Agricultural Cybernetics

Description

Agricultural systems are uniquely complex systems, given that agricultural systems are parts of natural and ecological systems. Those aspects bring in a substantial degree of uncertainty in system operation. Also, impact factors, such as weather factors, are critical in agricultural systems but these factors are uncontrollable in system management. Modern agriculture has been evolving through precision agriculture beginning in the late 1980s and biotechnological innovations in the early 2000s. Precision agriculture implements site-specific crop production management by integrating agricultural mechanization and information technology in geographic information system (GIS), global navigation satellite system (GNSS), and remote sensing. Now, precision agriculture is set to evolve into smart agriculture with advanced systematization, informatization, intelligence and automation. From precision agriculture to smart agriculture, there is a substantial amount of specific control and communication problems that have been investigated and will continue to be studied. In this book, the core ideas and methods from control problems in agricultural production systems are extracted, and a system view of agricultural production is formulated for the analysis and design of management strategies to control and optimize agricultural production systems while exploiting the intrinsic feedback information-exchanging mechanisms. On this basis, the theoretical framework of agricultural cybernetics is established to predict and control the behavior of agricultural production systems through control theory. 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