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Plants

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Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling.

Journal of the Royal Society, Interface
Branching in vascular networks and in overall organismic form is one of the most common and ancient features of multicellular plants, fungi and animals. By combining machine-learning techniques with new theory that relates vascular form to metabolic ...

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture.

PloS one
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such a...

Impacts of speciation and extinction measured by an evolutionary decay clock.

Nature
The hypothesis that destructive mass extinctions enable creative evolutionary radiations (creative destruction) is central to classic concepts of macroevolution. However, the relative impacts of extinction and radiation on the co-occurrence of specie...

Deep Learning Applied to Vegetation Identification and Removal Using Multidimensional Aerial Data.

Sensors (Basel, Switzerland)
When performing structural inspection, the generation of three-dimensional (3D) point clouds is a common resource. Those are usually generated from photogrammetry or through laser scan techniques. However, a significant drawback for complete inspecti...

Plant Leaf Position Estimation with Computer Vision.

Sensors (Basel, Switzerland)
Autonomous analysis of plants, such as for phenotyping and health monitoring etc., often requires the reliable identification and localization of single leaves, a task complicated by their complex and variable shape. Robotic sensor platforms commonly...

Quantifying Tropical Plant Diversity Requires an Integrated Technological Approach.

Trends in ecology & evolution
Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at large spatial scales is vital for many purposes. As macroecological approaches proliferate, the taxonomic uncertainties in species occurrence data are ...

Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery.

Sensors (Basel, Switzerland)
Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughp...

Leaf identification using radial basis function neural networks and SSA based support vector machine.

PloS one
In this research, an efficient scheme to identify leaf types is proposed. In that scheme, the leaf boundary points are fitted in a continuous contour using Radial Basis Function Neural Networks (RBFNN) to calculate the centroid of the leaf shape. Aft...

Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China.

Environmental pollution (Barking, Essex : 1987)
In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water...

Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review.

Sensors (Basel, Switzerland)
Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually beco...