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Machine Learning and AI for Precision Plant Epigenetics
Jen-Tsung Chen (Edited by), J–T Chen (Author)
9781394380282, Wiley
Hardback, published 30 June 2026
496 pages
24.4 x 17 x 1.5 cm, 0.68 kg
Harness artificial intelligence to develop stress-resilient crops for sustainable agriculture Machine Learning and AI for Precision Plant Epigenetics demonstrates how to develop climate-resilient crops by integrating AI with RNA-based epigenetic technologies. Edited by Professor Jen-Tsung Chen, a leader in plant biotechnology, this volume integrates insightful contributions from experts around the world that discuss how ML and AI models can revolutionize plant breeding and crop improvement to ensure food security under changing environmental conditions. The book explores applications across sixteen chapters, covering AI-driven epigenome engineering, CRISPR/Cas9-mediated precision editing, intelligent approaches to combat abiotic and biotic stresses, and AI-enabled RNA interference. It explores the use of AI models for studying non-coding RNAs, predicting plant epigenetic landscapes, unlocking heat stress memory mechanisms, and uncovering plant-microbiome interactions critical for productivity. The book: This essential resource is tailored for researchers in plant biology, stress physiology, crop breeding, computational biology, and bioinformatics. It offers a forward-looking perspective on developing sustainable agriculture systems that support global food security in an era of climate change and increasing environmental challenges.
List of Contributors xix About the Editor xxvii Preface xxix 1 Machine Learning for Precision Epigenetic Modification in Plants 1 1.1 Introduction 1 1.2 Epigenetic 3 1.3 Epigenetic Modifications 4 1.3.1 DNA Methylation Dynamics 4 1.3.2 RNA Modification Dynamics 6 1.3.3 Histone Modifications and Chromatin Dynamics 7 1.3.4 Chromatin Remodeling and Nucleosome Dynamics 8 1.4 ml in Plant Epigenetics 9 1.4.1 ml in DNA Methylation Dynamics 13 1.4.2 ml in Histone Modifications 15 1.4.3 ml in Chromatin Remodeling 16 1.4.4 ml in Chromatin Modification Dynamics 17 1.4.5 ml in Chromatin- Interaction Prediction 18 1.4.6 ml in ncRNA Prediction 20 1.4.7 ml in Epitranscriptomics 21 1.4.8 ml in Epigenetic Genome Editing 22 1.5 Challenges and Limitations 23 1.6 Future Perspectives 25 1.7 Conclusion 26 References 27 2 AI- Driven Precision Plant Epigenetic Regulation Under Changing Climate 43 2.1 Introduction 43 2.2 Foundations of Plant Epigenetics 44 2.2.1 DNA Methylation and Histone Modifications 44 2.2.2 Small RNAs and Noncoding RNA Regulation 45 2.2.3 Epigenetic Memory and Transgenerational Effects 46 2.3 Impacts of Climate Change on Plant Epigenomes 47 2.3.1 Abiotic Stress and Epigenomic Plasticity 47 2.3.2 Biotic Interactions in a Changing Climate 47 2.3.3 Case Studies 48 2.3.3.1 Epigenomic Responses to Salinity 48 2.3.3.2 Epigenomic Responses to Drought 49 2.3.3.3 Epigenomic Responses to Heat 50 2.4 Artificial Intelligence in Epigenetic Research 50 2.4.1 AI Tools and Algorithms for Omics Data Analysis 50 2.4.2 Machine Learning Models for Predicting Epigenetic Changes 51 2.4.3 Multiomics and Environmental Data Integrative Platforms 51 2.5 AI- Driven Precision Epigenetic Regulation 52 2.5.1 Epigenetic Biomarkers for Stress Tolerance 52 2.5.2 Targeted Epigenome Editing Strategy Design 52 2.5.3 AI- Enabled CRISPR/dCas Systems for Epigenetic Regulation 53 2.5.4 Reporting, Validation, and Ethics 53 2.5.4.1 Data and Code Transparency 53 2.5.4.2 Performance and Interpretation 53 2.5.4.3 Validation Ladder 54 2.5.4.4 Risk, Equity, and Access 55 2.6 Applications in Crop Improvement 55 2.6.1 Enhancing Resilience in Strategic Crops 55 2.6.1.1 Precision Breeding Through Genomic Tools 56 2.6.1.2 Systems Biology and Holistic Crop Management 56 2.6.1.3 Advanced Phenotyping for Precision Agriculture 57 2.6.2 Epigenetic Breeding and AI- Guided Selection 57 2.6.2.1 Mechanisms and Applications of Epigenetic Modulation 57 2.6.2.2 The Confluence of Epigenetics and Artificial Intelligence 58 2.6.2.3 Precision Epigenome Engineering 58 2.6.3 Data- Driven Decision- Making for Climate- Smart Agriculture 58 2.6.3.1 The Infrastructure of Precision Agriculture 59 2.6.3.2 Predictive Analytics for Proactive Management 59 2.6.3.3 Transparency and Efficiency Through Blockchain 60 2.7 Challenges, Ethics, and Future Directions 60 2.7.1 Data Limitations and Model Interpretability 60 2.7.2 Regulatory and Ethical Considerations 61 2.7.3 Prospects for Global Food Security 61 2.8 Conclusion 62 References 63 3 AI- Driven Plant Epigenome Engineering for Developing Resilient Crops 71 3.1 Introduction: Climate Change and the Need for Epigenetic Innovation 71 3.2 Epigenetic Mechanisms in Plant Stress Response 72 3.2.1 Epigenetic Memory in Environmental Stress Adaptation 74 3.3 Role of AI in Plant Epigenomic Decoding 75 3.3.1 AI- Driven Integration of Multiomics and Epigenetic Data 76 3.3.2 Deep Learning and Generative Network Approaches in Epigenetic Systems 77 3.3.3 Large Language Models in Plant Epigenomics 78 3.4 AI- Enabled Targeted Epigenome Editing 79 3.4.1 CRISPR/dCas9 Systems for Epigenetic Modifications: Designing Target Sites Through AI Prediction Models 80 3.5 Case Studies: Resilient Crop Development 81 3.5.1 Drought and Heat Tolerance Through Epigenetic Regulation 82 3.5.2 Salinity Resistance and Metabolic Adaptation 84 3.6 Conclusion and Future Perspectives 88 References 89 4 AI- Based Studies on Epigenetic Mechanisms: Highlighting Plant Adaptation and Domestication 99 4.1 Introduction 99 4.2 Epigenomic Data to Predictive Models: From Data to Discovery 101 4.3 AI/ML/DL Frameworks for Epigenetic Analysis in Plants 102 4.3.1 Machine Learning Approaches 102 4.3.2 Deep Learning for Epigenetic Pattern Recognition 103 4.3.2.1 CNNs for Spatial Epigenetic Patterns 103 4.3.2.2 RNNs for Temporal Epigenetic Dynamics 104 4.3.2.3 Graph Neural Networks for 3D Chromatin Architecture 105 4.3.2.4 Transformers for Multiomics Integration 107 4.3.2.5 Hybrid and Explainable AI Models 108 4.4 AI Insights into Epigenetic Mechanisms of Plant Adaptation 108 4.4.1 Drought Adaptation: Predictive Epigenomics and Transcriptomic Modeling 109 4.4.2 Salinity Stress: AI- Revealed Chromatin and Ion Homeostasis Networks 110 4.4.3 Heat Stress: AI- Modeled Epigenetic Dynamics and Thermotolerance 111 4.4.4 Cold Stress and Flooding: Temporal AI Modeling of Stress Memory 112 4.4.5 UV and Multistress Environments: Hybrid Adaptation Signatures 112 4.5 AI Insights into Plant Domestication Epigenomics 113 4.5.1 Epigenetic Footprints of Domestication in Crops 113 4.5.2 Cross- Species Generalization 115 4.6 Future Directions in AI- Driven Epigenomics 115 4.7 Conclusion 116 References 117 5 AI Models for Studying Plant Epigenetics and Epigenomics 127 5.1 Introduction 127 5.2 Plant Epigenetics and Epigenomics: An Overview 128 5.2.1 Major Epigenetic Mechanisms in Plants 128 5.2.2 Major Epigenomic Mechanisms in Plants 129 5.3 AI Models for Plant Epigenetics and Epigenomics 130 5.3.1 Why AI for Epigenomics? 130 5.3.2 AI Models for Plant Epigenetics and Epigenomics 131 5.4 Application of AI Models in Plant Epigenetics 131 5.5 Challenges and Limitations 136 5.6 Future Directions 136 5.7 Conclusion 137 References 137 6 AI- Based Approaches for Studying Plant Epigenetic Landscapes Under Abiotic Stress 143 6.1 Introduction 143 6.1.1 Plant Epigenetic Mechanisms 144 6.1.2 Epigenetic Regulation of Plant Responses to Abiotic Stress 145 6.1.3 Challenges in Handling and Integrating Large- Scale Epigenetic Datasets 146 6.2 AI and Machine Learning in Epigenetics 146 6.2.1 Types of AI Techniques 147 6.2.2 Data Preprocessing, Feature Extraction, Model Training, and Evaluation 148 6.3 Application of AI in Studying Plant Epigenetic Responses to Abiotic Stress 149 6.3.1 Pattern Recognition and Classification 149 6.3.2 Predictive Modeling 150 6.3.3 Multiomics Data Integration 151 6.4 Limitations and Drawbacks 153 6.5 Conclusion 154 References 154 7 AI- Based Whole- Genome Prediction for Diverse Plant Epigenetic Modulations 161 7.1 Introduction 161 7.2 Plant Epigenetic Modifications 163 7.2.1 DNA Methylation and Predictive Epigenomics 163 7.2.2 Histone Modifications and Chromatin Remodeling 163 7.2.3 Noncoding RNAs and Epigenetic Regulation 164 7.2.4 Transgenerational Epigenetic Inheritance 164 7.3 Data Sources and Profiling Technologies 165 7.3.1 Whole- Genome Bisulfite Sequencing 165 7.3.2 Chromatin Accessibility and Protein– DNA Interaction Profiling 165 7.3.3 RNA- Based Epigenomic Profiling 166 7.3.4 Single- Cell Epigenomics 166 7.3.5 Long- Read Sequencing and Direct Epigenetic Detection 166 7.3.6 Time Series and Multiomics Integration 167 7.4 AI and Machine Learning Frameworks for Epigenomic Prediction 167 7.4.1 Deep Learning Architectures for Epigenomic Inference 168 7.4.2 Transformer Models and Context- Aware Genomic Learning 168 7.4.3 Graph Neural Networks for 3D Genome Modeling 168 7.4.4 Generative Adversarial Networks for Synthetic Epigenomic Simulation 169 7.4.5 Federated Learning for Secure and Decentralized Epigenomic Prediction 169 7.5 Multiomics Integration Using AI 170 7.5.1 Systems Biology Approaches for Phenotype– Epigenome Prediction 170 7.5.2 Knowledge Graphs and Epigenetic Trait Modeling 171 7.6 AI- Based Whole- Genome Prediction Models 171 7.7 Applications in Plant Science and Agriculture 173 7.8 AI- Guided Epigenome Editing and Synthetic Biology 175 7.9 Explainable and Interpretable AI 176 7.10 Digital Twins and In Silico Plant Epigenomics 177 7.11 Quantum Computing and AI for Epigenetic Predictions 180 7.11.1 Hybrid Quantum- Classical AI Models in Epigenomic Prediction 180 7.11.2 Quantum- Enhanced Predictive Epigenomics in Agriculture 181 7.12 Challenges and Limitations 181 7.12.1 Data Sparsity, Noise, and Analytical Complexity 181 7.12.2 Computational and Biological Challenges in Genome- Wide Prediction 182 7.12.3 Cross- Species Transferability and Model Generalization 182 7.12.4 Ethical, Regulatory, and Biosafety Constraints 183 7.13 Future Perspectives 183 7.14 Conclusion 185 Abbreviations 185 Acknowledgement 186 Data Availability 186 References 187 8 Intelligent Priming System for Combating Biotic Stress 201 8.1 Introduction 201 8.2 Mechanisms Underlying Priming- Based Immunity 202 8.2.1 Signal Perception and Transduction 202 8.2.2 Role of Phytohormones 202 8.2.3 ROS Generation and Redox Signaling 203 8.3 Epigenetic and Molecular Basis of Priming Memory 203 8.3.1 DNA Methylation 203 8.3.2 Histone Modification 204 8.3.3 Transcriptional Reprogramming 204 8.3.4 Systemic Signal Transmission 205 8.4 Priming Agents and Triggers 206 8.4.1 Biotic: PGPR, Mycorrhizae, and Trichoderma spp. 206 8.4.2 Abiotic: Temperature Shifts, UV Light, and Chemical Inducers (e.g., BABA and SA) 206 8.4.3 Nanomaterials and Engineered Inducers 207 8.5 Integration with Omics and Smart Technologies 207 8.5.1 Transcriptomics, Proteomics, and Metabolomics in IPS Monitoring 207 8.5.2 Biosensors and Precision Agriculture Platforms in IPS Modulating 208 8.5.3 AI and Machine Learning for Predictive IPS Modeling 209 8.6 Applications in Major Crops 209 8.6.1 Case Studies in Tomato, Rice, Wheat, and Arabidopsis 209 8.6.2 IPS Deployment Under Field and Greenhouse Conditions 211 8.7 Transgenerational Priming and Long- Term Immunity 212 8.8 Advantages, Limitations, and Risk Assessment 213 8.9 Future Directions and Prospects 214 8.10 Conclusion 215 Acknowledgments 216 References 216 9 AI Technology for Studying Plant Noncoding RNAs 229 9.1 Introduction 229 9.2 Overview of Plant Noncoding RNAs 230 9.2.1 Classification of ncRNA 230 9.2.1.1 MicroRNA 231 9.2.1.2 Si- RNAs 231 9.2.1.3 Long Noncoding RNA 231 9.2.2 Functional Roles and Mechanisms in Plant Growth, Development, Stress Responses, and Adaptation 232 9.2.2.1 Role in Plant Growth and Development 232 9.2.2.2 Role in Stress Response and Adaptation 233 9.2.3 Challenges in Studying Plant ncRNA 235 9.3 Traditional Approaches for ncRNA Analysis in Plants 236 9.3.1 Laboratory- Based Detection: RNA- seq, qRT- PCR, Northern Blot, In Situ Hybridization 236 9.3.1.1 Northern Blotting for RNA Detection 236 9.3.1.2 RT- PCR and qRT- PCR for Plant ncRNA Expression Validation 236 9.3.1.3 In Situ Hybridization 237 9.3.2 Computational Biology Before AI: Sequence Alignment, Comparative Genomics, and Motif Analysis 237 9.3.2.1 Cloning and Sanger Sequencing of ncRNA Genes 237 9.3.2.2 Comparative Genomics 238 9.3.2.3 Motif Analysis 238 9.4 Rise of AI in Plant ncRNA Research 238 9.5 AI- Based Tools and Pipelines for Plant ncRNA Analysis 240 9.5.1 Pinc 240 9.5.2 PlantLncBoost 241 9.5.3 LncFinder- Plant and CPATplant 242 9.5.4 LncADeep, RNAplonc, and DeepPInc 242 9.5.5 Plant- LncPipe 243 9.6 Applications of AI in ncRNA Research 244 9.6.1 AI for Identification and Classification of Novel Plant ncRNAs 245 9.6.1.1 Machine Learning Tools 245 9.6.1.2 Deep Learning Architectures 246 9.6.1.3 Plant- Specific Databases and High- Throughput Annotation 246 9.6.2 Functional and Regulatory Role Prediction 246 9.6.3 Network Reconstruction and Discovery of Regulatory Modules 247 9.6.4 Integration of AI with Omics and Big Data Platforms 248 9.7 Major Obstacles and Constraints in AI- Driven ncRNA Research in Flora 250 9.7.1 Limitations of the Dataset and Quality of Curation 250 9.7.2 Cross- Species Generalization in Plants 251 9.7.3 Barriers to Biological Validation 251 9.7.4 Interpretability and Explainability of AI Models 252 9.7.5 Integration with Multiomics Data 252 9.7.6 Resource and Accessibility Constraints 253 9.7.7 Ethical and Biosafety Considerations 253 9.8 Future Perspectives of AI Technology for Studying Plant ncRNAs 253 9.8.1 More Advanced Multiclass and Functional Predictive Frameworks 254 9.8.2 Integration with Multiomics 254 9.8.3 User- Friendly Tools and Better Access for Plant Breeders and Molecular Biologists 255 9.9 Conclusion 256 References 256 10 AI- Enabled Plant RNA Interference 265 10.1 Introduction to Plant RNA Interference 265 10.2 Computational Biology Foundations of RNAi 266 10.3 Artificial Intelligence in RNAi Research 268 10.4 AI- Driven RNAi Design and Optimization 270 10.5 Functional Genomics Through AI- Enhanced RNAi 272 10.6 AI and RNAi in Plant Stress and Disease Management 273 10.7 Integration of Multiomics Data with AI and RNAi 275 10.8 AI- Enhanced Delivery Systems for RNAi in Plants 277 10.9 Challenges, Limitations, and Ethical Considerations 280 10.10 Future Prospects and Next- Generation Directions 282 10.11 Conclusion 283 Abbreviations 284 Acknowledgement 285 Data Availability 285 References 285 11 AI Models for Uncovering Plant– Microbiome Interactions 299 11.1 Introduction 299 11.2 Plant Microbiomes and Their Roles 301 11.2.1 Niches: Rhizosphere, Phyllosphere, and Endosphere 301 11.2.1.1 Function Activity of Microbial Consortia in the Rhizosphere 301 11.2.1.2 Rhizospheric Region: Dynamic Zone for Microbe- Driven 302 11.2.1.3 Microbe- to- Microbe Signaling 302 11.2.1.4 Plant- to- Microbe Signaling 303 11.2.1.5 Microbe- to- Plant Signaling 303 11.2.2 Functions in Growth Promotion and Immunity 303 11.3 AI Approaches in Microbiome Research 304 11.4 The Omics Toolkit: Mapping the Plant– Microbiome Interface 306 11.4.1 Metagenomics and the Functional Potential of Microbial Communities 307 11.4.2 Transcriptomics, Proteomics, and Metabolomics: Decoding Functional Expression 307 11.4.3 AI Pipelines for Feature Extraction and Data Harmonization 307 11.5 Predictive Applications 309 11.5.1 Biomarker Discovery: From Single Molecules to Functional Pathways 309 11.5.2 Disease Prediction and Early- Warning Systems (EWS) 310 11.6 Case Studies 312 11.7 Toward Predictive Microbiome Engineering 313 11.8 Challenges and Future Prospects 314 11.9 Conclusion 316 Acknowledgments 316 References 316 12 AI- Assisted Omics Tools for Predicting Functions of Plant RNAs 327 12.1 Introduction 327 12.2 Omics Data in Plant RNA Studies 328 12.3 AI Methodologies in Omics Analysis 328 12.4 AI- Assisted Omics Tools for Predicting Plant RNA Functions 330 12.4.1 Plant Long Noncoding RNA Prediction by Random Forests9 (PLncPRO) 330 12.4.2 Plant Target Prediction for microRNAs (P- TarPmiR) 331 12.4.3 Plant Long Non- coding RNA Identification Tool (PLIT) 332 12.4.4 Plant RNA– FM (Plant RNA Foundation Model) 333 12.4.5 Plant Long Noncoding RNA– Protein Interaction Method (PLRPIM) 334 12.4.6 Abiotic Stress Long Non- coding RNA Predictor (ASLnCR) 335 12.4.7 Alternative Splicing and microRNA Interaction Resource (ASmiR) 335 12.4.8 miRNA Finder/MicroRNA Finder 336 12.5 Challenges 337 12.6 Conclusion 338 References 338 13 The Integration of Artificial Intelligence and Big Data in Plant Epigenetics 341 13.1 Introduction 341 13.2 Fundamentals of Plant Epigenetics 343 13.2.1 Overview of Epigenetic Mechanisms 343 13.2.2 Function in Adaptation, Stress Response, and Plant Development 343 13.3 From Field to Cloud: Big Data Transforming Plant Research 344 13.4 AI Unraveled: Techniques for Biological Pattern Discovery 350 13.4.1 ml Approaches for Biological Data Analysis 350 13.4.2 dl Versus ml in Biological Data Analysis 351 13.5 Integrative Approaches Using AI and Big Data 354 13.5.1 AI- driven Identification of Epigenetic Biomarkers for Breeding 354 13.5.2 Network- Based Models for Epigenetic Regulation 355 13.5.3 Integrating Epigenomic, Transcriptomic, and Phenotypic Data 355 13.5.4 Case Study: AI- Based Identification of Rice Epigenetic Markers Responsive to Drought 357 13.5.5 Case Study: DL for Arabidopsis Chromatin Accessibility Prediction 358 13.6 From Code to Crops: AI Applications in Plant Epigenetics 358 13.6.1 Prediction of Histone Modification Sites and DM 358 13.6.2 Recognizing Regulatory Elements and Chromatin State 359 13.6.3 Categorizing Epigenetic Patterns Under Stress 359 13.6.4 Utilizing AI to Predict Genotypes to Epigenotypes 360 13.7 Future Perspectives 360 13.8 Conclusions 361 References 361 14 AI- Omics- Epigenetics Integration in Plants: Highlighting the Study of MicroRNAs 371 14.1 Introduction 371 14.2 Molecular Basis of Plant miRNA- Mediated Regulation 373 14.3 Epigenetic Roles of miRNAs in Plants 375 14.4 Artificial Intelligence in Biological Data Science 376 14.4.1 Relevance of AI to Plant miRNA Research 377 14.4.1.1 De Novo Discovery 377 14.4.2 Target Prediction and Interaction Modeling 377 14.4.3 Pattern Recognition in Multiomics 377 14.4.4 Translational Potential: From Basic Research to Precision Agriculture 377 14.5 AI and ML Techniques for MiRNA Discovery and Prediction 378 14.6 Computational Approaches in miRNA Discovery 379 14.7 ml Models for Plant miRNA Prediction 379 14.8 dl Applications and Target Prediction 380 14.8.1 Comparative Analysis of Computational Tools for Plant miRNA– Target Prediction 380 14.9 Conventional Prediction Tools and Their Limitations 380 14.10 dl Advancements 382 14.10.1 Convolutional Neural Networks 382 14.10.2 Graph Neural Networks 383 14.11 Integrating Multiomics and Epigenetic Data 383 14.11.1 Data Fusion Strategies 383 14.11.2 Epigenetic Features That Matter 384 14.11.3 Applications to Stress Biology 384 14.12 Challenges and Future Directions 384 14.12.1 AI- Driven Functional Analysis and Epigenetic Integration 385 14.12.2 Advancing Functional Analysis Through Artificial Intelligence 386 14.12.3 Decoding the Epigenetic Landscape with AI 386 14.12.4 Holistic Insights Through Multiomics Integration 387 14.12.5 Practical Applications in Modern Plant Science 388 14.12.6 Dynamic Modeling: Agent- Based and Reinforcement Learning 388 14.12.7 Ongoing Challenges and Future Trajectories 389 14.13 Conclusion 390 References 391 15 Machine Learning and Computational Biology- Based Epigenetics for Uncovering Plant Adaptive Evolution 397 15.1 Introduction 397 15.1.1 Computational Challenges in Epigenetics 397 15.1.2 The Challenge of Phenotypic Plasticity and Rapid Adaptation 398 15.1.3 Epigenetics: A Bridge Between Genome and Environment 399 15.1.4 Navigating the Data Deluge 399 15.2 Foundational Concepts in Plant Epigenetics 399 15.2.1 Key Epigenetic Marks 399 15.2.1.1 DNA Methylation 400 15.2.1.2 Histone Modifications 401 15.2.1.3 Noncoding RNA- Associated Gene Silencing 401 15.2.2 Mechanisms of Epigenetic Inheritance: Mitotic and Meiotic 402 15.2.3 Epigenetic Regulation of Key Agronomic Traits 402 15.2.3.1 Epigenetic Basis of Flowering Time 402 15.2.3.2 Epigenetic Basis of Stress Memory 402 15.2.3.3 Epigenetic Regulation of Disease Resistance 403 15.3 Acquiring and Processing Epigenomic Data 404 15.3.1 High- Throughput Sequencing Technologies for Epigenomics 404 15.3.1.1 Bisulfite Sequencing (BS- seq, WGBS) for DNA Methylation 404 15.3.1.2 ChIP- seq for Histone Modifications 404 15.3.1.3 ATAC- seq for Chromatin Accessibility 404 15.3.2 Preprocessing and Quality Control of NGS Data 404 15.3.3 Core Bioinformatics Pipelines: Alignment, Peak Calling, and Differential Analysis 405 15.4 Machine Learning for Decoding the Epigenomic Language 405 15.4.1 Dimensionality Reduction and Pattern Discovery 405 15.4.1.1 Unsupervised Learning for Epigenome Exploration 406 15.4.2 Supervised Learning for Predictive Epigenomics 406 15.4.3 Deep Learning Architectures for Sequence and Function 407 15.4.3.1 Convolutional Neural Networks (CNNs) for cis- Regulatory Element Detection 407 15.4.3.2 Recurrent Neural Networks (RNNs/LSTMs) for Modeling Epigenomic Dynamics 408 15.5 Integrative Computational Biology for Evolutionary Insights 408 15.5.1 Multiomics Data Integration 408 15.5.2 Phylogenetic Comparative Methods for Epigenetics 409 15.5.3 Identifying Epigenetic Footprints of Selection and Domestication 409 15.6 Challenges and Future Directions 410 15.7 Conclusion 411 References 411 16 Ethical and Regulatory Considerations of Artificial Intelligence in Agriculture 417 16.1 Introduction 417 16.2 Ethical Foundations and Philosophical Underpinnings of AI in Agriculture 420 16.2.1 Ethical Theories and Frameworks Relevant to AI 420 16.2.1.1 Utilitarianism and Its Role in Agricultural Decision- Making 420 16.2.1.2 Deontological Ethics in Algorithmic Accountability 421 16.2.1.3 Virtue Ethics and Sustainable AI Development 421 16.2.2 Ethical Dilemmas Specific to Agricultural AI Systems 422 16.2.2.1 Data Ownership and Consent in Agricultural Datasets 422 16.2.2.2 Moral Implications of Replacing Human Labor 422 16.2.2.3 Issues of Equity and Fair Access to AI Tools 422 16.2.3 AI and the Ethical Impacts on Agroecosystems 423 16.2.3.1 Balancing Technological Advancement with Biodiversity 423 16.2.3.2 Ethical Trade- offs in Resource Allocation and Sustainability 423 16.3 Data Privacy, Security, and Intellectual Property Rights 424 16.3.1 Data Collection and Privacy in Agricultural Settings 424 16.3.1.1 Informed Consent and Farmer Autonomy 425 16.3.1.2 Anonymization and Aggregation of Agricultural Data 425 16.3.2 Data Governance and Security Protocols 425 16.3.2.1 Cybersecurity Risks in AI- Enabled Agricultural Systems 426 16.3.2.2 Blockchain and Distributed Ledger Technologies for Data Integrity 426 16.3.3 Intellectual Property and Proprietary Algorithms 427 16.3.3.1 Ownership Rights over AI- Generated Outputs 427 16.3.3.2 Patentability of AI Tools in Precision Agriculture 428 16.3.3.3 Licensing and Open- Source Frameworks 428 16.4 Bias, Fairness, and Transparency in Agricultural AI Models 429 16.4.1 Algorithmic Bias in Agronomic Predictions 430 16.4.1.1 Sources of Bias in Agricultural Datasets 430 16.4.1.2 Impacts on Marginalized Farming Communities 430 16.4.2 Fairness in Decision- Making and Resource Allocation 431 16.4.2.1 Equity in Yield Prediction and Crop Recommendation Systems 431 16.4.2.2 Inclusion of Smallholder Farmers in AI Model Training 431 16.4.3 Model Explainability and Transparency 432 16.4.3.1 Interpretable AI Approaches in Plant Epigenetics 432 16.4.3.2 Auditable AI Systems and Ethical Benchmarks 432 16.5 Legal and Regulatory Frameworks Governing AI in Agriculture 433 16.5.1 Overview of Existing Legal Structures 433 16.5.1.1 International AI Ethics Guidelines and Declarations 434 16.5.1.2 National Policies on Digital Agriculture and AI 434 16.5.2 Sector- Specific Regulations and Compliance 435 16.5.2.1 Regulatory Frameworks for Precision Agriculture Tools 435 16.5.2.2 Compliance with Environmental and Biotechnological Laws 435 16.5.3 Need for Dynamic and Adaptive Regulatory Mechanisms 436 16.5.3.1 Challenges in Regulating Evolving AI Technologies 436 16.5.3.2 Stakeholder Participation in Policy Formation 436 16.6 Ethical Considerations in Automated Decision- Making Systems 437 16.6.1 Autonomy and Control in AI- Driven Agricultural Decisions 437 16.6.1.1 Human- in- the- Loop Versus Fully Automated Systems 437 16.6.1.2 Accountability in Autonomous Agricultural Machinery 438 16.6.2 Risk Assessment and Unintended Consequences 438 16.6.2.1 Systemic Risks in Crop and Soil Management Algorithms 438 16.6.2.2 Ethical Issues in Predictive Failure and False Recommendations 439 16.6.3 Redress and Liability Mechanisms 439 16.6.3.1 Legal Responsibility for AI- Induced Harm 439 16.6.3.2 Dispute Resolution and Farmer Rights 440 16.7 Governance, Ethics Integration, and Future Directions 440 16.7.1 Institutional Frameworks for Ethical Oversight 440 16.7.1.1 Ethical Review Boards for Agricultural AI Projects 441 16.7.1.2 Cross- disciplinary Ethical Committees 441 16.7.2 Ethics- by- Design and Responsible AI Development 442 16.7.2.1 Embedding Ethical Principles in AI System Design 442 16.7.2.2 Participatory Design Approaches in Agricultural Technology 442 16.7.3 Global Cooperation and Ethical Standardization 443 16.7.3.1 Harmonization of Global Ethical Standards 443 16.7.3.2 Role of International Bodies and Consortia 443 16.8 Conclusion 444 References 444 Index 451
Elshan Musazade, Suxin Yang, and Xianzhong Feng
Mohd Anas, Neha Chaurasia, Yamshi Arif, Mohammad Danish, Nashra Waqar, and Mohammad Ali
Neslihan Turgut Kara and Burcu Arıkan
Prioty Bandhan Roop and Sandip Debnath
Moahmed Kouighat, Abdelghani Bouchyoua, Anas Hamdani, and Yassine Mouniane
Sangeeta Sarma, Arpita Talukdar, Abdul Jalil, Bhanu Priya Pegu, Nazneen Hussain, Abhik Gogoi, Dhanawantari L. Singha, and Manabendra Dutta Choudhury
Manoj Mani, Nahidha Parveen Mohammed Koya, Vivek Patel, Vivek Kumar Varshney, Antony Prabhu Jeyabal Philomenathan, Maria Jerline Babu, Akilandeswari Govindraj, and Vijaya Anand Arumugam
Fatima- Ezzahra Soussani, Fatima- Zahra Akensous, Abdelhamid Aouabe, Naira Sbbar, Rachid Lahlali, and Abdelilah Meddich
Amarjeet Singh Bhogal, Rinee Doley, Shibani Ritusmita Borah, Niharika Saharia, Olympica Das, Rekha Sharma, Satwik Subhankar, Shyamalin Rajmedhi, Dikshita Hazarika, and Debojit Sarma
Manoj Mani, Gnana Sowndariyan Gnanasekaran, Poovarasan Manjeeswaran, Kathiresan Nagaraj, Antony Prabhu Jeyabal Philomenathan, Akilandeswari Govindraj, Maria Jerline Babu, and Vijaya Anand Arumugam
Mohammed Radi, Hakima Achetoui, Ilham Dehbi, Hajar Zennouhi, and Rachid Lahlali
Ather Manzoor, Umer Fayaz, Nelofar Lone, Asma Majid, Showkat A. Waza, and A. K. M. Aminul Islam
Isha Sharma, Varucha Misra, and A.K. Mall
Aditya Pratap Singh, Sanjivani Karki, Ashutosh Sawarkar, and Siddhartha Singh
Thiruvengadam Abarna, S. Gomathi, Shobana Devi Paulraj, Thirunethiran Karpagam, Angappan Shanmugapriya, and Ramasamy Manikandan
Harshit Mishra, Fredrick Kayusi, Rashmi Mishra, Ioannis Adamopoulos, and Arkan A. Ghaib
Subject Areas: Biology, life sciences [PS]
