Machine Learning for Plant Biology

• Autore: Chen Jen-Tsung
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2026; ©2025
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (371 pages)
• Soggetto topico: Machine learning; Plant anatomy
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Machine Learning for Plant Biology
• ISBN: 1-394-32964-4; 1-394-32963-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911042411903321
• Lo trovi qui: Univ. Federico II