SADHANA SINGH, Assistant Professor (Biochemistry), School of Sciences, U.P. Rajarshi Tandon Open University, Prayagraj, India. Sadhana?s research interests include-plant stress tolerance (model and non-model), molecular regulations controlling plant abiotic stresses, and the role of NAC genes towards abiotic stress tolerance. SANTOSH KUMAR UPADHYAY, Assistant Professor, Department of Botany, Panjab University, Chandigarh, India. He has been working in the field of plant biotechnology for more than 16 years.

• About the Editors xxiiiList of Contributors xxvPreface xxxi1 Introduction to Artificial Intelligence for Crop Improvement 1Madeeha Gul, Nikhil Raghuwanshi, and Noopur Singh1.1 Introduction 11.2 AI-Driven Multi-Omics Data Integration 31.3 Genomic Selection and Breeding 41.4 High-Throughput Phenotyping and Imaging 61.5 Generative AI in Plant Breeding 81.6 Advanced Pest and Disease Management 91.7 Climate-Smart Agriculture Using AI 111.8 AI-Powered Digital Twins for Agriculture 121.9 AI for Soil Microbiome Optimization 121.10 Ethical and Socioeconomic Aspects of AI in Agriculture 131.11 Challenges, Limitations, and Future Directions 141.12 Conclusion 152 Advances in Artificial Intelligence for Plant Biology and Crop Breeding: An Overview 27Diya Kapadia, Jayshree Pawar, and Kanti Kiran2.1 Introduction 272.2 Big Data: Handling and Analytics 272.3 Blockchain Technology to Trace and Store Huge Breeding Data 292.4 3D Printing in Plant Science 312.5 Machine Learning-Based Approaches in Modernized Plant Biology and Biotechnology 322.6 Supervised and Unsupervised Learning Algorithms 322.7 Artificial Neural Network and Genetic Algorithm Usage in Crops 332.8 Predictive Analytics for Plant Biology 342.9 Usage of Agents and Robotics in Breeding Programs 352.10 Sensors and Interpretations of Networking 372.11 Object Image Capture and Analysis of Data Used in Plant Biology 382.12 Application of ANNs in Plant Science 392.13 Future Perspectives of AI for Crop Improvements 392.14 Conclusion 403 The Role of Artificial Intelligence in Modern Agriculture and Crop Innovation 45Dil Khurram, Nadeem Iqbal, Muhammad Nauman, Riyazuddin Riyazuddin, and Guo Liu3.1 Introduction 453.2 Crop Improvement 463.3 Artificial Intelligence 483.4 The Role of AI in Agriculture 493.5 Overview of AI Integration in Crop Improvement 493.6 Role of Crop Growth Models 503.7 Machine Learning (ML) Models and Algorithms 523.8 Deep Learning 553.9 Computer Vision 573.10 AI for Multi-Omics Data Analysis 583.11 Natural Language Processing 583.12 Symbolic AI 623.13 Conclusions and Future Perspectives 634 Advancements in Phenotyping and High-Throughput Imaging 71Shrishti, Avani Vasudeva, and Papiya Mukherjee4.1 Introduction 714.2 Crop Phenotyping 724.3 Phenotyping Platforms 724.4 Plant Traits to Be Phenotyped 774.5 High-Throughput Imaging Systems 784.6 High-Throughput Image Data Processing 844.7 Future Prospectives 854.8 Conclusion 855 Applications of AI in the Genomic Analysis of Crop Plants for Improved Agricultural Outcomes 93Satwi Shah, Nancy Vora, and Manan Shah5.1 Introduction 935.2 AI Methods 955.3 Genomic Selection Models: Implementation in Plant Breeding 985.4 Challenges in AI Techniques for Genomic Selection in Plants 1065.5 Conclusions 1096 Current Applications of Artificial Intelligence and Machine Learning in Plant Functional Genomics 113Siddharth Singh, Sanjana Mishra, Amaan Arif, Prekshi Garg, and Prachi Srivastava6.1 Introduction 1136.2 Artificial Intelligence and Machine Learning in Plant Genomics 1186.3 Applications of Artificial Intelligence and Machine Learning in Plant Genomics 1236.4 Challenges in AI and ML Applications in Plant Genomics 1286.5 Future Prospects 1306.6 Conclusion 1317 AI Models for Studying and Integrating Plant Multiple Omics 141Maliheh Eftekhari and Mohammad Reza Naghavi7.1 Introduction 1417.2 Plant Multi-Omics: Data Types and Challenges 142\7.3 AI Models for Analyzing Individual Omics 1477.4 AI Approaches for Multi-Omics Integration 1507.5 Applications of AI-Driven Multi-Omics in Crop Improvement 1527.6 Challenges and Future Directions in AI-Driven Multi-Omics for Crop Science 1537.7 Conclusion 1548 Artificial Intelligence and Synthetic Biology for Crop Breeding 163Arukshita Chandra, Sakshi Singh, Divya Mohanty, Charu Sharma, Shrishti, Papiya Mukherjee, and Nupur Mondal8.1 Introduction 1638.2 History 1648.3 Methods, Models, and Algorithms 1668.4 Synthetic Genomics 1678.5 Artificial Intelligence (AI) 1678.6 Applications 1698.7 Global and National Progress 1718.8 Challenges 1728.9 Future Prospects 1738.10 Conclusion 1749 Hub Gene Prediction by Machine Learning for Regulating Plant Stress Responses 179Perumalla Srikanth, Ann Maxton, and Sam A. Masih9.1 Introduction 1799.2 Methods of Machine Learning for Gene Regulatory Network Prediction 1809.3 Prediction of Different Biological Systems 1829.4 Applications of Machine Learning for Regulating Plant Stress Responses 1869.5 Future Research and Challenges 1879.6 Conclusion 18810 Artificial Intelligence Models for Analysing Plant Transcriptomics 195Amaan Arif and Prachi Srivastava10.1 Introduction 19510.2 Fundamentals of Transcriptomics in Plants 19610.3 Key Challenges in Analyzing Transcriptomic Data 19710.4 Significance of Gene Expression and Regulatory Networks in Plant Development and Stress Response 19810.5 Introduction to AI, ML, and DL in the Context of Transcriptomics 20110.6 Advantages of AI-Driven Approaches Over Conventional Statistical Methods 20210.7 Key Applications of AI in Analyzing Plant Transcriptomic Data 20310.8 Machine Learning Models in Plant Transcriptomics 20410.9 Deep Learning Models in Plant Transcriptomics 20510.10 Integrating AI with RNA-Seq Data 20510.11 AI Models for Analyzing RNA-Seq Datasets 20710.12 Applications for AI in Functional Genomics 20910.13 Future Perspectives and Opportunities in AI-Driven Plant Transcriptomics 21210.14 Conclusion 21311 Integrating Artificial Intelligence Technologies with Plant Systems Biology 219Anukriti11.1 Introduction 21911.2 Case Studies 22611.3 Challenges and Opportunities 22711.4 The Future of AI-Powered Agriculture 22911.5 Conclusion 22912 Disease and Pest Management Using AI Technologies 235Acharya Balkrishna, Shalini Bhatt, Rakshit Pathak, and Vedpriya Arya12.1 Introduction 23512.2 AI Technologies in Disease Diagnosis 23712.3 AI in Pest Identification and Control 24712.4 AI for Predictive Modeling in Disease and Pest Management 25112.5 Economic and Environmental Impacts of AI in Pest and Disease Management 25112.6 Challenges 25312.7 Future Directions 25412.8 Conclusion 25513 Climate-Resilient Crop Improvement Through AI Technologies 263Akhouri Nishant Bhanu, Bangar Vaibhav, Mohammad Yasin, and Sadhana Singh13.1 Introduction 26313.2 Artificial Intelligence: Transforming the Future of Agriculture 26513.3 Integration of Artificial Intelligence in Plant Breeding for Crop Improvement 26813.4 Characterizing Germplasm Resources with AI to Produce Genomic Big Data 26913.5 AI for Overcoming Phenomics Bottlenecks Through Digitalization and Phenotyping Data Collection 27113.6 Examining AI's Potential for Genomic Predictions and Gene Function Analysis 27413.7 Multi-Omic Big Data Integration in Plant Breeding 27713.8 AI-Driven Integration to Bridge the Genotype–Phenotype Gap in Modern Crop Breeding 27913.9 AI for Functional Genomics and Gene Mining 28213.10 Using AI to Discover Exceptional Alleles and Causal Variants in Omic Data 28413.11 AI-Enabled Genomic Selection for Practical Plant Breeding by Phenotype Prediction 28613.12 Enhancing Gene Editing with Generative AI 28813.13 AI Providing Access to Envirotyping Data for Crop Breeding 29113.14 Conclusion 29514 Integration of AI with Precision Agriculture Technologies 307Tanisha Anand, Sumita Mishra, Sachin Kumar, Rajesh K. Tiwari, and Mala Trivedi14.1 Introduction 30714.2 Enabling Technologies for Precision Agriculture 30914.3 Internet of Things (IoT) 31014.4 Role/Applications of AI in Precision Agriculture 31214.5 Resource Management 31414.6 Climate Adaptation 31514.7 Supply Chain Optimization 31614.8 Result and Discussion 31614.9 Conclusion 31815 AI-Enabled IoT for Crop Improvement: A Paradigm Shift in Smart Agriculture 323Akhouri Nishant Bhanu, Bangar Vaibhav, and Satyam Sanodiya15.1 Introduction 32315.2 The Role of IoT in Crop Improvement 32415.3 IoT Technologies Used in Plant Breeding, Molecular Breeding, and Gene Editing 32615.4 Benefits and Challenges of Integrating AI and IoT in Crop Improvement 34115.5 Future Prospects of IoT and AI Integration in Crop Improvement 34515.6 Conclusion 34816 Optimizing Crop Management Practices Using AI Technologies 359Ambrina Sardar Khan and Prateek Srivastava16.1 Introduction 35916.2 Traditional Breeding: The Foundation of Agricultural Advancement 36016.3 Advancements in Molecular Breeding Techniques 36116.4 Speed Breeding: A New Era in Agriculture 36316.5 Traditional Meets Speed Breeding: Shaping Agricultural Progress 36416.6 Artificial Intelligence (AI) in Plant Breeding 36516.7 AI in Enhancing Plant Breeding Technologies 36716.8 Studying Biochemical Phenotype Through AI 36716.9 Integrating Phenomics with Genomics for Smart Breeding 36816.10 AI Technologies Benefiting Crop Breeding 37016.11 Role of Artificial Intelligence in Addressing Phenomics Challenges 37016.12 AI in Gene Function Analysis 37116.13 Artificial Intelligence in Genomics, Phenomics, and Envirotyping Data Accessibility 37116.14 Next-Generation (Next-Gen) Artificial Intelligence (AI) Augmented Farm 37216.15 Future Prospects of AI Breeding 37316.16 Conclusion 37317 Artificial Intelligence (AI)-Based Strategies for Plant Health andCropSafety 381Muhammad Nauman, Nadeem Iqbal, Dil Khurram, Hafiz Muhammad Ansab Jamil, Moniba Zahid Mahmood, Kalpita Singh, and Riyazuddin Riyazuddin17.1 Introduction to AI in Agriculture 38117.2 Machine Learning (ML) 38717.3 Role of Artificial Intelligence in Predictive Analytics and Real-Time Monitoring 38817.4 Applications of Artificial Intelligence in Disease and Pest Management 38917.5 Case Studies: DSS in Action 39217.6 Key Technologies in Artificial Intelligence-Driven Management 39317.7 Advantages of Internet of Things-Based Monitoring 39517.8 Machine Learning Models 39617.9 Conclusions and Future Perspectives 39918 Challenges and Uncertainties Associated with AI in Agriculture 403Vipin Bihari Mishra and Kanti Kiran18.1 Introduction 40318.2 Applicability of AI to Agricultural Practices 40418.3 Scope of Improvement of AI Strategies in the Agriculture Sector 40518.4 Recent Advancements in AI for Agriculture – Deep Learning in Agriculture 40718.5 New Opportunities for AI-Driven Initiatives in Agriculture 41018.6 Aspects of AI in Agriculture That are Ethical, Socioeconomic, and Environmental 41218.7 AI in Agriculture and Cross-Disciplinary Relationships 41418.8 Acceptance and Challenges of AI in Agriculture 41418.9 Data Bias and Its Implications in AI Agriculture 41518.10 Future Advancements and Plans 41618.11 Conclusions 41619 Ethical and Regulatory Considerations in AI-Driven Crop Improvement 421Praveen Kumar Maddheshiya, Kapil Gupta, Shubhra Gupta, Kiran Gupta, and Ravindra Pratap Singh19.1 Introduction 42119.2 The Potential of AI for Agriculture 42219.3 Ethical Issues of Using SIS in Agriculture 42519.4 Scenario of Digital Divide in Agriculture 42619.5 Ethical and Regulatory Considerations in AI-Driven Crop Improvement 42619.6 Ethical Framework for Assessment of Artificial Intelligence-Based Solutions in Agriculture 42919.7 Recommendations for Agricultural Technology Providers 43019.8 Conclusion 43120 Challenges and Opportunities Using AI Toward Crop Improvement 437Akhouri Nishant Bhanu, Bangar Vaibhav, and Saurav Raj20.1 Introduction 43720.2 Opportunities of Using AI in Crop Improvement 43920.3 Challenges for Using AI in Crop Improvement 44220.4 SWOT Analysis of AI Technologies in Crop Improvement 44420.5 Future Directions for AI in Crop Improvement 44620.6 Conclusion 447References 447Index 451