دانلود کتاب Agricultural Cybernetics

فهرست مطالب :

Preface
Contents
About the Authors
Chapter 1: Introduction
1.1 What Is Cybernetics?
1.2 Evolution of Cybernetics
1.2.1 Cybernetics, Wiener 1948
1.2.2 Engineering Cybernetics, Tsien 1954
1.2.3 Cybernetics Specialization and Advancement
1.3 Agricultural Cybernetics
1.3.1 Evaluation from Mechanized to Smart Agriculture through Precision Agriculture
1.3.2 Control and Communication in Agricultural Production Systems
1.4 Outline of the Book
Chapter 2: Mathematics, Statistics, and Representations for Cybernetic Systems
2.1 Mathematical Methods
2.1.1 Linear Algebra Methods
2.1.1.1 Orthogonal Vector and Orthogonal Matrix
2.1.1.2 Eigenvector and Eigenvalue
2.1.1.3 Singular Value Decomposition
2.1.2 Vector Calculus for Gradient
2.1.3 Optimization Techniques
2.2 Statistical Methods
2.2.1 Bayesian Analysis
2.2.2 Markov and Hidden Markov Processes
2.3 Information Entropy
2.4 Representations of Cybernetic Systems
2.4.1 Block Diagram Presentation of Cybernetic Systems
2.4.2 Mathematical Expression and Transformations
2.4.3 Presentations of Agricultural Cybernetic Systems
2.4.4 Basic System Configurations and Analysis
2.4.5 More Complicated Configurations
Chapter 3: Control and Communication Characteristics of Agricultural Production Systems
3.1 Mathematical Expression of Agricultural Production Systems
3.2 Dynamics of Agricultural Production Systems
3.3 Systems Parameters and Measurements in Crop Production
3.4 Variations, Uncertainties, and Uncontrollable Factors in Crop Production
3.5 Controllable Windows of Crop Production Systems
3.6 Multi-Stage, Adaptive Control in Crop Production
3.6.1 A Raised Problem
3.6.2 Stage-Specific Windows of Control in Crop Production
3.6.3 Adaptive Control
Chapter 4: Modeling of Crop Production Systems and System Characterization
4.1 Modeling for Agricultural Production System
4.1.1 Physics-Based Model
4.1.2 Empirical Model
4.1.3 Input-Output Model
4.1.3.1 An Example of Dynamic System Modeling
4.1.3.2 Model Structure Assumption
4.1.3.3 System Identification
Purpose Clarification of System Identification
Use of Prior Knowledge
Design of Experiment
Data Preprocessing
Model Structure Identification
Model Parameter Estimation
Model Validation
4.1.4 State-Space Model
4.1.4.1 System Controllability and Observability
4.1.4.2 Determination of Controllability within an Implementation Window
4.1.4.3 State Estimation
4.1.4.4 Kalman Filter
4.1.4.5 Implication to Precision Agriculture
4.1.5 Data-Driven Machine Learning Modeling
4.1.6 Neuro-Fuzzy Modeling
4.2 Crop Production Monitoring and Control
4.2.1 Crop Phenology
4.2.2 Crop Health Sensing and Phenotyping
4.2.3 Crop Growth Control
4.3 Controllable and Observable Variables in Crop Production Systems
Chapter 5: Control Theory for Agricultural Production
5.1 Classic Control Theory
5.2 Modern Control Theory
5.3 Optimal Control
5.4 Process Control and Unit Operation
5.5 Emerging Control Technologies
5.5.1 Model Predictive Control
5.5.2 Machine Learning and Data-Driven Model-Based Control
5.5.3 Neuro-Fuzzy Control
5.5.4 Data-Driven Model-Free Control
5.5.5 Advanced Adaptive Control
Chapter 6: Control of Agricultural Production Systems
6.1 Control of Precision Agricultural Operation
6.2 Control Strategies for Precision Agriculture
6.2.1 Prescriptive Control
6.2.2 Responsive Control
6.2.3 Feedback Control
6.2.4 Stage-Specific Control
6.2.4.1 Open- and Closed-Loop Controls in Stage-Specific Control
6.2.4.2 Enhanced Methods for Better Stage-Specific Control
6.3 Adoption of Control Theory in Precision Agriculture
6.4 A General Structure of Agricultural Cybernetic System
6.5 Repertoire in Agricultural Cybernetic Systems
6.5.1 Knowledge Representing Approach
6.5.2 Statistical Process Control Approach
6.5.3 Good Agricultural Practice Standard
Chapter 7: Learning from the Data
7.1 Big Data Analysis
7.2 Regression Analysis
7.3 Data-Driven Modeling Methods
7.3.1 Support Vector Machine
7.3.2 Random Forest
7.3.3 Long Short-Term Memory
7.3.4 Deep Learning Methods
7.3.5 Gray Relational Analysis
7.4 Further Topics
7.4.1 Model Overfitting
7.4.2 State-Space Representation of Neural Networks
7.4.3 Transfer Learning
7.4.4 Multitask Learning
Chapter 8: Outlook and Summary Remarks
8.1 Summary Remarks: What Will Agricultural Cybernetics Bring to Agriculture
8.2 Agricultural Cybernetics Controls a Large-Scale System
8.2.1 Plant Production Systems
8.2.2 Climate-Soil-Plant Systems
8.2.3 Machinery Systems
8.2.4 Human-Machine-Plant Systems
8.2.5 Production Chain Systems
8.3 Features of Agricultural Cybernetics
8.3.1 Intelligent Control System
8.3.2 Gray System
8.3.3 Knowledge-Intensive System
8.3.4 Scalable System
8.3.5 Chaos System
8.3.6 Dissipative System
8.4 The Future
References
Index