Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.
TL;DR: A comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios is provided.
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About: This article is published in Trends in Plant Science. The article was published on 01 Oct 2018. and is currently open access.
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TL;DR: A novel multi-modal sliding window-based support vector regression method for accurate prediction of complicated water stress, which is a plant status, from two data types, namely environmental and plant image data is proposed.
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Estimate, a New iPad Application for Assessment of Plant Disease Severity Using Photographic Standard Area Diagrams.
Sarah J. Pethybridge,Scot Nelson +1 more
TL;DR: A new iPad application called Estimate for researchers and crop managers for their use on a mobile device at the field-level for assessing plant disease severity in order to collect data or aid in treatment decisions and offers savings in time for data collection and processing.
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Leaf Doctor: A New Portable Application for Quantifying Plant Disease Severity.
Sarah J. Pethybridge,Scot Nelson +1 more
TL;DR: An interactive, iterative smartphone application was used on color images to distinguish diseased from healthy plant tissues and calculate percentage of disease severity and precision from Leaf Doctor were highly accurate and operationally defined as the ability of a rater to use Leaf Doctor and repeatedly obtain similar percentages of Disease severity for the same image.
A deep learning framework to discern and count microscopic nematode eggs
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TL;DR: It is shown that a deep learning architecture developed for rare object identification in clutter-filled images can identify and count the SCN eggs and illustrates the remarkable promise of applying deep learning approaches to phenotyping for pest assessment and management.