Machine Learning for Plant Biology

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 xixList of Contributors xxi1 Edge-Based Machine Learning for Computer Vision in Smart Plant Biology Imaging 1Julien Garnier, Simon Ravé, Nathan Drogue, Boris Adam, Pejman Rasti, David Rousseau1.1 Introduction 11.2 Electronic Devices for Embedded AI-driven Computer Vision 21.3 Light Deep Learning Strategies 31.4 Benchmark of Light Embedded Deep Learning on a Plant Imaging Use Case 41.4.1 Image Acquisition and Segmentation 41.4.2 Model Adaptation 41.4.3 Knowledge Distillation 61.4.4 Kolmogorov–Arnold Network 71.5 Discussion 81.6 Conclusion 92 Machine Learning for Studying Plant Evolutionary Developmental Biology 13Mani Manoj, Ramaraj Sivamano, Arunachalam Abitha, Mohammed Jaffer Shakeera Banu, Shanmugam Velayuthaprabhu, Kannan Vijayarani, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand2.1 Introduction to Plant Evolutionary Developmental Biology 132.1.1 Overview of Plant Evolutionary Developmental Biology 132.1.2 Key Concepts in Plant Evolution and Development 132.1.3 Importance of Evo-Devo in Understanding Plant Adaptations 142.1.4 Role of Computational and AI Tools in Evo-Devo Studies 142.2 Basics of ML in Biological Research 142.2.1 Fundamentals of ML in Biology 142.2.2 Supervised, Unsupervised, and Reinforcement Learning 152.2.3 Deep Learning and Neural Networks in Evo-Devo 152.2.4 ml Workflow: Data Collection, Processing, Model Selection, and Interpretation 152.2.5 Challenges of Applying ML to Evo-Devo Research 162.3 ml Applications in Plant Morphological Evolution 162.3.1 ml for Analyzing Fossilized Plant Structures 162.3.2 Shape and Trait Evolution Using CNNs and Autoencoders 162.3.3 3D Reconstruction of Plant Organs Through ML-based Image Processing 172.3.4 Quantitative Trait Analysis Using SVM 172.3.5 Integrating Phylogenetics and ML for Morphological Adaptation Studies 172.4 Genomic and Transcriptomic Insights Through ml 182.4.1 Evolutionary Genomics: Identifying Selection Signatures with ml 182.4.2 GRN Prediction Using Graph Neural Networks 182.4.3 ml for Comparative Genomics in Evolutionary Studies 182.4.4 Understanding Non-coding RNA Evolution with NLP-based ML Models 192.4.5 Unraveling Epigenetic Modifications in Plant Evolution Using ml 192.5 Inferring Evolutionary Developmental Pathways Using ml 192.5.1 ml Models for Predicting Gene Expression Patterns 192.5.2 Identifying Key Developmental Genes via Feature Selection Algorithms 202.5.3 Evolution of Transcription Factor Networks with ml 202.5.4 Bayesian ML for Inferring Ancestral Gene Interactions 202.5.5 Evolution of Polyploidy and Hybridization Analyzed Through ML Model 212.6 Phylogenetics and Evolutionary Tree Reconstruction Using ml 212.6.1 Phylogenetic Tree Prediction via Deep Learning 212.6.2 Bayesian ML for Inferring Evolutionary Relationships 212.6.3 Predicting Adaptive Radiation Events with ML Models 222.6.4 Network-based Approaches for Studying Horizontal Gene Transfer 232.6.5 Automating Phylogenomic Inference Using AI-based Pipelines 232.7 ml for Studying Developmental Plasticity and Environmental Adaptation 232.7.1 Predicting Phenotypic Plasticity with ML Algorithms 232.7.2 Climate-responsive Developmental Evolution Using ml 242.7.3 Adaptive Traits Discovery Using ML in Dynamic Environments 242.7.4 ML-based Prediction of Plant Evolution Under Climate Change 242.8 High-throughput Image-based ML Approaches in Evo-Devo 252.8.1 ml for Automated Plant Organ Recognition and Classification 252.8.2 Deep Learning for Leaf, Flower, and Root Morphological Evolution 252.8.3 CNNs for Large-scale Evolutionary Trait Analysis 252.8.4 Time-series ML for Tracking Developmental Transitions 262.8.5 Integrating ML with Phenotyping Platforms for Evo-Devo Research 272.9 Single-cell and Multi-omics ML Integration in Plant Evo-Devo 272.9.1 ml for Single-cell RNA Sequencing Data in Evolutionary Studies 272.9.2 Integrating Proteomics, Transcriptomics, and Metabolomics with ml 272.9.3 Deep Learning for Cell Fate and Differentiation Analysis in Evo-Devo 282.9.4 Predicting Evo-Devo Pathways Through Multi-omics Data Fusion 282.9.5 ml for Analyzing Spatial and Temporal Omics Data 282.10 Ethical, Computational, and Experimental Challenges 292.11 Conclusion 29Acknowledgement 30Data Availability 303 Machine Learning for Plant High-Throughput Phenotyping 39Dibyendu Seth, Sourish Pramanik, Ehsas Pachauri, Ankan Das, Sandip Debnath, Mehdi Rahimi3.1 Introduction 393.2 Overview of HTP 403.3 ml for Plant Phenotyping 413.3.1 What Is ML? 413.3.2 ml in Handling Big Data 423.4 Overview of ML Algorithms in Phenotyping 433.4.1 dl in Plant Phenotyping 443.4.2 Computer Vision in Phenotyping 453.5 Applications of ML in Plant Phenotyping 463.5.1 ml for Plant Recognition and Disease Detection 473.5.2 Spectral Analysis and Optical Imaging for Stress Detection 473.5.3 Hyperspectral and Multispectral Imaging 483.5.4 Thermal and Fluorescence Imaging for Stress Analysis 483.5.5 ml Approaches for Plant Stress Classification 483.5.6 Automated Image Analysis for Trait Extraction 483.5.6.1 Morphological Trait Measurement 483.5.6.2 Image-based Feature Extraction 493.5.7 Prediction Models for Yield Forecasting 493.6 Integration of ML with Emerging Technologies 503.7 Future Directions and Potential of ML in Agriculture 513.8 Case Studies and Real-world Applications 523.9 Conclusion 524 Machine Learning for Studying Plant Secondary Metabolites 59Saniya, Sarfraz Ahmad, Mohammad Ghani Raghib4.1 Introduction 594.2 ml Techniques in Metabolite Research 604.2.1 Supervised Learning Techniques 604.2.2 Unsupervised Learning Techniques 604.2.3 Deep Learning Techniques 614.2.4 Ensemble Learning Techniques 614.2.5 Reinforcement Learning in Metabolomics 614.2.6 Feature Selection and Dimensionality Reduction 624.2.7 Hybrid Models 624.2.8 Emerging Techniques in Metabolomics 624.3 Applications of ML in PSM Research 624.3.1 Predicting Metabolic Pathways 624.3.2 Identification of Key Biosynthetic Genes 644.3.3 Metabolite Profiling and Classification 644.3.4 Enhancing Plant Stress Response Through Metabolite Analysis 644.3.5 Chemotaxonomy and Species Identification 644.3.6 Pharmacological and Nutraceutical Research 654.3.7 Metabolic Engineering for Enhanced PSM Production 654.3.8 PSM-based Environmental Monitoring 654.3.9 Predictive Modeling for Agricultural Improvement 654.4 Challenges and Future Directions 654.4.1 Challenges in ML for PSM Research 654.4.1.1 Data-related Challenges 654.4.1.2 Algorithmic Challenges 664.4.1.3 Biological Complexity and Unknown Pathways 664.4.1.4 Computational Challenges 664.4.2 Future Directions in ML for PSM Research 664.4.2.1 Integrating Multi-omics Data 664.4.2.2 Advancing Explainable Artificial Intelligence 674.4.2.3 Improving Data Augmentation Strategies 674.4.2.4 Automation and High-throughput Analysis 674.4.2.5 Advancing Computational Infrastructure 674.4.2.6 Developing Crop-specific ML Models 674.4.3 Ethical Considerations and Regulatory Frameworks 674.5 Conclusion 685 Machine Learning for Plant Ecological Research 71Mani Manoj, Sahfigul Ameed Nihaal Fathima, Sathyalingam Sathya Trisha, Mohammed Jaffer Shakeera Banu, Shanmugam Gavaskar, Alagarsamy Sumitrha, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand5.1 Introduction to Machine Learning in Ecology 715.1.1 Overview of Machine Learning in Ecological Studies 715.1.2 Importance of ML in Plant Ecological Research 715.1.3 Comparison of Traditional vs. ML-based Ecological Analysis 725.2 Data Sources and Preprocessing for Plant Ecology 725.2.1 Types of Ecological Data 725.2.2 Data Collection Techniques 725.2.3 Data Cleaning, Normalization, and Feature Engineering 735.3 Machine Learning Techniques for Plant Ecology 745.3.1 Supervised Learning 745.3.2 Unsupervised Learning 745.3.3 Deep Learning 745.3.4 Reinforcement Learning 755.4 Applications of Machine Learning in Plant Ecology 755.5 Remote Sensing and AI in Plant Ecology 765.5.1 Use of Satellite and Drone Imagery for Plant Monitoring 765.5.2 Image Segmentation and Object Detection for Vegetation Analysis 775.5.3 AI Models for Automated Plant Health Assessment 785.6 Biodiversity Conservation and Ecosystem Monitoring 795.6.1 Machine Learning for Biodiversity Pattern Analysis 795.6.2 AI-driven Monitoring of Endangered Plant Species 795.6.3 Predicting Ecosystem Resilience and Response to Environmental Stressors 805.7 Predictive Modeling for Ecological Trends 805.7.1 Time-series Forecasting of Ecological Parameters 805.7.2 ml Models for Predicting Drought and Deforestation Impact 805.7.3 Climate–Plant Interaction Modeling 815.8 Challenges and Limitations of Machine Learning in Plant Ecology 815.9 Future Perspectives and Emerging Technologies 825.10 Conclusion 82Acknowledgement 83Data Availability 836 Machine Learning for Modeling Plant Abiotic Stress Responses 89Haragopal Dutta, Suman Dutta6.1 Introduction 896.2 Definition of Abiotic Stress 906.3 Effect of Abiotic Stress on Crops 916.4 Key Applications of Machine Learning in Abiotic Stress Research 926.4.1 Phenotypic Prediction 926.4.2 Gene Expression Modeling 956.4.3 High-throughput Image Analysis 956.4.4 Omics Data Integration 976.4.5 Stress Response Prediction and Simulation 996.5 ml Techniques Commonly Used for Abiotic Stress Modeling 996.5.1 Supervised Learning 996.5.2 Unsupervised Learning 1006.5.3 Deep Learning 1006.6 Challenges and Future Directions 1016.6.1 Data Availability and Quality 1016.6.2 Generalization 1016.6.3 Interpretability 1026.7 Conclusion 1027 Machine Learning for Modeling Plant–Pathogen Interactions 111Sourish Pramanik, Dibyendu Seth, Sandip Debnath7.1 Introduction 1117.2 Basics of PPI 1127.3 Basics of ML and Its Integration into Biological Systems 1137.3.1 Supervised Learning 1147.3.2 Unsupervised Learning 1147.3.3 Random Forests 1157.3.4 Naive Bayes 1157.3.5 Neural Networks 1157.3.6 Variational Autoencoders 1157.4 ml in PPI 1177.4.1 Integration of ML in PPI: Molecular Level 1187.4.2 Integration of ML in PPI: Field Level 1207.5 Conclusion 1228 Machine Learning-Enhanced Plant Disease Detection and Management 131Lellapalli Rithesh, Sucharita Mohapatra, Shimi Jose, Juel Debnath, Gyanisha Nayak, Mehjebin Rahman, Soumya Shephalika Dash, Anwesha Sharma, Sneha Mohan8.1 Introduction 1318.2 Fundamentals of ml 1368.2.1 Fundamental Principles of ml 1368.2.1.1 Supervised Learning 1368.2.1.2 Unsupervised Learning 1368.2.1.3 Reinforcement Learning 1368.2.2 Categories of ML Models Employed in Plant Disease Detection 1368.2.2.1 Decision Trees 1368.2.2.2 Support Vector Machines 1378.2.2.3 Random Forests 1378.2.2.4 Deep Learning 1378.2.2.5 Convolutional Neural Networks 1378.3 Data Collection and Preprocessing 1388.3.1 Data Types 1388.3.1.1 Image Data 1388.3.1.2 Spectral Data 1388.3.1.3 Sensor Data 1388.3.1.4 Genomic and Molecular Data 1388.3.1.5 Tabular Data 1388.3.2 Data Preprocessing Techniques 1388.3.2.1 Image Data Preprocessing 1398.3.2.2 Spectral Data Preprocessing 1398.3.2.3 Sensor Data Preprocessing 1398.3.2.4 Genomic and Molecular Data Preprocessing 1408.3.2.5 Tabular Data Preprocessing 1408.4 Image-based Pathology Identification 1408.4.1 The Role of Computer Vision in Plant Diseases Diagnosis 1408.4.2 Key Algorithms in Image Processing 1418.5 Sensor-based Disease Monitoring 1428.5.1 Role of Sensor-based Monitoring in Plant Disease Detection 1428.5.2 Role of IoT in Disease Detection and Management 1428.5.3 Disease Progression Using Environmental Sensors 1438.5.4 Integration with ml 1438.6 Genomic Approaches for Disease Prediction 1448.6.1 ml for Identification of Disease Resistance Traits Using Genomic Data 1458.7 Advances in ML Techniques for Disease Management 1468.7.1 AI-driven Disease Forecasting Models 1468.7.2 Predictive Modeling for Crop Management 1468.7.3 Transfer Learning and Model Generalization 1478.8 Challenges and Perspectives 1478.9 Conclusion 1479 Machine Learning for Analyzing and Integrating Multiple Omics 155Sarfraz Ahmad, Saniya, Mohammad Ghani Raghib, Rubina Khan, Vikas Belwal, Pankaj Kumar9.1 Introduction 1559.2 Characteristics of Omics Data 1569.2.1 High Dimensionality and Low Sample Size 1569.2.2 Data Heterogeneity 1579.2.3 Sparsity and Missing Values 1579.2.4 Noise and Technical Variability 1589.2.5 Dynamic and Temporal Variability 1589.2.6 Multicollinearity and Feature Interdependence 1589.2.7 High Biological Variability 1589.2.8 Class Imbalance in Omics Data 1599.2.9 Data Integration Complexity 1599.3 Data Preprocessing for Multi-omics Integration 1599.3.1 Data Normalization and Standardization 1599.3.2 Data Cleaning and Outlier Detection 1609.3.3 Data Imputation for Missing Values 1609.3.4 Batch Effect Correction 1609.3.5 Dimensionality Reduction 1609.3.6 Feature Selection 1619.3.7 Data Integration Strategies 1619.3.8 Data Transformation and Encoding 1619.3.9 Data Augmentation Techniques 1619.3.10 Data Quality Assessment 1629.4 ml Techniques for Multi-omics Analysis 1629.4.1 Supervised Learning Techniques 1629.4.1.1 Support Vector Machines 1629.4.1.2 Random Forest 1629.4.1.3 Elastic Net and LASSO Regression 1629.4.2 Unsupervised Learning Techniques 1639.4.2.1 Principal Component Analysis 1639.4.2.2 K-means Clustering 1639.4.2.3 Hierarchical Clustering 1639.4.3 Deep Learning Techniques 1639.4.3.1 Convolutional Neural Networks 1639.4.3.2 Recurrent Neural Networks 1639.4.3.3 Autoencoders 1639.4.4 Network-based Approaches 1649.4.4.1 Graph Neural Networks 1649.4.4.2 Bayesian Networks 1649.4.5 Hybrid and Ensemble Learning Approaches 1649.5 Applications of ML in Multi-omics Research 1649.5.1 Biomarker Discovery in Human Health 1659.5.1.1 Cancer Biomarker Identification 1659.5.1.2 Cardiovascular and Metabolic Disorders 1659.5.2 Disease Diagnosis and Classification 1659.5.2.1 Cancer Classification Models 1659.5.2.2 Neurological Disorders 1659.5.3 Drug Discovery and Personalized Medicine 1659.5.3.1 Drug–Target Interaction Prediction 1669.5.3.2 Personalized Medicine 1669.5.4 Applications in Plant Sciences 1669.5.4.1 Crop Trait Prediction 1669.5.4.2 Stress Response Prediction 1669.5.5 Environmental and Ecological Applications 1669.5.5.1 Soil Microbiome Analysis 1669.5.5.2 Environmental Stress Monitoring 1669.5.6 Systems Biology and Pathway Analysis 1689.6 Challenges and Future Directions 1689.6.1 Key Challenges in ML-driven Multi-omics Research 1689.6.1.1 Data Heterogeneity and Dimensionality 1689.6.1.2 Data Quality and Noise 1689.6.1.3 Model Interpretability and Biological Relevance 1689.6.1.4 Integration of Multi-scale Data 1689.6.1.5 Computational and Resource Limitations 1699.6.2 Future Directions in ML-driven Multi-omics Research 1699.6.2.1 Advancing Data Integration Techniques 1699.6.2.2 Improving Model Interpretability 1699.6.2.3 Enhancing Data Quality and Standardization 1699.6.2.4 Leveraging Transfer Learning and Few-shot Learning 1699.6.2.5 Advancing Cloud-based Solutions 1699.6.2.6 Ethical and Regulatory Considerations 1699.7 Conclusion 16910 Machine Learning for Plant Single-Cell RNA Sequencing 175Mani Manoj, Ravichandran Sneha, Esakkimuthu Balaji, Vadivelu Bharathi, Thamaraiselvan Nandhini Devi, Ramasamy Manikandan, Jeyabal Philomenathan Antony Prabhu, Arumugam Vijaya Anand10.1 Introduction to Plant Single-cell RNA Sequencing 17510.2 ml Techniques in scRNA-seq Data Analysis 17610.2.1 Preprocessing and Quality Control 17610.2.1.1 Data Filtering and Normalization 17610.2.1.2 Batch Effect Correction 17610.2.1.3 Dimensionality Reduction Techniques 17710.2.2 Feature Selection and Gene Expression Clustering 17710.2.2.1 Highly Variable Gene Selection 17710.2.2.2 Clustering Algorithms 17710.2.2.3 Deep Learning for Feature Selection 17810.2.3 Cell-type Identification and Annotation 17810.2.3.1 Supervised vs. Unsupervised Learning Approaches 17810.2.3.2 Decision Trees, Random Forest, and SVMs 17810.2.3.3 DL-based Cell-type Classification 17910.2.4 Trajectory and Pseudotime Inference 17910.2.4.1 Single-cell Developmental Trajectories 17910.2.4.2 Pseudotime Estimation 17910.2.4.3 Application of Graph Neural Networks 17910.2.5 Differential Gene Expression Analysis 18010.2.5.1 ml Models for Identifying DE Genes 18010.2.5.2 Bayesian Methods for Gene Expression Inference 18010.2.5.3 Application of Neural Networks in Predicting Gene Regulation 18010.2.6 Cell–Cell Interaction and Network Analysis 18110.2.6.1 Graph-based Approaches for Inferring Cell Communication 18110.2.6.2 Co-expression Network Analysis Using ml 18210.2.6.3 Integration with ST 18210.3 Deep Learning for Plant scRNA-seq Analysis 18210.3.1 Autoencoders for Dimensionality Reduction and Noise Removal 18210.3.1.1 VAEs in scRNA-seq 18210.3.1.2 GANs for Data Augmentation 18310.3.2 Convolutional and RNNs 18310.3.2.1 CNNs for ST in Plants 18310.3.2.2 RNNs for Temporal Gene Expression Analysis 18410.3.3 Transfer Learning in Plant scRNA-seq 18410.3.3.1 Pretrained Models for Plant Gene Expression Prediction 18410.3.3.2 Domain Adaptation Techniques for Cross-species Analysis 18510.4 Integrating Multi-omics Data with ml 18610.5 Challenges and Future Directions 18710.6 Conclusion 187Acknowledgement 187Data Availability 18811 Machine Learning for Plant Genomic Prediction 195Natasha Charaya, Sonika Kalia, Indra Rautela, Poorvi Yadav, Poornima Bhardwaj, Ritakshi Nautiyal, Monika Kalia, Vinay Sharma11.1 Introduction 19511.1.1 Genomic Prediction 19511.1.2 Genome Selection 19611.1.3 Machine Learning 19611.2 Types of Models Used in ml 19711.2.1 Supervised Models 19711.2.1.1 Classification 19811.2.1.2 Regression 19811.2.2 Unsupervised Models 19811.2.2.1 Clustering 19811.2.2.2 Dimensionality Reduction 19811.2.2.3 Anomaly Detection 19811.2.3 Semi-supervised Model 19911.2.3.1 Transductive SVM 19911.2.3.2 Generative Models 19911.2.4 Deep Learning 19911.2.4.1 Neural Networks 19911.3 Methods 20011.3.1 Linear Methods 20011.3.2 Kernel Methods 20011.3.3 Neural Networks 20111.3.4 Tree Ensembles 20111.4 Cross-validation 20111.4.1 Strategies for cv 20111.4.2 Validation Metrics 20211.4.3 Information 20211.5 Applications of ml 20311.5.1 Combining Trials to Increase the Sample Size 20311.5.2 An Explanation of the G × E Interaction Using Data Features 20411.5.3 Exploiting Information from Secondary Traits 20411.6 ml Challenges 20411.7 Summary 20512 Machine Learning-Assisted Plant Systems Biology 209Haragopal Dutta, Suman Dutta, Sudhir Kumar12.1 Introduction 20912.2 ml Algorithms in Plant Systems Biology 21112.2.1 Supervised Learning 21112.2.2 Unsupervised Learning 21112.2.3 Reinforcement Learning 21312.3 Key Applications of ML in Plant Systems Biology 21312.3.1 Multi-omics Data Integration 21312.3.2 Gene Regulatory Network 21512.3.3 Trait Prediction and Breeding 21612.3.4 Metabolic Pathway Analysis 21712.3.5 Stress Response and Adaptation 21712.3.6 Predictive Modeling of Plant–Environment Interactions 21812.3.7 Identification of Key Regulators in Synthetic Biology 21912.4 Challenges and Future Directions 22012.5 Conclusion 22113 Machine Learning-Driven Precision Plant Breeding 229Krishna Kumar Rai13.1 Introduction 22913.2 Conventional Breeding Approaches 23013.3 Molecular Breeding Innovations 23213.4 Speed Breeding and AI Integration 23313.5 Challenges and Future Directions 23513.6 Conclusion 23514 Machine Learning-Driven Smart Agriculture 241Rajdeep Mohanta, Soumik Dey Roy, Sahely Kanthal, Sanjay Mochary, Subhadwip Ghorai, Soham Hazra14.1 Introduction to Smart Agriculture 24114.1.1 Importance of Smart Agriculture in Modern Era 24114.1.2 The Role of Technology in Modern Agriculture 24114.1.3 Introduction to ML and AI in Agriculture 24214.2 The Role of ML in Agriculture 24214.3 Key Applications of ML in Smart Agriculture 24414.3.1 Precision Farming 24414.3.2 Pest and Disease Detection 24414.3.2.1 Disease Prediction and Control Measures 24514.3.3 Yield Prediction and Crop Management 24514.3.3.1 Predictive Models for Yield Based on Various Parameters (Soil, Weather, and Crop Type) 24514.3.3.2 Crop Rotation and Field Management Insights 24514.3.4 Climate Prediction and Weather Forecasting 24514.3.4.1 Predictive Analytics for Extreme Weather Events 24514.3.4.2 Mitigating the Impact of Climate Change on Agriculture 24514.4 Data Collection and Preprocessing in Agriculture 24614.4.1 Sources of Data in Agriculture 24614.4.1.1 Role of Technology in Agricultural Data Collection 24614.4.2 Types of Data 24714.4.2.1 Key Data Types in Smart Agriculture 24714.4.2.2 Weather Data 24814.4.2.3 Crop Health Data 24814.4.2.4 Livestock Monitoring Data 24814.4.3 Data Preprocessing Steps and Challenges in Agricultural Data 24914.4.3.1 Data Preprocessing Steps 24914.4.3.2 Challenges in Agricultural Data Preprocessing 25014.5 ml Techniques in Agriculture 25014.5.1 Categorization of ML Techniques 25114.5.1.1 SL (Supervised Learning) 25114.5.1.2 UL (Unsupervised Learning) 25114.6 Challenges and Limitations of ML in Agriculture 25214.7 Case Studies and Real-world Applications 25214.8 Future Prospects and Emerging Trends 25214.8.1 The Potential Impact of ML on Global Food Security Through Smart Agriculture 25214.8.1.1 The Role of ML in Smart Agriculture 25314.8.1.2 Benefits of ML-driven Smart Agriculture 25414.8.1.3 Challenges in Implementing ML in Agriculture 25414.8.1.4 Future Prospects and Recommendations 25414.8.2 Future Trends: Future Trends in ML-driven Smart Agriculture: AI Integration, Robotics, and Precision Breeding 25514.8.2.1 AI Integration in Agriculture 25514.8.2.2 Robotics in Smart Agriculture 25514.8.2.3 Precision Breeding with ml 25514.8.2.4 Challenges and Ethical Considerations 25514.8.2.5 Future Outlook and Recommendations 25614.8.3 Sustainability Implications and Environmental Impact of ML-driven Smart Agriculture 25614.8.3.1 Environmental Benefits of ML-driven Smart Agriculture 25614.8.3.2 Environmental Challenges of ML-driven Smart Agriculture 25714.8.3.3 Strategies for Sustainable Implementation 25714.9 Conclusion 25715 Plant Leaf Disease Detection and Classification Using Convolutional Neural Networks 265A. V. Senthil Kumar, N. Abinesh, Shanmugasundaram Hariharan, Kyla L. Tennin15.1 Introduction 26515.2 Plant Disease Challenges and Issues 26615.2.1 Data Quality and Availability 26615.2.2 Environmental Variability and Conditions 26615.2.3 Model Generalization Across Geographic Regions 26715.2.4 Complexity of Disease Symptom Expression 26715.2.5 Limited Generalization to New or Unknown Diseases 26715.2.6 Model Interpretability and Trust 26715.2.7 Scalability and Real-time Implementation 26815.3 Plant Disease Detection and Classification 26815.3.1 ml Techniques for Plant Disease Detection 26815.3.2 Supervised Learning Approaches 26815.3.3 Unsupervised Learning Approaches 26915.3.4 Deep Learning Approaches 27015.4 Data Sources and Feature Extraction 27015.4.1 Image-based Data 27015.4.2 Multispectral and Hyperspectral Data 27015.4.3 Feature Extraction Techniques 27015.5 Challenges in Plant Disease Detection Using ml 27015.5.1 The Reliability and Accessibility of Data 27115.5.2 Model Generalization and Overfitting 27115.5.3 Environmental Variability 27115.5.4 Interpretability and Trust 27115.6 Algorithm Description 27115.6.1 Algorithm Overview 27115.6.2 Data Collection and Preprocessing 27115.6.3 Data Preprocessing Involves Several Steps 27215.6.4 Feature Extraction 27215.6.5 Model Selection and Training 27315.6.5.1 Convolutional Neural Networks 27315.6.5.2 Support Vector Machine 27315.6.5.3 Decision Trees and RF 27315.6.5.4 Transfer Learning 27315.6.5.5 Model Evaluation and Tuning 27315.7 Deployment and Real-time Inference 27415.8 Challenges and Future Directions 27415.9 Proposed Methodology 27515.9.1 Overview of the Methodology 27515.9.1.1 Image Data Collection 27515.9.1.2 Sensor Data Collection 27515.9.1.3 Disease Annotation and Labeling 27515.9.2 Image Preprocessing 27515.9.2.1 Sensor Data Preprocessing 27615.9.2.2 Data Splitting 27615.9.3 Traditional Feature Extraction 27615.9.4 Deep Learning–based Feature Extraction 27615.9.4.1 Model Selection 27615.9.4.2 Model Training 27715.10 Conclusion 27716 The Future Farming: Machine Learning and Crop Health 281Sadhana 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 Samuthiravelu16.1 Introduction 28116.2 Background 28216.3 Significance 28216.4 Understanding ml 28316.5 Unsupervised Learning 28416.6 Supervised Learning 28416.7 Reinforced Learning 28416.8 Generic Functions of ML in Crop Health 28516.9 Advantages 28516.10 Challenges 28516.11 Future Perspectives 28616.12 Conclusion 28617 Social Impact of Machine Learning on Agricultural Communities 291Atef M. El-Sagheer, Eman A. Ahmed, Hamdy A. Sayed17.1 Introduction 29117.2 Impact of ML on Traditional Farming Practices 29117.2.1 Disruption of Traditional Knowledge Systems 29217.2.2 Precision Agriculture and Efficiency Gains 29217.2.3 Economic Impacts on Smallholder Farmers 29217.2.4 Environmental and Sustainability Considerations 29217.2.5 Adapting Traditional Farmers to the Digital Era 29317.2.6 Redefining Rural Employment in the Age of AI 29317.2.7 Automation and Job Displacement 29317.2.8 Emergence of New Job Roles 29317.2.9 Education and Skill Development 29417.2.10 Opportunities for Entrepreneurship 29417.2.11 The Role of Policy in Supporting Rural Employment 29417.3 ml and Farmer Autonomy: Decision-making in a Data-driven World 29417.3.1 The Role of ML in Decision-making 29417.3.2 Impact on Farmer Autonomy 29517.3.3 Data Ownership and Control 29517.3.4 Maintaining Autonomy Through Explainable AI 29517.3.5 Collaborative Models of AI and Farmer Expertise 29617.4 Cultural Shifts and Acceptance of Technology in Agriculture 29617.4.1 Technology Adoption in Agricultural Communities 29617.4.2 Trust and Technology Providers 29617.4.3 Cultural Resistance to Change 29717.4.4 Facilitating Cultural Shifts 29717.4.5 Socioeconomic Disparities in Access to ML Technologies 29817.5 Economic Factors 29817.5.1 Educational Disparities 29817.5.2 Infrastructural Challenges 29817.5.3 Impact on Different Socioeconomic Groups 29817.5.4 Strategies for Mitigating Disparities 29817.5.5 Gender and Social Equity in ML-driven Agriculture 29917.6 Gender Disparities in ML-driven Agriculture 29917.6.1 Social Equity and Access to ML Technologies 29917.6.2 Impact of ML Technologies on Gender and Social Equity 29917.6.3 Strategies for Promoting Equity in ML-driven Agriculture 29917.7 Digital Literacy and Skill Development in Farming Communities 30017.7.1 State of Digital Literacy in Farming Communities 30017.7.2 Barriers to Digital Skill Development 30017.7.3 Impact of Digital Literacy on Agricultural Productivity 30017.7.4 Strategies for Enhancing Digital Literacy in Farming Communities 30117.8 Balancing Technological Innovation with Social Equity 30117.8.1 Technological Innovation and Its Impact on Equity 30117.8.2 Barriers to Equitable Access 30117.8.3 Case Studies of Technological Disparities 30217.8.4 Strategies for Promoting Social Equity 30217.9 The Role of ML in Shaping Rural Economies 30217.9.1 ml in Agriculture 30217.9.2 Economic Development and Diversification 30317.9.3 Challenges and Barriers 30317.9.4 Strategies for Effective Implementation 30317.10 Ethical Dilemmas of AI-driven Agriculture in Developing Communities 30317.10.1 Equity and Access Issues 30417.10.2 Privacy and Data Ownership 30417.10.3 Algorithmic Bias and Fairness 30417.10.4 Environmental and Social Implications 30417.10.5 Strategies for Ethical AI Implementation 30417.11 Community Resilience and Adaptation to Technological Change 30517.11.1 Understanding Community Resilience 30517.11.2 Adaptation Strategies for Technological Change 30517.11.2.1 Education and Skill Development 30517.11.2.2 Community Engagement and Participation 30517.11.2.3 Building Social Capital 30517.11.2.4 Infrastructure Development 30617.11.3 Challenges in Adapting to Technological Change 30617.11.3.1 Digital Divide 30617.11.3.2 Resistance to Change 30617.11.3.3 Economic Constraints 30617.11.4 Case Studies of Successful Adaptation 30617.11.4.1 The Digital Green Initiative 30617.11.4.2 Smart Cities and Urban Resilience 30617.12 Long-term Social Impacts: Sustainability and Food Security 30617.12.1 Defining Sustainability and Food Security 30717.12.2 The Role of Sustainability in Food Security 30717.12.2.1 Sustainable Agricultural Practices 30717.12.2.2 Climate Change Mitigation 30717.12.3 Social Effects of Sustainability and Food Security 30717.12.3.1 Equitable Access to Resources 30717.12.3.2 Health and Nutrition 30717.12.3.3 Economic Stability and Livelihoods 30817.12.4 Long-term Challenges to Sustainability and Food Security 30817.12.4.1 Resource Depletion 30817.12.4.2 Global Trade and Food Systems 30817.13 Collaborative Models: Integrating Local Knowledge with AI Systems 30817.13.1 The Significance of Local Knowledge 30817.13.2 Collaborative Models in AI and Local Knowledge Integration 30917.13.2.1 Participatory AI Development 30917.13.2.2 Knowledge Co-production 30917.13.3 Challenges in Integrating Local Knowledge and AI Systems 30917.13.3.1 Data Standardization and Representation 30917.13.3.2 Power Dynamics and Knowledge Hierarchies 30917.13.4 Case Studies of Successful Integration 31017.13.4.1 AI for Climate-resilient Agriculture in Sub-Saharan Africa 31017.13.4.2 Indigenous Knowledge and AI for Fire Management in Australia 31017.14 Conclusion 31018 Ethical and Regulatory Considerations of Machine Learning in Modern Agriculture 317Atef M. El-Sagheer, Mohamed M. M. Hamd18.1 Introduction 31718.2 Data Privacy and Security in Agricultural ML Systems 31718.2.1 Agricultural Data and Its Sensitivity 31818.2.2 Security Threats in Agricultural Machine Learning Systems 31818.2.2.1 Data Breaches 31818.2.2.2 Adversarial Attacks 31818.2.2.3 Model Theft and Data Leakage 31818.2.2.4 Unauthorized Access 31818.2.3 Privacy Concerns in Agricultural Machine Learning Systems 31918.2.3.1 Data Ownership and Consent 31918.2.3.2 Data Obfuscation 31918.2.3.3 Profiling and Surveillance 31918.2.4 Addressing Data Privacy and Security in Agricultural ML Systems 31918.2.4.1 Secure Data Storage and Transmission 31918.2.4.2 Access Control and Authentication 31918.2.4.3 Federated Learning 31918.2.4.4 Adversarial ML Defenses 32018.2.4.5 Ethical and Regulatory Frameworks 32018.2.5 Future Directions and Challenges 32018.3 Bias and Fairness in Artificial Intelligence Models for Plant Disease Prediction 32018.3.1 Sources of Bias in ML for Plant Disease Prediction 32018.3.1.1 Data Imbalance 32018.3.1.2 Sampling Bias 32018.3.1.3 Labeling Bias 32118.3.1.4 Algorithmic Bias 32118.3.1.5 Geographical and Climatic Bias 32118.3.2 Impact of Bias on Agricultural Communities 32118.3.2.1 Disparities in Disease Management 32118.3.2.2 Inequity in Predictions for Minority Crops 32118.3.2.3 Economic and Environmental Consequences 32118.3.3 Strategies to Mitigate Bias in Prediction Models of Plant Disease 32218.3.3.1 Diversifying Training Data 32218.3.3.2 Synthetic Data Generation 32218.3.3.3 Fairness-aware Algorithms 32218.3.3.4 Transparency and Explainability 32218.3.3.5 Continuous Monitoring and Audits 32218.3.4 Challenges in Ensuring Fairness 32218.4 Environmental and Ecological Implications of AI in Agriculture 32318.4.1 AI-driven Precision Agriculture and Resource Optimization 32318.4.1.1 Water Conservation 32318.4.1.2 Reduction of Chemical Inputs 32318.4.1.3 Energy Efficiency 32318.4.2 Impact on Biodiversity and Ecosystem Services 32318.4.2.1 Preservation of Biodiversity 32318.4.2.2 Risk of Monoculture Intensification 32318.4.2.3 Wildlife Habitat Displacement 32418.4.3 Carbon Footprint of AI in Agriculture 32418.4.3.1 Energy Use in AI Training 32418.4.3.2 Efforts to Reduce AI’s Carbon Footprint 32418.4.4 Ecological Impacts of AI-powered Autonomous Systems 32418.4.4.1 Soil Compaction 32418.4.4.2 E-waste and Resource Depletion 32418.4.4.3 Opportunities for Regenerative Agriculture 32418.4.5 Ethical and Ecological Governance of AI in Agriculture 32518.4.5.1 Sustainable AI Development 32518.4.5.2 Incorporating Ecological Indicators in AI Models 32518.4.5.3 Inclusive AI for Global Agriculture 32518.5 Transparency and Explain Ability in AI-driven Agricultural Solutions 32518.5.1 The Need for Transparency in AI-driven Agriculture 32518.5.1.1 Building Trust Among Stakeholders 32518.5.1.2 Ethical and Legal Implications 32518.5.2 Challenges in Achieving Explainability in Agricultural AI Systems 32618.5.2.1 Complexity of AI Algorithms 32618.5.2.2 Data Quality and Bias 32618.5.2.3 Trade-off Between Accuracy and Interpretability 32618.5.3 Strategies for Enhancing Transparency and Explainability 32618.5.3.1 Post hoc Explainability Techniques 32618.5.3.2 Model-agnostic Interpretability Frameworks 32618.5.3.3 Simplified User Interfaces for Farmers 32618.5.3.4 Collaboration Between AI Developers and Agronomists 32718.5.4 Ethical Implications of Transparency in AI-driven Agriculture 32718.5.4.1 Equity and Accessibility 32718.5.4.2 Accountability in Decision-making 32718.5.5 Future Directions in Transparent AI for Agriculture 32718.5.5.1 Interpretable AI Models 32718.5.5.2 Regulation and Standardization 32718.5.5.3 Education and Training 32718.6 Balancing Innovation with Tradition: Ethical Challenges in Technological Adoption 32818.6.1 Technological Innovation in Agriculture 32818.6.1.1 Advancements in Agricultural Technology 32818.6.1.2 AI and Machine Learning in Crop Management 32818.6.2 The Role of Tradition in Sustainable Farming 32818.6.2.1 Traditional Farming Methods 32818.6.2.2 Cultural Significance of Farming Traditions 32818.6.3 Ethical Challenges in Technological Adoption 32918.6.3.1 Equity and Access to Technology 32918.6.3.2 Environmental Sustainability vs. Technological Efficiency 32918.6.3.3 The Threat to Smallholder Farmers 32918.6.3.4 Technological Overload and Farmer Autonomy 32918.6.4 Balancing Innovation and Tradition 32918.6.4.1 Integrating Traditional Knowledge with Modern Technology 32918.6.4.2 Participatory Approaches to Technology Development 32918.6.4.3 Policy and Regulation for Ethical Technology Adoption 33018.7 Equity in Access to Machine Learning Technologies for Sustainable Agriculture 33018.7.1 Challenges in Achieving Equity in Access to ML Technologies 33018.7.1.1 Cost Barriers and Economic Disparities 33018.7.1.2 Lack of Technical Expertise 33018.7.1.3 Data Availability and Quality 33018.7.1.4 Infrastructure Limitations 33018.7.2 Strategies for Promoting Equity in Access to ML Technologies 33118.7.2.1 Subsidies and Financial Support 33118.7.2.2 Capacity Building and Training 33118.7.2.3 Improving Data Accessibility and Quality 33118.7.2.4 Developing Infrastructure and Connectivity 33118.7.2.5 Promoting Open-source and Inclusive Technologies 33118.7.3 Case Studies and Examples 33118.7.3.1 Precision Agriculture in India 33118.7.3.2 Agricultural Data Platforms in Africa 33218.7.3.3 Internet Connectivity Projects in Rural Areas 33218.8 Human–AI Collaboration: Ethical Guidelines for Decision-making in Agriculture 33218.8.1 Ethical Challenges in Human–AI Collaboration 33218.8.1.1 Transparency and Explainability 33218.8.1.2 Accountability and Responsibility 33218.8.1.3 Bias and Fairness 33218.8.1.4 Human Autonomy and Decision-making 33318.8.2 Ethical Guidelines for Human–AI Collaboration 33318.8.2.1 Develop Transparent and Explainable AI Systems 33318.8.2.2 Establish Accountability Frameworks 33318.8.2.3 Implement Bias Mitigation Strategies 33318.8.2.4 Promote Human–AI Collaboration and Oversight 33318.8.2.5 Foster Continuous Ethical Review and Improvement 33318.8.3 Case Studies and Examples 33318.8.3.1 AI for Precision Agriculture in the United States 33318.8.3.2 AI-assisted Pest Management in India 33418.8.3.3 Bias Mitigation in Agricultural Lending in Africa 33418.9 Regulatory Frameworks for ML in Agricultural Biotechnology 33418.9.1 Current Regulatory Frameworks 33418.9.1.1 Global and Regional Regulations 33418.9.1.2 Data Privacy and Security Regulations 33418.9.1.3 Ethical and Safety Guidelines 33418.9.2 Challenges and Gaps in Regulatory Frameworks 33518.9.2.1 Rapid Technological Advancements 33518.9.2.2 Integration of ML into Existing Frameworks 33518.9.2.3 Global Consistency and Harmonization 33518.9.3 Proposed Guidelines for Future Regulation 33518.9.3.1 Dynamic and Adaptive Regulatory Frameworks 33518.9.3.2 Enhanced Transparency and Explainability Requirements 33518.9.3.3 Risk Assessment and Management Protocols 33518.9.3.4 International Collaboration and Harmonization 33518.9.4 Case Studies and Examples 33618.9.4.1 EU Regulations for GMOs and ml 33618.9.4.2 FDA’s Approach to Biotechnology and AI 33618.9.4.3 Global Harmonization Efforts 33618.10 Conclusion 336