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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.
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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.
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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. Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming.