemble Methods for Improving Predictions
17.6.4 Transfer Learning and Domain Adaptation
17.7 Challenges and Future Directions
17.7.1 Explainability and Interpretability of the Models
17.8 Conclusion
17.8.1 Importance of Machine Learning in Optimizing Crop Yield Prediction
17.8.2 Applications
References
18. Analyzing Smart Farming Technologies: A Study on Indian Farmers’ Adoption Trends
Nilesh Tejrao Kate, Upasana Acharya and Chandraprabha Vaidya
18.1 Introduction
18.2 Research Methodology
18.3 Objectives
18.4 Literature Review
18.5 Data Analysis
18.6 Key Findings
18.7 Discussions and Recommendations
18.8 Conclusion
References
19. Leveraging Linear Regression Model to Address Food Insecurity in the United States: A Smart Agritech Approach
Anoushka Tomar, Shivani Dubey, Vikas Singhal and Ajay Kumar Sahu
19.1 Introduction
19.2 Literature Review
19.3 Data Sources
19.4 Preprocessing
19.5 Data Dictionary
19.6 Exploratory Data Analysis
19.6.1 Question 1: In What Ways Have Metrics Like Unemployment, Houselessness, and Food Insecurity Rates Evolved Over Time?
19.6.2 Question 2: What is the Geographic Variability in Unemployment, Race, and Food Insecurity Rates?
19.6.3 Question 3: How are Factors Such as Rent Prices, Unemployment, Houselessness, and Race Related to the Food Insecurity Rate?
19.6.4 Conclusion for Exploratory Data Analysis
19.7 Feature Engineering
19.7.1 Feature Selection and Modeling
19.8 Predictions
19.9 Conclusion
References
About the Editors
Index
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