Automated and accurate segmentation of leaf venation networks via deep learning
نویسندگان
چکیده
منابع مشابه
Inferring climate from angiosperm leaf venation networks.
Leaf venation networks provide an integrative linkage between plant form, function and climate niche, because leaf water transport underlies variation in plant performance. Here, we develop theory based on leaf physiology that uses community-mean vein density to predict growing season temperature and atmospheric CO2 concentration. The key assumption is that leaf water supply is matched to water...
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There have been numerous attempts to derive general models for the structure and function of resource delivery networks in biology. Such theories typically predict the quantitative structure of vascular networks across scales. For example, fractal branching models of plant structure predict that the network dimensions within plant stems or leaves should be scale-free. However, very few empirica...
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We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together. Our similarity metric is based on a deep, fully convolutional embedding model. Our grouping method is based on selecting all points that are sufficiently similar to a set of “seed points’, chosen from a deep, fully c...
متن کاملLinking Auxin with Photosynthetic Rate via Leaf Venation.
Land plants lose vast quantities of water to the atmosphere during photosynthetic gas exchange. In angiosperms, a complex network of veins irrigates the leaf, and it is widely held that the density and placement of these veins determines maximum leaf hydraulic capacity and thus maximum photosynthetic rate. This theory is largely based on interspecific comparisons and has never been tested using...
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ژورنال
عنوان ژورنال: New Phytologist
سال: 2020
ISSN: 0028-646X,1469-8137
DOI: 10.1111/nph.16923