{"product_id":"machine-learning-for-plant-biology-hardback-9781394329618","title":"Machine Learning for Plant Biology (Hardback) 9781394329618","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning for Plant Biology\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eJen-Tsung Chen (Edited by), Chen (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394329618, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 1 January 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e28.5 x 22.2 x 3.4 cm, 1.106 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eA comprehensive and current summary of machine learning-based strategies for constructing digital plant biology\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eMachine Learning for Plant Biology\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eMachine Learning for Plant Biology\u003c\/i\u003e includes information on: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e Intelligent breeding for stress-resistant and high-yield crops, contributing to sustainable agriculture, the Sustainable Development Goals (SDGs), and the Paris Agreement\u003c\/li\u003e\n\u003cli\u003e Interactions between plants, pathogens, and environmental stresses through omics approaches, functional genomics, genome editing, and high-throughput technologies\u003c\/li\u003e\n\u003cli\u003e State-of-the-art AI tools, including machine and deep learning models, as well as generative AI\u003c\/li\u003e\n\u003cli\u003e Applications include species identification, systems biology, functional genomics, genomic selection, phenotyping, synthetic biology, spatial omics, plant disease diagnosis and protection, and plant secondary metabolism\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Plant Biology\u003c\/i\u003e 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eList of Contributors xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Edge-Based Machine Learning for Computer Vision in Smart Plant Biology Imaging 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJulien Garnier, Simon Ravé, Nathan Drogue, Boris Adam, Pejman Rasti, David Rousseau\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Electronic Devices for Embedded AI-driven Computer Vision 2\u003c\/p\u003e \u003cp\u003e1.3 Light Deep Learning Strategies 3\u003c\/p\u003e \u003cp\u003e1.4 Benchmark of Light Embedded Deep Learning on a Plant Imaging Use Case 4\u003c\/p\u003e \u003cp\u003e1.4.1 Image Acquisition and Segmentation 4\u003c\/p\u003e \u003cp\u003e1.4.2 Model Adaptation 4\u003c\/p\u003e \u003cp\u003e1.4.3 Knowledge Distillation 6\u003c\/p\u003e \u003cp\u003e1.4.4 Kolmogorov–Arnold Network 7\u003c\/p\u003e \u003cp\u003e1.5 Discussion 8\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Machine Learning for Studying Plant Evolutionary Developmental Biology 13\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMani Manoj, Ramaraj Sivamano, Arunachalam Abitha, Mohammed Jaffer Shakeera Banu, Shanmugam Velayuthaprabhu, Kannan Vijayarani, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction to Plant Evolutionary Developmental Biology 13\u003c\/p\u003e \u003cp\u003e2.1.1 Overview of Plant Evolutionary Developmental Biology 13\u003c\/p\u003e \u003cp\u003e2.1.2 Key Concepts in Plant Evolution and Development 13\u003c\/p\u003e \u003cp\u003e2.1.3 Importance of Evo-Devo in Understanding Plant Adaptations 14\u003c\/p\u003e \u003cp\u003e2.1.4 Role of Computational and AI Tools in Evo-Devo Studies 14\u003c\/p\u003e \u003cp\u003e2.2 Basics of ML in Biological Research 14\u003c\/p\u003e \u003cp\u003e2.2.1 Fundamentals of ML in Biology 14\u003c\/p\u003e \u003cp\u003e2.2.2 Supervised, Unsupervised, and Reinforcement Learning 15\u003c\/p\u003e \u003cp\u003e2.2.3 Deep Learning and Neural Networks in Evo-Devo 15\u003c\/p\u003e \u003cp\u003e2.2.4 ml Workflow: Data Collection, Processing, Model Selection, and Interpretation 15\u003c\/p\u003e \u003cp\u003e2.2.5 Challenges of Applying ML to Evo-Devo Research 16\u003c\/p\u003e \u003cp\u003e2.3 ml Applications in Plant Morphological Evolution 16\u003c\/p\u003e \u003cp\u003e2.3.1 ml for Analyzing Fossilized Plant Structures 16\u003c\/p\u003e \u003cp\u003e2.3.2 Shape and Trait Evolution Using CNNs and Autoencoders 16\u003c\/p\u003e \u003cp\u003e2.3.3 3D Reconstruction of Plant Organs Through ML-based Image Processing 17\u003c\/p\u003e \u003cp\u003e2.3.4 Quantitative Trait Analysis Using SVM 17\u003c\/p\u003e \u003cp\u003e2.3.5 Integrating Phylogenetics and ML for Morphological Adaptation Studies 17\u003c\/p\u003e \u003cp\u003e2.4 Genomic and Transcriptomic Insights Through ml 18\u003c\/p\u003e \u003cp\u003e2.4.1 Evolutionary Genomics: Identifying Selection Signatures with ml 18\u003c\/p\u003e \u003cp\u003e2.4.2 GRN Prediction Using Graph Neural Networks 18\u003c\/p\u003e \u003cp\u003e2.4.3 ml for Comparative Genomics in Evolutionary Studies 18\u003c\/p\u003e \u003cp\u003e2.4.4 Understanding Non-coding RNA Evolution with NLP-based ML Models 19\u003c\/p\u003e \u003cp\u003e2.4.5 Unraveling Epigenetic Modifications in Plant Evolution Using ml 19\u003c\/p\u003e \u003cp\u003e2.5 Inferring Evolutionary Developmental Pathways Using ml 19\u003c\/p\u003e \u003cp\u003e2.5.1 ml Models for Predicting Gene Expression Patterns 19\u003c\/p\u003e \u003cp\u003e2.5.2 Identifying Key Developmental Genes via Feature Selection Algorithms 20\u003c\/p\u003e \u003cp\u003e2.5.3 Evolution of Transcription Factor Networks with ml 20\u003c\/p\u003e \u003cp\u003e2.5.4 Bayesian ML for Inferring Ancestral Gene Interactions 20\u003c\/p\u003e \u003cp\u003e2.5.5 Evolution of Polyploidy and Hybridization Analyzed Through ML Model 21\u003c\/p\u003e \u003cp\u003e2.6 Phylogenetics and Evolutionary Tree Reconstruction Using ml 21\u003c\/p\u003e \u003cp\u003e2.6.1 Phylogenetic Tree Prediction via Deep Learning 21\u003c\/p\u003e \u003cp\u003e2.6.2 Bayesian ML for Inferring Evolutionary Relationships 21\u003c\/p\u003e \u003cp\u003e2.6.3 Predicting Adaptive Radiation Events with ML Models 22\u003c\/p\u003e \u003cp\u003e2.6.4 Network-based Approaches for Studying Horizontal Gene Transfer 23\u003c\/p\u003e \u003cp\u003e2.6.5 Automating Phylogenomic Inference Using AI-based Pipelines 23\u003c\/p\u003e \u003cp\u003e2.7 ml for Studying Developmental Plasticity and Environmental Adaptation 23\u003c\/p\u003e \u003cp\u003e2.7.1 Predicting Phenotypic Plasticity with ML Algorithms 23\u003c\/p\u003e \u003cp\u003e2.7.2 Climate-responsive Developmental Evolution Using ml 24\u003c\/p\u003e \u003cp\u003e2.7.3 Adaptive Traits Discovery Using ML in Dynamic Environments 24\u003c\/p\u003e \u003cp\u003e2.7.4 ML-based Prediction of Plant Evolution Under Climate Change 24\u003c\/p\u003e \u003cp\u003e2.8 High-throughput Image-based ML Approaches in Evo-Devo 25\u003c\/p\u003e \u003cp\u003e2.8.1 ml for Automated Plant Organ Recognition and Classification 25\u003c\/p\u003e \u003cp\u003e2.8.2 Deep Learning for Leaf, Flower, and Root Morphological Evolution 25\u003c\/p\u003e \u003cp\u003e2.8.3 CNNs for Large-scale Evolutionary Trait Analysis 25\u003c\/p\u003e \u003cp\u003e2.8.4 Time-series ML for Tracking Developmental Transitions 26\u003c\/p\u003e \u003cp\u003e2.8.5 Integrating ML with Phenotyping Platforms for Evo-Devo Research 27\u003c\/p\u003e \u003cp\u003e2.9 Single-cell and Multi-omics ML Integration in Plant Evo-Devo 27\u003c\/p\u003e \u003cp\u003e2.9.1 ml for Single-cell RNA Sequencing Data in Evolutionary Studies 27\u003c\/p\u003e \u003cp\u003e2.9.2 Integrating Proteomics, Transcriptomics, and Metabolomics with ml 27\u003c\/p\u003e \u003cp\u003e2.9.3 Deep Learning for Cell Fate and Differentiation Analysis in Evo-Devo 28\u003c\/p\u003e \u003cp\u003e2.9.4 Predicting Evo-Devo Pathways Through Multi-omics Data Fusion 28\u003c\/p\u003e \u003cp\u003e2.9.5 ml for Analyzing Spatial and Temporal Omics Data 28\u003c\/p\u003e \u003cp\u003e2.10 Ethical, Computational, and Experimental Challenges 29\u003c\/p\u003e \u003cp\u003e2.11 Conclusion 29\u003c\/p\u003e \u003cp\u003eAcknowledgement 30\u003c\/p\u003e \u003cp\u003eData Availability 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Machine Learning for Plant High-Throughput Phenotyping 39\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDibyendu Seth, Sourish Pramanik, Ehsas Pachauri, Ankan Das, Sandip Debnath, Mehdi Rahimi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 39\u003c\/p\u003e \u003cp\u003e3.2 Overview of HTP 40\u003c\/p\u003e \u003cp\u003e3.3 ml for Plant Phenotyping 41\u003c\/p\u003e \u003cp\u003e3.3.1 What Is ML? 41\u003c\/p\u003e \u003cp\u003e3.3.2 ml in Handling Big Data 42\u003c\/p\u003e \u003cp\u003e3.4 Overview of ML Algorithms in Phenotyping 43\u003c\/p\u003e \u003cp\u003e3.4.1 dl in Plant Phenotyping 44\u003c\/p\u003e \u003cp\u003e3.4.2 Computer Vision in Phenotyping 45\u003c\/p\u003e \u003cp\u003e3.5 Applications of ML in Plant Phenotyping 46\u003c\/p\u003e \u003cp\u003e3.5.1 ml for Plant Recognition and Disease Detection 47\u003c\/p\u003e \u003cp\u003e3.5.2 Spectral Analysis and Optical Imaging for Stress Detection 47\u003c\/p\u003e \u003cp\u003e3.5.3 Hyperspectral and Multispectral Imaging 48\u003c\/p\u003e \u003cp\u003e3.5.4 Thermal and Fluorescence Imaging for Stress Analysis 48\u003c\/p\u003e \u003cp\u003e3.5.5 ml Approaches for Plant Stress Classification 48\u003c\/p\u003e \u003cp\u003e3.5.6 Automated Image Analysis for Trait Extraction 48\u003c\/p\u003e \u003cp\u003e3.5.6.1 Morphological Trait Measurement 48\u003c\/p\u003e \u003cp\u003e3.5.6.2 Image-based Feature Extraction 49\u003c\/p\u003e \u003cp\u003e3.5.7 Prediction Models for Yield Forecasting 49\u003c\/p\u003e \u003cp\u003e3.6 Integration of ML with Emerging Technologies 50\u003c\/p\u003e \u003cp\u003e3.7 Future Directions and Potential of ML in Agriculture 51\u003c\/p\u003e \u003cp\u003e3.8 Case Studies and Real-world Applications 52\u003c\/p\u003e \u003cp\u003e3.9 Conclusion 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Machine Learning for Studying Plant Secondary Metabolites 59\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSaniya, Sarfraz Ahmad, Mohammad Ghani Raghib\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 59\u003c\/p\u003e \u003cp\u003e4.2 ml Techniques in Metabolite Research 60\u003c\/p\u003e \u003cp\u003e4.2.1 Supervised Learning Techniques 60\u003c\/p\u003e \u003cp\u003e4.2.2 Unsupervised Learning Techniques 60\u003c\/p\u003e \u003cp\u003e4.2.3 Deep Learning Techniques 61\u003c\/p\u003e \u003cp\u003e4.2.4 Ensemble Learning Techniques 61\u003c\/p\u003e \u003cp\u003e4.2.5 Reinforcement Learning in Metabolomics 61\u003c\/p\u003e \u003cp\u003e4.2.6 Feature Selection and Dimensionality Reduction 62\u003c\/p\u003e \u003cp\u003e4.2.7 Hybrid Models 62\u003c\/p\u003e \u003cp\u003e4.2.8 Emerging Techniques in Metabolomics 62\u003c\/p\u003e \u003cp\u003e4.3 Applications of ML in PSM Research 62\u003c\/p\u003e \u003cp\u003e4.3.1 Predicting Metabolic Pathways 62\u003c\/p\u003e \u003cp\u003e4.3.2 Identification of Key Biosynthetic Genes 64\u003c\/p\u003e \u003cp\u003e4.3.3 Metabolite Profiling and Classification 64\u003c\/p\u003e \u003cp\u003e4.3.4 Enhancing Plant Stress Response Through Metabolite Analysis 64\u003c\/p\u003e \u003cp\u003e4.3.5 Chemotaxonomy and Species Identification 64\u003c\/p\u003e \u003cp\u003e4.3.6 Pharmacological and Nutraceutical Research 65\u003c\/p\u003e \u003cp\u003e4.3.7 Metabolic Engineering for Enhanced PSM Production 65\u003c\/p\u003e \u003cp\u003e4.3.8 PSM-based Environmental Monitoring 65\u003c\/p\u003e \u003cp\u003e4.3.9 Predictive Modeling for Agricultural Improvement 65\u003c\/p\u003e \u003cp\u003e4.4 Challenges and Future Directions 65\u003c\/p\u003e \u003cp\u003e4.4.1 Challenges in ML for PSM Research 65\u003c\/p\u003e \u003cp\u003e4.4.1.1 Data-related Challenges 65\u003c\/p\u003e \u003cp\u003e4.4.1.2 Algorithmic Challenges 66\u003c\/p\u003e \u003cp\u003e4.4.1.3 Biological Complexity and Unknown Pathways 66\u003c\/p\u003e \u003cp\u003e4.4.1.4 Computational Challenges 66\u003c\/p\u003e \u003cp\u003e4.4.2 Future Directions in ML for PSM Research 66\u003c\/p\u003e \u003cp\u003e4.4.2.1 Integrating Multi-omics Data 66\u003c\/p\u003e \u003cp\u003e4.4.2.2 Advancing Explainable Artificial Intelligence 67\u003c\/p\u003e \u003cp\u003e4.4.2.3 Improving Data Augmentation Strategies 67\u003c\/p\u003e \u003cp\u003e4.4.2.4 Automation and High-throughput Analysis 67\u003c\/p\u003e \u003cp\u003e4.4.2.5 Advancing Computational Infrastructure 67\u003c\/p\u003e \u003cp\u003e4.4.2.6 Developing Crop-specific ML Models 67\u003c\/p\u003e \u003cp\u003e4.4.3 Ethical Considerations and Regulatory Frameworks 67\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Machine Learning for Plant Ecological Research 71\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMani Manoj, Sahfigul Ameed Nihaal Fathima, Sathyalingam Sathya Trisha, Mohammed Jaffer Shakeera Banu, Shanmugam Gavaskar, Alagarsamy Sumitrha, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction to Machine Learning in Ecology 71\u003c\/p\u003e \u003cp\u003e5.1.1 Overview of Machine Learning in Ecological Studies 71\u003c\/p\u003e \u003cp\u003e5.1.2 Importance of ML in Plant Ecological Research 71\u003c\/p\u003e \u003cp\u003e5.1.3 Comparison of Traditional vs. ML-based Ecological Analysis 72\u003c\/p\u003e \u003cp\u003e5.2 Data Sources and Preprocessing for Plant Ecology 72\u003c\/p\u003e \u003cp\u003e5.2.1 Types of Ecological Data 72\u003c\/p\u003e \u003cp\u003e5.2.2 Data Collection Techniques 72\u003c\/p\u003e \u003cp\u003e5.2.3 Data Cleaning, Normalization, and Feature Engineering 73\u003c\/p\u003e \u003cp\u003e5.3 Machine Learning Techniques for Plant Ecology 74\u003c\/p\u003e \u003cp\u003e5.3.1 Supervised Learning 74\u003c\/p\u003e \u003cp\u003e5.3.2 Unsupervised Learning 74\u003c\/p\u003e \u003cp\u003e5.3.3 Deep Learning 74\u003c\/p\u003e \u003cp\u003e5.3.4 Reinforcement Learning 75\u003c\/p\u003e \u003cp\u003e5.4 Applications of Machine Learning in Plant Ecology 75\u003c\/p\u003e \u003cp\u003e5.5 Remote Sensing and AI in Plant Ecology 76\u003c\/p\u003e \u003cp\u003e5.5.1 Use of Satellite and Drone Imagery for Plant Monitoring 76\u003c\/p\u003e \u003cp\u003e5.5.2 Image Segmentation and Object Detection for Vegetation Analysis 77\u003c\/p\u003e \u003cp\u003e5.5.3 AI Models for Automated Plant Health Assessment 78\u003c\/p\u003e \u003cp\u003e5.6 Biodiversity Conservation and Ecosystem Monitoring 79\u003c\/p\u003e \u003cp\u003e5.6.1 Machine Learning for Biodiversity Pattern Analysis 79\u003c\/p\u003e \u003cp\u003e5.6.2 AI-driven Monitoring of Endangered Plant Species 79\u003c\/p\u003e \u003cp\u003e5.6.3 Predicting Ecosystem Resilience and Response to Environmental Stressors 80\u003c\/p\u003e \u003cp\u003e5.7 Predictive Modeling for Ecological Trends 80\u003c\/p\u003e \u003cp\u003e5.7.1 Time-series Forecasting of Ecological Parameters 80\u003c\/p\u003e \u003cp\u003e5.7.2 ml Models for Predicting Drought and Deforestation Impact 80\u003c\/p\u003e \u003cp\u003e5.7.3 Climate–Plant Interaction Modeling 81\u003c\/p\u003e \u003cp\u003e5.8 Challenges and Limitations of Machine Learning in Plant Ecology 81\u003c\/p\u003e \u003cp\u003e5.9 Future Perspectives and Emerging Technologies 82\u003c\/p\u003e \u003cp\u003e5.10 Conclusion 82\u003c\/p\u003e \u003cp\u003eAcknowledgement 83\u003c\/p\u003e \u003cp\u003eData Availability 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Machine Learning for Modeling Plant Abiotic Stress Responses 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHaragopal Dutta, Suman Dutta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 89\u003c\/p\u003e \u003cp\u003e6.2 Definition of Abiotic Stress 90\u003c\/p\u003e \u003cp\u003e6.3 Effect of Abiotic Stress on Crops 91\u003c\/p\u003e \u003cp\u003e6.4 Key Applications of Machine Learning in Abiotic Stress Research 92\u003c\/p\u003e \u003cp\u003e6.4.1 Phenotypic Prediction 92\u003c\/p\u003e \u003cp\u003e6.4.2 Gene Expression Modeling 95\u003c\/p\u003e \u003cp\u003e6.4.3 High-throughput Image Analysis 95\u003c\/p\u003e \u003cp\u003e6.4.4 Omics Data Integration 97\u003c\/p\u003e \u003cp\u003e6.4.5 Stress Response Prediction and Simulation 99\u003c\/p\u003e \u003cp\u003e6.5 ml Techniques Commonly Used for Abiotic Stress Modeling 99\u003c\/p\u003e \u003cp\u003e6.5.1 Supervised Learning 99\u003c\/p\u003e \u003cp\u003e6.5.2 Unsupervised Learning 100\u003c\/p\u003e \u003cp\u003e6.5.3 Deep Learning 100\u003c\/p\u003e \u003cp\u003e6.6 Challenges and Future Directions 101\u003c\/p\u003e \u003cp\u003e6.6.1 Data Availability and Quality 101\u003c\/p\u003e \u003cp\u003e6.6.2 Generalization 101\u003c\/p\u003e \u003cp\u003e6.6.3 Interpretability 102\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Machine Learning for Modeling Plant–Pathogen Interactions 111\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSourish Pramanik, Dibyendu Seth, Sandip Debnath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 111\u003c\/p\u003e \u003cp\u003e7.2 Basics of PPI 112\u003c\/p\u003e \u003cp\u003e7.3 Basics of ML and Its Integration into Biological Systems 113\u003c\/p\u003e \u003cp\u003e7.3.1 Supervised Learning 114\u003c\/p\u003e \u003cp\u003e7.3.2 Unsupervised Learning 114\u003c\/p\u003e \u003cp\u003e7.3.3 Random Forests 115\u003c\/p\u003e \u003cp\u003e7.3.4 Naive Bayes 115\u003c\/p\u003e \u003cp\u003e7.3.5 Neural Networks 115\u003c\/p\u003e \u003cp\u003e7.3.6 Variational Autoencoders 115\u003c\/p\u003e \u003cp\u003e7.4 ml in PPI 117\u003c\/p\u003e \u003cp\u003e7.4.1 Integration of ML in PPI: Molecular Level 118\u003c\/p\u003e \u003cp\u003e7.4.2 Integration of ML in PPI: Field Level 120\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Machine Learning-Enhanced Plant Disease Detection and Management 131\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLellapalli Rithesh, Sucharita Mohapatra, Shimi Jose, Juel Debnath, Gyanisha Nayak, Mehjebin Rahman, Soumya Shephalika Dash, Anwesha Sharma, Sneha Mohan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 131\u003c\/p\u003e \u003cp\u003e8.2 Fundamentals of ml 136\u003c\/p\u003e \u003cp\u003e8.2.1 Fundamental Principles of ml 136\u003c\/p\u003e \u003cp\u003e8.2.1.1 Supervised Learning 136\u003c\/p\u003e \u003cp\u003e8.2.1.2 Unsupervised Learning 136\u003c\/p\u003e \u003cp\u003e8.2.1.3 Reinforcement Learning 136\u003c\/p\u003e \u003cp\u003e8.2.2 Categories of ML Models Employed in Plant Disease Detection 136\u003c\/p\u003e \u003cp\u003e8.2.2.1 Decision Trees 136\u003c\/p\u003e \u003cp\u003e8.2.2.2 Support Vector Machines 137\u003c\/p\u003e \u003cp\u003e8.2.2.3 Random Forests 137\u003c\/p\u003e \u003cp\u003e8.2.2.4 Deep Learning 137\u003c\/p\u003e \u003cp\u003e8.2.2.5 Convolutional Neural Networks 137\u003c\/p\u003e \u003cp\u003e8.3 Data Collection and Preprocessing 138\u003c\/p\u003e \u003cp\u003e8.3.1 Data Types 138\u003c\/p\u003e \u003cp\u003e8.3.1.1 Image Data 138\u003c\/p\u003e \u003cp\u003e8.3.1.2 Spectral Data 138\u003c\/p\u003e \u003cp\u003e8.3.1.3 Sensor Data 138\u003c\/p\u003e \u003cp\u003e8.3.1.4 Genomic and Molecular Data 138\u003c\/p\u003e \u003cp\u003e8.3.1.5 Tabular Data 138\u003c\/p\u003e \u003cp\u003e8.3.2 Data Preprocessing Techniques 138\u003c\/p\u003e \u003cp\u003e8.3.2.1 Image Data Preprocessing 139\u003c\/p\u003e \u003cp\u003e8.3.2.2 Spectral Data Preprocessing 139\u003c\/p\u003e \u003cp\u003e8.3.2.3 Sensor Data Preprocessing 139\u003c\/p\u003e \u003cp\u003e8.3.2.4 Genomic and Molecular Data Preprocessing 140\u003c\/p\u003e \u003cp\u003e8.3.2.5 Tabular Data Preprocessing 140\u003c\/p\u003e \u003cp\u003e8.4 Image-based Pathology Identification 140\u003c\/p\u003e \u003cp\u003e8.4.1 The Role of Computer Vision in Plant Diseases Diagnosis 140\u003c\/p\u003e \u003cp\u003e8.4.2 Key Algorithms in Image Processing 141\u003c\/p\u003e \u003cp\u003e8.5 Sensor-based Disease Monitoring 142\u003c\/p\u003e \u003cp\u003e8.5.1 Role of Sensor-based Monitoring in Plant Disease Detection 142\u003c\/p\u003e \u003cp\u003e8.5.2 Role of IoT in Disease Detection and Management 142\u003c\/p\u003e \u003cp\u003e8.5.3 Disease Progression Using Environmental Sensors 143\u003c\/p\u003e \u003cp\u003e8.5.4 Integration with ml 143\u003c\/p\u003e \u003cp\u003e8.6 Genomic Approaches for Disease Prediction 144\u003c\/p\u003e \u003cp\u003e8.6.1 ml for Identification of Disease Resistance Traits Using Genomic Data 145\u003c\/p\u003e \u003cp\u003e8.7 Advances in ML Techniques for Disease Management 146\u003c\/p\u003e \u003cp\u003e8.7.1 AI-driven Disease Forecasting Models 146\u003c\/p\u003e \u003cp\u003e8.7.2 Predictive Modeling for Crop Management 146\u003c\/p\u003e \u003cp\u003e8.7.3 Transfer Learning and Model Generalization 147\u003c\/p\u003e \u003cp\u003e8.8 Challenges and Perspectives 147\u003c\/p\u003e \u003cp\u003e8.9 Conclusion 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Machine Learning for Analyzing and Integrating Multiple Omics 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSarfraz Ahmad, Saniya, Mohammad Ghani Raghib, Rubina Khan, Vikas Belwal, Pankaj Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 155\u003c\/p\u003e \u003cp\u003e9.2 Characteristics of Omics Data 156\u003c\/p\u003e \u003cp\u003e9.2.1 High Dimensionality and Low Sample Size 156\u003c\/p\u003e \u003cp\u003e9.2.2 Data Heterogeneity 157\u003c\/p\u003e \u003cp\u003e9.2.3 Sparsity and Missing Values 157\u003c\/p\u003e \u003cp\u003e9.2.4 Noise and Technical Variability 158\u003c\/p\u003e \u003cp\u003e9.2.5 Dynamic and Temporal Variability 158\u003c\/p\u003e \u003cp\u003e9.2.6 Multicollinearity and Feature Interdependence 158\u003c\/p\u003e \u003cp\u003e9.2.7 High Biological Variability 158\u003c\/p\u003e \u003cp\u003e9.2.8 Class Imbalance in Omics Data 159\u003c\/p\u003e \u003cp\u003e9.2.9 Data Integration Complexity 159\u003c\/p\u003e \u003cp\u003e9.3 Data Preprocessing for Multi-omics Integration 159\u003c\/p\u003e \u003cp\u003e9.3.1 Data Normalization and Standardization 159\u003c\/p\u003e \u003cp\u003e9.3.2 Data Cleaning and Outlier Detection 160\u003c\/p\u003e \u003cp\u003e9.3.3 Data Imputation for Missing Values 160\u003c\/p\u003e \u003cp\u003e9.3.4 Batch Effect Correction 160\u003c\/p\u003e \u003cp\u003e9.3.5 Dimensionality Reduction 160\u003c\/p\u003e \u003cp\u003e9.3.6 Feature Selection 161\u003c\/p\u003e \u003cp\u003e9.3.7 Data Integration Strategies 161\u003c\/p\u003e \u003cp\u003e9.3.8 Data Transformation and Encoding 161\u003c\/p\u003e \u003cp\u003e9.3.9 Data Augmentation Techniques 161\u003c\/p\u003e \u003cp\u003e9.3.10 Data Quality Assessment 162\u003c\/p\u003e \u003cp\u003e9.4 ml Techniques for Multi-omics Analysis 162\u003c\/p\u003e \u003cp\u003e9.4.1 Supervised Learning Techniques 162\u003c\/p\u003e \u003cp\u003e9.4.1.1 Support Vector Machines 162\u003c\/p\u003e \u003cp\u003e9.4.1.2 Random Forest 162\u003c\/p\u003e \u003cp\u003e9.4.1.3 Elastic Net and LASSO Regression 162\u003c\/p\u003e \u003cp\u003e9.4.2 Unsupervised Learning Techniques 163\u003c\/p\u003e \u003cp\u003e9.4.2.1 Principal Component Analysis 163\u003c\/p\u003e \u003cp\u003e9.4.2.2 K-means Clustering 163\u003c\/p\u003e \u003cp\u003e9.4.2.3 Hierarchical Clustering 163\u003c\/p\u003e \u003cp\u003e9.4.3 Deep Learning Techniques 163\u003c\/p\u003e \u003cp\u003e9.4.3.1 Convolutional Neural Networks 163\u003c\/p\u003e \u003cp\u003e9.4.3.2 Recurrent Neural Networks 163\u003c\/p\u003e \u003cp\u003e9.4.3.3 Autoencoders 163\u003c\/p\u003e \u003cp\u003e9.4.4 Network-based Approaches 164\u003c\/p\u003e \u003cp\u003e9.4.4.1 Graph Neural Networks 164\u003c\/p\u003e \u003cp\u003e9.4.4.2 Bayesian Networks 164\u003c\/p\u003e \u003cp\u003e9.4.5 Hybrid and Ensemble Learning Approaches 164\u003c\/p\u003e \u003cp\u003e9.5 Applications of ML in Multi-omics Research 164\u003c\/p\u003e \u003cp\u003e9.5.1 Biomarker Discovery in Human Health 165\u003c\/p\u003e \u003cp\u003e9.5.1.1 Cancer Biomarker Identification 165\u003c\/p\u003e \u003cp\u003e9.5.1.2 Cardiovascular and Metabolic Disorders 165\u003c\/p\u003e \u003cp\u003e9.5.2 Disease Diagnosis and Classification 165\u003c\/p\u003e \u003cp\u003e9.5.2.1 Cancer Classification Models 165\u003c\/p\u003e \u003cp\u003e9.5.2.2 Neurological Disorders 165\u003c\/p\u003e \u003cp\u003e9.5.3 Drug Discovery and Personalized Medicine 165\u003c\/p\u003e \u003cp\u003e9.5.3.1 Drug–Target Interaction Prediction 166\u003c\/p\u003e \u003cp\u003e9.5.3.2 Personalized Medicine 166\u003c\/p\u003e \u003cp\u003e9.5.4 Applications in Plant Sciences 166\u003c\/p\u003e \u003cp\u003e9.5.4.1 Crop Trait Prediction 166\u003c\/p\u003e \u003cp\u003e9.5.4.2 Stress Response Prediction 166\u003c\/p\u003e \u003cp\u003e9.5.5 Environmental and Ecological Applications 166\u003c\/p\u003e \u003cp\u003e9.5.5.1 Soil Microbiome Analysis 166\u003c\/p\u003e \u003cp\u003e9.5.5.2 Environmental Stress Monitoring 166\u003c\/p\u003e \u003cp\u003e9.5.6 Systems Biology and Pathway Analysis 168\u003c\/p\u003e \u003cp\u003e9.6 Challenges and Future Directions 168\u003c\/p\u003e \u003cp\u003e9.6.1 Key Challenges in ML-driven Multi-omics Research 168\u003c\/p\u003e \u003cp\u003e9.6.1.1 Data Heterogeneity and Dimensionality 168\u003c\/p\u003e \u003cp\u003e9.6.1.2 Data Quality and Noise 168\u003c\/p\u003e \u003cp\u003e9.6.1.3 Model Interpretability and Biological Relevance 168\u003c\/p\u003e \u003cp\u003e9.6.1.4 Integration of Multi-scale Data 168\u003c\/p\u003e \u003cp\u003e9.6.1.5 Computational and Resource Limitations 169\u003c\/p\u003e \u003cp\u003e9.6.2 Future Directions in ML-driven Multi-omics Research 169\u003c\/p\u003e \u003cp\u003e9.6.2.1 Advancing Data Integration Techniques 169\u003c\/p\u003e \u003cp\u003e9.6.2.2 Improving Model Interpretability 169\u003c\/p\u003e \u003cp\u003e9.6.2.3 Enhancing Data Quality and Standardization 169\u003c\/p\u003e \u003cp\u003e9.6.2.4 Leveraging Transfer Learning and Few-shot Learning 169\u003c\/p\u003e \u003cp\u003e9.6.2.5 Advancing Cloud-based Solutions 169\u003c\/p\u003e \u003cp\u003e9.6.2.6 Ethical and Regulatory Considerations 169\u003c\/p\u003e \u003cp\u003e9.7 Conclusion 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Machine Learning for Plant Single-Cell RNA Sequencing 175\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMani Manoj, Ravichandran Sneha, Esakkimuthu Balaji, Vadivelu Bharathi, Thamaraiselvan Nandhini Devi, Ramasamy Manikandan, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction to Plant Single-cell RNA Sequencing 175\u003c\/p\u003e \u003cp\u003e10.2 ml Techniques in scRNA-seq Data Analysis 176\u003c\/p\u003e \u003cp\u003e10.2.1 Preprocessing and Quality Control 176\u003c\/p\u003e \u003cp\u003e10.2.1.1 Data Filtering and Normalization 176\u003c\/p\u003e \u003cp\u003e10.2.1.2 Batch Effect Correction 176\u003c\/p\u003e \u003cp\u003e10.2.1.3 Dimensionality Reduction Techniques 177\u003c\/p\u003e \u003cp\u003e10.2.2 Feature Selection and Gene Expression Clustering 177\u003c\/p\u003e \u003cp\u003e10.2.2.1 Highly Variable Gene Selection 177\u003c\/p\u003e \u003cp\u003e10.2.2.2 Clustering Algorithms 177\u003c\/p\u003e \u003cp\u003e10.2.2.3 Deep Learning for Feature Selection 178\u003c\/p\u003e \u003cp\u003e10.2.3 Cell-type Identification and Annotation 178\u003c\/p\u003e \u003cp\u003e10.2.3.1 Supervised vs. Unsupervised Learning Approaches 178\u003c\/p\u003e \u003cp\u003e10.2.3.2 Decision Trees, Random Forest, and SVMs 178\u003c\/p\u003e \u003cp\u003e10.2.3.3 DL-based Cell-type Classification 179\u003c\/p\u003e \u003cp\u003e10.2.4 Trajectory and Pseudotime Inference 179\u003c\/p\u003e \u003cp\u003e10.2.4.1 Single-cell Developmental Trajectories 179\u003c\/p\u003e \u003cp\u003e10.2.4.2 Pseudotime Estimation 179\u003c\/p\u003e \u003cp\u003e10.2.4.3 Application of Graph Neural Networks 179\u003c\/p\u003e \u003cp\u003e10.2.5 Differential Gene Expression Analysis 180\u003c\/p\u003e \u003cp\u003e10.2.5.1 ml Models for Identifying DE Genes 180\u003c\/p\u003e \u003cp\u003e10.2.5.2 Bayesian Methods for Gene Expression Inference 180\u003c\/p\u003e \u003cp\u003e10.2.5.3 Application of Neural Networks in Predicting Gene Regulation 180\u003c\/p\u003e \u003cp\u003e10.2.6 Cell–Cell Interaction and Network Analysis 181\u003c\/p\u003e \u003cp\u003e10.2.6.1 Graph-based Approaches for Inferring Cell Communication 181\u003c\/p\u003e \u003cp\u003e10.2.6.2 Co-expression Network Analysis Using ml 182\u003c\/p\u003e \u003cp\u003e10.2.6.3 Integration with ST 182\u003c\/p\u003e \u003cp\u003e10.3 Deep Learning for Plant scRNA-seq Analysis 182\u003c\/p\u003e \u003cp\u003e10.3.1 Autoencoders for Dimensionality Reduction and Noise Removal 182\u003c\/p\u003e \u003cp\u003e10.3.1.1 VAEs in scRNA-seq 182\u003c\/p\u003e \u003cp\u003e10.3.1.2 GANs for Data Augmentation 183\u003c\/p\u003e \u003cp\u003e10.3.2 Convolutional and RNNs 183\u003c\/p\u003e \u003cp\u003e10.3.2.1 CNNs for ST in Plants 183\u003c\/p\u003e \u003cp\u003e10.3.2.2 RNNs for Temporal Gene Expression Analysis 184\u003c\/p\u003e \u003cp\u003e10.3.3 Transfer Learning in Plant scRNA-seq 184\u003c\/p\u003e \u003cp\u003e10.3.3.1 Pretrained Models for Plant Gene Expression Prediction 184\u003c\/p\u003e \u003cp\u003e10.3.3.2 Domain Adaptation Techniques for Cross-species Analysis 185\u003c\/p\u003e \u003cp\u003e10.4 Integrating Multi-omics Data with ml 186\u003c\/p\u003e \u003cp\u003e10.5 Challenges and Future Directions 187\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 187\u003c\/p\u003e \u003cp\u003eAcknowledgement 187\u003c\/p\u003e \u003cp\u003eData Availability 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Machine Learning for Plant Genomic Prediction 195\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNatasha Charaya, Sonika Kalia, Indra Rautela, Poorvi Yadav, Poornima Bhardwaj, Ritakshi Nautiyal, Monika Kalia, Vinay Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 195\u003c\/p\u003e \u003cp\u003e11.1.1 Genomic Prediction 195\u003c\/p\u003e \u003cp\u003e11.1.2 Genome Selection 196\u003c\/p\u003e \u003cp\u003e11.1.3 Machine Learning 196\u003c\/p\u003e \u003cp\u003e11.2 Types of Models Used in ml 197\u003c\/p\u003e \u003cp\u003e11.2.1 Supervised Models 197\u003c\/p\u003e \u003cp\u003e11.2.1.1 Classification 198\u003c\/p\u003e \u003cp\u003e11.2.1.2 Regression 198\u003c\/p\u003e \u003cp\u003e11.2.2 Unsupervised Models 198\u003c\/p\u003e \u003cp\u003e11.2.2.1 Clustering 198\u003c\/p\u003e \u003cp\u003e11.2.2.2 Dimensionality Reduction 198\u003c\/p\u003e \u003cp\u003e11.2.2.3 Anomaly Detection 198\u003c\/p\u003e \u003cp\u003e11.2.3 Semi-supervised Model 199\u003c\/p\u003e \u003cp\u003e11.2.3.1 Transductive SVM 199\u003c\/p\u003e \u003cp\u003e11.2.3.2 Generative Models 199\u003c\/p\u003e \u003cp\u003e11.2.4 Deep Learning 199\u003c\/p\u003e \u003cp\u003e11.2.4.1 Neural Networks 199\u003c\/p\u003e \u003cp\u003e11.3 Methods 200\u003c\/p\u003e \u003cp\u003e11.3.1 Linear Methods 200\u003c\/p\u003e \u003cp\u003e11.3.2 Kernel Methods 200\u003c\/p\u003e \u003cp\u003e11.3.3 Neural Networks 201\u003c\/p\u003e \u003cp\u003e11.3.4 Tree Ensembles 201\u003c\/p\u003e \u003cp\u003e11.4 Cross-validation 201\u003c\/p\u003e \u003cp\u003e11.4.1 Strategies for cv 201\u003c\/p\u003e \u003cp\u003e11.4.2 Validation Metrics 202\u003c\/p\u003e \u003cp\u003e11.4.3 Information 202\u003c\/p\u003e \u003cp\u003e11.5 Applications of ml 203\u003c\/p\u003e \u003cp\u003e11.5.1 Combining Trials to Increase the Sample Size 203\u003c\/p\u003e \u003cp\u003e11.5.2 An Explanation of the G × E Interaction Using Data Features 204\u003c\/p\u003e \u003cp\u003e11.5.3 Exploiting Information from Secondary Traits 204\u003c\/p\u003e \u003cp\u003e11.6 ml Challenges 204\u003c\/p\u003e \u003cp\u003e11.7 Summary 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Machine Learning-Assisted Plant Systems Biology 209\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHaragopal Dutta, Suman Dutta, Sudhir Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 209\u003c\/p\u003e \u003cp\u003e12.2 ml Algorithms in Plant Systems Biology 211\u003c\/p\u003e \u003cp\u003e12.2.1 Supervised Learning 211\u003c\/p\u003e \u003cp\u003e12.2.2 Unsupervised Learning 211\u003c\/p\u003e \u003cp\u003e12.2.3 Reinforcement Learning 213\u003c\/p\u003e \u003cp\u003e12.3 Key Applications of ML in Plant Systems Biology 213\u003c\/p\u003e \u003cp\u003e12.3.1 Multi-omics Data Integration 213\u003c\/p\u003e \u003cp\u003e12.3.2 Gene Regulatory Network 215\u003c\/p\u003e \u003cp\u003e12.3.3 Trait Prediction and Breeding 216\u003c\/p\u003e \u003cp\u003e12.3.4 Metabolic Pathway Analysis 217\u003c\/p\u003e \u003cp\u003e12.3.5 Stress Response and Adaptation 217\u003c\/p\u003e \u003cp\u003e12.3.6 Predictive Modeling of Plant–Environment Interactions 218\u003c\/p\u003e \u003cp\u003e12.3.7 Identification of Key Regulators in Synthetic Biology 219\u003c\/p\u003e \u003cp\u003e12.4 Challenges and Future Directions 220\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Machine Learning-Driven Precision Plant Breeding 229\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKrishna Kumar Rai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 229\u003c\/p\u003e \u003cp\u003e13.2 Conventional Breeding Approaches 230\u003c\/p\u003e \u003cp\u003e13.3 Molecular Breeding Innovations 232\u003c\/p\u003e \u003cp\u003e13.4 Speed Breeding and AI Integration 233\u003c\/p\u003e \u003cp\u003e13.5 Challenges and Future Directions 235\u003c\/p\u003e \u003cp\u003e13.6 Conclusion 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Machine Learning-Driven Smart Agriculture 241\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajdeep Mohanta, Soumik Dey Roy, Sahely Kanthal, Sanjay Mochary, Subhadwip Ghorai, Soham Hazra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction to Smart Agriculture 241\u003c\/p\u003e \u003cp\u003e14.1.1 Importance of Smart Agriculture in Modern Era 241\u003c\/p\u003e \u003cp\u003e14.1.2 The Role of Technology in Modern Agriculture 241\u003c\/p\u003e \u003cp\u003e14.1.3 Introduction to ML and AI in Agriculture 242\u003c\/p\u003e \u003cp\u003e14.2 The Role of ML in Agriculture 242\u003c\/p\u003e \u003cp\u003e14.3 Key Applications of ML in Smart Agriculture 244\u003c\/p\u003e \u003cp\u003e14.3.1 Precision Farming 244\u003c\/p\u003e \u003cp\u003e14.3.2 Pest and Disease Detection 244\u003c\/p\u003e \u003cp\u003e14.3.2.1 Disease Prediction and Control Measures 245\u003c\/p\u003e \u003cp\u003e14.3.3 Yield Prediction and Crop Management 245\u003c\/p\u003e \u003cp\u003e14.3.3.1 Predictive Models for Yield Based on Various Parameters (Soil, Weather, and Crop Type) 245\u003c\/p\u003e \u003cp\u003e14.3.3.2 Crop Rotation and Field Management Insights 245\u003c\/p\u003e \u003cp\u003e14.3.4 Climate Prediction and Weather Forecasting 245\u003c\/p\u003e \u003cp\u003e14.3.4.1 Predictive Analytics for Extreme Weather Events 245\u003c\/p\u003e \u003cp\u003e14.3.4.2 Mitigating the Impact of Climate Change on Agriculture 245\u003c\/p\u003e \u003cp\u003e14.4 Data Collection and Preprocessing in Agriculture 246\u003c\/p\u003e \u003cp\u003e14.4.1 Sources of Data in Agriculture 246\u003c\/p\u003e \u003cp\u003e14.4.1.1 Role of Technology in Agricultural Data Collection 246\u003c\/p\u003e \u003cp\u003e14.4.2 Types of Data 247\u003c\/p\u003e \u003cp\u003e14.4.2.1 Key Data Types in Smart Agriculture 247\u003c\/p\u003e \u003cp\u003e14.4.2.2 Weather Data 248\u003c\/p\u003e \u003cp\u003e14.4.2.3 Crop Health Data 248\u003c\/p\u003e \u003cp\u003e14.4.2.4 Livestock Monitoring Data 248\u003c\/p\u003e \u003cp\u003e14.4.3 Data Preprocessing Steps and Challenges in Agricultural Data 249\u003c\/p\u003e \u003cp\u003e14.4.3.1 Data Preprocessing Steps 249\u003c\/p\u003e \u003cp\u003e14.4.3.2 Challenges in Agricultural Data Preprocessing 250\u003c\/p\u003e \u003cp\u003e14.5 ml Techniques in Agriculture 250\u003c\/p\u003e \u003cp\u003e14.5.1 Categorization of ML Techniques 251\u003c\/p\u003e \u003cp\u003e14.5.1.1 SL (Supervised Learning) 251\u003c\/p\u003e \u003cp\u003e14.5.1.2 UL (Unsupervised Learning) 251\u003c\/p\u003e \u003cp\u003e14.6 Challenges and Limitations of ML in Agriculture 252\u003c\/p\u003e \u003cp\u003e14.7 Case Studies and Real-world Applications 252\u003c\/p\u003e \u003cp\u003e14.8 Future Prospects and Emerging Trends 252\u003c\/p\u003e \u003cp\u003e14.8.1 The Potential Impact of ML on Global Food Security Through Smart Agriculture 252\u003c\/p\u003e \u003cp\u003e14.8.1.1 The Role of ML in Smart Agriculture 253\u003c\/p\u003e \u003cp\u003e14.8.1.2 Benefits of ML-driven Smart Agriculture 254\u003c\/p\u003e \u003cp\u003e14.8.1.3 Challenges in Implementing ML in Agriculture 254\u003c\/p\u003e \u003cp\u003e14.8.1.4 Future Prospects and Recommendations 254\u003c\/p\u003e \u003cp\u003e14.8.2 Future Trends: Future Trends in ML-driven Smart Agriculture: AI Integration, Robotics, and Precision Breeding 255\u003c\/p\u003e \u003cp\u003e14.8.2.1 AI Integration in Agriculture 255\u003c\/p\u003e \u003cp\u003e14.8.2.2 Robotics in Smart Agriculture 255\u003c\/p\u003e \u003cp\u003e14.8.2.3 Precision Breeding with ml 255\u003c\/p\u003e \u003cp\u003e14.8.2.4 Challenges and Ethical Considerations 255\u003c\/p\u003e \u003cp\u003e14.8.2.5 Future Outlook and Recommendations 256\u003c\/p\u003e \u003cp\u003e14.8.3 Sustainability Implications and Environmental Impact of ML-driven Smart Agriculture 256\u003c\/p\u003e \u003cp\u003e14.8.3.1 Environmental Benefits of ML-driven Smart Agriculture 256\u003c\/p\u003e \u003cp\u003e14.8.3.2 Environmental Challenges of ML-driven Smart Agriculture 257\u003c\/p\u003e \u003cp\u003e14.8.3.3 Strategies for Sustainable Implementation 257\u003c\/p\u003e \u003cp\u003e14.9 Conclusion 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Plant Leaf Disease Detection and Classification Using Convolutional Neural Networks 265\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eA. V. Senthil Kumar, N. Abinesh, Shanmugasundaram Hariharan, Kyla L. Tennin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 265\u003c\/p\u003e \u003cp\u003e15.2 Plant Disease Challenges and Issues 266\u003c\/p\u003e \u003cp\u003e15.2.1 Data Quality and Availability 266\u003c\/p\u003e \u003cp\u003e15.2.2 Environmental Variability and Conditions 266\u003c\/p\u003e \u003cp\u003e15.2.3 Model Generalization Across Geographic Regions 267\u003c\/p\u003e \u003cp\u003e15.2.4 Complexity of Disease Symptom Expression 267\u003c\/p\u003e \u003cp\u003e15.2.5 Limited Generalization to New or Unknown Diseases 267\u003c\/p\u003e \u003cp\u003e15.2.6 Model Interpretability and Trust 267\u003c\/p\u003e \u003cp\u003e15.2.7 Scalability and Real-time Implementation 268\u003c\/p\u003e \u003cp\u003e15.3 Plant Disease Detection and Classification 268\u003c\/p\u003e \u003cp\u003e15.3.1 ml Techniques for Plant Disease Detection 268\u003c\/p\u003e \u003cp\u003e15.3.2 Supervised Learning Approaches 268\u003c\/p\u003e \u003cp\u003e15.3.3 Unsupervised Learning Approaches 269\u003c\/p\u003e \u003cp\u003e15.3.4 Deep Learning Approaches 270\u003c\/p\u003e \u003cp\u003e15.4 Data Sources and Feature Extraction 270\u003c\/p\u003e \u003cp\u003e15.4.1 Image-based Data 270\u003c\/p\u003e \u003cp\u003e15.4.2 Multispectral and Hyperspectral Data 270\u003c\/p\u003e \u003cp\u003e15.4.3 Feature Extraction Techniques 270\u003c\/p\u003e \u003cp\u003e15.5 Challenges in Plant Disease Detection Using ml 270\u003c\/p\u003e \u003cp\u003e15.5.1 The Reliability and Accessibility of Data 271\u003c\/p\u003e \u003cp\u003e15.5.2 Model Generalization and Overfitting 271\u003c\/p\u003e \u003cp\u003e15.5.3 Environmental Variability 271\u003c\/p\u003e \u003cp\u003e15.5.4 Interpretability and Trust 271\u003c\/p\u003e \u003cp\u003e15.6 Algorithm Description 271\u003c\/p\u003e \u003cp\u003e15.6.1 Algorithm Overview 271\u003c\/p\u003e \u003cp\u003e15.6.2 Data Collection and Preprocessing 271\u003c\/p\u003e \u003cp\u003e15.6.3 Data Preprocessing Involves Several Steps 272\u003c\/p\u003e \u003cp\u003e15.6.4 Feature Extraction 272\u003c\/p\u003e \u003cp\u003e15.6.5 Model Selection and Training 273\u003c\/p\u003e \u003cp\u003e15.6.5.1 Convolutional Neural Networks 273\u003c\/p\u003e \u003cp\u003e15.6.5.2 Support Vector Machine 273\u003c\/p\u003e \u003cp\u003e15.6.5.3 Decision Trees and RF 273\u003c\/p\u003e \u003cp\u003e15.6.5.4 Transfer Learning 273\u003c\/p\u003e \u003cp\u003e15.6.5.5 Model Evaluation and Tuning 273\u003c\/p\u003e \u003cp\u003e15.7 Deployment and Real-time Inference 274\u003c\/p\u003e \u003cp\u003e15.8 Challenges and Future Directions 274\u003c\/p\u003e \u003cp\u003e15.9 Proposed Methodology 275\u003c\/p\u003e \u003cp\u003e15.9.1 Overview of the Methodology 275\u003c\/p\u003e \u003cp\u003e15.9.1.1 Image Data Collection 275\u003c\/p\u003e \u003cp\u003e15.9.1.2 Sensor Data Collection 275\u003c\/p\u003e \u003cp\u003e15.9.1.3 Disease Annotation and Labeling 275\u003c\/p\u003e \u003cp\u003e15.9.2 Image Preprocessing 275\u003c\/p\u003e \u003cp\u003e15.9.2.1 Sensor Data Preprocessing 276\u003c\/p\u003e \u003cp\u003e15.9.2.2 Data Splitting 276\u003c\/p\u003e \u003cp\u003e15.9.3 Traditional Feature Extraction 276\u003c\/p\u003e \u003cp\u003e15.9.4 Deep Learning–based Feature Extraction 276\u003c\/p\u003e \u003cp\u003e15.9.4.1 Model Selection 276\u003c\/p\u003e \u003cp\u003e15.9.4.2 Model Training 277\u003c\/p\u003e \u003cp\u003e15.10 Conclusion 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 The Future Farming: Machine Learning and Crop Health 281\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSadhana 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\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 281\u003c\/p\u003e \u003cp\u003e16.2 Background 282\u003c\/p\u003e \u003cp\u003e16.3 Significance 282\u003c\/p\u003e \u003cp\u003e16.4 Understanding ml 283\u003c\/p\u003e \u003cp\u003e16.5 Unsupervised Learning 284\u003c\/p\u003e \u003cp\u003e16.6 Supervised Learning 284\u003c\/p\u003e \u003cp\u003e16.7 Reinforced Learning 284\u003c\/p\u003e \u003cp\u003e16.8 Generic Functions of ML in Crop Health 285\u003c\/p\u003e \u003cp\u003e16.9 Advantages 285\u003c\/p\u003e \u003cp\u003e16.10 Challenges 285\u003c\/p\u003e \u003cp\u003e16.11 Future Perspectives 286\u003c\/p\u003e \u003cp\u003e16.12 Conclusion 286\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Social Impact of Machine Learning on Agricultural Communities 291\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAtef M. El-Sagheer, Eman A. Ahmed, Hamdy A. Sayed\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 291\u003c\/p\u003e \u003cp\u003e17.2 Impact of ML on Traditional Farming Practices 291\u003c\/p\u003e \u003cp\u003e17.2.1 Disruption of Traditional Knowledge Systems 292\u003c\/p\u003e \u003cp\u003e17.2.2 Precision Agriculture and Efficiency Gains 292\u003c\/p\u003e \u003cp\u003e17.2.3 Economic Impacts on Smallholder Farmers 292\u003c\/p\u003e \u003cp\u003e17.2.4 Environmental and Sustainability Considerations 292\u003c\/p\u003e \u003cp\u003e17.2.5 Adapting Traditional Farmers to the Digital Era 293\u003c\/p\u003e \u003cp\u003e17.2.6 Redefining Rural Employment in the Age of AI 293\u003c\/p\u003e \u003cp\u003e17.2.7 Automation and Job Displacement 293\u003c\/p\u003e \u003cp\u003e17.2.8 Emergence of New Job Roles 293\u003c\/p\u003e \u003cp\u003e17.2.9 Education and Skill Development 294\u003c\/p\u003e \u003cp\u003e17.2.10 Opportunities for Entrepreneurship 294\u003c\/p\u003e \u003cp\u003e17.2.11 The Role of Policy in Supporting Rural Employment 294\u003c\/p\u003e \u003cp\u003e17.3 ml and Farmer Autonomy: Decision-making in a Data-driven World 294\u003c\/p\u003e \u003cp\u003e17.3.1 The Role of ML in Decision-making 294\u003c\/p\u003e \u003cp\u003e17.3.2 Impact on Farmer Autonomy 295\u003c\/p\u003e \u003cp\u003e17.3.3 Data Ownership and Control 295\u003c\/p\u003e \u003cp\u003e17.3.4 Maintaining Autonomy Through Explainable AI 295\u003c\/p\u003e \u003cp\u003e17.3.5 Collaborative Models of AI and Farmer Expertise 296\u003c\/p\u003e \u003cp\u003e17.4 Cultural Shifts and Acceptance of Technology in Agriculture 296\u003c\/p\u003e \u003cp\u003e17.4.1 Technology Adoption in Agricultural Communities 296\u003c\/p\u003e \u003cp\u003e17.4.2 Trust and Technology Providers 296\u003c\/p\u003e \u003cp\u003e17.4.3 Cultural Resistance to Change 297\u003c\/p\u003e \u003cp\u003e17.4.4 Facilitating Cultural Shifts 297\u003c\/p\u003e \u003cp\u003e17.4.5 Socioeconomic Disparities in Access to ML Technologies 298\u003c\/p\u003e \u003cp\u003e17.5 Economic Factors 298\u003c\/p\u003e \u003cp\u003e17.5.1 Educational Disparities 298\u003c\/p\u003e \u003cp\u003e17.5.2 Infrastructural Challenges 298\u003c\/p\u003e \u003cp\u003e17.5.3 Impact on Different Socioeconomic Groups 298\u003c\/p\u003e \u003cp\u003e17.5.4 Strategies for Mitigating Disparities 298\u003c\/p\u003e \u003cp\u003e17.5.5 Gender and Social Equity in ML-driven Agriculture 299\u003c\/p\u003e \u003cp\u003e17.6 Gender Disparities in ML-driven Agriculture 299\u003c\/p\u003e \u003cp\u003e17.6.1 Social Equity and Access to ML Technologies 299\u003c\/p\u003e \u003cp\u003e17.6.2 Impact of ML Technologies on Gender and Social Equity 299\u003c\/p\u003e \u003cp\u003e17.6.3 Strategies for Promoting Equity in ML-driven Agriculture 299\u003c\/p\u003e \u003cp\u003e17.7 Digital Literacy and Skill Development in Farming Communities 300\u003c\/p\u003e \u003cp\u003e17.7.1 State of Digital Literacy in Farming Communities 300\u003c\/p\u003e \u003cp\u003e17.7.2 Barriers to Digital Skill Development 300\u003c\/p\u003e \u003cp\u003e17.7.3 Impact of Digital Literacy on Agricultural Productivity 300\u003c\/p\u003e \u003cp\u003e17.7.4 Strategies for Enhancing Digital Literacy in Farming Communities 301\u003c\/p\u003e \u003cp\u003e17.8 Balancing Technological Innovation with Social Equity 301\u003c\/p\u003e \u003cp\u003e17.8.1 Technological Innovation and Its Impact on Equity 301\u003c\/p\u003e \u003cp\u003e17.8.2 Barriers to Equitable Access 301\u003c\/p\u003e \u003cp\u003e17.8.3 Case Studies of Technological Disparities 302\u003c\/p\u003e \u003cp\u003e17.8.4 Strategies for Promoting Social Equity 302\u003c\/p\u003e \u003cp\u003e17.9 The Role of ML in Shaping Rural Economies 302\u003c\/p\u003e \u003cp\u003e17.9.1 ml in Agriculture 302\u003c\/p\u003e \u003cp\u003e17.9.2 Economic Development and Diversification 303\u003c\/p\u003e \u003cp\u003e17.9.3 Challenges and Barriers 303\u003c\/p\u003e \u003cp\u003e17.9.4 Strategies for Effective Implementation 303\u003c\/p\u003e \u003cp\u003e17.10 Ethical Dilemmas of AI-driven Agriculture in Developing Communities 303\u003c\/p\u003e \u003cp\u003e17.10.1 Equity and Access Issues 304\u003c\/p\u003e \u003cp\u003e17.10.2 Privacy and Data Ownership 304\u003c\/p\u003e \u003cp\u003e17.10.3 Algorithmic Bias and Fairness 304\u003c\/p\u003e \u003cp\u003e17.10.4 Environmental and Social Implications 304\u003c\/p\u003e \u003cp\u003e17.10.5 Strategies for Ethical AI Implementation 304\u003c\/p\u003e \u003cp\u003e17.11 Community Resilience and Adaptation to Technological Change 305\u003c\/p\u003e \u003cp\u003e17.11.1 Understanding Community Resilience 305\u003c\/p\u003e \u003cp\u003e17.11.2 Adaptation Strategies for Technological Change 305\u003c\/p\u003e \u003cp\u003e17.11.2.1 Education and Skill Development 305\u003c\/p\u003e \u003cp\u003e17.11.2.2 Community Engagement and Participation 305\u003c\/p\u003e \u003cp\u003e17.11.2.3 Building Social Capital 305\u003c\/p\u003e \u003cp\u003e17.11.2.4 Infrastructure Development 306\u003c\/p\u003e \u003cp\u003e17.11.3 Challenges in Adapting to Technological Change 306\u003c\/p\u003e \u003cp\u003e17.11.3.1 Digital Divide 306\u003c\/p\u003e \u003cp\u003e17.11.3.2 Resistance to Change 306\u003c\/p\u003e \u003cp\u003e17.11.3.3 Economic Constraints 306\u003c\/p\u003e \u003cp\u003e17.11.4 Case Studies of Successful Adaptation 306\u003c\/p\u003e \u003cp\u003e17.11.4.1 The Digital Green Initiative 306\u003c\/p\u003e \u003cp\u003e17.11.4.2 Smart Cities and Urban Resilience 306\u003c\/p\u003e \u003cp\u003e17.12 Long-term Social Impacts: Sustainability and Food Security 306\u003c\/p\u003e \u003cp\u003e17.12.1 Defining Sustainability and Food Security 307\u003c\/p\u003e \u003cp\u003e17.12.2 The Role of Sustainability in Food Security 307\u003c\/p\u003e \u003cp\u003e17.12.2.1 Sustainable Agricultural Practices 307\u003c\/p\u003e \u003cp\u003e17.12.2.2 Climate Change Mitigation 307\u003c\/p\u003e \u003cp\u003e17.12.3 Social Effects of Sustainability and Food Security 307\u003c\/p\u003e \u003cp\u003e17.12.3.1 Equitable Access to Resources 307\u003c\/p\u003e \u003cp\u003e17.12.3.2 Health and Nutrition 307\u003c\/p\u003e \u003cp\u003e17.12.3.3 Economic Stability and Livelihoods 308\u003c\/p\u003e \u003cp\u003e17.12.4 Long-term Challenges to Sustainability and Food Security 308\u003c\/p\u003e \u003cp\u003e17.12.4.1 Resource Depletion 308\u003c\/p\u003e \u003cp\u003e17.12.4.2 Global Trade and Food Systems 308\u003c\/p\u003e \u003cp\u003e17.13 Collaborative Models: Integrating Local Knowledge with AI Systems 308\u003c\/p\u003e \u003cp\u003e17.13.1 The Significance of Local Knowledge 308\u003c\/p\u003e \u003cp\u003e17.13.2 Collaborative Models in AI and Local Knowledge Integration 309\u003c\/p\u003e \u003cp\u003e17.13.2.1 Participatory AI Development 309\u003c\/p\u003e \u003cp\u003e17.13.2.2 Knowledge Co-production 309\u003c\/p\u003e \u003cp\u003e17.13.3 Challenges in Integrating Local Knowledge and AI Systems 309\u003c\/p\u003e \u003cp\u003e17.13.3.1 Data Standardization and Representation 309\u003c\/p\u003e \u003cp\u003e17.13.3.2 Power Dynamics and Knowledge Hierarchies 309\u003c\/p\u003e \u003cp\u003e17.13.4 Case Studies of Successful Integration 310\u003c\/p\u003e \u003cp\u003e17.13.4.1 AI for Climate-resilient Agriculture in Sub-Saharan Africa 310\u003c\/p\u003e \u003cp\u003e17.13.4.2 Indigenous Knowledge and AI for Fire Management in Australia 310\u003c\/p\u003e \u003cp\u003e17.14 Conclusion 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Ethical and Regulatory Considerations of Machine Learning in Modern Agriculture 317\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAtef M. El-Sagheer, Mohamed M. M. Hamd\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 317\u003c\/p\u003e \u003cp\u003e18.2 Data Privacy and Security in Agricultural ML Systems 317\u003c\/p\u003e \u003cp\u003e18.2.1 Agricultural Data and Its Sensitivity 318\u003c\/p\u003e \u003cp\u003e18.2.2 Security Threats in Agricultural Machine Learning Systems 318\u003c\/p\u003e \u003cp\u003e18.2.2.1 Data Breaches 318\u003c\/p\u003e \u003cp\u003e18.2.2.2 Adversarial Attacks 318\u003c\/p\u003e \u003cp\u003e18.2.2.3 Model Theft and Data Leakage 318\u003c\/p\u003e \u003cp\u003e18.2.2.4 Unauthorized Access 318\u003c\/p\u003e \u003cp\u003e18.2.3 Privacy Concerns in Agricultural Machine Learning Systems 319\u003c\/p\u003e \u003cp\u003e18.2.3.1 Data Ownership and Consent 319\u003c\/p\u003e \u003cp\u003e18.2.3.2 Data Obfuscation 319\u003c\/p\u003e \u003cp\u003e18.2.3.3 Profiling and Surveillance 319\u003c\/p\u003e \u003cp\u003e18.2.4 Addressing Data Privacy and Security in Agricultural ML Systems 319\u003c\/p\u003e \u003cp\u003e18.2.4.1 Secure Data Storage and Transmission 319\u003c\/p\u003e \u003cp\u003e18.2.4.2 Access Control and Authentication 319\u003c\/p\u003e \u003cp\u003e18.2.4.3 Federated Learning 319\u003c\/p\u003e \u003cp\u003e18.2.4.4 Adversarial ML Defenses 320\u003c\/p\u003e \u003cp\u003e18.2.4.5 Ethical and Regulatory Frameworks 320\u003c\/p\u003e \u003cp\u003e18.2.5 Future Directions and Challenges 320\u003c\/p\u003e \u003cp\u003e18.3 Bias and Fairness in Artificial Intelligence Models for Plant Disease Prediction 320\u003c\/p\u003e \u003cp\u003e18.3.1 Sources of Bias in ML for Plant Disease Prediction 320\u003c\/p\u003e \u003cp\u003e18.3.1.1 Data Imbalance 320\u003c\/p\u003e \u003cp\u003e18.3.1.2 Sampling Bias 320\u003c\/p\u003e \u003cp\u003e18.3.1.3 Labeling Bias 321\u003c\/p\u003e \u003cp\u003e18.3.1.4 Algorithmic Bias 321\u003c\/p\u003e \u003cp\u003e18.3.1.5 Geographical and Climatic Bias 321\u003c\/p\u003e \u003cp\u003e18.3.2 Impact of Bias on Agricultural Communities 321\u003c\/p\u003e \u003cp\u003e18.3.2.1 Disparities in Disease Management 321\u003c\/p\u003e \u003cp\u003e18.3.2.2 Inequity in Predictions for Minority Crops 321\u003c\/p\u003e \u003cp\u003e18.3.2.3 Economic and Environmental Consequences 321\u003c\/p\u003e \u003cp\u003e18.3.3 Strategies to Mitigate Bias in Prediction Models of Plant Disease 322\u003c\/p\u003e \u003cp\u003e18.3.3.1 Diversifying Training Data 322\u003c\/p\u003e \u003cp\u003e18.3.3.2 Synthetic Data Generation 322\u003c\/p\u003e \u003cp\u003e18.3.3.3 Fairness-aware Algorithms 322\u003c\/p\u003e \u003cp\u003e18.3.3.4 Transparency and Explainability 322\u003c\/p\u003e \u003cp\u003e18.3.3.5 Continuous Monitoring and Audits 322\u003c\/p\u003e \u003cp\u003e18.3.4 Challenges in Ensuring Fairness 322\u003c\/p\u003e \u003cp\u003e18.4 Environmental and Ecological Implications of AI in Agriculture 323\u003c\/p\u003e \u003cp\u003e18.4.1 AI-driven Precision Agriculture and Resource Optimization 323\u003c\/p\u003e \u003cp\u003e18.4.1.1 Water Conservation 323\u003c\/p\u003e \u003cp\u003e18.4.1.2 Reduction of Chemical Inputs 323\u003c\/p\u003e \u003cp\u003e18.4.1.3 Energy Efficiency 323\u003c\/p\u003e \u003cp\u003e18.4.2 Impact on Biodiversity and Ecosystem Services 323\u003c\/p\u003e \u003cp\u003e18.4.2.1 Preservation of Biodiversity 323\u003c\/p\u003e \u003cp\u003e18.4.2.2 Risk of Monoculture Intensification 323\u003c\/p\u003e \u003cp\u003e18.4.2.3 Wildlife Habitat Displacement 324\u003c\/p\u003e \u003cp\u003e18.4.3 Carbon Footprint of AI in Agriculture 324\u003c\/p\u003e \u003cp\u003e18.4.3.1 Energy Use in AI Training 324\u003c\/p\u003e \u003cp\u003e18.4.3.2 Efforts to Reduce AI’s Carbon Footprint 324\u003c\/p\u003e \u003cp\u003e18.4.4 Ecological Impacts of AI-powered Autonomous Systems 324\u003c\/p\u003e \u003cp\u003e18.4.4.1 Soil Compaction 324\u003c\/p\u003e \u003cp\u003e18.4.4.2 E-waste and Resource Depletion 324\u003c\/p\u003e \u003cp\u003e18.4.4.3 Opportunities for Regenerative Agriculture 324\u003c\/p\u003e \u003cp\u003e18.4.5 Ethical and Ecological Governance of AI in Agriculture 325\u003c\/p\u003e \u003cp\u003e18.4.5.1 Sustainable AI Development 325\u003c\/p\u003e \u003cp\u003e18.4.5.2 Incorporating Ecological Indicators in AI Models 325\u003c\/p\u003e \u003cp\u003e18.4.5.3 Inclusive AI for Global Agriculture 325\u003c\/p\u003e \u003cp\u003e18.5 Transparency and Explain Ability in AI-driven Agricultural Solutions 325\u003c\/p\u003e \u003cp\u003e18.5.1 The Need for Transparency in AI-driven Agriculture 325\u003c\/p\u003e \u003cp\u003e18.5.1.1 Building Trust Among Stakeholders 325\u003c\/p\u003e \u003cp\u003e18.5.1.2 Ethical and Legal Implications 325\u003c\/p\u003e \u003cp\u003e18.5.2 Challenges in Achieving Explainability in Agricultural AI Systems 326\u003c\/p\u003e \u003cp\u003e18.5.2.1 Complexity of AI Algorithms 326\u003c\/p\u003e \u003cp\u003e18.5.2.2 Data Quality and Bias 326\u003c\/p\u003e \u003cp\u003e18.5.2.3 Trade-off Between Accuracy and Interpretability 326\u003c\/p\u003e \u003cp\u003e18.5.3 Strategies for Enhancing Transparency and Explainability 326\u003c\/p\u003e \u003cp\u003e18.5.3.1 Post hoc Explainability Techniques 326\u003c\/p\u003e \u003cp\u003e18.5.3.2 Model-agnostic Interpretability Frameworks 326\u003c\/p\u003e \u003cp\u003e18.5.3.3 Simplified User Interfaces for Farmers 326\u003c\/p\u003e \u003cp\u003e18.5.3.4 Collaboration Between AI Developers and Agronomists 327\u003c\/p\u003e \u003cp\u003e18.5.4 Ethical Implications of Transparency in AI-driven Agriculture 327\u003c\/p\u003e \u003cp\u003e18.5.4.1 Equity and Accessibility 327\u003c\/p\u003e \u003cp\u003e18.5.4.2 Accountability in Decision-making 327\u003c\/p\u003e \u003cp\u003e18.5.5 Future Directions in Transparent AI for Agriculture 327\u003c\/p\u003e \u003cp\u003e18.5.5.1 Interpretable AI Models 327\u003c\/p\u003e \u003cp\u003e18.5.5.2 Regulation and Standardization 327\u003c\/p\u003e \u003cp\u003e18.5.5.3 Education and Training 327\u003c\/p\u003e \u003cp\u003e18.6 Balancing Innovation with Tradition: Ethical Challenges in Technological Adoption 328\u003c\/p\u003e \u003cp\u003e18.6.1 Technological Innovation in Agriculture 328\u003c\/p\u003e \u003cp\u003e18.6.1.1 Advancements in Agricultural Technology 328\u003c\/p\u003e \u003cp\u003e18.6.1.2 AI and Machine Learning in Crop Management 328\u003c\/p\u003e \u003cp\u003e18.6.2 The Role of Tradition in Sustainable Farming 328\u003c\/p\u003e \u003cp\u003e18.6.2.1 Traditional Farming Methods 328\u003c\/p\u003e \u003cp\u003e18.6.2.2 Cultural Significance of Farming Traditions 328\u003c\/p\u003e \u003cp\u003e18.6.3 Ethical Challenges in Technological Adoption 329\u003c\/p\u003e \u003cp\u003e18.6.3.1 Equity and Access to Technology 329\u003c\/p\u003e \u003cp\u003e18.6.3.2 Environmental Sustainability vs. Technological Efficiency 329\u003c\/p\u003e \u003cp\u003e18.6.3.3 The Threat to Smallholder Farmers 329\u003c\/p\u003e \u003cp\u003e18.6.3.4 Technological Overload and Farmer Autonomy 329\u003c\/p\u003e \u003cp\u003e18.6.4 Balancing Innovation and Tradition 329\u003c\/p\u003e \u003cp\u003e18.6.4.1 Integrating Traditional Knowledge with Modern Technology 329\u003c\/p\u003e \u003cp\u003e18.6.4.2 Participatory Approaches to Technology Development 329\u003c\/p\u003e \u003cp\u003e18.6.4.3 Policy and Regulation for Ethical Technology Adoption 330\u003c\/p\u003e \u003cp\u003e18.7 Equity in Access to Machine Learning Technologies for Sustainable Agriculture 330\u003c\/p\u003e \u003cp\u003e18.7.1 Challenges in Achieving Equity in Access to ML Technologies 330\u003c\/p\u003e \u003cp\u003e18.7.1.1 Cost Barriers and Economic Disparities 330\u003c\/p\u003e \u003cp\u003e18.7.1.2 Lack of Technical Expertise 330\u003c\/p\u003e \u003cp\u003e18.7.1.3 Data Availability and Quality 330\u003c\/p\u003e \u003cp\u003e18.7.1.4 Infrastructure Limitations 330\u003c\/p\u003e \u003cp\u003e18.7.2 Strategies for Promoting Equity in Access to ML Technologies 331\u003c\/p\u003e \u003cp\u003e18.7.2.1 Subsidies and Financial Support 331\u003c\/p\u003e \u003cp\u003e18.7.2.2 Capacity Building and Training 331\u003c\/p\u003e \u003cp\u003e18.7.2.3 Improving Data Accessibility and Quality 331\u003c\/p\u003e \u003cp\u003e18.7.2.4 Developing Infrastructure and Connectivity 331\u003c\/p\u003e \u003cp\u003e18.7.2.5 Promoting Open-source and Inclusive Technologies 331\u003c\/p\u003e \u003cp\u003e18.7.3 Case Studies and Examples 331\u003c\/p\u003e \u003cp\u003e18.7.3.1 Precision Agriculture in India 331\u003c\/p\u003e \u003cp\u003e18.7.3.2 Agricultural Data Platforms in Africa 332\u003c\/p\u003e \u003cp\u003e18.7.3.3 Internet Connectivity Projects in Rural Areas 332\u003c\/p\u003e \u003cp\u003e18.8 Human–AI Collaboration: Ethical Guidelines for Decision-making in Agriculture 332\u003c\/p\u003e \u003cp\u003e18.8.1 Ethical Challenges in Human–AI Collaboration 332\u003c\/p\u003e \u003cp\u003e18.8.1.1 Transparency and Explainability 332\u003c\/p\u003e \u003cp\u003e18.8.1.2 Accountability and Responsibility 332\u003c\/p\u003e \u003cp\u003e18.8.1.3 Bias and Fairness 332\u003c\/p\u003e \u003cp\u003e18.8.1.4 Human Autonomy and Decision-making 333\u003c\/p\u003e \u003cp\u003e18.8.2 Ethical Guidelines for Human–AI Collaboration 333\u003c\/p\u003e \u003cp\u003e18.8.2.1 Develop Transparent and Explainable AI Systems 333\u003c\/p\u003e \u003cp\u003e18.8.2.2 Establish Accountability Frameworks 333\u003c\/p\u003e \u003cp\u003e18.8.2.3 Implement Bias Mitigation Strategies 333\u003c\/p\u003e \u003cp\u003e18.8.2.4 Promote Human–AI Collaboration and Oversight 333\u003c\/p\u003e \u003cp\u003e18.8.2.5 Foster Continuous Ethical Review and Improvement 333\u003c\/p\u003e \u003cp\u003e18.8.3 Case Studies and Examples 333\u003c\/p\u003e \u003cp\u003e18.8.3.1 AI for Precision Agriculture in the United States 333\u003c\/p\u003e \u003cp\u003e18.8.3.2 AI-assisted Pest Management in India 334\u003c\/p\u003e \u003cp\u003e18.8.3.3 Bias Mitigation in Agricultural Lending in Africa 334\u003c\/p\u003e \u003cp\u003e18.9 Regulatory Frameworks for ML in Agricultural Biotechnology 334\u003c\/p\u003e \u003cp\u003e18.9.1 Current Regulatory Frameworks 334\u003c\/p\u003e \u003cp\u003e18.9.1.1 Global and Regional Regulations 334\u003c\/p\u003e \u003cp\u003e18.9.1.2 Data Privacy and Security Regulations 334\u003c\/p\u003e \u003cp\u003e18.9.1.3 Ethical and Safety Guidelines 334\u003c\/p\u003e \u003cp\u003e18.9.2 Challenges and Gaps in Regulatory Frameworks 335\u003c\/p\u003e \u003cp\u003e18.9.2.1 Rapid Technological Advancements 335\u003c\/p\u003e \u003cp\u003e18.9.2.2 Integration of ML into Existing Frameworks 335\u003c\/p\u003e \u003cp\u003e18.9.2.3 Global Consistency and Harmonization 335\u003c\/p\u003e \u003cp\u003e18.9.3 Proposed Guidelines for Future Regulation 335\u003c\/p\u003e \u003cp\u003e18.9.3.1 Dynamic and Adaptive Regulatory Frameworks 335\u003c\/p\u003e \u003cp\u003e18.9.3.2 Enhanced Transparency and Explainability Requirements 335\u003c\/p\u003e \u003cp\u003e18.9.3.3 Risk Assessment and Management Protocols 335\u003c\/p\u003e \u003cp\u003e18.9.3.4 International Collaboration and Harmonization 335\u003c\/p\u003e \u003cp\u003e18.9.4 Case Studies and Examples 336\u003c\/p\u003e \u003cp\u003e18.9.4.1 EU Regulations for GMOs and ml 336\u003c\/p\u003e \u003cp\u003e18.9.4.2 FDA’s Approach to Biotechnology and AI 336\u003c\/p\u003e \u003cp\u003e18.9.4.3 Global Harmonization Efforts 336\u003c\/p\u003e \u003cp\u003e18.10 Conclusion 336\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Science: general issues [\u003ca title=\"See our other books on Science: general issues\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Science:%20general%20issues%20%5BPD%5D%22\"\u003ePD\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52433752883480,"sku":"9781394329618","price":104.57,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394329618.jpg?v=1784853696","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/machine-learning-for-plant-biology-hardback-9781394329618","provider":"Freshly Printed Books","version":"1.0","type":"link"}