Barley, for example, may be affected by various foliar pathogens during the vegetation period, and significant quantitative and qualitative yield losses are caused by diseases like powdery mildew, net blotch and brown rust 5. ![]() Unfortunately, these scale badly to the growing amounts of data in plant phenotyping and are prone to human conformation bias. Today’s approaches to disease detection and planning of plant protection measures still very much rely on human experts and/or on prognosis models. Especially the detection of plant diseases is an important task in crop production to avoid yield losses, and in plant breeding for the selection of diseases resistant genotypes. Within this context, non-invasive sensors and computer based technologies demonstrated their potential to equip todays agriculture with tools to solve current and future challenges 4. Recently, phenotyping is defined as a set of methodologies and protocols to assess plant parameters at different scales 2, 3. The plant phenotype is of importance to evaluate the performance of a crop as the interaction between a plant genotype and its environment 1. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we “wordify” the hyperspectral images, i.e., we turn the images into a corpus of text documents. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. Addressing both security functionality and assurance helps to ensure that information technology component products and the information systems built from those products using sound system and security engineering principles are sufficiently trustworthy.Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. ![]() Finally, the catalog of security controls addresses security from both a functionality perspective (the strength of security functions and mechanisms provided) and an assurance perspective (the measures of confidence in the implemented security capability). ![]() The publication also describes how to develop specialized sets of controls, or overlays, tailored for specific types of missions/business functions, technologies, or environments of operation. The controls address a diverse set of security and privacy requirements across the federal government and critical infrastructure, derived from legislation, Executive Orders, policies, directives, regulations, standards, and/or mission/business needs. The security and privacy controls are customizable and implemented as part of an organization-wide process that manages information security and privacy risk. This publication provides a catalog of security and privacy controls for federal information systems and organizations and a process for selecting controls to protect organizational operations (including mission, functions, image, and reputation), organizational assets, individuals, other organizations, and the Nation from a diverse set of threats including hostile cyber attacks, natural disasters, structural failures, and human errors (both intentional and unintentional).
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