کتاب تکنیک‌های هوش مصنوعی در کشاورزی هوشمند

Foreword
Preface
Acknowledgment
Contents
Editors and Contributors
About the Editors
Contributors
1: Assessing the Importance and Need of Artificial Intelligence for Precision Agriculture
1.1 Introduction
1.1.1 Disciplines Essential to Achieve AI
1.1.2 Types of AI
1.1.3 Components of AI
1.1.4 Advantages of AI
1.1.5 Disadvantages of AI
1.1.6 Assessment and Need of AI in Agriculture
References
2: Challenges in Achieving Artificial Intelligence in Agriculture
2.1 Introduction
2.2 Current Applications of AI in Agriculture
2.2.1 Precision Agriculture
2.2.2 Crop, Soil, and Livestock Monitoring
2.2.3 Weather Forecasting and Climate Modeling
2.2.4 Agriculture Robots and Autonomous Equipment
2.2.5 Analyzing Agricultural Big Data
2.2.6 Agricultural Predictive Analytics
2.2.7 Logistics and Supply Chain Management
2.3 Challenges for Achieving AI in Agriculture
2.3.1 Lack of Data Availability and Quality of Data
2.3.2 Barriers to Accessing High Technology
2.3.3 High Level of Input Cost
2.3.4 Social Cultural Barriers
2.3.5 Policy Barriers
2.4 Strategies to Overcome Achieving AI Challenges in Agriculture
2.4.1 Strategies to Increase Data Availability and Quality of Data
2.4.2 Strategies to Increase High Technology Accessibility
2.4.3 Strategies to Reduce High Input Cost
2.4.4 Strategies to Overcome Social Barriers
2.4.5 Strategies to Overcome Policy Barriers
2.5 Future Directions
2.6 Conclusion
References
3: Introduction to Artificial Intelligence Techniques in Agricultural Applications and Their Future Aspects
3.1 Introduction
3.2 Key Components and Techniques in AI
3.3 Major Applications of AI in Agriculture
3.3.1 Precision Farming Management
3.3.2 Soil and Irrigation Management
3.3.2.1 Soil Management
3.3.2.2 Irrigation Management
3.3.3 Livestock Management
3.3.3.1 Animal Identification
3.3.3.2 Automated Weighing Systems
3.3.3.3 Automated Health Monitoring
3.3.4 Aquaculture System
3.3.4.1 Automated Monitoring and Control
3.3.4.2 Precision Aquaculture
3.3.5 Supply Chain Management
3.4 Future Aspects of AI in Agriculture
3.5 Opportunity of AI Technology in Agriculture
3.6 Challenges in Adoption of AI Technology in Agriculture
3.7 Conclusions
References
4: Agricultural Artificial Intelligence: Obstacles and Opportunities
4.1 Introduction
4.2 Limitations of Implementing Artificial Intelligence Techniques in Agriculture Field: A Practical Approach
4.2.1 Lack of Competent Labor
4.2.1.1 Solution
4.2.2 The Affordability of AI Adoption in Agriculture
4.2.2.1 Factors Increasing AI’s Expensive Cost in Agriculture
4.2.3 Financial Effects on Farming Businesses
4.2.3.1 Agricultural AI’s Expensive Costs: Strategies for Sustainable Adoption
4.2.3.2 Solution: Multiple Strategies Can Be Employed to Reduce Expenses and Encourage Broader Availability of AI Technologies
4.2.4 Difficulty in Data Handling
4.2.4.1 Lack of Access to Data
4.2.5 Extreme Climatic Condition as a Challenge for Implementing AI in Agriculture
4.2.6 Requirement of High Accuracy and Precision
4.2.7 Security Risks
4.3 Conclusion
References
5: Smart Farming Management System: Pre and Post-Production Interventions
5.1 Introduction
5.2 Problem with Modern Agriculture
5.2.1 Why Smart Farm Management
5.2.2 Agriculture Interventions
5.3 Irrigation Water Management
5.4 Farm Mechanization
5.4.1 Precision Machinery
5.4.2 Robotics
5.5 Postharvest Management
5.6 Recommendations, Limitation, and Suggestions for Future
References
6: Introduction to Various Intelligent Devices and Implementation Platforms
6.1 The Dawn of the Intelligent Age
6.2 A Spectrum of Intelligence
6.2.1 Implementation Platforms: Bridging the Gap
6.2.1.1 Road Ahead
6.3 Beyond the Text
6.3.1 Raspberry Pi
6.3.2 Arduino
6.3.3 TensorFlow Lite
6.3.4 OpenHAB
6.3.5 Amazon Web Services (AWS) IoT Core
6.4 Case Studies: Intelligent Devices Reimagine Industries
6.4.1 Healthcare
6.4.2 Agriculture
6.4.3 Manufacturing
6.5 Conclusion
References
7: Fruit Counting and Analysis Using Artificial Intelligence Approaches
7.1 Introduction
7.1.1 Challenges Associated with Traditional Manual Counting Methods
7.1.2 Role of Artificial Intelligence in Addressing These Challenges
7.2 Need for Automation in Fruit Counting
7.2.1 Importance of Efficient Fruit Counting for Yield Estimation
7.2.2 Limitations of Manual Counting Methods
7.3 Computer Vision and Its Applications
7.3.1 Object Detection Algorithms for Identifying and Locating Fruits in Images
7.3.1.1 One-Stage Object Detection Algorithms
7.3.1.2 Two-Stage Object Detection Algorithms
7.3.1.3 Artificial Neural Networks (ANNs)
7.3.1.4 Convolutional Neural Networks (CNNs)
7.3.2 Image Segmentation Techniques for Precise Fruit Boundary Delineation
7.4 Machine Learning Models for Counting Accuracy
7.4.1 Overview of Convolutional Neural Network and Support Vector Machines
7.5 Data Acquisition and Preprocessing
7.5.1 Use of Labeled Datasets and the Challenges Associated with Data Collection
7.6 Case Studies and Success Stories
7.7 Limitations and Opportunities of AI in Agriculture
7.7.1 Challenges and Future Directions in Fruit Counting Using AI
7.7.2 Exploration of Some Emerging Technologies
7.8 Conclusion
References
8: Deep Learning-Based Plant Stress Diagnosis: An Optimized Generative Augmentation Model Approach
8.1 Introduction
8.2 Literature Review
8.3 Proposed Model for Plant Stress Identification
8.3.1 Training and Testing with MBGD Optimization
8.4 Result and Discussion
8.4.1 Dataset Distribution and Augmentation
8.4.2 DCNN Performance Analysis
8.4.3 Comparative Analysis of Disease Identification
8.4.4 Comparative Assessment with Previous Methods
8.5 Conclusion
References
9: Transformative Impact of AI-Driven Computer Vision in Agriculture
9.1 Sampling of Agricultural Soil Using UAV
9.2 Computer Vision-Based Prototype of Picking System for Fruit
9.3 Canopy Height Estimation
9.4 Computer Vision-Based Fruit Grading System for Quality Evaluation
9.5 A Generalized Computer Vision Approach to Mapping Crop Fields in Heterogeneous Agricultural Landscapes
9.6 Potato Crop Stress Prediction of Aerial Images Using Naïve Bayes Classifier
9.7 Vision-Based Navigation for Autonomous Vehicles in Agricultural Fields: A Novel Texture Tracking Approach
9.8 Water Necessity Assessment Using Computer Vision and Drone Technology
9.9 Different Vegetation Indices Measurement Using Computer Vision
9.10 Plant Disease Detection and Classification
9.11 limitations and Opportunities of AI
9.12 Conclusion
References
10: An In-Depth Analysis of Artificial Intelligence-Based Crop Pest Management and Water Supply Regulation
10.1 Introduction
10.2 Impact of AI on Indian Agriculture
10.3 AI in Irrigation Systems
10.4 Reviving the Irrigation System Through AI Model
10.4.1 Machine Learning Algorithms
10.4.2 Supervised Irrigation Model
10.4.2.1 Linear Regression
10.4.2.2 Support Vector Machine (SVM)
10.4.3 Unsupervised Irrigation Model
10.4.3.1 K Means Clustering
10.4.4 Artificial Neural Network (ANN)
10.4.4.1 Deep Learning
10.4.5 Convolutional Neural Network (CNN)
10.4.6 Recurrent Neural Network (RNN)
10.4.7 Long Short-Term Memory (LSTM)
10.4.8 Reinforcement Learning (RL)
10.5 For Future: Optimize Agriculture with Smart Irrigation
10.6 Case Studies
10.6.1 Irrigation with NASA and AI (Application: EVAPO)
10.6.1.1 Core Value
10.6.2 AI-Powered Crop Monitoring and Irrigation (Application: Nano Ganesh)
10.6.2.1 Core Value
10.7 AI in Pest Management
10.8 Startups Using AI and Machine Learning in Agriculture
10.8.1 Taranis
10.8.2 Trap View
10.8.3 EcoRobotix
10.8.4 Kishan Know
10.8.5 Greeneye Technology
10.9 Pest Detection and Monitoring Systems
10.10 Decision Support System
10.11 Precision Pest Management
10.12 GIS and GPS Modules
10.13 Merits of AI
10.13.1 Reduced Use of Pesticides
10.13.2 Increased Efficiency
10.13.3 Lower Costs of Pest Control
10.14 Limitations of AI
10.14.1 Response Time and Accuracy
10.14.2 Large Data Requirement
10.14.3 Methodology
10.14.4 High Data Cost
10.14.5 Flexibility
10.15 Challenges While Using AI
10.16 Conclusion and Future Perspectives
References
11: AI for Data-Driven Decision-Making in Smart Agriculture: From Field to Farm Management
11.1 Introduction
11.2 Overview of Smart Agriculture
11.2.1 Definition and Scope
11.2.1.1 Definition
11.2.1.2 Scope
11.2.2 Components of Smart Agriculture
11.2.2.1 Sensing and Monitoring
11.2.2.2 Data Collection
11.2.2.3 Communication Technologies
11.2.2.4 Decision Support Systems (DSS)
11.3 Role of Data in Smart Agriculture
11.3.1 Importance of Data-Driven Decision-Making
11.3.2 Types of Data in Agriculture
11.3.2.1 Environmental Data
11.3.2.2 Crop Data
11.3.2.3 Livestock Data
11.3.3 Challenges in Handling Agricultural Data
11.3.3.1 Data Security and Privacy
11.3.3.2 Data Integration
11.3.3.3 Connectivity Issues
11.4 AI in Agriculture
11.4.1 Introduction to AI
11.4.2 AI Techniques in Agriculture
11.4.2.1 Machine Learning
11.4.2.2 Deep Learning
11.4.2.3 Computer Vision
11.4.3 Applications of AI in Smart Agriculture
11.4.3.1 Crop Monitoring
11.4.3.2 Pest and Disease Detection
11.4.3.3 Yield Prediction
11.5 Data-Driven Decision Making
11.5.1 Concept and Principles
11.5.2 Integration of AI in Decision-Making
11.5.3 Benefits and Challenges
11.5.3.1 Benefits
11.5.3.2 Challenges
11.6 From Field to Farm Management
11.6.1 Precision Agriculture
11.6.2 Intelligent Farm Equipment
11.6.3 Automation in Farm Management
11.7 Data Integration and Management
11.7.1 Data Integration Platforms
11.7.2 Challenges in Data Management
11.7.3 Security and Privacy Concerns
11.8 Future Trends in AI for Smart Agriculture
11.8.1 Emerging Technologies
11.8.1.1 Internet of Things (IoT) Integration
11.8.1.2 Robotics and Automation
11.8.1.3 Edge Computing
11.8.2 Potential Innovations
11.8.2.1 Predictive Analytics for Crop Management
11.8.2.2 Precision Agriculture at Scale
11.8.2.3 Blockchain for Supply Chain Transparency
11.8.3 Sustainability and Ethical Considerations
11.8.3.1 Resource Optimization and Environmental Impact
11.8.3.2 Ethical Use of Data
11.8.3.3 Addressing Socioeconomic Impact
11.9 Conclusion
References
12: AI-Based Regulation of Water Supply and Pest Management in Farming
12.1 Introduction
12.1.1 The Rise of AI and Its Application in Agriculture Water Management
12.1.2 Predictive Analytics for Weather and Water Availability
12.1.2.1 Machine Learning Algorithms
12.1.2.2 Real-time Monitoring Systems
12.1.3 AI to Detect Irrigation Malfunctions or Leaks
12.1.4 Sensor-based AI Solutions to Optimize Irrigation Scheduling
12.1.5 Smart Irrigation Systems and Water Conservation
12.1.5.1 AI-Powered Drip Irrigation
12.1.5.2 Soil Moisture Sensors
12.1.6 AI in Different Types of Irrigation Systems
12.1.6.1 Sprinkler Irrigation
12.1.6.2 Centre Pivot Irrigation
12.1.7 Key Areas for Policy Action
12.1.8 Advancing: Effective Partnerships Between Government and Private Sector
12.2 Significance of Artificial Intelligence in Pest Management
12.2.1 Pest Detection, Identification, and Classification
12.2.2 Pest Monitoring
12.2.3 Pest Prediction and Decision-Making by Employing AI
12.2.4 Pesticide Application Using AI
12.3 AI in Agriculture: Opportunities
12.4 AI in Agriculture: Challenges
12.5 Conclusions
References
13: Advancement and Challenges of Implementing Artificial Intelligence of Things in Precision Agriculture
13.1 Introduction
13.2 Traditional Agriculture to Smart Agriculture
13.2.1 Artificial Intelligence of Things (AIoT)
13.3 Technological Integration in Smart Agriculture
13.4 Application of Smart Agriculture
13.4.1 Soil Management
13.4.2 Water Management
13.4.3 Weed Management
13.4.4 Diseases and Pest Management
13.4.5 Crop Yield Prediction
13.4.6 Harvesting
13.4.7 Weather Forecast
13.4.8 Supply Chain Management
13.5 Challenges in Smart Agriculture
13.5.1 Data Level Challenges
13.5.2 Network Layer Challenges
13.5.3 Storage and Processing Challenges
13.5.4 Data Analysis Challenges
13.5.5 Decision-Making and Recommendation Challenges
13.5.6 General Issues Challenges
13.6 Cybersecurity Challenges in the Smart Farming Ecosystem
13.6.1 Data Attacks
13.6.2 Networking and Equipment Attacks
13.6.3 Supply Chain Attacks
13.7 Conclusions
References
14: Enabling Digital Platforms: Toward Smart Agriculture
14.1 Introduction
14.2 Internet of Things in Agriculture
14.3 Application of Digitized Platforms in Genetics and Plant Breeding
14.3.1 Genomic Sequencing in Rice Cultivation
14.3.2 Isolation, Cloning, and Functional Validation of Pi54 Gene for Resistance to Rice Blast (Adopted from ICAR Gene Bank, 2022)
14.3.3 Application of Internet of Things (IoT) in Next-Generation Sequencing (NGS) in Genetics and Plant Breeding
14.3.3.1 Advantages of Next-Generation Sequencing in Agriculture
14.3.4 Application of Digitized Platforms in Soil Health Management
14.3.4.1 Sentinel-2 Satellite Data for Spatiotemporal Mapping of Deep Pools for Monitoring the Riverine Connectivity (Adopted from Zhang et al. 2021)
14.3.5 Application of Digitized Platforms in Animal Husbandry
14.3.5.1 Disease Informatics (Table 14.1; Fig. 14.7)
14.4 Conclusion
References
15: IoT and Drone-Based Field Monitoring and Surveillance System
15.1 Introduction
15.2 A Brief Idea About Drones Used in Agriculture
15.2.1 Sensors Used in Drones
15.2.2 Method of Processing Data Acquired by Sensors
15.3 Components of Sensor Platform Based on IoT
15.4 “Modus Operandi”: Method of Data Acquisition and Transmission
15.5 Instances of Field Monitoring and Surveillance Based on IoT and Drones
15.5.1 Crop Health Monitoring
15.5.2 Crop Growth Monitoring and Yield Estimation
15.5.3 Water Stress Monitoring
15.5.4 Weed Mapping
15.5.5 Nutrient Status
15.6 Conclusion
15.7 Future Prospect
References
16: IoT-Based Real-Time Farm Management System for Smart Agriculture
16.1 Introduction
16.2 Major Sensors Used in IoT for Smart Agriculture
16.2.1 Soil Moisture Sensor
16.2.2 Soil Nutrient Sensor
16.2.3 Actuators
16.2.4 Encoders
16.2.5 RGB, Multispectral, and Hyperspectral Camera
16.2.6 Thermal Camera
16.2.7 Leaf Area Index (LAI) Sensors
16.2.8 Weather Station
16.3 IoT-Based Technologies
16.4 Data Communication, Storage, and Mining
16.4.1 Data Communication
16.4.2 Cloud Storage
16.4.3 Data Mining for IoT
16.5 Application of AI in Agricultural IoT
16.5.1 Crop and Yield Management
16.5.2 Disease/Pest Management
16.5.3 Autonomous Tractor and Field Robots
16.5.4 Soil Analysis
16.5.5 Irrigation Management
16.5.6 Frost Management
16.6 Conclusion
References