Chunhua Zhang

This book features five peer-reviewed reviews on the use of unmanned aircraft systems in an array of agricultural settings.The first chapter provides an overview of unmanned aerial system (UAS) platforms and sensors, flight planning and imagery acquisition, before moving on to consider stitching and ortho-rectification in UAS image processing.The second chapter discusses approaches to unmanned aerial vehicle (UAV) remote sensing and data analysis for high-throughput field phenotyping and ecophysiological research. The chapter reviews the use of UAV remote sensing to measure key plant traits, such as canopy cover and leaf area index.The third chapter presents a useful overview of the advantages and limitations of UAV remote sensing platforms and their applications in precision agriculture.The fourth chapter discusses the quantification of plant water status and the various methods used to assess plant water stress. A section on optical remote sensing, thermal infrared remote sensing and microwave remote sensing of plant status is also included.The final chapter reviews the application of small unmanned aircraft systems (sUAS) and remote sensing technology in turfgrass systems. The chapter also highlights the various sUAS platforms and sensors necessary to measure and monitor the target of interest.

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations.To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature. Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.