11010nam 22005053 450 991104241190332120251115060326.01-394-32964-41-394-32963-6(CKB)42587204200041(MiAaPQ)EBC32409816(Au-PeEL)EBL32409816(OCoLC)1551395247(EXLCZ)994258720420004120251115d2026 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierMachine Learning for Plant Biology1st ed.Newark :John Wiley & Sons, Incorporated,2026.©2025.1 online resource (371 pages)1-394-32961-X Cover -- Title Page -- Copyright -- Table of Contents -- Preface -- List of Contributors -- Chapter 1: Edge-Based Machine Learning for Computer Vision in Smart Plant Biology Imaging -- 1.1 Introduction -- 1.2 Electronic Devices for Embedded AI-driven Computer Vision -- 1.3 Light Deep Learning Strategies -- 1.4 Benchmark of Light Embedded Deep Learning on a Plant Imaging Use Case -- 1.4.1 Image Acquisition and Segmentation -- 1.4.2 Model Adaptation -- 1.4.3 Knowledge Distillation -- 1.4.4 Kolmogorov-Arnold Network -- 1.5 Discussion -- 1.6 Conclusion -- Chapter 2: Machine Learning for Studying Plant Evolutionary Developmental Biology -- 2.1 Introduction to Plant Evolutionary Developmental Biology -- 2.1.1 Overview of Plant Evolutionary Developmental Biology -- 2.1.2 Key Concepts in Plant Evolution and Development -- 2.1.3 Importance of Evo-Devo in Understanding Plant Adaptations -- 2.1.4 Role of Computational and AI Tools in Evo-Devo Studies -- 2.2 Basics of ML in Biological Research -- 2.2.1 Fundamentals of ML in Biology -- 2.2.2 Supervised, Unsupervised, and Reinforcement Learning -- 2.2.3 Deep Learning and Neural Networks in Evo-Devo -- 2.2.4 ML Workflow: Data Collection, Processing, Model Selection, and Interpretation -- 2.2.5 Challenges of Applying ML to Evo-Devo Research -- 2.3 ML Applications in Plant Morphological Evolution -- 2.3.1 ML for Analyzing Fossilized Plant Structures -- 2.3.2 Shape and Trait Evolution Using CNNs and Autoencoders -- 2.3.3 3D Reconstruction of Plant Organs Through ML-based Image Processing -- 2.3.4 Quantitative Trait Analysis Using SVM -- 2.3.5 Integrating Phylogenetics and ML for Morphological Adaptation Studies -- 2.4 Genomic and Transcriptomic Insights Through ML -- 2.4.1 Evolutionary Genomics: Identifying Selection Signatures with ML -- 2.4.2 GRN Prediction Using Graph Neural Networks.2.4.3 ML for Comparative Genomics in Evolutionary Studies -- 2.4.4 Understanding Non-coding RNA Evolution with NLP-based ML Models -- 2.4.5 Unraveling Epigenetic Modifications in Plant Evolution Using ML -- 2.5 Inferring Evolutionary Developmental Pathways Using ML -- 2.5.1 ML Models for Predicting Gene Expression Patterns -- 2.5.2 Identifying Key Developmental Genes via Feature Selection Algorithms -- 2.5.3 Evolution of Transcription Factor Networks with ML -- 2.5.4 Bayesian ML for Inferring Ancestral Gene Interactions -- 2.5.5 Evolution of Polyploidy and Hybridization Analyzed Through ML Model -- 2.6 Phylogenetics and Evolutionary Tree Reconstruction Using ML -- 2.6.1 Phylogenetic Tree Prediction via Deep Learning -- 2.6.2 Bayesian ML for Inferring Evolutionary Relationships -- 2.6.3 Predicting Adaptive Radiation Events with ML Models -- 2.6.4 Network-based Approaches for Studying Horizontal Gene Transfer -- 2.6.5 Automating Phylogenomic Inference Using AI-based Pipelines -- 2.7 ML for Studying Developmental Plasticity and Environmental Adaptation -- 2.7.1 Predicting Phenotypic Plasticity with ML Algorithms -- 2.7.2 Climate-responsive Developmental Evolution Using ML -- 2.7.3 Adaptive Traits Discovery Using ML in Dynamic Environments -- 2.7.4 ML-based Prediction of Plant Evolution Under Climate Change -- 2.8 High-throughput Image-based ML Approaches in Evo-Devo -- 2.8.1 ML for Automated Plant Organ Recognition and Classification -- 2.8.2 Deep Learning for Leaf, Flower, and Root Morphological Evolution -- 2.8.3 CNNs for Large-scale Evolutionary Trait Analysis -- 2.8.4 Time-series ML for Tracking Developmental Transitions -- 2.8.5 Integrating ML with Phenotyping Platforms for Evo-Devo Research -- 2.9 Single-cell and Multi-omics ML Integration in Plant Evo-Devo -- 2.9.1 ML for Single-cell RNA Sequencing Data in Evolutionary Studies.2.9.2 Integrating Proteomics, Transcriptomics, and Metabolomics with ML -- 2.9.3 Deep Learning for Cell Fate and Differentiation Analysis in Evo-Devo -- 2.9.4 Predicting Evo-Devo Pathways Through Multi-omics Data Fusion -- 2.9.5 ML for Analyzing Spatial and Temporal Omics Data -- 2.10 Ethical, Computational, and Experimental Challenges -- 2.11 Conclusion -- Acknowledgement -- Data Availability -- Chapter 3: Machine Learning for Plant High-Throughput Phenotyping -- 3.1 Introduction -- 3.2 Overview of HTP -- 3.3 ML for Plant Phenotyping -- 3.3.1 What Is ML -- 3.3.2 ML in Handling Big Data -- 3.4 Overview of ML Algorithms in Phenotyping -- 3.4.1 DL in Plant Phenotyping -- 3.4.2 Computer Vision in Phenotyping -- 3.5 Applications of ML in Plant Phenotyping -- 3.5.1 ML for Plant Recognition and Disease Detection -- 3.5.2 Spectral Analysis and Optical Imaging for Stress Detection -- 3.5.3 Hyperspectral and Multispectral Imaging -- 3.5.4 Thermal and Fluorescence Imaging for Stress Analysis -- 3.5.5 ML Approaches for Plant Stress Classification -- 3.5.6 Automated Image Analysis for Trait Extraction -- 3.5.6.1 Morphological Trait Measurement -- 3.5.6.2 Image-based Feature Extraction -- 3.5.7 Prediction Models for Yield Forecasting -- 3.6 Integration of ML with Emerging Technologies -- 3.7 Future Directions and Potential of ML in Agriculture -- 3.8 Case Studies and Real-world Applications -- 3.9 Conclusion -- Chapter 4: Machine Learning for Studying Plant Secondary Metabolites -- 4.1 Introduction -- 4.2 ML Techniques in Metabolite Research -- 4.2.1 Supervised Learning Techniques -- 4.2.2 Unsupervised Learning Techniques -- 4.2.3 Deep Learning Techniques -- 4.2.4 Ensemble Learning Techniques -- 4.2.5 Reinforcement Learning in Metabolomics -- 4.2.6 Feature Selection and Dimensionality Reduction -- 4.2.7 Hybrid Models -- 4.2.8 Emerging Techniques in Metabolomics.4.3 Applications of ML in PSM Research -- 4.3.1 Predicting Metabolic Pathways -- 4.3.2 Identification of Key Biosynthetic Genes -- 4.3.3 Metabolite Profiling and Classification -- 4.3.4 Enhancing Plant Stress Response Through Metabolite Analysis -- 4.3.5 Chemotaxonomy and Species Identification -- 4.3.6 Pharmacological and Nutraceutical Research -- 4.3.7 Metabolic Engineering for Enhanced PSM Production -- 4.3.8 PSM-based Environmental Monitoring -- 4.3.9 Predictive Modeling for Agricultural Improvement -- 4.4 Challenges and Future Directions -- 4.4.1 Challenges in ML for PSM Research -- 4.4.1.1 Data-related Challenges -- 4.4.1.2 Algorithmic Challenges -- 4.4.1.3 Biological Complexity and Unknown Pathways -- 4.4.1.4 Computational Challenges -- 4.4.2 Future Directions in ML for PSM Research -- 4.4.2.1 Integrating Multi-omics Data -- 4.4.2.2 Advancing Explainable Artificial Intelligence -- 4.4.2.3 Improving Data Augmentation Strategies -- 4.4.2.4 Automation and High-throughput Analysis -- 4.4.2.5 Advancing Computational Infrastructure -- 4.4.2.6 Developing Crop-specific ML Models -- 4.4.3 Ethical Considerations and Regulatory Frameworks -- 4.5 Conclusion -- Chapter 5: Machine Learning for Plant Ecological Research -- 5.1 Introduction to Machine Learning in Ecology -- 5.1.1 Overview of Machine Learning in Ecological Studies -- 5.1.2 Importance of ML in Plant Ecological Research -- 5.1.3 Comparison of Traditional vs. ML-based Ecological Analysis -- 5.2 Data Sources and Preprocessing for Plant Ecology -- 5.2.1 Types of Ecological Data -- 5.2.2 Data Collection Techniques -- 5.2.3 Data Cleaning, Normalization, and Feature Engineering -- 5.3 Machine Learning Techniques for Plant Ecology -- 5.3.1 Supervised Learning -- 5.3.2 Unsupervised Learning -- 5.3.3 Deep Learning -- 5.3.4 Reinforcement Learning -- 5.4 Applications of Machine Learning in Plant Ecology.5.5 Remote Sensing and AI in Plant Ecology -- 5.5.1 Use of Satellite and Drone Imagery for Plant Monitoring -- 5.5.2 Image Segmentation and Object Detection for Vegetation Analysis -- 5.5.3 AI Models for Automated Plant Health Assessment -- 5.6 Biodiversity Conservation and Ecosystem Monitoring -- 5.6.1 Machine Learning for Biodiversity Pattern Analysis -- 5.6.2 AI-driven Monitoring of Endangered Plant Species -- 5.6.3 Predicting Ecosystem Resilience and Response to Environmental Stressors -- 5.7 Predictive Modeling for Ecological Trends -- 5.7.1 Time-series Forecasting of Ecological Parameters -- 5.7.2 ML Models for Predicting Drought and Deforestation Impact -- 5.7.3 Climate-Plant Interaction Modeling -- 5.8 Challenges and Limitations of Machine Learning in Plant Ecology -- 5.9 Future Perspectives and Emerging Technologies -- 5.10 Conclusion -- Acknowledgement -- Data Availability -- Chapter 6: Machine Learning for Modeling Plant Abiotic Stress Responses -- 6.1 Introduction -- 6.2 Definition of Abiotic Stress -- 6.3 Effect of Abiotic Stress on Crops -- 6.4 Key Applications of Machine Learning in Abiotic Stress Research -- 6.4.1 Phenotypic Prediction -- 6.4.2 Gene Expression Modeling -- 6.4.3 High-throughput Image Analysis -- 6.4.4 Omics Data Integration -- 6.4.5 Stress Response Prediction and Simulation -- 6.5 ML Techniques Commonly Used for Abiotic Stress Modeling -- 6.5.1 Supervised Learning -- 6.5.2 Unsupervised Learning -- 6.5.3 Deep Learning -- 6.6 Challenges and Future Directions -- 6.6.1 Data Availability and Quality -- 6.6.2 Generalization -- 6.6.3 Interpretability -- 6.7 Conclusion -- Chapter 7: Machine Learning for Modeling Plant-Pathogen Interactions -- 7.1 Introduction -- 7.2 Basics of PPI -- 7.3 Basics of ML and Its Integration into Biological Systems -- 7.3.1 Supervised Learning -- 7.3.2 Unsupervised Learning -- 7.3.3 Random Forests.7.3.4 Naive Bayes.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.Machine learningGenerated by AIPlant anatomyGenerated by AIMachine learning.Plant anatomy.Chen Jen-Tsung1253370MiAaPQMiAaPQMiAaPQBOOK9911042411903321Machine Learning for Plant Biology4458795UNINA