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Machine Learning for Plant Biology
Jen-Tsung Chen (Edited by)
9781394329618, Wiley
Hardback, published 1 January 2026
368 pages
28.5 x 22.2 x 3.4 cm, 1.106 kg
A comprehensive and current summary of machine learning-based strategies for constructing digital plant biology Machine Learning for Plant Biology provides a comprehensive summary of the latest developments in machine learning (ML) technologies, emphasizing their role in analyzing complex biological networks of plants and in modeling the responses of major crops to biotic and abiotic stresses. The combinatorial strategies discussed in this book enable readers to further their understanding of plant biology, stress physiology, and protection. Machine Learning for Plant Biology includes information on: Machine Learning for Plant Biology is an essential reference on the subject for scientists, plant biologists, crop breeders, and students interested in the development of sustainable agriculture in the face of a changing global climate.
Preface xix List of Contributors xxi 1 Edge-Based Machine Learning for Computer Vision in Smart Plant Biology Imaging 1 1.1 Introduction 1 1.2 Electronic Devices for Embedded AI-driven Computer Vision 2 1.3 Light Deep Learning Strategies 3 1.4 Benchmark of Light Embedded Deep Learning on a Plant Imaging Use Case 4 1.5 Discussion 8 1.6 Conclusion 9 2 Machine Learning for Studying Plant Evolutionary Developmental Biology 13 2.1 Introduction to Plant Evolutionary Developmental Biology 13 2.2 Basics of ML in Biological Research 14 2.3 ml Applications in Plant Morphological Evolution 16 2.4 Genomic and Transcriptomic Insights Through ml 18 2.5 Inferring Evolutionary Developmental Pathways Using ml 19 2.6 Phylogenetics and Evolutionary Tree Reconstruction Using ml 21 2.7 ml for Studying Developmental Plasticity and Environmental Adaptation 23 2.8 High-throughput Image-based ML Approaches in Evo-Devo 25 2.9 Single-cell and Multi-omics ML Integration in Plant Evo-Devo 27 2.10 Ethical, Computational, and Experimental Challenges 29 2.11 Conclusion 29 Acknowledgement 30 Data Availability 30 3 Machine Learning for Plant High-Throughput Phenotyping 39 3.1 Introduction 39 3.2 Overview of HTP 40 3.3 ml for Plant Phenotyping 41 3.4 Overview of ML Algorithms in Phenotyping 43 3.5 Applications of ML in Plant Phenotyping 46 3.6 Integration of ML with Emerging Technologies 50 3.7 Future Directions and Potential of ML in Agriculture 51 3.8 Case Studies and Real-world Applications 52 3.9 Conclusion 52 4 Machine Learning for Studying Plant Secondary Metabolites 59 4.1 Introduction 59 4.2 ml Techniques in Metabolite Research 60 4.3 Applications of ML in PSM Research 62 4.4 Challenges and Future Directions 65 4.5 Conclusion 68 5 Machine Learning for Plant Ecological Research 71 5.1 Introduction to Machine Learning in Ecology 71 5.2 Data Sources and Preprocessing for Plant Ecology 72 5.3 Machine Learning Techniques for Plant Ecology 74 5.4 Applications of Machine Learning in Plant Ecology 75 5.5 Remote Sensing and AI in Plant Ecology 76 5.6 Biodiversity Conservation and Ecosystem Monitoring 79 5.7 Predictive Modeling for Ecological Trends 80 5.8 Challenges and Limitations of Machine Learning in Plant Ecology 81 5.9 Future Perspectives and Emerging Technologies 82 5.10 Conclusion 82 Acknowledgement 83 Data Availability 83 6 Machine Learning for Modeling Plant Abiotic Stress Responses 89 6.1 Introduction 89 6.2 Definition of Abiotic Stress 90 6.3 Effect of Abiotic Stress on Crops 91 6.4 Key Applications of Machine Learning in Abiotic Stress Research 92 6.5 ml Techniques Commonly Used for Abiotic Stress Modeling 99 6.6 Challenges and Future Directions 101 6.7 Conclusion 102 7 Machine Learning for Modeling Plant–Pathogen Interactions 111 7.1 Introduction 111 7.2 Basics of PPI 112 7.3 Basics of ML and Its Integration into Biological Systems 113 7.4 ml in PPI 117 7.5 Conclusion 122 8 Machine Learning-Enhanced Plant Disease Detection and Management 131 8.1 Introduction 131 8.2 Fundamentals of ml 136 8.3 Data Collection and Preprocessing 138 8.4 Image-based Pathology Identification 140 8.5 Sensor-based Disease Monitoring 142 8.6 Genomic Approaches for Disease Prediction 144 8.7 Advances in ML Techniques for Disease Management 146 8.8 Challenges and Perspectives 147 8.9 Conclusion 147 9 Machine Learning for Analyzing and Integrating Multiple Omics 155 9.1 Introduction 155 9.2 Characteristics of Omics Data 156 9.3 Data Preprocessing for Multi-omics Integration 159 9.4 ml Techniques for Multi-omics Analysis 162 9.5 Applications of ML in Multi-omics Research 164 9.5.1 Biomarker Discovery in Human Health 165 9.6 Challenges and Future Directions 168 9.7 Conclusion 169 10 Machine Learning for Plant Single-Cell RNA Sequencing 175 10.1 Introduction to Plant Single-cell RNA Sequencing 175 10.2 ml Techniques in scRNA-seq Data Analysis 176 10.3 Deep Learning for Plant scRNA-seq Analysis 182 10.4 Integrating Multi-omics Data with ml 186 10.5 Challenges and Future Directions 187 10.6 Conclusion 187 Acknowledgement 187 Data Availability 188 11 Machine Learning for Plant Genomic Prediction 195 11.1 Introduction 195 11.1.1 Genomic Prediction 195 11.1.2 Genome Selection 196 11.1.3 Machine Learning 196 11.2 Types of Models Used in ml 197 11.2.1 Supervised Models 197 11.2.1.1 Classification 198 11.2.1.2 Regression 198 11.2.2 Unsupervised Models 198 11.2.2.1 Clustering 198 11.2.2.2 Dimensionality Reduction 198 11.2.2.3 Anomaly Detection 198 11.2.3 Semi-supervised Model 199 11.2.3.1 Transductive SVM 199 11.2.3.2 Generative Models 199 11.2.4 Deep Learning 199 11.2.4.1 Neural Networks 199 11.3 Methods 200 11.3.1 Linear Methods 200 11.3.2 Kernel Methods 200 11.3.3 Neural Networks 201 11.3.4 Tree Ensembles 201 11.4 Cross-validation 201 11.4.1 Strategies for cv 201 11.4.2 Validation Metrics 202 11.4.3 Information 202 11.5 Applications of ml 203 11.5.1 Combining Trials to Increase the Sample Size 203 11.5.2 An Explanation of the G × E Interaction Using Data Features 204 11.5.3 Exploiting Information from Secondary Traits 204 11.6 ml Challenges 204 11.7 Summary 205 12 Machine Learning-Assisted Plant Systems Biology 209 12.1 Introduction 209 12.2 ml Algorithms in Plant Systems Biology 211 12.2.1 Supervised Learning 211 12.2.2 Unsupervised Learning 211 12.2.3 Reinforcement Learning 213 12.3 Key Applications of ML in Plant Systems Biology 213 12.3.1 Multi-omics Data Integration 213 12.3.2 Gene Regulatory Network 215 12.3.3 Trait Prediction and Breeding 216 12.3.4 Metabolic Pathway Analysis 217 12.3.5 Stress Response and Adaptation 217 12.3.6 Predictive Modeling of Plant–Environment Interactions 218 12.3.7 Identification of Key Regulators in Synthetic Biology 219 12.4 Challenges and Future Directions 220 12.5 Conclusion 221 13 Machine Learning-Driven Precision Plant Breeding 229 13.1 Introduction 229 13.2 Conventional Breeding Approaches 230 13.3 Molecular Breeding Innovations 232 13.4 Speed Breeding and AI Integration 233 13.5 Challenges and Future Directions 235 13.6 Conclusion 235 14 Machine Learning-Driven Smart Agriculture 241 14.1 Introduction to Smart Agriculture 241 14.1.1 Importance of Smart Agriculture in Modern Era 241 14.1.2 The Role of Technology in Modern Agriculture 241 14.1.3 Introduction to ML and AI in Agriculture 242 14.2 The Role of ML in Agriculture 242 14.3 Key Applications of ML in Smart Agriculture 244 14.3.1 Precision Farming 244 14.3.2 Pest and Disease Detection 244 14.3.2.1 Disease Prediction and Control Measures 245 14.3.3 Yield Prediction and Crop Management 245 14.3.3.1 Predictive Models for Yield Based on Various Parameters (Soil, Weather, and Crop Type) 245 14.3.3.2 Crop Rotation and Field Management Insights 245 14.3.4 Climate Prediction and Weather Forecasting 245 14.3.4.1 Predictive Analytics for Extreme Weather Events 245 14.3.4.2 Mitigating the Impact of Climate Change on Agriculture 245 14.4 Data Collection and Preprocessing in Agriculture 246 14.4.1 Sources of Data in Agriculture 246 14.4.1.1 Role of Technology in Agricultural Data Collection 246 14.4.2 Types of Data 247 14.4.2.1 Key Data Types in Smart Agriculture 247 14.4.2.2 Weather Data 248 14.4.2.3 Crop Health Data 248 14.4.2.4 Livestock Monitoring Data 248 14.4.3 Data Preprocessing Steps and Challenges in Agricultural Data 249 14.4.3.1 Data Preprocessing Steps 249 14.4.3.2 Challenges in Agricultural Data Preprocessing 250 14.5 ml Techniques in Agriculture 250 14.5.1 Categorization of ML Techniques 251 14.5.1.1 SL (Supervised Learning) 251 14.5.1.2 UL (Unsupervised Learning) 251 14.6 Challenges and Limitations of ML in Agriculture 252 14.7 Case Studies and Real-world Applications 252 14.8 Future Prospects and Emerging Trends 252 14.8.1 The Potential Impact of ML on Global Food Security Through Smart Agriculture 252 14.8.1.1 The Role of ML in Smart Agriculture 253 14.8.1.2 Benefits of ML-driven Smart Agriculture 254 14.8.1.3 Challenges in Implementing ML in Agriculture 254 14.8.1.4 Future Prospects and Recommendations 254 14.8.2 Future Trends: Future Trends in ML-driven Smart Agriculture: AI Integration, Robotics, and Precision Breeding 255 14.8.2.1 AI Integration in Agriculture 255 14.8.2.2 Robotics in Smart Agriculture 255 14.8.2.3 Precision Breeding with ml 255 14.8.2.4 Challenges and Ethical Considerations 255 14.8.2.5 Future Outlook and Recommendations 256 14.8.3 Sustainability Implications and Environmental Impact of ML-driven Smart Agriculture 256 14.8.3.1 Environmental Benefits of ML-driven Smart Agriculture 256 14.8.3.2 Environmental Challenges of ML-driven Smart Agriculture 257 14.8.3.3 Strategies for Sustainable Implementation 257 14.9 Conclusion 257 15 Plant Leaf Disease Detection and Classification Using Convolutional Neural Networks 265 15.1 Introduction 265 15.2 Plant Disease Challenges and Issues 266 15.2.1 Data Quality and Availability 266 15.2.2 Environmental Variability and Conditions 266 15.2.3 Model Generalization Across Geographic Regions 267 15.2.4 Complexity of Disease Symptom Expression 267 15.2.5 Limited Generalization to New or Unknown Diseases 267 15.2.6 Model Interpretability and Trust 267 15.2.7 Scalability and Real-time Implementation 268 15.3 Plant Disease Detection and Classification 268 15.3.1 ml Techniques for Plant Disease Detection 268 15.3.2 Supervised Learning Approaches 268 15.3.3 Unsupervised Learning Approaches 269 15.3.4 Deep Learning Approaches 270 15.4 Data Sources and Feature Extraction 270 15.4.1 Image-based Data 270 15.4.2 Multispectral and Hyperspectral Data 270 15.4.3 Feature Extraction Techniques 270 15.5 Challenges in Plant Disease Detection Using ml 270 15.5.1 The Reliability and Accessibility of Data 271 15.5.2 Model Generalization and Overfitting 271 15.5.3 Environmental Variability 271 15.5.4 Interpretability and Trust 271 15.6 Algorithm Description 271 15.6.1 Algorithm Overview 271 15.6.2 Data Collection and Preprocessing 271 15.6.3 Data Preprocessing Involves Several Steps 272 15.6.4 Feature Extraction 272 15.6.5 Model Selection and Training 273 15.6.5.1 Convolutional Neural Networks 273 15.6.5.2 Support Vector Machine 273 15.6.5.3 Decision Trees and RF 273 15.6.5.4 Transfer Learning 273 15.6.5.5 Model Evaluation and Tuning 273 15.7 Deployment and Real-time Inference 274 15.8 Challenges and Future Directions 274 15.9 Proposed Methodology 275 15.9.1 Overview of the Methodology 275 15.9.1.1 Image Data Collection 275 15.9.1.2 Sensor Data Collection 275 15.9.1.3 Disease Annotation and Labeling 275 15.9.2 Image Preprocessing 275 15.9.2.1 Sensor Data Preprocessing 276 15.9.2.2 Data Splitting 276 15.9.3 Traditional Feature Extraction 276 15.9.4 Deep Learning–based Feature Extraction 276 15.9.4.1 Model Selection 276 15.9.4.2 Model Training 277 15.10 Conclusion 277 16 The Future Farming: Machine Learning and Crop Health 281 16.1 Introduction 281 16.2 Background 282 16.3 Significance 282 16.4 Understanding ml 283 16.5 Unsupervised Learning 284 16.6 Supervised Learning 284 16.7 Reinforced Learning 284 16.8 Generic Functions of ML in Crop Health 285 16.9 Advantages 285 16.10 Challenges 285 16.11 Future Perspectives 286 16.12 Conclusion 286 17 Social Impact of Machine Learning on Agricultural Communities 291 17.1 Introduction 291 17.2 Impact of ML on Traditional Farming Practices 291 17.2.1 Disruption of Traditional Knowledge Systems 292 17.2.2 Precision Agriculture and Efficiency Gains 292 17.2.3 Economic Impacts on Smallholder Farmers 292 17.2.4 Environmental and Sustainability Considerations 292 17.2.5 Adapting Traditional Farmers to the Digital Era 293 17.2.6 Redefining Rural Employment in the Age of AI 293 17.2.7 Automation and Job Displacement 293 17.2.8 Emergence of New Job Roles 293 17.2.9 Education and Skill Development 294 17.2.10 Opportunities for Entrepreneurship 294 17.2.11 The Role of Policy in Supporting Rural Employment 294 17.3 ml and Farmer Autonomy: Decision-making in a Data-driven World 294 17.3.1 The Role of ML in Decision-making 294 17.3.2 Impact on Farmer Autonomy 295 17.3.3 Data Ownership and Control 295 17.3.4 Maintaining Autonomy Through Explainable AI 295 17.3.5 Collaborative Models of AI and Farmer Expertise 296 17.4 Cultural Shifts and Acceptance of Technology in Agriculture 296 17.4.1 Technology Adoption in Agricultural Communities 296 17.4.2 Trust and Technology Providers 296 17.4.3 Cultural Resistance to Change 297 17.4.4 Facilitating Cultural Shifts 297 17.4.5 Socioeconomic Disparities in Access to ML Technologies 298 17.5 Economic Factors 298 17.5.1 Educational Disparities 298 17.5.2 Infrastructural Challenges 298 17.5.3 Impact on Different Socioeconomic Groups 298 17.5.4 Strategies for Mitigating Disparities 298 17.5.5 Gender and Social Equity in ML-driven Agriculture 299 17.6 Gender Disparities in ML-driven Agriculture 299 17.6.1 Social Equity and Access to ML Technologies 299 17.6.2 Impact of ML Technologies on Gender and Social Equity 299 17.6.3 Strategies for Promoting Equity in ML-driven Agriculture 299 17.7 Digital Literacy and Skill Development in Farming Communities 300 17.7.1 State of Digital Literacy in Farming Communities 300 17.7.2 Barriers to Digital Skill Development 300 17.7.3 Impact of Digital Literacy on Agricultural Productivity 300 17.7.4 Strategies for Enhancing Digital Literacy in Farming Communities 301 17.8 Balancing Technological Innovation with Social Equity 301 17.8.1 Technological Innovation and Its Impact on Equity 301 17.8.2 Barriers to Equitable Access 301 17.8.3 Case Studies of Technological Disparities 302 17.8.4 Strategies for Promoting Social Equity 302 17.9 The Role of ML in Shaping Rural Economies 302 17.9.1 ml in Agriculture 302 17.9.2 Economic Development and Diversification 303 17.9.3 Challenges and Barriers 303 17.9.4 Strategies for Effective Implementation 303 17.10 Ethical Dilemmas of AI-driven Agriculture in Developing Communities 303 17.10.1 Equity and Access Issues 304 17.10.2 Privacy and Data Ownership 304 17.10.3 Algorithmic Bias and Fairness 304 17.10.4 Environmental and Social Implications 304 17.10.5 Strategies for Ethical AI Implementation 304 17.11 Community Resilience and Adaptation to Technological Change 305 17.11.1 Understanding Community Resilience 305 17.11.2 Adaptation Strategies for Technological Change 305 17.11.2.1 Education and Skill Development 305 17.11.2.2 Community Engagement and Participation 305 17.11.2.3 Building Social Capital 305 17.11.2.4 Infrastructure Development 306 17.11.3 Challenges in Adapting to Technological Change 306 17.11.3.1 Digital Divide 306 17.11.3.2 Resistance to Change 306 17.11.3.3 Economic Constraints 306 17.11.4 Case Studies of Successful Adaptation 306 17.11.4.1 The Digital Green Initiative 306 17.11.4.2 Smart Cities and Urban Resilience 306 17.12 Long-term Social Impacts: Sustainability and Food Security 306 17.12.1 Defining Sustainability and Food Security 307 17.12.2 The Role of Sustainability in Food Security 307 17.12.2.1 Sustainable Agricultural Practices 307 17.12.2.2 Climate Change Mitigation 307 17.12.3 Social Effects of Sustainability and Food Security 307 17.12.3.1 Equitable Access to Resources 307 17.12.3.2 Health and Nutrition 307 17.12.3.3 Economic Stability and Livelihoods 308 17.12.4 Long-term Challenges to Sustainability and Food Security 308 17.12.4.1 Resource Depletion 308 17.12.4.2 Global Trade and Food Systems 308 17.13 Collaborative Models: Integrating Local Knowledge with AI Systems 308 17.13.1 The Significance of Local Knowledge 308 17.13.2 Collaborative Models in AI and Local Knowledge Integration 309 17.13.2.1 Participatory AI Development 309 17.13.2.2 Knowledge Co-production 309 17.13.3 Challenges in Integrating Local Knowledge and AI Systems 309 17.13.3.1 Data Standardization and Representation 309 17.13.3.2 Power Dynamics and Knowledge Hierarchies 309 17.13.4 Case Studies of Successful Integration 310 17.13.4.1 AI for Climate-resilient Agriculture in Sub-Saharan Africa 310 17.13.4.2 Indigenous Knowledge and AI for Fire Management in Australia 310 17.14 Conclusion 310 18 Ethical and Regulatory Considerations of Machine Learning in Modern Agriculture 317 18.1 Introduction 317 18.2 Data Privacy and Security in Agricultural ML Systems 317 18.2.1 Agricultural Data and Its Sensitivity 318 18.2.2 Security Threats in Agricultural Machine Learning Systems 318 18.2.2.1 Data Breaches 318 18.2.2.2 Adversarial Attacks 318 18.2.2.3 Model Theft and Data Leakage 318 18.2.2.4 Unauthorized Access 318 18.2.3 Privacy Concerns in Agricultural Machine Learning Systems 319 18.2.3.1 Data Ownership and Consent 319 18.2.3.2 Data Obfuscation 319 18.2.3.3 Profiling and Surveillance 319 18.2.4 Addressing Data Privacy and Security in Agricultural ML Systems 319 18.2.4.1 Secure Data Storage and Transmission 319 18.2.4.2 Access Control and Authentication 319 18.2.4.3 Federated Learning 319 18.2.4.4 Adversarial ML Defenses 320 18.2.4.5 Ethical and Regulatory Frameworks 320 18.2.5 Future Directions and Challenges 320 18.3 Bias and Fairness in Artificial Intelligence Models for Plant Disease Prediction 320 18.3.1 Sources of Bias in ML for Plant Disease Prediction 320 18.3.1.1 Data Imbalance 320 18.3.1.2 Sampling Bias 320 18.3.1.3 Labeling Bias 321 18.3.1.4 Algorithmic Bias 321 18.3.1.5 Geographical and Climatic Bias 321 18.3.2 Impact of Bias on Agricultural Communities 321 18.3.2.1 Disparities in Disease Management 321 18.3.2.2 Inequity in Predictions for Minority Crops 321 18.3.2.3 Economic and Environmental Consequences 321 18.3.3 Strategies to Mitigate Bias in Prediction Models of Plant Disease 322 18.3.3.1 Diversifying Training Data 322 18.3.3.2 Synthetic Data Generation 322 18.3.3.3 Fairness-aware Algorithms 322 18.3.3.4 Transparency and Explainability 322 18.3.3.5 Continuous Monitoring and Audits 322 18.3.4 Challenges in Ensuring Fairness 322 18.4 Environmental and Ecological Implications of AI in Agriculture 323 18.4.1 AI-driven Precision Agriculture and Resource Optimization 323 18.4.1.1 Water Conservation 323 18.4.1.2 Reduction of Chemical Inputs 323 18.4.1.3 Energy Efficiency 323 18.4.2 Impact on Biodiversity and Ecosystem Services 323 18.4.2.1 Preservation of Biodiversity 323 18.4.2.2 Risk of Monoculture Intensification 323 18.4.2.3 Wildlife Habitat Displacement 324 18.4.3 Carbon Footprint of AI in Agriculture 324 18.4.3.1 Energy Use in AI Training 324 18.4.3.2 Efforts to Reduce AI’s Carbon Footprint 324 18.4.4 Ecological Impacts of AI-powered Autonomous Systems 324 18.4.4.1 Soil Compaction 324 18.4.4.2 E-waste and Resource Depletion 324 18.4.4.3 Opportunities for Regenerative Agriculture 324 18.4.5 Ethical and Ecological Governance of AI in Agriculture 325 18.4.5.1 Sustainable AI Development 325 18.4.5.2 Incorporating Ecological Indicators in AI Models 325 18.4.5.3 Inclusive AI for Global Agriculture 325 18.5 Transparency and Explain Ability in AI-driven Agricultural Solutions 325 18.5.1 The Need for Transparency in AI-driven Agriculture 325 18.5.1.1 Building Trust Among Stakeholders 325 18.5.1.2 Ethical and Legal Implications 325 18.5.2 Challenges in Achieving Explainability in Agricultural AI Systems 326 18.5.2.1 Complexity of AI Algorithms 326 18.5.2.2 Data Quality and Bias 326 18.5.2.3 Trade-off Between Accuracy and Interpretability 326 18.5.3 Strategies for Enhancing Transparency and Explainability 326 18.5.3.1 Post hoc Explainability Techniques 326 18.5.3.2 Model-agnostic Interpretability Frameworks 326 18.5.3.3 Simplified User Interfaces for Farmers 326 18.5.3.4 Collaboration Between AI Developers and Agronomists 327 18.5.4 Ethical Implications of Transparency in AI-driven Agriculture 327 18.5.4.1 Equity and Accessibility 327 18.5.4.2 Accountability in Decision-making 327 18.5.5 Future Directions in Transparent AI for Agriculture 327 18.5.5.1 Interpretable AI Models 327 18.5.5.2 Regulation and Standardization 327 18.5.5.3 Education and Training 327 18.6 Balancing Innovation with Tradition: Ethical Challenges in Technological Adoption 328 18.6.1 Technological Innovation in Agriculture 328 18.6.1.1 Advancements in Agricultural Technology 328 18.6.1.2 AI and Machine Learning in Crop Management 328 18.6.2 The Role of Tradition in Sustainable Farming 328 18.6.2.1 Traditional Farming Methods 328 18.6.2.2 Cultural Significance of Farming Traditions 328 18.6.3 Ethical Challenges in Technological Adoption 329 18.6.3.1 Equity and Access to Technology 329 18.6.3.2 Environmental Sustainability vs. Technological Efficiency 329 18.6.3.3 The Threat to Smallholder Farmers 329 18.6.3.4 Technological Overload and Farmer Autonomy 329 18.6.4 Balancing Innovation and Tradition 329 18.6.4.1 Integrating Traditional Knowledge with Modern Technology 329 18.6.4.2 Participatory Approaches to Technology Development 329 18.6.4.3 Policy and Regulation for Ethical Technology Adoption 330 18.7 Equity in Access to Machine Learning Technologies for Sustainable Agriculture 330 18.7.1 Challenges in Achieving Equity in Access to ML Technologies 330 18.7.1.1 Cost Barriers and Economic Disparities 330 18.7.1.2 Lack of Technical Expertise 330 18.7.1.3 Data Availability and Quality 330 18.7.1.4 Infrastructure Limitations 330 18.7.2 Strategies for Promoting Equity in Access to ML Technologies 331 18.7.2.1 Subsidies and Financial Support 331 18.7.2.2 Capacity Building and Training 331 18.7.2.3 Improving Data Accessibility and Quality 331 18.7.2.4 Developing Infrastructure and Connectivity 331 18.7.2.5 Promoting Open-source and Inclusive Technologies 331 18.7.3 Case Studies and Examples 331 18.7.3.1 Precision Agriculture in India 331 18.7.3.2 Agricultural Data Platforms in Africa 332 18.7.3.3 Internet Connectivity Projects in Rural Areas 332 18.8 Human–AI Collaboration: Ethical Guidelines for Decision-making in Agriculture 332 18.8.1 Ethical Challenges in Human–AI Collaboration 332 18.8.1.1 Transparency and Explainability 332 18.8.1.2 Accountability and Responsibility 332 18.8.1.3 Bias and Fairness 332 18.8.1.4 Human Autonomy and Decision-making 333 18.8.2 Ethical Guidelines for Human–AI Collaboration 333 18.8.2.1 Develop Transparent and Explainable AI Systems 333 18.8.2.2 Establish Accountability Frameworks 333 18.8.2.3 Implement Bias Mitigation Strategies 333 18.8.2.4 Promote Human–AI Collaboration and Oversight 333 18.8.2.5 Foster Continuous Ethical Review and Improvement 333 18.8.3 Case Studies and Examples 333 18.8.3.1 AI for Precision Agriculture in the United States 333 18.8.3.2 AI-assisted Pest Management in India 334 18.8.3.3 Bias Mitigation in Agricultural Lending in Africa 334 18.9 Regulatory Frameworks for ML in Agricultural Biotechnology 334 18.9.1 Current Regulatory Frameworks 334 18.9.1.1 Global and Regional Regulations 334 18.9.1.2 Data Privacy and Security Regulations 334 18.9.1.3 Ethical and Safety Guidelines 334 18.9.2 Challenges and Gaps in Regulatory Frameworks 335 18.9.2.1 Rapid Technological Advancements 335 18.9.2.2 Integration of ML into Existing Frameworks 335 18.9.2.3 Global Consistency and Harmonization 335 18.9.3 Proposed Guidelines for Future Regulation 335 18.9.3.1 Dynamic and Adaptive Regulatory Frameworks 335 18.9.3.2 Enhanced Transparency and Explainability Requirements 335 18.9.3.3 Risk Assessment and Management Protocols 335 18.9.3.4 International Collaboration and Harmonization 335 18.9.4 Case Studies and Examples 336 18.9.4.1 EU Regulations for GMOs and ml 336 18.9.4.2 FDA’s Approach to Biotechnology and AI 336 18.9.4.3 Global Harmonization Efforts 336 18.10 Conclusion 336
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Subject Areas: Science: general issues [PD]
