Optimizing AI Applications for Sustainable Agriculture
Description
Embrace the future of sustainable food production with this comprehensive guide that explores how artificial intelligence and emerging technologies are revolutionizing agriculture.
In an era marked by climate change, resource depletion, and population growth, innovation is not a luxury-it is a necessity. Integrating AI into agricultural practices offers a promising solution. From precision farming and crop monitoring to predictive analytics and decision support systems, AI has the potential to revolutionize how we grow, manage, and distribute food. This book is a comprehensive guide that delves into the transformative potential of artificial intelligence and emerging technologies in the field of agriculture. An in-depth exploration of various AI technologies, such as machine learning, deep learning, natural language processing, and computer vision, will demonstrate the wide applications these tools have for agricultural practices. It covers emerging technologies like the Internet of Things, drones, precision farming, and agro-technology. The primary focus is on how these technologies can enhance sustainability in agriculture by improving crop yields, reducing water consumption, minimizing chemical use, and promoting eco-friendly farming practices. This essential guide will give readers a deep understanding of how cutting-edge technology can be harnessed to create a more sustainable future for agriculture.
Readers will find the volume:
- Dives into the latest research and innovations in AI and emerging technologies that are transforming agricultural practices;
- Provides real-world examples and case studies that show how these technologies can be implemented in farming;
- Explores how these modern technologies align with global sustainability goals and how they can be integrated into national strategies;
- Introduces the role of AI and emerging technologies in promoting sustainable agricultural practices that protect the environment.
Audience
Researchers, computer and agricultural scientists, farmers, and policymakers looking to leverage the potential of artificial intelligence and machine learning for the benefit of farmers.
More details
Other editions
Additional editions
Persons
Roheet Bhatnagar, PhD is a Professor in the Department of Computer Science and Engineering at Manipal University, Jaipur, Rajasthan, India with over 22 years of experience. He has published more than 100 research papers in reputed conferences and journals and edited five books. His research focuses on soft computing, data structure, and software engineering.
Chandan Kumar Panda, PhD is an Assistant Professor at Bihar Agricultural University, Sabour, Bihar, India with over eight years of research and teaching experience. He has published three books, 16 book chapters, and more than 50 research papers in international journals and conferences. He is an acclaimed researcher in ICT in the agriculture sector. His research interests include agricultural extension, rural development, and information and communication technology in agriculture.
Mahmoud Yasin Shams, PhD is an Associate Professor of Machine Learning and Information Retrieval in the School of Artificial Intelligence, Kafrelsheikh University, Kafr el-Sheikh, Egypt. With over 70 papers and conference presentations published in top-tier journals he has made significant contributions to the field. He specializes in artificial intelligence, machine learning, pattern recognition, and classification.
Content
Preface xxi
Part I: Artificial Intelligence-Assisted Sustainable Agriculture 1
1 AI and Emerging Technologies for Precision Agriculture: A Survey 3
Brajesh Kumar Khare
1.1 Introduction 4
1.2 Precision Agriculture 5
1.3 Artificial Intelligence 9
1.3.1 Role of AI in Agriculture 11
1.4 Internet of Things (IoT) 11
1.4.1 Basics of IoT in Agriculture 13
1.4.2 Role of IoT 15
1.5 Blockchain Technology 15
1.6 Technologies Used in Smart Farming 17
1.6.1 Global Positioning System (GPS) 17
1.6.2 Sensor Technologies 17
1.6.3 Variable Rate Technology and Grid Soil Sampling 18
1.6.4 Geographic Information System (GIS) 19
1.6.5 Crop Management 19
1.6.6 Soil and Plant Sensors 20
1.6.7 Yield Monitor 20
1.7 Challenges 24
1.8 Future Research 26
1.9 Conclusion 29
References 29
2 AI-Enabled Framework for Sustainable Agriculture Practices 33
Yukti Batra, Suman Bhatia and Ankit Verma
2.1 Introduction 34
2.2 Sustainable Agriculture Imperatives 35
2.2.1 Environmental Degradation 36
2.2.2 Biodiversity Loss 36
2.2.3 Climate Change Impacts 36
2.2.4 Resource Scarcity 37
2.2.5 Food Security and Economic Stability 37
2.2.6 Public Health Concerns 37
2.2.7 Social Equity and Rural Livelihoods 37
2.2.8 Global Food Shortage Concerns 38
2.2.9 Empowerment and Awareness 38
2.3 Social Relevance of Sustainable Practices in Agriculture 38
2.3.1 Livelihood Security 39
2.3.2 Community Health and Well-Being 39
2.3.3 Social Equity and Inclusion 39
2.3.4 Rural Empowerment and Resilience 40
2.4 Sustainable Agriculture Indicators 40
2.4.1 Food Grain Productivity 40
2.4.2 Population Density 41
2.4.3 Cropping Intensity 42
2.5 Sustainable Agriculture Practices Followed Till Date 42
2.5.1 Agroforestry 42
2.5.2 Integrated Pest Management (IPM) 44
2.5.3 Crop Rotation 44
2.5.4 Cover Cropping 44
2.5.5 Organic Farming 44
2.5.6 No-Till Farming 44
2.6 AI-Enabled Conceptual Framework 44
2.6.1 Perception from Environment Using IoT Sensors 45
2.6.1.1 Remote Sensing 45
2.6.1.2 IoT Sensors 46
2.6.2 Data Storage 46
2.6.3 Data Processing 47
2.6.4 Training and Testing by ML Models 47
2.7 Applications of Artificial Intelligence in Agriculture 48
2.8 Challenges and Barriers to Sustainable Agriculture 51
2.8.1 Theoretical Obstacles 51
2.8.2 Methodological Obstacles 52
2.8.3 Personal Obstacles 53
2.8.4 Practical Obstacles 54
2.9 Future Directions 55
2.10 Conclusion 57
References 58
3 The Impact of Artificial Intelligence on Agriculture: Revolutionizing Efficiency and Sustainability 61
Santhiya S., P. Jayadharshini, N. Abinaya, Sharmila C., Srigha S. and Sruthi K.
Applications 62
3.1 Introduction 62
3.2 Precision Farming 64
3.2.1 Data Collection and Analytics 64
3.2.2 Disease Detection 65
3.2.3 Yield Production and Optimization 65
3.2.4 Precision Irrigation 66
3.3 Crop Monitoring 67
3.3.1 Remote Sensing and Satellite Imagery 67
3.3.2 Drones 67
3.3.3 Computer Vision and Image Analysis 68
3.3.4 Sensor Network and IoT 68
3.3.5 Weed Detection Management 68
3.4 AI in Aquaculture 69
3.4.1 Monitoring Water Quality 69
3.4.2 Feed Management 70
3.4.3 Breeding Technique 70
3.4.4 Autonomous Systems and Market Optimization 70
3.5 Predictive Analysis 71
3.5.1 Irrigation Optimization 71
3.5.2 Supply Chain Management 72
3.5.3 Weather and Climate Modeling 72
3.5.4 Equipment Maintenance 73
3.6 Robotics and Automation in AI Agriculture 73
3.6.1 Robotic Planting System 73
3.6.2 Automated Irrigation Systems 74
3.6.3 AI-Driven Crop Monitoring 75
3.6.4 Harvesting Robots 75
3.7 Livestock Monitoring 75
3.7.1 Video and Image Analysis 76
3.7.2 Health Monitoring 76
3.7.3 Behavior Analysis 77
3.7.4 Predictive Analysis 77
3.7.5 Environment Analysis 77
3.7.6 Disease Analysis and Prediction 78
3.8 AI for Climate Smart Agriculture 78
3.8.1 Climate Prediction and Weather Forecasting 79
3.8.2 Enhancing Resilience to Climate Variability 79
3.8.3 Water Management 80
3.8.4 Reducing Greenhouse Gas Emissions 80
3.8.5 Increasing Productivity and Sustainability 80
3.9 AI in Agroecology 81
3.9.1 Decision Support Systems 81
3.9.2 Biodiversity Conservation 82
3.9.3 Soil Health Management 82
3.10 Soil Analysis 83
3.10.1 Soil Classification 83
3.10.2 Soil Nutrient Management 83
3.10.3 Disease and Pest Detection 84
3.10.4 Soil Moisture Monitoring 84
3.10.5 Precision Agriculture 84
3.10.6 Soil Erosion Prediction 85
3.10.7 Soil Remediation 85
3.11 Conclusion 86
Bibliography 87
4 Integrating Artificial Intelligence into Sustainable Agriculture: Advancements, Challenges, and Applications 89
Djamel Saba and Abdelkader Hadidi
4.1 Introduction 90
4.2 Literature Review 92
4.3 Key Critical Challenges of Conventional Agriculture 97
4.3.1 Overview of Conventional Agriculture 97
4.3.2 The Distinction Between Agriculture in the Past and Now 99
4.4 AI Technologies and Sustainable Agriculture 103
4.5 Artificial Intelligence's Practical Use in Farming 104
4.6 Challenges and Ethical Considerations 107
4.6.1 Challenges 107
4.6.1.1 Data Privacy and Security 107
4.6.1.2 Accessibility and Inclusivity 107
4.6.1.3 Algorithm Bias 107
4.6.1.4 Interoperability and Standardization 107
4.6.1.5 Job Displacement 108
4.6.2 Ethical Considerations 108
4.6.2.1 Transparency and Accountability 108
4.6.2.2 Environmental Impact 108
4.6.2.3 Informed Consent 108
4.6.2.4 Fair Distribution of Benefits 109
4.6.2.5 Long-Term Sustainability 109
4.7 Conclusions and Further Work 109
References 110
5 Artificial Intelligence for Sustainable and Smart Agriculture 117
Djamel Saba and Abdelkader Hadidi
5.1 Introduction 118
5.2 Literature Review 120
5.3 AI Techniques for Revolutionizing Traditional Farming 125
5.4 Role of the IoT in Smart Farms 128
5.4.1 Smart Farming Technologies 130
5.4.1.1 Precision Agriculture 130
5.4.1.2 Livestock Monitoring 130
5.4.1.3 Crop Monitoring 130
5.4.2 Climate Management and Weather Forecasting 130
5.4.3 Supply Chain Optimization 131
5.4.4 Analytics and Assistance for Decision-Making 131
5.4.5 The Advantages and Difficulties of IoT in Agriculture 131
5.4.5.1 Advantages 131
5.4.5.2 Difficulties 131
5.5 Environmental Concerns Related to Agriculture 132
5.5.1 Environmental Concerns Related to Sustainable Agriculture 132
5.5.2 Environmental Concerns Related to Smart Agriculture 132
5.6 Challenges and Considerations 135
5.7 Conclusions and Further Work 137
References 142
6 Data-Driven Approaches for Sustainable Agriculture and Food Security 145
S.C. Vetrivel, V. Sabareeshwari, K.C. Sowmiya and V.P. Arun
6.1 Introduction 146
6.1.1 The Role of Data in Agriculture 146
6.1.2 Importance of Sustainability and Food Security 147
6.1.3 Overview of Data-Driven Technologies 148
6.2 Big Data in Agriculture 150
6.2.1 Definition and Characteristics of Big Data 150
6.2.2 Applications of Big Data in Agriculture 151
6.2.3 Challenges and Opportunities 152
6.2.3.1 Challenges 152
6.2.3.2 Opportunities 153
6.3 Internet of Things (IoT) in Agriculture 154
6.3.1 Understanding IoT and Its Components 154
6.3.2 IoT Applications in Farming 155
6.3.3 Benefits and Challenges of IoT Implementation 156
6.4 Artificial Intelligence and Machine Learning in Agriculture 157
6.4.1 Fundamentals of AI and Machine Learning 157
6.4.2 AI and ML Applications in Crop Monitoring and Management 158
6.4.3 Predictive Analytics for Yield Optimization 159
6.5 Remote Sensing and GIS in Agriculture 159
6.5.1 Remote Sensing Technologies Overview 159
6.5.2 GIS Mapping for Precision Agriculture 160
6.5.3 Monitoring Environmental Impact and Land Use 161
6.6 Data-Driven Approaches for Sustainable Crop Management 162
6.6.1 Precision Agriculture Techniques 162
6.6.2 Crop Disease Detection and Management 162
6.6.3 Water Management and Irrigation Systems 163
6.7 Data-Driven Livestock Management 163
6.7.1 Monitoring Animal Health and Welfare 163
6.7.2 Precision Livestock Farming 164
6.7.3 Sustainable Feed Management 164
6.8 Supply Chain Management and Food Security 165
6.8.1 Traceability and Transparency in the Food Supply Chain 165
6.8.2 Data-Driven Approaches for Food Distribution 165
6.8.3 Enhancing Food Security through Data Analytics 166
6.9 Policy Implications and Ethical Considerations 167
6.9.1 Regulatory Frameworks for Data-Driven Agriculture 167
6.9.2 Ethical Issues Surrounding Data Collection and Privacy 167
6.9.3 Balancing Innovation with Social Responsibility 168
6.10 Future Trends and Conclusion 168
6.10.1 Emerging Technologies and Trends 168
6.10.2 Potential Impact on Sustainable Agriculture and Food Security 169
6.11 Conclusion 170
References 170
Part II: Recent Developments in Crop Disease Detection and Prevention 175
7 Advances in Plant Disease Detection and Classification Systems 177
Bhakti Sanket Puranik, Karanbir Singh Pelia, Shrivatsasingh Khushal Rathore and Vaibhav Vikas Dighe
7.1 Introduction 178
7.2 Literature Review 179
7.3 Methodologies and Techniques 185
7.3.1 CNN Architectures 185
7.3.2 Activation Functions 186
7.3.3 Loss Functions 187
7.3.4 Learning Rate Schedulers 187
7.3.5 Early Stopping 188
7.3.6 Checkpoints and Callbacks 188
7.3.7 Data Preprocessing 189
7.3.8 Data Augmentation 189
7.3.9 Transfer Learning 190
7.3.10 Ensemble Learning 191
7.4 Challenges and Limitations 191
7.4.1 Dataset Scarcity 192
7.4.2 Image Variability 192
7.4.3 Label Inconsistency 193
7.4.4 Model Interpretability 193
7.5 Proposed Model 194
7.5.1 Model Architecture 195
7.5.2 Training Mechanism 196
7.6 Future Scope 198
7.6.1 Development of Comprehensive Datasets 199
7.6.2 Exploration of Novel Architectures 199
7.6.3 Integration of Advanced Technologies 200
7.6.4 Crowdsourcing New Data 201
7.6.5 Adaptation and Interaction 201
7.6.6 Integrated Remediation Strategies 202
7.7 Conclusion 203
References 204
8 Ensemble-Based Crop Disease Biomarker Multi-Domain Feature Analysis (ECDBMFA) 207
Chilakalapudi Malathi and Sheela J.
8.1 Introduction 208
8.2 Literature Survey 208
8.3 Design of ECDBMFA 210
8.4 Result Evaluation and Comparative Analysis with Existing Techniques 217
8.5 Conclusion 226
References 226
9 Artificial Intelligence and Machine Learning in Crop Yield Prediction and Pest Control 231
Archana Negi, Jitendra Singh, Robin Kumar, Atin Kumar, Nisha and Sharad Sachan
Introduction 232
Artificial Intelligence 234
Machine Learning 235
AI-Based ML Algorithm Models 237
Some Important Evaluation Metrics Used in AI-Based Predictive Models 239
Applications of Artificial Intelligence and Machine Learning in Crop Yield Prediction Models 241
AI-Based Crop Yield Prediction Method-Case Study 242
Steps for Crop Yield Prediction 243
Applications of Artificial Intelligence and Machine Learning in Pest and Disease Management 244
Advantages of Using Artificial Intelligence/Machine Learning in Agriculture 248
Challenges of Artificial Intelligence and Machine Learning Application in Agriculture 249
Conclusion and Future Prospects 250
References 250
10 Farming in the Digital Age: A Machine Learning Enhanced Crop Yield Prediction and Recommendation System 257
Arti Sonawane, Akanksha Ranade, Apurva Kolte, Siddharth Daundkar and Shreyas Rajage
10.1 Background 258
10.2 Introduction 260
10.3 Importance 261
10.4 Machine Learning in Agriculture 262
10.5 Objectives 267
10.6 Related Work 267
10.6.1 Research Gaps 276
10.7 Proposed Methodology 277
10.7.1 Data Collection 277
10.7.2 Data Preprocessing 277
10.7.3 Training and Testing Model 278
10.7.4 Decision Tree Repressor 278
10.7.5 Random Forest Regressor 279
10.8 Implications for Farmers 282
10.9 Future Directions 284
10.10 Conclusion 285
References 285
Part III: IoT and Modern Agriculture 289
11 Digital Agriculture: IoT Applications and Technological Advancement 291
K. Aditya Shastry
11.1 Introduction 292
11.2 Related Work 296
11.3 Emerging Technologies and Related Applications in Smart Agriculture 299
11.3.1 Internet of Things (IoT) in Agriculture 300
11.3.2 Artificial Intelligence (AI) and Machine Learning (ml) 300
11.3.3 Remote Sensing (RS) and Satellite Technology 302
11.3.4 Blockchain Technology 305
11.3.5 Robotics and Automation 309
11.3.6 Sustainable Agriculture Practices 310
11.4 Challenges in Smart Farming 315
11.5 Future Trends in Smart Farming 317
11.6 Conclusion 320
References 320
12 IoT in Climate-Smart Farming 323
Maitreyi Darbha, S. V. Sanjay Kumar, S. R. Mani Sekhar and Sanjay H. A.
12.1 Introduction 323
12.2 IoT in Agriculture 325
12.2.1 What is IoT? 325
12.2.2 Methods Involved in the Incorporation of IoT in Agriculture 325
12.2.2.1 Greenhouse Farming 325
12.2.2.2 Vertical Farming 326
12.2.2.3 Hydroponics 326
12.2.2.4 Phenotyping 327
12.2.3 Resources Required for the Incorporation 328
12.3 Climate-Smart Farming Practices 329
12.3.1 What is Climate-Smart Farming? 329
12.3.2 Integration of IoT 330
12.3.2.1 Precision Farming 330
12.3.2.2 Smart Irrigation 331
12.3.2.3 Crop Monitoring 331
12.3.2.4 Livestock Management 331
12.3.3 Environmental Impact and Resilience to Climate Change 332
12.4 Case Studies 333
12.4.1 IoT Applications in Precision Agriculture 333
12.4.1.1 Weather Monitoring 333
12.4.1.2 Soil Content Monitoring 333
12.4.1.3 Diseases Monitoring 334
12.4.2 IoT Applications in Greenhouse 334
12.5 Evaluation of IoT Technologies 336
12.5.1 Effectiveness of IoT Technologies 336
12.5.2 Comparison with Traditional Methods 336
12.5.3 Advantages and Disadvantages 337
12.6 Relevance to Current-Day Global Issues 338
12.6.1 Future Scope 338
12.7 Conclusion 339
References 340
Part IV: Technological Trends and Advancements in the Agricultural Sector 345
13 Sustainable Agriculture Practices with ICT for Soil Health Management 347
Bhabani Prasad Mondal, Anshuman Kohli, Ingle Sagar Nandulal, Roheet Bhatnagar, Chandan Kumar Panda, Sonal Kumari, Bharat Lal, Sai Parasar Das, Chandrabhan Patel, Vimal Kumar, Achin Kumar, Karad Gaurav Uttamrao, Suman Dutta and Ali R.A. Moursy
13.1 Introduction 348
13.2 Advanced ICT Technologies 350
13.2.1 Gps 350
13.2.2 Gis 351
13.2.3 Dss 352
13.2.4 Remote Sensing 352
13.2.5 IoT 353
13.2.6 Sensor Technology 354
13.2.7 Grid Soil Sampling and Variable Rate Technology (vrt) 356
13.2.8 Agricultural Robotics 357
13.3 Application of ICT in Soil Health Management 358
13.3.1 Artificial Intelligence in Analyzing Soil Health Parameters 358
13.3.1.1 Data Collection 358
13.3.1.2 Data Preprocessing 358
13.3.1.3 Feature Selection 358
13.3.1.4 Model Training 359
13.3.1.5 Model Validation 359
13.3.1.6 Soil Health Parameter Prediction 359
13.3.2 Fertilizer Recommendation Using ICT 359
13.3.2.1 Soil App 360
13.3.2.2 Multimodal DSS in Soil Fertility Management 360
13.3.3 Smart Soil Health Management Using Sensor-Based Technology 362
13.3.3.1 Sensor Selection 362
13.3.3.2 Sensor Placement 362
13.3.3.3 Data Collection 362
13.3.3.4 Data Processing 362
13.3.4 Real-Time Monitoring 363
13.3.4.1 Sensors' Efficiency Evaluation 363
13.3.5 Satellite and Drone-Based Remote Sensing Technology in Soil Health Management 363
13.3.6 ICT-Based Soil Conservation for Soil Health Management 364
13.3.7 Autonomous Robots in Efficient Soil Health Management 365
13.4 Challenges in Implementing ICT-Based Technologies 365
13.4.1 Lack of Availability of Accurate Data 365
13.4.2 High Cost of Technology and Higher Investment 366
13.4.3 Lack of Sound Skill and Knowledge of Farmers 366
13.4.4 Lack of Communication Structure and Support 367
13.4.5 Low-Risk-Bearing Capacity of Farmers 367
13.5 Opportunities or Pathways to Tackle the Issues in ICT-Based Soil Management 367
13.6 Conclusion 369
Acknowledgment 370
References 370
14 Water Resource Management Model for Smart Agriculture 375
Aysulu Aydarova
Introduction 375
Main Part 376
Conclusion 397
References 398
15 A Big Data Analytics-Based Architecture for Smart Farming 399
Tanvi Chawla, Tamanna Gahlawat and TanyaShree Thakur
15.1 Introduction 400
15.2 Related Work 402
15.3 Research Issues in Big Data for Smart Agriculture 404
15.4 Applications of Big Data Analytics in Smart Agriculture 405
15.5 Types of Big Data in Agriculture 407
15.6 Proposed Work 408
15.7 Conclusion and Future Work 414
References 414
16 Adoption of Blockchain Technology for Transparent and Secure Agricultural Transactions 417
S.C. Vetrivel, V. Sabareeshwari, K.C. Sowmiya and V.P. Arun
16.1 Introduction to Blockchain Technology 418
16.1.1 Definition and Overview 418
16.1.2 Evolution of Blockchain 418
16.1.3 Basic Components and Principles 419
16.1.4 Blockchain's Significance in Agriculture 419
16.2 Challenges in Traditional Agricultural Transactions 420
16.2.1 Lack of Transparency 420
16.2.2 Security Issues 420
16.2.3 Trust Deficit 421
16.2.4 Inefficiencies in Supply Chain 421
16.3 Understanding Blockchain Solutions 422
16.3.1 How Blockchain Operates 422
16.3.2 Types of Blockchain 423
16.3.3 Smart Contracts and Their Role 424
16.3.4 Benefits of Blockchain in Agriculture 425
16.4 Use Cases of Blockchain in Agriculture 427
16.4.1 Produce Traceability 427
16.4.1.1 Tracking Farm to Fork 427
16.4.1.2 Quality Assurance 427
16.4.2 Supply Chain Management 428
16.4.2.1 Inventory Tracking 428
16.4.2.2 Real-Time Monitoring 428
16.4.3 Payment and Financing Solutions 428
16.4.3.1 Microfinancing for Farmers 428
16.4.3.2 Instant and Secure Payments 430
16.5 Implementing Blockchain in Agriculture 430
16.5.1 Infrastructure Requirements 430
16.5.2 Data Management and Integration 432
16.5.3 Regulatory Considerations 432
16.5.4 Challenges in Adoption 432
16.6 Case Studies and Success Stories 434
16.6.1 IBM Food Trust 434
16.6.2 Provenance 434
16.6.3 AgriDigital 434
16.7 Future Trends and Opportunities 435
16.7.1 Integration with IoT and AI 435
16.7.2 Expansion of Blockchain Applications 435
16.7.3 Potential Impact on Global Food Security 437
16.8 Conclusion 439
References 439
17 AI-Assisted Environmental Parameter Monitoring of Plants in Greenhouse Farming 445
K. Sujatha, N.P.G. Bhavani, R. S. Ponmagal, N. Shanmugasundaram, C. Tamilselvi, A. Ganesan and Suqun Cao
17.1 Introduction 446
17.2 Background 447
17.3 Importance of Smart Agriculture 448
17.4 Artificial Neural Network (ANN) 449
17.4.1 Mayfly Optimization 451
17.5 Problem Statement 453
17.6 Objectives 454
17.7 Strategy for Polyhouse Monitoring 454
17.8 Results and Discussion 460
17.9 Conclusion 467
References 469
18 Metaverse in Agricultural Training and Simulation 471
Syed Quadir Moinuddin, Himam Saheb Shaik, md Atiqur Rahman and Borigorla Venu
18.1 Introduction 471
18.2 AI in Agriculture 473
18.3 Metaverse 475
18.3.1 Agriculture with AI-Based Metaverse 476
18.4 Augmented Reality (AR) 478
18.5 Virtual Reality (VR) 480
18.6 Mixed Reality (MR) 482
18.7 Agriculture Training Simulations 485
18.8 Metaverse in Agriculture Trainings 487
18.9 Conclusions 488
Acknowledgment 489
References 489
19 Sustainable Farming in the Digital Era: AI and IoT Technologies Transforming Agriculture 493
Arti Sonawane, Suvarna Patil and Atul Kathole
19.1 Introduction 494
19.1.1 The Role of Artificial Intelligence in Agriculture 495
19.1.2 The Role of the Internet of Things in Agriculture 495
19.1.3 The Intersection of AI and IoT in Agriculture 496
19.1.4 The Importance of Sustainability in Agriculture 496
19.1.5 Problem Statement 497
19.1.6 Motivation 497
19.1.7 Objective 497
19.2 Related Work 498
19.2.1 Comparative Analysis of Existing Challenges 499
19.2.1.1 Precision Agriculture: Challenges in Future IoT (2023) 501
19.2.1.2 AI-Driven Precision Agriculture: Challenges and Perspectives (2023) 502
19.2.1.3 IoT and AI in Agriculture: An Overview (2022) 502
19.2.1.4 Smart Farming with IoT and AI: Benefits and Challenges (2022) 502
19.2.1.5 AI and IoT-Based Crop Monitoring: A Review (2023) 502
19.2.1.6 Integration of AI and IoT in Agriculture: State-of-the-Art and Future Trends (2023) 502
19.2.1.7 Sustainable Agriculture: The Role of IoT and AI (2022) 503
19.2.1.8 Advances in IoT and AI for Precision Agriculture (2022) 503
19.3 Discussion of Proposed Approach 503
19.3.1 System Architecture 504
19.3.2 Components and Tools 505
19.3.3 Result and Discussion 506
19.4 Application 508
19.5 Advantages and Disadvantages of System 509
19.6 Conclusion 510
Future Scope 510
References 511
20 Precision Agriculture with Unmanned Aerial Vehicles 513
Suresh S., Sampath Boopathi, Elayaraja R., Velmurugan D. and Selvapriya R.
20.1 Introduction 514
20.2 Agri-UAV Construction and Controls 516
20.3 Applications of UAVs in Agriculture 519
20.3.1 Crop Spraying 520
20.3.2 Crop Health Monitoring 524
20.3.3 Drone Seeding 527
20.4 Conclusion 529
References 530
Index 535
1
AI and Emerging Technologies for Precision Agriculture: A Survey
Brajesh Kumar Khare
Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, Uttar Pradesh, India
Abstract
The urgent need for sustainable agriculture practices is increasingly recognized in light of escalating global food demands and environmental pressures. This paper examines the role of artificial intelligence (AI) and other emerging technologies including Internet of Things, blockchain, robotics and automated machinery, and vertical farming in optimizing agricultural processes to achieve sustainability goals. These technologies promise enhanced efficiency, reduced resource consumption, improved crop yields, and minimized environmental impacts. Robotics and automated machinery represent transformative tools in reducing labor costs and increasing precision, whereas vertical farming offers a sustainable solution to urban food production challenges by minimizing land use and optimizing water and nutrient cycles. Additionally, this paper discusses the significant barriers to technology adoption, such as data privacy concerns, lack of infrastructure, and the need for farmer training and technological literacy. This paper also explores future research directions in the field of agriculture. Key areas include AI-driven precision irrigation and nutrient management to optimize resource use, climate resilience modeling to mitigate climate change impacts, and data integration platforms utilizing big data and blockchain for transparency and efficiency. Enhanced remote sensing techniques will provide detailed insights into crop and soil health, whereas user-friendly AI interfaces and farmer training programs will ensure widespread adoption.
Keywords: Agriculture, Internet of Things (IoT), blockchain, machine learning (ML), sensor
1.1 Introduction
Farming, essential for human survival since ancient times, now faces significant hurdles due to environmental shifts, rising populations, and growing food needs. To address these challenges, a new approach called precision agriculture has emerged, blending AI with advanced technologies. This innovative field aims to revolutionize traditional farming methods through research and technological progress. Precision agriculture strives to maximize crop yields by harnessing sophisticated technologies to collect and analyze data on diverse elements, including soil characteristics, meteorological conditions, crop vigor, and pest infestations. By combining this data with AI algorithms, precision agriculture can provide farmers with actionable insights and recommendations tailored to their specific fields and crops. The need to address the many issues confronting the global agricultural sector-such as the need for sustainable practices, resource constraints, climate change, and increased food production to feed a growing population-is the foundation of emerging technologies in agriculture. The United Nations projects that the global population will hit 9.7 billion by 2050. This figure represents a significant increase from the current world population and highlights the pressing need to address challenges related to food security, resource management, and sustainable development to support the growing number of people inhabiting our planet over the next few decades [1]. This population growth places tremendous pressure on agriculture to produce more food, often referred to as the "food security challenge." Farming activities require vast quantities of land, water, and energy resources. As the availability of these vital resources diminishes over time, it is becoming increasingly crucial to adopt more efficient and environmentally friendly agricultural methods that promote sustainability and conservation [2]. The phenomena of climate change are having a profound impact on agricultural practices around the world. Rising global temperatures, shifts in rainfall and precipitation patterns, as well as an escalation in the occurrence of severe weather events are all factors contributing to disruptions in the agricultural sector. These climate-related challenges pose significant threats to crop yields, food production, and the overall sustainability of farming activities on a global scale [3]. This necessitates adaptation strategies and mitigation efforts within the agricultural sector [4]. Modern information technologies are being leveraged in the concept of "smart farming" to enhance and optimize the intricate systems involved in agricultural operations. Information and communication technologies are being integrated into agricultural production systems with the goal of improving overall efficiency and yields. Agriculture is one of the key industries in the field of production, given its critical role in sustaining human populations.
AI techniques, play a pivotal role in precision agriculture. These techniques enable the analysis of vast amounts of data including remote sensing devices and soil sensors. AI algorithms analyze agricultural data to identify patterns, enabling predictions and recommendations for crop yields, irrigation, fertilizer use, and pest control. This data-driven approach helps potentially improving agricultural productivity and sustainability.
Emerging technologies, such as Internet of Things (IoT) devices, drones, and robotics, are also revolutionizing precision agriculture. IoT sensors embedded in fields can continuously monitor Environmental conditions, providing real-time data for AI algorithms to process. Unmanned aerial vehicles, commonly known as drones, outfitted with advanced multispectral imaging cameras, possess the capability to capture exceptionally high-resolution visual data of crop fields. This technological application allows for the timely identification of early warning signs that crops may be experiencing stress or deficiencies in essential nutrients. Moreover, robotic systems designed specifically for agricultural applications can automate and take over labor-intensive tasks such as planting seeds, removing unwanted vegetation, and harvesting mature crops. The integration of these robotic solutions into farming operations has the potential to significantly reduce labor costs while concurrently boosting overall operational efficiency.
As AI and emerging technologies continue to evolve, their potential applications in precision agriculture will expand, paving the way for a more efficient, sustainable, and productive agricultural sector globally.
1.2 Precision Agriculture
Precision agriculture represents an innovative and technologically advanced approach to farming that aims to optimize multiple facets of agricultural productivity. This methodology employs data-driven strategies and cutting-edge technologies to precisely manage and allocate critical resources like pesticides and seeds according to the specific conditions present within different areas of a cultivated field. The overarching objectives of precision agriculture are to boost crop yields, minimize the costs associated with agricultural inputs and resources, and reduce the environmental impacts of farming activities, all while promoting the long-term sustainability of agricultural practices. By precisely tailoring resource allocation to the unique needs of localized sections within a field, precision agriculture techniques strive to maximize efficiency, profitability, and environmental stewardship across the entire farming operation [5]. Data collected from diverse sources, including Global Positioning System (GPS), soil sensors, and drones. These data are meticulously analyzed to comprehend field variability, assess crop conditions, and inform decision-making [6]. Variable rate technology (VRT) facilitates the pesticides and irrigation based on specific field conditions. This tailored approach ensures efficient resource utilization and minimizes wastage [7]. GPS technology is integral for precise field mapping, navigation, and the georeferencing of data. It enables the accurate tracking of field operations and the creation of detailed field maps [8]. At the core of precision agriculture lies the collection and analysis of data from remote sensing devices, weather stations, soil sensors, and yield monitors. This data is then processed and interpreted using specialized software and algorithms, often incorporating machine learning (ML) and AI techniques. Precision agriculture relies heavily on zone-based field management, a technique that segments farmland into distinct areas according to various agricultural parameters including soil composition, landscape features, and past production records. This approach recognizes that even within a single field, there can be significant variations in soil fertility, moisture levels, and other factors that affect crop growth and productivity. By accurately mapping and analyzing these variations, precision agriculture allows for the precise application of inputs such as fertilizers, pesticides, and water. VRT enables tailored input application based on each field zone's unique requirements. VRT systems are often integrated with GPS-guided machinery and automated control systems, ensuring accurate and efficient application of inputs.
Another critical aspect of precision agriculture is cropping monitoring and yield mapping. Advanced sensing technologies, such as multispectral and hyperspectral imaging systems mounted on drones or satellites, can provide detailed information about crop health, nutrient status, and potential yield. Precision data collection identifies field areas needing specific treatments. Yield mapping...
