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.
Table of Contents:
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
1.4 Internet of Things (IoT) 11
1.5 Blockchain Technology 15
1.6 Technologies Used in Smart Farming 17
1.7 Challenges 24
1.8 Future Research 26
1.9 Conclusion 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
2.3 Social Relevance of Sustainable Practices in Agriculture 38
2.4 Sustainable Agriculture Indicators 40
2.5 Sustainable Agriculture Practices Followed Till Date 42
2.6 AI-Enabled Conceptual Framework 44
2.7 Applications of Artificial Intelligence in Agriculture 48
2.8 Challenges and Barriers to Sustainable Agriculture 51
2.9 Future Directions 55
2.10 Conclusion 57
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.
3.1 Introduction 62
3.2 Precision Farming 64
3.3 Crop Monitoring 67
3.4 AI in Aquaculture 69
3.5 Predictive Analysis 71
3.6 Robotics and Automation in AI Agriculture 73
3.7 Livestock Monitoring 75
3.8 AI for Climate Smart Agriculture 78
3.9 AI in Agroecology 81
3.10 Soil Analysis 83
3.11 Conclusion 86
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
4.4 AI Technologies and Sustainable Agriculture 103
4.5 Artificial Intelligence’s Practical Use in Farming 104
4.6 Challenges and Ethical Considerations 107
4.7 Conclusions and Further Work 109
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
5.5 Environmental Concerns Related to Agriculture 132
5.6 Challenges and Considerations 135
5.7 Conclusions and Further Work 137
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
6.2 Big Data in Agriculture 150
6.3 Internet of Things (IoT) in Agriculture 154
6.4 Artificial Intelligence and Machine Learning in Agriculture 157
6.5 Remote Sensing and GIS in Agriculture 159
6.6 Data-Driven Approaches for Sustainable Crop Management 162
6.7 Data-Driven Livestock Management 163
6.8 Supply Chain Management and Food Security 165
6.9 Policy Implications and Ethical Considerations 167
6.10 Future Trends and Conclusion 168
6.11 Conclusion 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
7.4 Challenges and Limitations 191
7.5 Proposed Model 194
7.6 Future Scope 198
7.7 Conclusion 203
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
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
9.1 Introduction 232
9.2 Artificial Intelligence 234
9.3 Machine Learning 235
9.4 AI-Based ML Algorithm Models 237
9.5 Some Important Evaluation Metrics Used in AI-Based Predictive Models 239
9.6 Applications of Artificial Intelligence and Machine Learning in Crop Yield Prediction Models 241
9.7 AI-Based Crop Yield Prediction Method—Case Study 242
9.8 Steps for Crop Yield Prediction 243
9.9 Applications of Artificial Intelligence and Machine Learning in Pest and Disease Management 244
9.10 Advantages of Using Artificial Intelligence/Machine Learning in Agriculture 248
9.11 Challenges of Artificial Intelligence and Machine Learning Application in Agriculture 249
9.12 Conclusion and Future Prospects 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
10.7 Proposed Methodology 277
10.8 Implications for Farmers 282
10.9 Future Directions 284
10.10 Conclusion 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
11.4 Challenges in Smart Farming 315
11.5 Future Trends in Smart Farming 317
11.6 Conclusion 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
12.3 Climate-Smart Farming Practices 329
12.4 Case Studies 333
12.5 Evaluation of IoT Technologies 336
12.6 Relevance to Current-Day Global Issues 338
12.7 Conclusion 339
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
13.3 Application of ICT in Soil Health Management 358
13.4 Challenges in Implementing ICT-Based Technologies 365
13.5 Opportunities or Pathways to Tackle the Issues in ICT-Based Soil Management 367
13.6 Conclusion 369
14 Water Resource Management Model for Smart Agriculture 375
Aysulu Aydarova
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
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
16.2 Challenges in Traditional Agricultural Transactions 420
16.3 Understanding Blockchain Solutions 422
16.4 Use Cases of Blockchain in Agriculture 427
16.5 Implementing Blockchain in Agriculture 430
16.6 Case Studies and Success Stories 434
16.7 Future Trends and Opportunities 435
6.8 Conclusion 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
17.5 Problem Statement 453
17.6 Objectives 454
17.7 Strategy for Polyhouse Monitoring 454
17.8 Results and Discussion 460
17.9 Conclusion 467
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
18.4 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
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
19.2 Related Work 498
19.3 Discussion of Proposed Approach 503
19.4 Application 508
19.5 Advantages and Disadvantages of System 509
19.6 Conclusion 510
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
20.4 Conclusion 529
References 530
Index 535
