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Submit a ProposalComputer Vision in Smart Agriculture and Crop Management
 | Edited by Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Prithi Samuel, Malathy Sathyamoorthy, and Ali Kashif Bashir Copyright: 2025 | Expected Pub Date:2024// ISBN: 9781394186297 | Hardcover | 394 pages Price: $225 USD  |
One Line Description
This book is essential for anyone interested in understanding how smart agriculture, utilizing information and technology such as computer vision and deep learning, can revolutionize agriculture productivity, resolve ongoing concerns, and enhance economic and general effectiveness in farming.
Audience
Researchers and academics who want to use computer vision approaches in smart agriculture and industry professionals working in the domains of agriculture, computer vision, and smart farming
Researchers and academics who want to use computer vision approaches in smart agriculture and industry professionals working in the domains of agriculture, computer vision, and smart farming
Description
The need for a sufficient food supply and the needs of society have led to the development of smart agriculture. Smart agriculture, which makes use of information and technology, can help farmers in isolated places. A crucial part of the development of agricultural automation systems is computer vision (CV) technology. The use of computer vision in conjunction with other intelligent systems, such as deep learning, could be used to handle agriculture productivity in relation to massive data in order to resolve ongoing agricultural concerns, as well as enhance the economic and general effectiveness of agricultural automation devices in order to drive it more intelligently. Automation in agriculture helps to attain benefits like minimal cost, high performance, and high accuracy, all of which contribute to sustained progress. During the growing season, aerial imaging is typically utilized to monitor crops. High-throughput phenotypic research is anticipated to offer substantial yield attributes in large proportions and facilitate effective crop management choices. With the help of contemporary computer vision technologies, crop field security may be completely automated. Computer vision technology has advanced quickly in agriculture because of its automation and detection capabilities. Farmers are effectively helped by machine learning and computer vision techniques to discriminate between rich soil, natural remedies, and insect controls. The color, size, thickness, and surface texture of a crop can be used to identify impurities in the agricultural yield, as well as unclean food products. The detection and monitoring of plant health can be best accomplished using computer vision-based artificial intelligence (AI) models. In addition, AI supports the development of seasonal forecasting techniques to increase accuracy, production, and operational efficiencies. In order to detect and manage plants, weeds, diseases, and pests and safeguard the destiny of sustainable agriculture and farming, breakthroughs in computer vision, intelligent systems, and machine learning will eventually lead to the development of remote sensing technologies. The purpose of this special area is to use computer vision technologies and other emerging technologies to develop smart agriculture and crop surveillance for the benefit of society. To achieve smart agriculture and sustainable farming, this book focuses on deep learning for retrieving relevant image content features from digital photogrammetry and applying data mining and machine learning techniques for handling the content of image features into different clusters for intelligent applications.
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The need for a sufficient food supply and the needs of society have led to the development of smart agriculture. Smart agriculture, which makes use of information and technology, can help farmers in isolated places. A crucial part of the development of agricultural automation systems is computer vision (CV) technology. The use of computer vision in conjunction with other intelligent systems, such as deep learning, could be used to handle agriculture productivity in relation to massive data in order to resolve ongoing agricultural concerns, as well as enhance the economic and general effectiveness of agricultural automation devices in order to drive it more intelligently. Automation in agriculture helps to attain benefits like minimal cost, high performance, and high accuracy, all of which contribute to sustained progress. During the growing season, aerial imaging is typically utilized to monitor crops. High-throughput phenotypic research is anticipated to offer substantial yield attributes in large proportions and facilitate effective crop management choices. With the help of contemporary computer vision technologies, crop field security may be completely automated. Computer vision technology has advanced quickly in agriculture because of its automation and detection capabilities. Farmers are effectively helped by machine learning and computer vision techniques to discriminate between rich soil, natural remedies, and insect controls. The color, size, thickness, and surface texture of a crop can be used to identify impurities in the agricultural yield, as well as unclean food products. The detection and monitoring of plant health can be best accomplished using computer vision-based artificial intelligence (AI) models. In addition, AI supports the development of seasonal forecasting techniques to increase accuracy, production, and operational efficiencies. In order to detect and manage plants, weeds, diseases, and pests and safeguard the destiny of sustainable agriculture and farming, breakthroughs in computer vision, intelligent systems, and machine learning will eventually lead to the development of remote sensing technologies. The purpose of this special area is to use computer vision technologies and other emerging technologies to develop smart agriculture and crop surveillance for the benefit of society. To achieve smart agriculture and sustainable farming, this book focuses on deep learning for retrieving relevant image content features from digital photogrammetry and applying data mining and machine learning techniques for handling the content of image features into different clusters for intelligent applications.
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Author / Editor Details
Rajesh Kumar Dhanaraj, PhD is a professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed to over 25 books on various technologies, 21 patents, and 53 articles and papers in various refereed journals and international conferences. He is a Senior Member of the Institute of Electrical and Electronics Engineers, member of the Computer Science Teacher Association and International Association of Engineers, and an Expert Advisory Panel Member of Texas Instruments Inc., USA. His research interests include Machine Learning, Cyber-Physical Systems, and Wireless Sensor Networks.
Rajesh Kumar Dhanaraj, PhD is a professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed to over 25 books on various technologies, 21 patents, and 53 articles and papers in various refereed journals and international conferences. He is a Senior Member of the Institute of Electrical and Electronics Engineers, member of the Computer Science Teacher Association and International Association of Engineers, and an Expert Advisory Panel Member of Texas Instruments Inc., USA. His research interests include Machine Learning, Cyber-Physical Systems, and Wireless Sensor Networks.
Balamurugan Balusamy, PhD is an associate dean student at Shiv Nadar University, Delhi, India with over 12 years of experience. He has published over 200 papers, edited and authored over 80 books, and collaborated with professors across the world from top ranked universities. Additionally, he has several top-notch conferences on his resume, serves on the advisory committee for several startups and forums, and does consultancy work for the industry on industrial IoT and has given over 195 talks at various events and symposiums.
Prithi Samuel, PhD is an assistant professor in the Department of Computational Intelligence at the SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India with over 15 years of teaching experience in reputed engineering colleges. She is a pioneer researcher in the areas of automation theory, machine learning, deep learning, computational intelligence techniques, and the Internet of Things. She has published papers in leading international journals and conferences and published books and book chapters for several renowned publishing houses. She is an active member of the Institute of Electrical and Electronics Engineering and Association for Computing Machinery and holds an International Society for Technology in Education and International Association of Engineers lifetime membership.
Malathy Sathyamoorthy is an assistant professor in the department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India. She is a life member of the Indian Society for Technical Education and International Association of Engineers. She has also published over 20 research papers in various journals, 15 papers in international conferences, two patents, and four book chapters. Her areas of interest include wireless sensor networks, networking, security, and machine learning.
Ali Kashif Bashir, PhD is a reader of Networks and Security at the Manchester Metropolitan University, United Kingdom. He is also affiliated with the University of Electronic Science and Technology of China, National University of Science and Technology, Islamabad, Pakistan, and University of Guelph, Canada. He is managing several research and industrial projects and reviews funding proposals for the Engineering and Physical Sciences Research Council, UK, Commonwealth, UK, National Science and Engineering Research Council, Canada, Mitacs, Canada, the Irish Research Council, and Qatar National Research Fund. He has delivered more than 30 talks across the globe, organized over 40 guest editorials, and chaired more than 35 conferences and workshops.
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Table of Contents
Preface
1. Computer Vision-Based Innovations for Smart Agriculture and Crop Surveillance: Evolution, Trends, and Future Challenges
M. Nalini and B. Yoga Bhuvaneswari
1.1 Introduction
1.2 Artificial Intelligence in Agriculture
1.3 Evolution of Smart Agriculture
1.4 AI Technology Trends in Computer Vision
1.5 Benefits of Artificial Intelligence in Agriculture
1.5.1 Improving the Whole Supply Chain
1.5.2 Agricultural Robotics
1.5.3 Policy, Governance and Market Access
1.5.4 Early Warning System
1.5.5 Food Safety and Traceability
1.5.6 Financial Inclusion and Risk Management
1.5.7 Capacity Building and Empowerment
1.5.8 Growth Driven by IoT
1.5.9 Image-Dependent Insight Generation
1.5.10 Identification of Optimal Mix for Agronomic Products
1.5.11 Monitoring of Crops and Soil Health
1.5.12 Automation Techniques in Irrigation and Enabling Farmers
1.5.13 Drones: The New Buzz in AI-Driven Agriculture
1.6 Precision Farming
1.7 Future Challenges
1.8 Conclusion
References
2. Cyber Biosecurity Solutions for Protecting Smart Agriculture and Precision Farming
Balakesava Reddy Parvathala and Srinivas Kolli
2.1 Introduction
2.2 Cyber-Attacks on SF and PA
2.3 Network and Related Equipment Attacks
2.3.1 Attacks on Data
2.3.2 Attacks on Code (Applications)
2.3.3 Attacks on Support Chain
2.3.4 Misuse Attacks
2.4 Security Threats to SF and PA Using the Cyber-Kill-Chain (CKC) Taxonomy
2.5 The Taxonomy
2.5.1 Threats Pertaining to the Phase of Reconnaissance
2.6 Data Collection
2.6.1 Participants
2.7 Vulnerability of the Food and Agricultural System and the Bio Economy
2.7.1 Risk Mitigation Strategies and Countermeasure
2.7.2 A Consideration of the Diversity Within and Between the Plant, Animal, and Environmental Sectors of the Food and Agricultural System
2.7.3 Technological Solutions for the Intelligent Production of Food
2.7.3.1 Structures of Greenhouses, Micro-Tunnels, and Macro-Tunnels
2.7.3.2 Monitoring, Control, and Automation Systems
2.8 The APTs in SF and PA
2.8.1 APT Attacks on SF and PA
2.8.1.1 The Anatomy of an APT Attack on SF or PA
2.9 Challenges in the Implementation of Technologies in the Agricultural Sector
2.10 Open Challenges and Research Areas
2.11 Conclusions
References
3. Precision Smart Farming and Cultivation with Virtual Reality/Augmented Reality Technology - Applications and Use Cases
Himani Sharma, Atin Kumar and Rohit Kumar
3.1 Introduction
3.2 Advantages of Precision Smart Farming
3.2.1 Precision Agriculture Enhances Crop Cultivation
3.2.2 Precision Farming Helps with Real-Time Data
3.2.3 Precision Farming Helps in Optimizing the Use of Resources
3.2.4 Decrease in the Use of Pesticides, Fertilizers, and Water
3.2.5 Reduce Strain on the Environment
3.2.6 Ability to Make Informed Decisions
3.3 Disadvantages of Precision-Smart Farming
3.4 How Could India Benefit from Precision Farming?
3.5 Challenges in Adopting Precision Farming in India
3.6 Cultivation with Virtual Reality/Augmented Reality Technology
3.7 Benefits of Cultivation with Virtual/Augmented Reality Technology
3.7.1 Augmented Reality in Agriculture for Simulated Training
3.7.2 Augmented Reality in Agriculture for Weather Updates
3.7.2.1 Virtual Reality Saves Money on Plants Protection Tools
3.7.2.2 How Augmented Reality Works in Agriculture
3.7.2.3 Is Augmented Reality the Future of Productive Farming?
3.7.2.4 Why Does the Agriculture Sector Face Challenges with Augmented Reality?
3.8 Conclusion
3.9 Summary
References
4. Stereo Vision Subsystem and Scene Segmentation Self-Steering Tractors in Smart Agriculture
Dileep Pulugu, Revathy Pulugu, K. Muthumanickam, S. Gopinath and A. Manikandan
4.1 Introduction
4.2 Global Positioning System
4.3 Self-Steering Tractors with Vision Have Evolved
4.4 Safety Issues
4.5 The System Architecture of Self-Guiding Tractors
4.6 Basic Modeling
4.7 Building with a Vision
4.8 Path Tracking Control System
4.9 Development of a Tractor-Based Agricultural Row Detection System Using Stereovision
4.9.1 System Architecture
4.9.2 Stereo Vision Crop Row Detecting System
4.10 Creation of a Crop Row Detecting Method Using Stereo Vision
4.10.1 Overview of the System and Picture Preparation
4.10.2 Visual Odometry Based Relative Localization
4.10.3 Visual Odometry Using Feature Tracking
4.10.4 Visible Odometry Using a Single Camera
4.11 Stereo Vision for Absolute Localization
4.12 Multi-Vision Methods
4.13 Conclusions
References
5. Vision-Based Image Classification and Image Segmentation Algorithms for Plant Disease Diagnostics
N. Ashokkumar, A. Manikandan, S. Hariprasath and P. Vijayalakshmi
5.1 Introduction
5.2 Signs and Symptoms of Plant Disease
5.2.1 The Importance of Plant Diseases in the US
5.2.2 Different Types of Plant Disease
5.3 Techniques and Algorithms for Detecting Plant Disease
5.3.1 Detection Techniques Used Today for Agricultural Diseases
5.3.2 Clustering Algorithms
5.3.3 Image Preprocessing
5.4 Dataset for Diagnosis Plant Disease
5.4.1 DiaMOS Plant Dataset
5.4.2 RoCoLe Dataset
5.4.3 Plant Pathology Dataset
5.4.4 Plant Village Dataset
5.5 Segmentation
5.5.1 Segmentation using the RCNN
5.5.2 Segmentation using TGVFCMS
5.6 Classification
5.6.1 Convolutional Neural Network (CNN)
5.6.1.1 Convolutional Layer
5.6.1.2 Pooling Layer
5.6.1.3 Fully Connected Layer
5.6.2 Classification Using Artificial Neural Networks
5.6.3 Classification Using K-Nearest Neighbor (K-NN)
5.6.4 Classification Using an Adaptive Neuro-Fuzzy Classification Model (ANFIS)
5.7 Conclusion
References
6. Smart Dust Technology for Monitoring and Control Systems in Smart Agriculture and Crop Surveillance Systems
M. Yogeshwari and A. Prasanth
6.1 Introduction
6.2 Smart Dust Technology in Smart Agriculture
6.2.1 WSN-Based Precision Agriculture
6.2.2 IoT in Agriculture
6.2.3 Machine Learning in WSN and Its Agriculture-Based Applications
6.3 Precision Agriculture and Its Functional Elements
6.4 Yield Monitoring and Forecast
6.4.1 Information Sources Required for Yield Monitoring
6.4.2 Demand for Irrigation Planning
6.5 Advanced Agricultural Practices
6.6 Conclusion
References
7. An Advanced Application of UAV – Drone Technologies in Precision Agriculture for Seed Dropping, Fertilizers and Pesticides Spraying and Field Monitoring
Daniel Lawrence I., A. Rehash Rushmi Pavitra, Ragupathy Karu, and M.P. Saravanan
7.1 Introduction
7.2 Irrigation Management
7.3 Seed Dropping
7.4 Pesticide and Fertilizer Spraying System
7.5 Improving Soil Productivity
7.6 Supporting Crop Growth
7.7 Crop Management Strategies
7.8 Increasing Crop Yield
7.9 Preventing Crop Disease
7.10 Predicting Crop Yield
7.11 Conclusion
References
8. Cognitive Intelligence and Distributed Computing Systems Applications in Smart Farming
Sangeetha Radhakrishnan and A. Prasanth
8.1 Introduction
8.1.1 Smart Farming
8.1.2 Challenges in Traditional Farming
8.1.3 Techniques Used in Smart Farming
8.1.3.1 Machine Learning
8.1.3.2 Deep Learning
8.1.3.3 Image Processing
8.1.3.4 Internet of Things
8.1.3.5 Artificial Neural Networks
8.1.3.6 Wireless Sensor Network Technology
8.1.4 Benefits of Smart Farming
8.1.5 Applications of Smart Farming
8.1.5.1 Disease Detection
8.1.5.2 Crop Phenotyping
8.2 Cognitive Intelligence
8.2.1 Applications of Smart Farming
8.2.1.1 Retail Industry
8.2.1.2 Logistics
8.2.1.3 Banking and Finance
8.2.1.4 Cyber Security
8.2.1.5 Power and Energy
8.2.1.6 Healthcare
8.2.1.7 Education
8.2.1.8 Agriculture
8.2.2 Agricultural Robotics
8.2.3 Benefits of Cognitive Computing
8.2.4 Scope of Cognitive Computing
8.2.4.1 Engagement
8.2.4.2 Decision
8.2.4.3 Discovery
8.2.5 Limitations of Cognitive Computing
8.2.5.1 Limited Analysis of Risk
8.2.5.2 Meticulous Training Process
8.2.5.3 More Intelligence Augmentation Rather than Artificial Intelligence
8.3 Distributed Computing
8.3.1 Evolution of Distributed Computing Systems
8.3.2 Features of Distributed Computing System
8.3.2.1 No Common Physical Clock
8.3.2.2 No Shared Memory
8.3.2.3 Geographical Separation
8.3.2.4 Autonomy and Heterogeneity
8.3.2.5 Distributed Computing System Model
8.3.2.6 Minicomputer Model
8.3.2.7 Workstation Model
8.3.2.8 Processor Pool Model
8.3.2.9 Hybrid Model
8.3.3 Advantages of Distributed Computing System
8.3.3.1 Inherently Distributed Computation
8.3.3.2 Resource Sharing
8.3.3.3 Access to the Geographically Remote Data and Resources
8.3.3.4 Enhanced Reliability
8.3.3.5 Increased Performance/Cost Ratio
8.3.3.6 Scalability
8.3.3.7 Modularity and Incremental Expandability
8.3.4 Scope of Distributed Computing
8.4 Cognitive Intelligence and Distributed Computing in Smart Farming
8.4.1 Need for Digitization of Agriculture
8.4.2 Existing System
8.4.3 Technologies Used in Cognitive Intelligence and Distributed Computing in Smart Farming
8.4.3.1 Genetic Algorithm, Fuzzy Logic, ANN Technology
8.4.3.2 Remote Sensing, Decision Tree, Hyper Spectral Data Mining
8.4.3.3 WSN Technology
8.4.3.4 Image Processing, K-Mean Clustering
8.4.3.5 Machine Vision, Image Processing Techniques
8.4.3.6 Hyperspectral Imaging, Artificial Neural Networks
8.5 Conclusion and Summary
8.5.1 Concluding Thoughts
8.5.2 Future Enhancement
References
9. Blockchain-Based Smart Agriculture with the Internet of Things: A Revolutionary Approach in Agriculture and Food Supply Chain
Vasanth R. and Pandian A.
9.1 Introduction
9.1.1 Food Supply Chain in Agriculture’s Context
9.1.2 The Purpose of Blockchain Technology
9.1.3 The Synergy Between Blockchain and IoT
9.1.4 An Overview of IoT and Its Role in Agriculture
9.1.4.1 Sensing and Data Collection
9.1.4.2 Data Sensing and Gathering
9.1.4.3 Automation and Control
9.1.4.4 Animal Observation
9.1.4.5 Supply Chain Management
9.1.4.6 Environmental Monitoring
9.1.4.7 Technologies for Supporting Decisions
9.1.4.8 Scalability and Honesty
9.2 Literature Review
9.2.1 Blockchain Technology and Its Applications in Agriculture
9.2.1.1 Enhancing Traceability and Transparency
9.2.1.2 Supply Chain Efficiency
9.2.1.3 Reduction of Food Fraud
9.2.1.4 Data Sharing and Cooperation
9.2.1.5 Sustainability and Certification
9.2.1.6 Adoption Problems and Roadblocks
9.2.1.7 Future Perspectives
9.2.2 Exploration on IoT in Agriculture and Its Benefits
9.2.3 Challenges in the Agriculture and Food Supply Chain
9.3 Methodology
9.3.1 Data Collections
9.3.2 Data Analysis
9.3.3 Experiments and Case Studies
9.3.3.1 Monitoring the Supply Chain Trial
9.3.3.2 Case Research on Crop Monitoring
9.3.3.3 The Experiment on Intelligent Irrigation
9.3.3.4 Livestock Tracking Case Study
9.3.3.5 Quality Control Test
9.3.3.6 A Decentralized Market Case Study
9.3.3.7 The Carbon Footprint-Reducing Test
9.3.3.8 Case Study for Prediction of Crop Yield
9.3.3.9 Farmer Cooperative Experiment
9.3.3.10 Case Study on Pest and Disease Monitoring
9.3.4 Selection Criteria for Blockchain and IoT Technologies in Agriculture
9.3.5 Data Sources and Tools
9.3.5.1 Data Sources
9.3.5.2 Tools and Technologies
9.4 Blockchain Technology in Agriculture
9.4.1 Several Ways of Blockchain Technology for Agriculture
9.4.1.1 Traceability of the Supply Chain
9.4.1.2 Responsibility and Openness
9.4.1.3 Security and Confidence
9.4.1.4 Efficiency and Reduced Waste
9.4.1.5 Data Sharing and Cooperation
9.4.1.6 Consumer Involvement
9.4.1.7 Reduction in Food Fraud
9.4.1.8 Catastrophe or Pollution
9.4.1.9 Sustainable Environmental Practices
9.4.2 Case Studies Blockchain Implementation in Agriculture
9.4.2.1 IBM Food Trust
9.5 Internet of Things in Agriculture
9.5.1 Examples of IoT Applications in Smart Agriculture
9.6 Integration of Blockchain and IoT in Agriculture
9.6.1 Advantages of Combining Technologies
9.7 Case Studies
9.8 Challenges and Future Directions
9.9 Conclusion
References
10. Computer Vision Systems in Livestock Farming, Poultry Farming, and Fish Farming: Applications, Use Cases, and Research Directions
Balasubramaniam S., Vijesh Joe C., A. Prasanth and K. Satheesh Kumar
10.1 Introduction
10.2 Smart Agriculture
10.2.1 Livestock Farming
10.2.2 Poultry Farming
10.2.3 Fish Farming
10.3 Computer Vision
10.3.1 Primary Operations of Computer Vision
10.3.1.1 Capturing an Image
10.3.1.2 Image Editing Procedure
10.3.1.3 Analyzing and Taking Required Action
10.4 Primary Computer Vision Techniques
10.4.1 Classification of Images
10.4.2 Detection of Objects
10.4.3 Tracking of Objects
10.4.4 Semantic Segmentation
10.4.5 Instance Segmentation
10.4.6 AI Techniques
10.4.6.1 Convolution and Feature Extraction
10.4.6.2 Convolutional Neural Networks
10.4.6.3 Algorithms for Detecting Targets in Two Stages
10.4.6.4 Algorithms for Detecting Targets in a Single Stage
10.5 Computer Vision-Based Systems in Livestock Farming, Poultry Farming, and Fish Farming
10.5.1 Livestock Management
10.5.1.1 Individual Cattle Tracing and Health Monitoring
10.5.1.2 Automatic Prediction of Swine Stress Using Infrared Skin Temperature
(Subscrofa)
10.5.1.3 CNN-Based Image Analysis of Milking Cows for Individual Identification
and Tracking of Feeding Behavior
10.5.1.4 An Algorithm Based on Regularities for Deducing When Cattle are Grazing
or Ruminating from their Auditory Signals
10.5.1.5 Individual Pig Weights can be Automatically Estimated Using Image Processing Software
10.5.2 Poultry Farming
10.5.2.1 Estimating Broiler Weight Using Computer Vision and Neural Networks
10.5.2.2 Deep Convolutional Neural Network-Based Poop Recognition and Classification in Broilers
10.5.2.3 Imaging Investigation of Flock Behavior at Multiple Feeders for Broiler Chickens
10.5.2.4 Chicken Net, a Computer Vision-Based Method for Assessing Commercial
Laying Hen Plumage
10.5.2.5 Effects of Flooding on Litter-Directed Behavior, Anxiety, and the Pursuit
of Novel Stimuli in Young Grill Chickens
10.5.3 Fish Farming
10.5.3.1 Fish Behavior Analysis with Artificial Intelligence
10.5.3.2 Strong School-Tracking with a CNN for Individual Fish Identification
10.5.3.3 Fish Detection and Classification in Aquatic Environments Using Deep
Learning and Temporal Data
10.5.3.4 Computational Object Classification in Marine Observation Using Active
Learning System for Marine Observation Images (ALMI), a General-Purpose Active Learning System
10.5.3.5 Use of Machine Vision for Fish Weight Estimation
10.6 Computer Vision Systems for Intelligent Farming: Current Research Challenges
10.6.1 Legal Documents
10.6.2 Privacy and Data Protection
10.7 Conclusion and Future Scope
References
11. Forestry Management with AI and Drone Technology – Digital Forestry
M. Shanthalakshmi, M. Jeevasree, R. Kavitha, V. Madhumathi, S. Mythreye and A. Naafiah Yusra
11.1 Introduction
11.2 Drone Technology
11.3 Drones Employed for Disaster Management
11.4 Drones Equipped with Remote Sensing, GIS and LiDar for Geographical Dispersal Maintenance and Surveillance
11.5 Drones for Livestock Management
11.6 Conclusion
References
12. Drone Application and Use Cases in Smart Agriculture and Crop Surveillance: Future Research Directions
Nilotpal Das, Atin Kumar and Rohit Kumar
12.1 Introduction
12.2 Definition of Drones
12.3 Classification of Drones
12.3.1 Components of Agriculture Drones
12.3.2 Drones for Agriculture by Offering
12.3.2.1 Hardware
12.3.2.2 Software and Services
12.4 Application of Drones in Agriculture
12.5 Agriculture Using Drone Technology
12.5.1 Execution Strategy
12.5.1.1 Production of Drones
12.5.1.2 Suppliers of Drone Services
12.5.1.3 Trained Human Resources
12.5.1.4 Ease of Doing Business
12.5.1.5 Application of Drone Research
12.5.1.6 Extension System
12.5.1.7 Custom Hiring Centers
12.5.2 Scope for Public-Private Partnership (PPP) Mode Operations
12.6 Drone Use Rules and Regulations in India
12.7 Policy Need
12.8 Another Benefits of Drones in Agriculture
12.9 Drawbacks of Drones in Agriculture
12.10 Drone Agriculture Cost
12.11 Future Research Direction
12.12 Summary
References
13. A Comprehensive Study on Machine Vision Techniques for an Automatic Weeding Strategy in Plantations
Manikandan J., Rhikshitha K., Sathya Sudarsen G. S. and Saran J. U.
13.1 Introduction
13.2 Related Study
13.3 Methodology
13.3.1 Collection of Datasets of Images of the Undesired Weed
13.3.2 Data Labeling
13.3.3 Pre-Processing of Dataset
13.3.4 Transformation into Binary or Grayscale Image
13.3.5 Image Segmentation
13.3.6 Feature Extraction
13.3.7 Classification
13.3.8 Execute Action
13.4 Experimentation and Analysis
13.4.1 Dataset
13.4.2 Performance Evaluation Metrics
13.4.3 Results and Discussion
13.5 Conclusion and Future Enhancements
References
14. An Effective Study on the Machine Vision-Based Automatic Control and Monitoring in Furrow Irrigation and Precision Irrigation
Manikandan J., Saran J. U., Samitha S. and Rhikshitha K.
14.1 Introduction
14.1.1 Introduction to Smart Irrigation and Machine Vision
14.1.2 A Brief on Furrow Irrigation Water Management
14.1.3 A Brief on Precision Irrigation Water Management
14.1.4 Existing Irrigation Systems
14.2 Methodology
14.2.1 System Design
14.2.2 Data Collection (as Images)
14.2.3 Image Processing
14.2.4 Control Algorithms
14.2.5 Classification
14.2.5.1 Convolutional Neural Networks (CNNs)
14.2.5.2 Support Vector Machine (SVM)
14.2.5.3 Random Forests
14.2.5.4 Recurrent Neural Networks (RNNs)
14.2.5.5 Deep Reinforcement Learning
14.2.5.6 Artificial Neural Network (ANN)
14.2.5.7 Feedback and Optimization
14.3 Maintenance and Upgrades
14.4 Experimentation and Analysis
14.4.1 Dataset Summary
14.4.2 Performance Evaluation Metrics
14.4.2.1 Accuracy
14.4.2.2 Specificity
14.4.2.3 Precision
14.4.2.4 Sensitivity/Recall
14.4.2.5 F1 Measure/F1 Score
14.4.3 Result and Discussion
14.5 Conclusion and Future Enhancement
References
15. Applications in Agriculture for Assessing and Monitoring Soil Using Smart Sensing and Edge Computing
G. Padmapriya, V. Vennila, Prithi Samuel, Rajesh Kumar Dhanaraj, Balamurugan Balusamy and Malathy Sathyamoorthy
15.1 Introduction
15.1.1 IoT-Based Smart Agriculture
15.1.2 Smart Sensing
15.1.2.1 Smart Sensing
15.1.2.2 Edge Computing
15.1.2.3 Internet of Things (IoT)
15.1.2.4 Predictive Analytics
15.1.2.5 Precision Farming
15.1.3 Assessing and Monitoring Soil Using Smart Sensing
15.1.4 Smart Agriculture Using Edge Computing
15.1.4.1 Autonomous Tractors and Robotic Machinery
15.1.4.2 Farm Automation
15.2 Smart Agriculture Using Smart Sensing and Edge Computing
15.2.1 Pest Control
15.2.2 Harvesting
15.2.3 Integrated Precision Agriculture Systems
15.3 IoT-Based Smart Agriculture
15.3.1 Sensor Data
15.3.2 Semantic Knowledge Base
15.4 KNN-Based Smart IoT System
15.4.1 K-Nearest Neighbors Algorithm
15.4.2 Smart IoT System Using KNN
15.4.2.1 HL-69 Soil Hygrometer Sensor
15.4.2.2 AM2302 DHT11 Sensor
15.4.2.3 BH1750 FVI Light Sensor
15.5 Results and Discussion
15.6 Performance Evaluation
15.7 Conclusion
References
Index
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Preface
1. Computer Vision-Based Innovations for Smart Agriculture and Crop Surveillance: Evolution, Trends, and Future Challenges
M. Nalini and B. Yoga Bhuvaneswari
1.1 Introduction
1.2 Artificial Intelligence in Agriculture
1.3 Evolution of Smart Agriculture
1.4 AI Technology Trends in Computer Vision
1.5 Benefits of Artificial Intelligence in Agriculture
1.5.1 Improving the Whole Supply Chain
1.5.2 Agricultural Robotics
1.5.3 Policy, Governance and Market Access
1.5.4 Early Warning System
1.5.5 Food Safety and Traceability
1.5.6 Financial Inclusion and Risk Management
1.5.7 Capacity Building and Empowerment
1.5.8 Growth Driven by IoT
1.5.9 Image-Dependent Insight Generation
1.5.10 Identification of Optimal Mix for Agronomic Products
1.5.11 Monitoring of Crops and Soil Health
1.5.12 Automation Techniques in Irrigation and Enabling Farmers
1.5.13 Drones: The New Buzz in AI-Driven Agriculture
1.6 Precision Farming
1.7 Future Challenges
1.8 Conclusion
References
2. Cyber Biosecurity Solutions for Protecting Smart Agriculture and Precision Farming
Balakesava Reddy Parvathala and Srinivas Kolli
2.1 Introduction
2.2 Cyber-Attacks on SF and PA
2.3 Network and Related Equipment Attacks
2.3.1 Attacks on Data
2.3.2 Attacks on Code (Applications)
2.3.3 Attacks on Support Chain
2.3.4 Misuse Attacks
2.4 Security Threats to SF and PA Using the Cyber-Kill-Chain (CKC) Taxonomy
2.5 The Taxonomy
2.5.1 Threats Pertaining to the Phase of Reconnaissance
2.6 Data Collection
2.6.1 Participants
2.7 Vulnerability of the Food and Agricultural System and the Bio Economy
2.7.1 Risk Mitigation Strategies and Countermeasure
2.7.2 A Consideration of the Diversity Within and Between the Plant, Animal, and Environmental Sectors of the Food and Agricultural System
2.7.3 Technological Solutions for the Intelligent Production of Food
2.7.3.1 Structures of Greenhouses, Micro-Tunnels, and Macro-Tunnels
2.7.3.2 Monitoring, Control, and Automation Systems
2.8 The APTs in SF and PA
2.8.1 APT Attacks on SF and PA
2.8.1.1 The Anatomy of an APT Attack on SF or PA
2.9 Challenges in the Implementation of Technologies in the Agricultural Sector
2.10 Open Challenges and Research Areas
2.11 Conclusions
References
3. Precision Smart Farming and Cultivation with Virtual Reality/Augmented Reality Technology - Applications and Use Cases
Himani Sharma, Atin Kumar and Rohit Kumar
3.1 Introduction
3.2 Advantages of Precision Smart Farming
3.2.1 Precision Agriculture Enhances Crop Cultivation
3.2.2 Precision Farming Helps with Real-Time Data
3.2.3 Precision Farming Helps in Optimizing the Use of Resources
3.2.4 Decrease in the Use of Pesticides, Fertilizers, and Water
3.2.5 Reduce Strain on the Environment
3.2.6 Ability to Make Informed Decisions
3.3 Disadvantages of Precision-Smart Farming
3.4 How Could India Benefit from Precision Farming?
3.5 Challenges in Adopting Precision Farming in India
3.6 Cultivation with Virtual Reality/Augmented Reality Technology
3.7 Benefits of Cultivation with Virtual/Augmented Reality Technology
3.7.1 Augmented Reality in Agriculture for Simulated Training
3.7.2 Augmented Reality in Agriculture for Weather Updates
3.7.2.1 Virtual Reality Saves Money on Plants Protection Tools
3.7.2.2 How Augmented Reality Works in Agriculture
3.7.2.3 Is Augmented Reality the Future of Productive Farming?
3.7.2.4 Why Does the Agriculture Sector Face Challenges with Augmented Reality?
3.8 Conclusion
3.9 Summary
References
4. Stereo Vision Subsystem and Scene Segmentation Self-Steering Tractors in Smart Agriculture
Dileep Pulugu, Revathy Pulugu, K. Muthumanickam, S. Gopinath and A. Manikandan
4.1 Introduction
4.2 Global Positioning System
4.3 Self-Steering Tractors with Vision Have Evolved
4.4 Safety Issues
4.5 The System Architecture of Self-Guiding Tractors
4.6 Basic Modeling
4.7 Building with a Vision
4.8 Path Tracking Control System
4.9 Development of a Tractor-Based Agricultural Row Detection System Using Stereovision
4.9.1 System Architecture
4.9.2 Stereo Vision Crop Row Detecting System
4.10 Creation of a Crop Row Detecting Method Using Stereo Vision
4.10.1 Overview of the System and Picture Preparation
4.10.2 Visual Odometry Based Relative Localization
4.10.3 Visual Odometry Using Feature Tracking
4.10.4 Visible Odometry Using a Single Camera
4.11 Stereo Vision for Absolute Localization
4.12 Multi-Vision Methods
4.13 Conclusions
References
5. Vision-Based Image Classification and Image Segmentation Algorithms for Plant Disease Diagnostics
N. Ashokkumar, A. Manikandan, S. Hariprasath and P. Vijayalakshmi
5.1 Introduction
5.2 Signs and Symptoms of Plant Disease
5.2.1 The Importance of Plant Diseases in the US
5.2.2 Different Types of Plant Disease
5.3 Techniques and Algorithms for Detecting Plant Disease
5.3.1 Detection Techniques Used Today for Agricultural Diseases
5.3.2 Clustering Algorithms
5.3.3 Image Preprocessing
5.4 Dataset for Diagnosis Plant Disease
5.4.1 DiaMOS Plant Dataset
5.4.2 RoCoLe Dataset
5.4.3 Plant Pathology Dataset
5.4.4 Plant Village Dataset
5.5 Segmentation
5.5.1 Segmentation using the RCNN
5.5.2 Segmentation using TGVFCMS
5.6 Classification
5.6.1 Convolutional Neural Network (CNN)
5.6.1.1 Convolutional Layer
5.6.1.2 Pooling Layer
5.6.1.3 Fully Connected Layer
5.6.2 Classification Using Artificial Neural Networks
5.6.3 Classification Using K-Nearest Neighbor (K-NN)
5.6.4 Classification Using an Adaptive Neuro-Fuzzy Classification Model (ANFIS)
5.7 Conclusion
References
6. Smart Dust Technology for Monitoring and Control Systems in Smart Agriculture and Crop Surveillance Systems
M. Yogeshwari and A. Prasanth
6.1 Introduction
6.2 Smart Dust Technology in Smart Agriculture
6.2.1 WSN-Based Precision Agriculture
6.2.2 IoT in Agriculture
6.2.3 Machine Learning in WSN and Its Agriculture-Based Applications
6.3 Precision Agriculture and Its Functional Elements
6.4 Yield Monitoring and Forecast
6.4.1 Information Sources Required for Yield Monitoring
6.4.2 Demand for Irrigation Planning
6.5 Advanced Agricultural Practices
6.6 Conclusion
References
7. An Advanced Application of UAV – Drone Technologies in Precision Agriculture for Seed Dropping, Fertilizers and Pesticides Spraying and Field Monitoring
Daniel Lawrence I., A. Rehash Rushmi Pavitra, Ragupathy Karu, and M.P. Saravanan
7.1 Introduction
7.2 Irrigation Management
7.3 Seed Dropping
7.4 Pesticide and Fertilizer Spraying System
7.5 Improving Soil Productivity
7.6 Supporting Crop Growth
7.7 Crop Management Strategies
7.8 Increasing Crop Yield
7.9 Preventing Crop Disease
7.10 Predicting Crop Yield
7.11 Conclusion
References
8. Cognitive Intelligence and Distributed Computing Systems Applications in Smart Farming
Sangeetha Radhakrishnan and A. Prasanth
8.1 Introduction
8.1.1 Smart Farming
8.1.2 Challenges in Traditional Farming
8.1.3 Techniques Used in Smart Farming
8.1.3.1 Machine Learning
8.1.3.2 Deep Learning
8.1.3.3 Image Processing
8.1.3.4 Internet of Things
8.1.3.5 Artificial Neural Networks
8.1.3.6 Wireless Sensor Network Technology
8.1.4 Benefits of Smart Farming
8.1.5 Applications of Smart Farming
8.1.5.1 Disease Detection
8.1.5.2 Crop Phenotyping
8.2 Cognitive Intelligence
8.2.1 Applications of Smart Farming
8.2.1.1 Retail Industry
8.2.1.2 Logistics
8.2.1.3 Banking and Finance
8.2.1.4 Cyber Security
8.2.1.5 Power and Energy
8.2.1.6 Healthcare
8.2.1.7 Education
8.2.1.8 Agriculture
8.2.2 Agricultural Robotics
8.2.3 Benefits of Cognitive Computing
8.2.4 Scope of Cognitive Computing
8.2.4.1 Engagement
8.2.4.2 Decision
8.2.4.3 Discovery
8.2.5 Limitations of Cognitive Computing
8.2.5.1 Limited Analysis of Risk
8.2.5.2 Meticulous Training Process
8.2.5.3 More Intelligence Augmentation Rather than Artificial Intelligence
8.3 Distributed Computing
8.3.1 Evolution of Distributed Computing Systems
8.3.2 Features of Distributed Computing System
8.3.2.1 No Common Physical Clock
8.3.2.2 No Shared Memory
8.3.2.3 Geographical Separation
8.3.2.4 Autonomy and Heterogeneity
8.3.2.5 Distributed Computing System Model
8.3.2.6 Minicomputer Model
8.3.2.7 Workstation Model
8.3.2.8 Processor Pool Model
8.3.2.9 Hybrid Model
8.3.3 Advantages of Distributed Computing System
8.3.3.1 Inherently Distributed Computation
8.3.3.2 Resource Sharing
8.3.3.3 Access to the Geographically Remote Data and Resources
8.3.3.4 Enhanced Reliability
8.3.3.5 Increased Performance/Cost Ratio
8.3.3.6 Scalability
8.3.3.7 Modularity and Incremental Expandability
8.3.4 Scope of Distributed Computing
8.4 Cognitive Intelligence and Distributed Computing in Smart Farming
8.4.1 Need for Digitization of Agriculture
8.4.2 Existing System
8.4.3 Technologies Used in Cognitive Intelligence and Distributed Computing in Smart Farming
8.4.3.1 Genetic Algorithm, Fuzzy Logic, ANN Technology
8.4.3.2 Remote Sensing, Decision Tree, Hyper Spectral Data Mining
8.4.3.3 WSN Technology
8.4.3.4 Image Processing, K-Mean Clustering
8.4.3.5 Machine Vision, Image Processing Techniques
8.4.3.6 Hyperspectral Imaging, Artificial Neural Networks
8.5 Conclusion and Summary
8.5.1 Concluding Thoughts
8.5.2 Future Enhancement
References
9. Blockchain-Based Smart Agriculture with the Internet of Things: A Revolutionary Approach in Agriculture and Food Supply Chain
Vasanth R. and Pandian A.
9.1 Introduction
9.1.1 Food Supply Chain in Agriculture’s Context
9.1.2 The Purpose of Blockchain Technology
9.1.3 The Synergy Between Blockchain and IoT
9.1.4 An Overview of IoT and Its Role in Agriculture
9.1.4.1 Sensing and Data Collection
9.1.4.2 Data Sensing and Gathering
9.1.4.3 Automation and Control
9.1.4.4 Animal Observation
9.1.4.5 Supply Chain Management
9.1.4.6 Environmental Monitoring
9.1.4.7 Technologies for Supporting Decisions
9.1.4.8 Scalability and Honesty
9.2 Literature Review
9.2.1 Blockchain Technology and Its Applications in Agriculture
9.2.1.1 Enhancing Traceability and Transparency
9.2.1.2 Supply Chain Efficiency
9.2.1.3 Reduction of Food Fraud
9.2.1.4 Data Sharing and Cooperation
9.2.1.5 Sustainability and Certification
9.2.1.6 Adoption Problems and Roadblocks
9.2.1.7 Future Perspectives
9.2.2 Exploration on IoT in Agriculture and Its Benefits
9.2.3 Challenges in the Agriculture and Food Supply Chain
9.3 Methodology
9.3.1 Data Collections
9.3.2 Data Analysis
9.3.3 Experiments and Case Studies
9.3.3.1 Monitoring the Supply Chain Trial
9.3.3.2 Case Research on Crop Monitoring
9.3.3.3 The Experiment on Intelligent Irrigation
9.3.3.4 Livestock Tracking Case Study
9.3.3.5 Quality Control Test
9.3.3.6 A Decentralized Market Case Study
9.3.3.7 The Carbon Footprint-Reducing Test
9.3.3.8 Case Study for Prediction of Crop Yield
9.3.3.9 Farmer Cooperative Experiment
9.3.3.10 Case Study on Pest and Disease Monitoring
9.3.4 Selection Criteria for Blockchain and IoT Technologies in Agriculture
9.3.5 Data Sources and Tools
9.3.5.1 Data Sources
9.3.5.2 Tools and Technologies
9.4 Blockchain Technology in Agriculture
9.4.1 Several Ways of Blockchain Technology for Agriculture
9.4.1.1 Traceability of the Supply Chain
9.4.1.2 Responsibility and Openness
9.4.1.3 Security and Confidence
9.4.1.4 Efficiency and Reduced Waste
9.4.1.5 Data Sharing and Cooperation
9.4.1.6 Consumer Involvement
9.4.1.7 Reduction in Food Fraud
9.4.1.8 Catastrophe or Pollution
9.4.1.9 Sustainable Environmental Practices
9.4.2 Case Studies Blockchain Implementation in Agriculture
9.4.2.1 IBM Food Trust
9.5 Internet of Things in Agriculture
9.5.1 Examples of IoT Applications in Smart Agriculture
9.6 Integration of Blockchain and IoT in Agriculture
9.6.1 Advantages of Combining Technologies
9.7 Case Studies
9.8 Challenges and Future Directions
9.9 Conclusion
References
10. Computer Vision Systems in Livestock Farming, Poultry Farming, and Fish Farming: Applications, Use Cases, and Research Directions
Balasubramaniam S., Vijesh Joe C., A. Prasanth and K. Satheesh Kumar
10.1 Introduction
10.2 Smart Agriculture
10.2.1 Livestock Farming
10.2.2 Poultry Farming
10.2.3 Fish Farming
10.3 Computer Vision
10.3.1 Primary Operations of Computer Vision
10.3.1.1 Capturing an Image
10.3.1.2 Image Editing Procedure
10.3.1.3 Analyzing and Taking Required Action
10.4 Primary Computer Vision Techniques
10.4.1 Classification of Images
10.4.2 Detection of Objects
10.4.3 Tracking of Objects
10.4.4 Semantic Segmentation
10.4.5 Instance Segmentation
10.4.6 AI Techniques
10.4.6.1 Convolution and Feature Extraction
10.4.6.2 Convolutional Neural Networks
10.4.6.3 Algorithms for Detecting Targets in Two Stages
10.4.6.4 Algorithms for Detecting Targets in a Single Stage
10.5 Computer Vision-Based Systems in Livestock Farming, Poultry Farming, and Fish Farming
10.5.1 Livestock Management
10.5.1.1 Individual Cattle Tracing and Health Monitoring
10.5.1.2 Automatic Prediction of Swine Stress Using Infrared Skin Temperature
(Subscrofa)
10.5.1.3 CNN-Based Image Analysis of Milking Cows for Individual Identification
and Tracking of Feeding Behavior
10.5.1.4 An Algorithm Based on Regularities for Deducing When Cattle are Grazing
or Ruminating from their Auditory Signals
10.5.1.5 Individual Pig Weights can be Automatically Estimated Using Image Processing Software
10.5.2 Poultry Farming
10.5.2.1 Estimating Broiler Weight Using Computer Vision and Neural Networks
10.5.2.2 Deep Convolutional Neural Network-Based Poop Recognition and Classification in Broilers
10.5.2.3 Imaging Investigation of Flock Behavior at Multiple Feeders for Broiler Chickens
10.5.2.4 Chicken Net, a Computer Vision-Based Method for Assessing Commercial
Laying Hen Plumage
10.5.2.5 Effects of Flooding on Litter-Directed Behavior, Anxiety, and the Pursuit
of Novel Stimuli in Young Grill Chickens
10.5.3 Fish Farming
10.5.3.1 Fish Behavior Analysis with Artificial Intelligence
10.5.3.2 Strong School-Tracking with a CNN for Individual Fish Identification
10.5.3.3 Fish Detection and Classification in Aquatic Environments Using Deep
Learning and Temporal Data
10.5.3.4 Computational Object Classification in Marine Observation Using Active
Learning System for Marine Observation Images (ALMI), a General-Purpose Active Learning System
10.5.3.5 Use of Machine Vision for Fish Weight Estimation
10.6 Computer Vision Systems for Intelligent Farming: Current Research Challenges
10.6.1 Legal Documents
10.6.2 Privacy and Data Protection
10.7 Conclusion and Future Scope
References
11. Forestry Management with AI and Drone Technology – Digital Forestry
M. Shanthalakshmi, M. Jeevasree, R. Kavitha, V. Madhumathi, S. Mythreye and A. Naafiah Yusra
11.1 Introduction
11.2 Drone Technology
11.3 Drones Employed for Disaster Management
11.4 Drones Equipped with Remote Sensing, GIS and LiDar for Geographical Dispersal Maintenance and Surveillance
11.5 Drones for Livestock Management
11.6 Conclusion
References
12. Drone Application and Use Cases in Smart Agriculture and Crop Surveillance: Future Research Directions
Nilotpal Das, Atin Kumar and Rohit Kumar
12.1 Introduction
12.2 Definition of Drones
12.3 Classification of Drones
12.3.1 Components of Agriculture Drones
12.3.2 Drones for Agriculture by Offering
12.3.2.1 Hardware
12.3.2.2 Software and Services
12.4 Application of Drones in Agriculture
12.5 Agriculture Using Drone Technology
12.5.1 Execution Strategy
12.5.1.1 Production of Drones
12.5.1.2 Suppliers of Drone Services
12.5.1.3 Trained Human Resources
12.5.1.4 Ease of Doing Business
12.5.1.5 Application of Drone Research
12.5.1.6 Extension System
12.5.1.7 Custom Hiring Centers
12.5.2 Scope for Public-Private Partnership (PPP) Mode Operations
12.6 Drone Use Rules and Regulations in India
12.7 Policy Need
12.8 Another Benefits of Drones in Agriculture
12.9 Drawbacks of Drones in Agriculture
12.10 Drone Agriculture Cost
12.11 Future Research Direction
12.12 Summary
References
13. A Comprehensive Study on Machine Vision Techniques for an Automatic Weeding Strategy in Plantations
Manikandan J., Rhikshitha K., Sathya Sudarsen G. S. and Saran J. U.
13.1 Introduction
13.2 Related Study
13.3 Methodology
13.3.1 Collection of Datasets of Images of the Undesired Weed
13.3.2 Data Labeling
13.3.3 Pre-Processing of Dataset
13.3.4 Transformation into Binary or Grayscale Image
13.3.5 Image Segmentation
13.3.6 Feature Extraction
13.3.7 Classification
13.3.8 Execute Action
13.4 Experimentation and Analysis
13.4.1 Dataset
13.4.2 Performance Evaluation Metrics
13.4.3 Results and Discussion
13.5 Conclusion and Future Enhancements
References
14. An Effective Study on the Machine Vision-Based Automatic Control and Monitoring in Furrow Irrigation and Precision Irrigation
Manikandan J., Saran J. U., Samitha S. and Rhikshitha K.
14.1 Introduction
14.1.1 Introduction to Smart Irrigation and Machine Vision
14.1.2 A Brief on Furrow Irrigation Water Management
14.1.3 A Brief on Precision Irrigation Water Management
14.1.4 Existing Irrigation Systems
14.2 Methodology
14.2.1 System Design
14.2.2 Data Collection (as Images)
14.2.3 Image Processing
14.2.4 Control Algorithms
14.2.5 Classification
14.2.5.1 Convolutional Neural Networks (CNNs)
14.2.5.2 Support Vector Machine (SVM)
14.2.5.3 Random Forests
14.2.5.4 Recurrent Neural Networks (RNNs)
14.2.5.5 Deep Reinforcement Learning
14.2.5.6 Artificial Neural Network (ANN)
14.2.5.7 Feedback and Optimization
14.3 Maintenance and Upgrades
14.4 Experimentation and Analysis
14.4.1 Dataset Summary
14.4.2 Performance Evaluation Metrics
14.4.2.1 Accuracy
14.4.2.2 Specificity
14.4.2.3 Precision
14.4.2.4 Sensitivity/Recall
14.4.2.5 F1 Measure/F1 Score
14.4.3 Result and Discussion
14.5 Conclusion and Future Enhancement
References
15. Applications in Agriculture for Assessing and Monitoring Soil Using Smart Sensing and Edge Computing
G. Padmapriya, V. Vennila, Prithi Samuel, Rajesh Kumar Dhanaraj, Balamurugan Balusamy and Malathy Sathyamoorthy
15.1 Introduction
15.1.1 IoT-Based Smart Agriculture
15.1.2 Smart Sensing
15.1.2.1 Smart Sensing
15.1.2.2 Edge Computing
15.1.2.3 Internet of Things (IoT)
15.1.2.4 Predictive Analytics
15.1.2.5 Precision Farming
15.1.3 Assessing and Monitoring Soil Using Smart Sensing
15.1.4 Smart Agriculture Using Edge Computing
15.1.4.1 Autonomous Tractors and Robotic Machinery
15.1.4.2 Farm Automation
15.2 Smart Agriculture Using Smart Sensing and Edge Computing
15.2.1 Pest Control
15.2.2 Harvesting
15.2.3 Integrated Precision Agriculture Systems
15.3 IoT-Based Smart Agriculture
15.3.1 Sensor Data
15.3.2 Semantic Knowledge Base
15.4 KNN-Based Smart IoT System
15.4.1 K-Nearest Neighbors Algorithm
15.4.2 Smart IoT System Using KNN
15.4.2.1 HL-69 Soil Hygrometer Sensor
15.4.2.2 AM2302 DHT11 Sensor
15.4.2.3 BH1750 FVI Light Sensor
15.5 Results and Discussion
15.6 Performance Evaluation
15.7 Conclusion
References
Index
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