The application of imaging techniques in plant and agricultural sciences had previously been confined to images obtained through remote sensing techniques. Technological advancements now allow image analysis for the nondestructive and objective evaluation of biological objects. This has opened a new window in the field of plant science. Plant Image

Domesticated crops are the result of artificial selection for particular phenotypes or, in some cases, natural selection for an adaptive trait. Plant traits can be identified through image-based plant phenotyping, a process that was, until recently, strenous and time-consuming. Intelligent Image Analysis for Plant Phenotyping reviews information on time-saving techniques, using computer vision and imaging technologies. These methodologies provide an automated, non-invasive, and scalable mechanism by which to define and collect plant phenotypes. Beautifully illustrated, with numerous color images, the book focuses on phenotypes measured from individual plants under controlled experimental conditions, which are widely available in high-throughput systems. Features: Presents methodologies for image processing, including data-driven and machine learning techniques for plant phenotyping. Features information on advanced techniques for extracting phenotypes through images and image sequences captured in a variety of modalities. Includes real-world scientific problems, including predicting yield by modeling interactions between plant data and environmental information. Discusses the challenge of translating images into biologically informative quantitative phenotypes. A practical resource for students, researchers, and practitioners, this book is invaluable for those working in the emerging fields at the intersection of computer vision and plant sciences.

The application of imaging techniques in plant and agricultural sciences had previously been confined to images obtained through remote sensing techniques. Technological advancements now allow image analysis for the nondestructive and objective evaluation of biological objects. This has opened a new window in the field of plant science. Plant Image Analysis: Fundamentals and Applications introduces the basic concepts of image analysis and discusses various techniques in plant imaging, their applications, and future potential. Several types of imaging techniques are discussed including RGB, hyperspectral, thermal, PRI, chlorophyll fluorescence, ROS, and chromosome imaging. The book also covers the use of these techniques in assessing plant growth, early detection of disease and stress, fruit crop yield, plant chromosome analysis, plant phenotyping, and nutrient status both in vivo and in vitro. The book is an authoritative guide for researchers and those teaching in the fields of stress physiology, precision agriculture, agricultural biotechnology, and cell and developmental biology. Graduate students and professionals using machine vision in plant science will also benefit from this comprehensive resource.

Digital color analysis has become an increasingly popular and cost-effective method utilized by resource managers and scientists for evaluating foliar nutrition and health in response to environmental stresses. We developed and tested a new method of digital image analysis that uses Scion Image or NIH image public domain software to quantify leaf color. This publication provides instructions for using this software to measure the percentage green and red in leaves, colors of particular importance for the assessment of plant health. Comparisons of results from digital analyses of 326 scanned images of leaves and concurrent spectrophotometric measures of chlorophyll a, chlorophyll b, and anthocyanins verify that image analysis provides a reliable quantitative measure of leaf color and the relative concentrations of underlying plant pigments.

The Handbook of Reference Methods for Plant Analysis is an outstanding resource of plant analysis procedures, outlined in easy-to-follow steps and laboratory-ready for implementation. Plant laboratory preparation methods such as dry ashing and acid and microwave digestion are discussed in detail. Extraction techniques for analysis of readily soluble elements (petiole analysis) and quick test kits for field testing are also presented. This handbook consolidates proven, time tested methods in one convenient source. Plant scientists in production agriculture, forestry, horticulture, environmental sciences, and other related disciplines will find the Handbook a standard laboratory reference. The Handbook was written for the Soil and Plant Analysis Council, Inc., of which the editor is a board member. The council aims to promote uniform soil test and plant analysis methods, use, interpretation, and terminology; and to stimulate research on the calibration and use of soil testing and plant analysis. This reference will help readers reach these important goals in their own research.

Plant Analysis: An Interpretation Manual 2nd Edition is an easily accessible compilation of data summarising the range of nutrient concentration limits for crops, pastures, vegetables, fruit trees, vines, ornamentals and forest species. This information is valuable in assessing the effectiveness of fertiliser programs and for monitoring longer term changes in crop nutritional status. New to this edition: *Volume and scope of information accessed from the literature has expanded several-fold. Interpretation criteria for 294 species have been compiled in the tables from more than 1872 published papers. *New chapter on nutrient criteria for forest species. *Includes guidelines for collecting, handling and analysing plant material. An entire chapter is devoted to the identification of nutrient deficiency and toxicity symptoms.

This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

The third edition of a standard resource, this book offers a state-of-the-art, multi-disciplinary presentation of plant roots. It examines structure and development, assemblage of root systems, metabolism and growth, stressful environments, and interactions at the rhizosphere. Reflecting the explosion of advances and emerging technologies in the field, the book presents developments in the study of root origin, composition, formation, and behavior for the production of novel pharmaceutical and medicinal compounds, agrochemicals, dyes, flavors, and pesticides. It details breakthroughs in genetics, molecular biology, growth substance physiology, biotechnology, and biomechanics.

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.

Targeted at beginners as well as experienced users, this handy reference explains the benefits and uses of flow cytometery in the study of plants and their genomes. Following a brief introduction that highlights general considerations when analyzing plant cells by flow cytometric methods, the book goes on to discuss examples of application in plant genetics, genomic analysis, cell cycle analysis, marine organism analysis and breeding studies. With its list of general reading and a glossary of terms, this first reference on FCM in plants fills a real gap by providing first-hand practical hints for the growing community of plant geneticists.