Machine Learning for Plant Biology
John Wiley & Sons Inc (Verlag)
978-1-394-32961-8 (ISBN)
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:
Intelligent breeding for stress-resistant and high-yield crops, contributing to sustainable agriculture, the Sustainable Development Goals (SDGs), and the Paris Agreement
Interactions between plants, pathogens, and environmental stresses through omics approaches, functional genomics, genome editing, and high-throughput technologies
State-of-the-art AI tools, including machine and deep learning models, as well as generative AI
Applications include species identification, systems biology, functional genomics, genomic selection, phenotyping, synthetic biology, spatial omics, plant disease diagnosis and protection, and plant secondary metabolism
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.
JEN-TSUNG CHEN is a Professor of Cell Biology at the Department of Life Sciences, National University of Kaohsiung, Taiwan, where he teaches courses on cell biology, genomics, proteomics, plant physiology, and plant biotechnology. His research interests include bioactive compounds, chromatography techniques, plant molecular biology, plant biotechnology, bioinformatics, and systems pharmacology. In 2023 and 2024, Elsevier and Stanford University recognized Dr. Chen as one of the “World’s Top 2% Scientists”.
Preface xix
List of Contributors xxi
1 Edge-Based Machine Learning for Computer Vision in Smart Plant Biology Imaging 1
Julien Garnier, Simon Ravé, Nathan Drogue, Boris Adam, Pejman Rasti, David Rousseau
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
1.4.1 Image Acquisition and Segmentation 4
1.4.2 Model Adaptation 4
1.4.3 Knowledge Distillation 6
1.4.4 Kolmogorov–Arnold Network 7
1.5 Discussion 8
1.6 Conclusion 9
2 Machine Learning for Studying Plant Evolutionary Developmental Biology 13
Mani Manoj, Ramaraj Sivamano, Arunachalam Abitha, Mohammed Jaffer Shakeera Banu, Shanmugam Velayuthaprabhu, Kannan Vijayarani, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand
2.1 Introduction to Plant Evolutionary Developmental Biology 13
2.1.1 Overview of Plant Evolutionary Developmental Biology 13
2.1.2 Key Concepts in Plant Evolution and Development 13
2.1.3 Importance of Evo-Devo in Understanding Plant Adaptations 14
2.1.4 Role of Computational and AI Tools in Evo-Devo Studies 14
2.2 Basics of ML in Biological Research 14
2.2.1 Fundamentals of ML in Biology 14
2.2.2 Supervised, Unsupervised, and Reinforcement Learning 15
2.2.3 Deep Learning and Neural Networks in Evo-Devo 15
2.2.4 ml Workflow: Data Collection, Processing, Model Selection, and Interpretation 15
2.2.5 Challenges of Applying ML to Evo-Devo Research 16
2.3 ml Applications in Plant Morphological Evolution 16
2.3.1 ml for Analyzing Fossilized Plant Structures 16
2.3.2 Shape and Trait Evolution Using CNNs and Autoencoders 16
2.3.3 3D Reconstruction of Plant Organs Through ML-based Image Processing 17
2.3.4 Quantitative Trait Analysis Using SVM 17
2.3.5 Integrating Phylogenetics and ML for Morphological Adaptation Studies 17
2.4 Genomic and Transcriptomic Insights Through ml 18
2.4.1 Evolutionary Genomics: Identifying Selection Signatures with ml 18
2.4.2 GRN Prediction Using Graph Neural Networks 18
2.4.3 ml for Comparative Genomics in Evolutionary Studies 18
2.4.4 Understanding Non-coding RNA Evolution with NLP-based ML Models 19
2.4.5 Unraveling Epigenetic Modifications in Plant Evolution Using ml 19
2.5 Inferring Evolutionary Developmental Pathways Using ml 19
2.5.1 ml Models for Predicting Gene Expression Patterns 19
2.5.2 Identifying Key Developmental Genes via Feature Selection Algorithms 20
2.5.3 Evolution of Transcription Factor Networks with ml 20
2.5.4 Bayesian ML for Inferring Ancestral Gene Interactions 20
2.5.5 Evolution of Polyploidy and Hybridization Analyzed Through ML Model 21
2.6 Phylogenetics and Evolutionary Tree Reconstruction Using ml 21
2.6.1 Phylogenetic Tree Prediction via Deep Learning 21
2.6.2 Bayesian ML for Inferring Evolutionary Relationships 21
2.6.3 Predicting Adaptive Radiation Events with ML Models 22
2.6.4 Network-based Approaches for Studying Horizontal Gene Transfer 23
2.6.5 Automating Phylogenomic Inference Using AI-based Pipelines 23
2.7 ml for Studying Developmental Plasticity and Environmental Adaptation 23
2.7.1 Predicting Phenotypic Plasticity with ML Algorithms 23
2.7.2 Climate-responsive Developmental Evolution Using ml 24
2.7.3 Adaptive Traits Discovery Using ML in Dynamic Environments 24
2.7.4 ML-based Prediction of Plant Evolution Under Climate Change 24
2.8 High-throughput Image-based ML Approaches in Evo-Devo 25
2.8.1 ml for Automated Plant Organ Recognition and Classification 25
2.8.2 Deep Learning for Leaf, Flower, and Root Morphological Evolution 25
2.8.3 CNNs for Large-scale Evolutionary Trait Analysis 25
2.8.4 Time-series ML for Tracking Developmental Transitions 26
2.8.5 Integrating ML with Phenotyping Platforms for Evo-Devo Research 27
2.9 Single-cell and Multi-omics ML Integration in Plant Evo-Devo 27
2.9.1 ml for Single-cell RNA Sequencing Data in Evolutionary Studies 27
2.9.2 Integrating Proteomics, Transcriptomics, and Metabolomics with ml 27
2.9.3 Deep Learning for Cell Fate and Differentiation Analysis in Evo-Devo 28
2.9.4 Predicting Evo-Devo Pathways Through Multi-omics Data Fusion 28
2.9.5 ml for Analyzing Spatial and Temporal Omics Data 28
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
Dibyendu Seth, Sourish Pramanik, Ehsas Pachauri, Ankan Das, Sandip Debnath, Mehdi Rahimi
3.1 Introduction 39
3.2 Overview of HTP 40
3.3 ml for Plant Phenotyping 41
3.3.1 What Is ML? 41
3.3.2 ml in Handling Big Data 42
3.4 Overview of ML Algorithms in Phenotyping 43
3.4.1 dl in Plant Phenotyping 44
3.4.2 Computer Vision in Phenotyping 45
3.5 Applications of ML in Plant Phenotyping 46
3.5.1 ml for Plant Recognition and Disease Detection 47
3.5.2 Spectral Analysis and Optical Imaging for Stress Detection 47
3.5.3 Hyperspectral and Multispectral Imaging 48
3.5.4 Thermal and Fluorescence Imaging for Stress Analysis 48
3.5.5 ml Approaches for Plant Stress Classification 48
3.5.6 Automated Image Analysis for Trait Extraction 48
3.5.6.1 Morphological Trait Measurement 48
3.5.6.2 Image-based Feature Extraction 49
3.5.7 Prediction Models for Yield Forecasting 49
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
Saniya, Sarfraz Ahmad, Mohammad Ghani Raghib
4.1 Introduction 59
4.2 ml Techniques in Metabolite Research 60
4.2.1 Supervised Learning Techniques 60
4.2.2 Unsupervised Learning Techniques 60
4.2.3 Deep Learning Techniques 61
4.2.4 Ensemble Learning Techniques 61
4.2.5 Reinforcement Learning in Metabolomics 61
4.2.6 Feature Selection and Dimensionality Reduction 62
4.2.7 Hybrid Models 62
4.2.8 Emerging Techniques in Metabolomics 62
4.3 Applications of ML in PSM Research 62
4.3.1 Predicting Metabolic Pathways 62
4.3.2 Identification of Key Biosynthetic Genes 64
4.3.3 Metabolite Profiling and Classification 64
4.3.4 Enhancing Plant Stress Response Through Metabolite Analysis 64
4.3.5 Chemotaxonomy and Species Identification 64
4.3.6 Pharmacological and Nutraceutical Research 65
4.3.7 Metabolic Engineering for Enhanced PSM Production 65
4.3.8 PSM-based Environmental Monitoring 65
4.3.9 Predictive Modeling for Agricultural Improvement 65
4.4 Challenges and Future Directions 65
4.4.1 Challenges in ML for PSM Research 65
4.4.1.1 Data-related Challenges 65
4.4.1.2 Algorithmic Challenges 66
4.4.1.3 Biological Complexity and Unknown Pathways 66
4.4.1.4 Computational Challenges 66
4.4.2 Future Directions in ML for PSM Research 66
4.4.2.1 Integrating Multi-omics Data 66
4.4.2.2 Advancing Explainable Artificial Intelligence 67
4.4.2.3 Improving Data Augmentation Strategies 67
4.4.2.4 Automation and High-throughput Analysis 67
4.4.2.5 Advancing Computational Infrastructure 67
4.4.2.6 Developing Crop-specific ML Models 67
4.4.3 Ethical Considerations and Regulatory Frameworks 67
4.5 Conclusion 68
5 Machine Learning for Plant Ecological Research 71
Mani Manoj, Sahfigul Ameed Nihaal Fathima, Sathyalingam Sathya Trisha, Mohammed Jaffer Shakeera Banu, Shanmugam Gavaskar, Alagarsamy Sumitrha, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand
5.1 Introduction to Machine Learning in Ecology 71
5.1.1 Overview of Machine Learning in Ecological Studies 71
5.1.2 Importance of ML in Plant Ecological Research 71
5.1.3 Comparison of Traditional vs. ML-based Ecological Analysis 72
5.2 Data Sources and Preprocessing for Plant Ecology 72
5.2.1 Types of Ecological Data 72
5.2.2 Data Collection Techniques 72
5.2.3 Data Cleaning, Normalization, and Feature Engineering 73
5.3 Machine Learning Techniques for Plant Ecology 74
5.3.1 Supervised Learning 74
5.3.2 Unsupervised Learning 74
5.3.3 Deep Learning 74
5.3.4 Reinforcement Learning 75
5.4 Applications of Machine Learning in Plant Ecology 75
5.5 Remote Sensing and AI in Plant Ecology 76
5.5.1 Use of Satellite and Drone Imagery for Plant Monitoring 76
5.5.2 Image Segmentation and Object Detection for Vegetation Analysis 77
5.5.3 AI Models for Automated Plant Health Assessment 78
5.6 Biodiversity Conservation and Ecosystem Monitoring 79
5.6.1 Machine Learning for Biodiversity Pattern Analysis 79
5.6.2 AI-driven Monitoring of Endangered Plant Species 79
5.6.3 Predicting Ecosystem Resilience and Response to Environmental Stressors 80
5.7 Predictive Modeling for Ecological Trends 80
5.7.1 Time-series Forecasting of Ecological Parameters 80
5.7.2 ml Models for Predicting Drought and Deforestation Impact 80
5.7.3 Climate–Plant Interaction Modeling 81
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
Haragopal Dutta, Suman Dutta
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
6.4.1 Phenotypic Prediction 92
6.4.2 Gene Expression Modeling 95
6.4.3 High-throughput Image Analysis 95
6.4.4 Omics Data Integration 97
6.4.5 Stress Response Prediction and Simulation 99
6.5 ml Techniques Commonly Used for Abiotic Stress Modeling 99
6.5.1 Supervised Learning 99
6.5.2 Unsupervised Learning 100
6.5.3 Deep Learning 100
6.6 Challenges and Future Directions 101
6.6.1 Data Availability and Quality 101
6.6.2 Generalization 101
6.6.3 Interpretability 102
6.7 Conclusion 102
7 Machine Learning for Modeling Plant–Pathogen Interactions 111
Sourish Pramanik, Dibyendu Seth, Sandip Debnath
7.1 Introduction 111
7.2 Basics of PPI 112
7.3 Basics of ML and Its Integration into Biological Systems 113
7.3.1 Supervised Learning 114
7.3.2 Unsupervised Learning 114
7.3.3 Random Forests 115
7.3.4 Naive Bayes 115
7.3.5 Neural Networks 115
7.3.6 Variational Autoencoders 115
7.4 ml in PPI 117
7.4.1 Integration of ML in PPI: Molecular Level 118
7.4.2 Integration of ML in PPI: Field Level 120
7.5 Conclusion 122
8 Machine Learning-Enhanced Plant Disease Detection and Management 131
Lellapalli Rithesh, Sucharita Mohapatra, Shimi Jose, Juel Debnath, Gyanisha Nayak, Mehjebin Rahman, Soumya Shephalika Dash, Anwesha Sharma, Sneha Mohan
8.1 Introduction 131
8.2 Fundamentals of ml 136
8.2.1 Fundamental Principles of ml 136
8.2.1.1 Supervised Learning 136
8.2.1.2 Unsupervised Learning 136
8.2.1.3 Reinforcement Learning 136
8.2.2 Categories of ML Models Employed in Plant Disease Detection 136
8.2.2.1 Decision Trees 136
8.2.2.2 Support Vector Machines 137
8.2.2.3 Random Forests 137
8.2.2.4 Deep Learning 137
8.2.2.5 Convolutional Neural Networks 137
8.3 Data Collection and Preprocessing 138
8.3.1 Data Types 138
8.3.1.1 Image Data 138
8.3.1.2 Spectral Data 138
8.3.1.3 Sensor Data 138
8.3.1.4 Genomic and Molecular Data 138
8.3.1.5 Tabular Data 138
8.3.2 Data Preprocessing Techniques 138
8.3.2.1 Image Data Preprocessing 139
8.3.2.2 Spectral Data Preprocessing 139
8.3.2.3 Sensor Data Preprocessing 139
8.3.2.4 Genomic and Molecular Data Preprocessing 140
8.3.2.5 Tabular Data Preprocessing 140
8.4 Image-based Pathology Identification 140
8.4.1 The Role of Computer Vision in Plant Diseases Diagnosis 140
8.4.2 Key Algorithms in Image Processing 141
8.5 Sensor-based Disease Monitoring 142
8.5.1 Role of Sensor-based Monitoring in Plant Disease Detection 142
8.5.2 Role of IoT in Disease Detection and Management 142
8.5.3 Disease Progression Using Environmental Sensors 143
8.5.4 Integration with ml 143
8.6 Genomic Approaches for Disease Prediction 144
8.6.1 ml for Identification of Disease Resistance Traits Using Genomic Data 145
8.7 Advances in ML Techniques for Disease Management 146
8.7.1 AI-driven Disease Forecasting Models 146
8.7.2 Predictive Modeling for Crop Management 146
8.7.3 Transfer Learning and Model Generalization 147
8.8 Challenges and Perspectives 147
8.9 Conclusion 147
9 Machine Learning for Analyzing and Integrating Multiple Omics 155
Sarfraz Ahmad, Saniya, Mohammad Ghani Raghib, Rubina Khan, Vikas Belwal, Pankaj Kumar
9.1 Introduction 155
9.2 Characteristics of Omics Data 156
9.2.1 High Dimensionality and Low Sample Size 156
9.2.2 Data Heterogeneity 157
9.2.3 Sparsity and Missing Values 157
9.2.4 Noise and Technical Variability 158
9.2.5 Dynamic and Temporal Variability 158
9.2.6 Multicollinearity and Feature Interdependence 158
9.2.7 High Biological Variability 158
9.2.8 Class Imbalance in Omics Data 159
9.2.9 Data Integration Complexity 159
9.3 Data Preprocessing for Multi-omics Integration 159
9.3.1 Data Normalization and Standardization 159
9.3.2 Data Cleaning and Outlier Detection 160
9.3.3 Data Imputation for Missing Values 160
9.3.4 Batch Effect Correction 160
9.3.5 Dimensionality Reduction 160
9.3.6 Feature Selection 161
9.3.7 Data Integration Strategies 161
9.3.8 Data Transformation and Encoding 161
9.3.9 Data Augmentation Techniques 161
9.3.10 Data Quality Assessment 162
9.4 ml Techniques for Multi-omics Analysis 162
9.4.1 Supervised Learning Techniques 162
9.4.1.1 Support Vector Machines 162
9.4.1.2 Random Forest 162
9.4.1.3 Elastic Net and LASSO Regression 162
9.4.2 Unsupervised Learning Techniques 163
9.4.2.1 Principal Component Analysis 163
9.4.2.2 K-means Clustering 163
9.4.2.3 Hierarchical Clustering 163
9.4.3 Deep Learning Techniques 163
9.4.3.1 Convolutional Neural Networks 163
9.4.3.2 Recurrent Neural Networks 163
9.4.3.3 Autoencoders 163
9.4.4 Network-based Approaches 164
9.4.4.1 Graph Neural Networks 164
9.4.4.2 Bayesian Networks 164
9.4.5 Hybrid and Ensemble Learning Approaches 164
9.5 Applications of ML in Multi-omics Research 164
9.5.1 Biomarker Discovery in Human Health 165
9.5.1.1 Cancer Biomarker Identification 165
9.5.1.2 Cardiovascular and Metabolic Disorders 165
9.5.2 Disease Diagnosis and Classification 165
9.5.2.1 Cancer Classification Models 165
9.5.2.2 Neurological Disorders 165
9.5.3 Drug Discovery and Personalized Medicine 165
9.5.3.1 Drug–Target Interaction Prediction 166
9.5.3.2 Personalized Medicine 166
9.5.4 Applications in Plant Sciences 166
9.5.4.1 Crop Trait Prediction 166
9.5.4.2 Stress Response Prediction 166
9.5.5 Environmental and Ecological Applications 166
9.5.5.1 Soil Microbiome Analysis 166
9.5.5.2 Environmental Stress Monitoring 166
9.5.6 Systems Biology and Pathway Analysis 168
9.6 Challenges and Future Directions 168
9.6.1 Key Challenges in ML-driven Multi-omics Research 168
9.6.1.1 Data Heterogeneity and Dimensionality 168
9.6.1.2 Data Quality and Noise 168
9.6.1.3 Model Interpretability and Biological Relevance 168
9.6.1.4 Integration of Multi-scale Data 168
9.6.1.5 Computational and Resource Limitations 169
9.6.2 Future Directions in ML-driven Multi-omics Research 169
9.6.2.1 Advancing Data Integration Techniques 169
9.6.2.2 Improving Model Interpretability 169
9.6.2.3 Enhancing Data Quality and Standardization 169
9.6.2.4 Leveraging Transfer Learning and Few-shot Learning 169
9.6.2.5 Advancing Cloud-based Solutions 169
9.6.2.6 Ethical and Regulatory Considerations 169
9.7 Conclusion 169
10 Machine Learning for Plant Single-Cell RNA Sequencing 175
Mani Manoj, Ravichandran Sneha, Esakkimuthu Balaji, Vadivelu Bharathi, Thamaraiselvan Nandhini Devi, Ramasamy Manikandan, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand
10.1 Introduction to Plant Single-cell RNA Sequencing 175
10.2 ml Techniques in scRNA-seq Data Analysis 176
10.2.1 Preprocessing and Quality Control 176
10.2.1.1 Data Filtering and Normalization 176
10.2.1.2 Batch Effect Correction 176
10.2.1.3 Dimensionality Reduction Techniques 177
10.2.2 Feature Selection and Gene Expression Clustering 177
10.2.2.1 Highly Variable Gene Selection 177
10.2.2.2 Clustering Algorithms 177
10.2.2.3 Deep Learning for Feature Selection 178
10.2.3 Cell-type Identification and Annotation 178
10.2.3.1 Supervised vs. Unsupervised Learning Approaches 178
10.2.3.2 Decision Trees, Random Forest, and SVMs 178
10.2.3.3 DL-based Cell-type Classification 179
10.2.4 Trajectory and Pseudotime Inference 179
10.2.4.1 Single-cell Developmental Trajectories 179
10.2.4.2 Pseudotime Estimation 179
10.2.4.3 Application of Graph Neural Networks 179
10.2.5 Differential Gene Expression Analysis 180
10.2.5.1 ml Models for Identifying DE Genes 180
10.2.5.2 Bayesian Methods for Gene Expression Inference 180
10.2.5.3 Application of Neural Networks in Predicting Gene Regulation 180
10.2.6 Cell–Cell Interaction and Network Analysis 181
10.2.6.1 Graph-based Approaches for Inferring Cell Communication 181
10.2.6.2 Co-expression Network Analysis Using ml 182
10.2.6.3 Integration with ST 182
10.3 Deep Learning for Plant scRNA-seq Analysis 182
10.3.1 Autoencoders for Dimensionality Reduction and Noise Removal 182
10.3.1.1 VAEs in scRNA-seq 182
10.3.1.2 GANs for Data Augmentation 183
10.3.2 Convolutional and RNNs 183
10.3.2.1 CNNs for ST in Plants 183
10.3.2.2 RNNs for Temporal Gene Expression Analysis 184
10.3.3 Transfer Learning in Plant scRNA-seq 184
10.3.3.1 Pretrained Models for Plant Gene Expression Prediction 184
10.3.3.2 Domain Adaptation Techniques for Cross-species Analysis 185
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
Natasha Charaya, Sonika Kalia, Indra Rautela, Poorvi Yadav, Poornima Bhardwaj, Ritakshi Nautiyal, Monika Kalia, Vinay Sharma
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
Haragopal Dutta, Suman Dutta, Sudhir Kumar
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
Krishna Kumar Rai
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
Rajdeep Mohanta, Soumik Dey Roy, Sahely Kanthal, Sanjay Mochary, Subhadwip Ghorai, Soham Hazra
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
A. V. Senthil Kumar, N. Abinesh, Shanmugasundaram Hariharan, Kyla L. Tennin
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
Sadhana Veeramani, Jeya Rani Maria Michael, Kalaichelvi Kalaignan, Ehab A A Salama, Annasamy Kaliyan, Thiruveni Thangaraj, Anantha Raju Pokkaru, Raveena Ravi, Karthiba Loganathan, Murali Sankar Perumal, Manasa Samuthiravelu
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
Atef M. El-Sagheer, Eman A. Ahmed, Hamdy A. Sayed
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
Atef M. El-Sagheer, Mohamed M. M. Hamd
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
| Erscheinungsdatum | 03.01.2026 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 222 x 285 mm |
| Gewicht | 1106 g |
| Themenwelt | Naturwissenschaften |
| ISBN-10 | 1-394-32961-X / 139432961X |
| ISBN-13 | 978-1-394-32961-8 / 9781394329618 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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