Yanbo Huang – författare

In the past ten years electronics and computer technologies have significantly pushed forward the progress of automation in the food industry. The application of these technologies to automation for food engineering will produce more nutritious, better quality, and safer items for consumers. Automation for Food Engineering: Food Quality Quantization and Process Control explores the usage of advanced methods, such as wavelet analysis and artificial neural networks, to automated food quality evaluation and process control. It introduces novel system prototypes, such as machine vision, elastography, and the electronic nose, for food quality measurement, analysis, and prediction.The book discusses advanced techniques, such as medical imaging, mathematical analysis, and statistical modeling, which have proven successful in food engineering. The authors use the characteristics of food processes to describe concepts, and they employ data from food engineering applications to explain the methods. To aid in the comprehension of technical information, they provide real-world examples and case studies from food engineering projects.The material covers the frameworks, techniques, designs, algorithms, tests and implementation of data acquisition, analysis, modeling, prediction, and control in automation for food engineering. It demonstrates the techniques for automation of food engineering, and helps you in the development of techniques for your own applications. Automation for Food Engineering: Food Quality Quantization and Process Control is the first and only book that gives a systematical study and summary about concepts, principles, methods, and practices in food quality quantization and process control.

Automation for Food Engineering introduces the frameworks, methods, designs, algorithms, tests, and implementation of data acquisition, analysis, modeling, prediction, and control in automation for food engineering. Using numerous examples and case studies from food engineering projects, the book introduces basic methods systematically and describes advanced techniques, such as medical imaging, mathematical analysis, and statistical modeling, which have proven successful in food engineering. The authors use the characteristics of food processes to describe concepts and employ data from food engineering applications to explain the methods.

Automation for Food Engineering introduces the frameworks, methods, designs, algorithms, tests, and implementation of data acquisition, analysis, modeling, prediction, and control in automation for food engineering. Using numerous examples and case studies from food engineering projects, the book introduces basic methods systematically and describes advanced techniques, such as medical imaging, mathematical analysis, and statistical modeling, which have proven successful in food engineering. The authors use the characteristics of food processes to describe concepts and employ data from food engineering applications to explain the methods.

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.

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.