Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]

By Massimo Minervini, Hanno Scharr, Sotirios A. Tsaftaris. 2015

242 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Molecular Biology, Plant Science

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Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “In fact, most experts now agree that lack of reliable and automated algorithms to analyze these vast datasets forms a new bottleneck in our understanding of plant biology and function (Minervini et al., 2015b).”
An interactive tool for semi-automated leaf annotation (2015, background): “In fact, most experts now agree that lack of reliable and automated algorithms to extract information from these vast datasets forms a new bottleneck in our understanding of plant biology and function [25].”
Learning to Count Leaves in Rosette Plants (2015, background): “com would increase even more the reach of automated high throughput phenotyping [27].”
High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture (2019, background): “Plant phenotyping is still the bottleneck as the development of techniques for the precise and accurate recording of important agronomical traits and crop monitoring are lagging behind [13,14].”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “In fact, most experts now agree that lack of reliable and automated algorithms to extract ﬁne-grained information from these vast datasets forms a new bottleneck in our understanding of plant biology and function [39].”
Multi-modality imagery database for plant phenotyping (2016, background): “Due to diverse variations of leaf shape, appearance, layout, growth and movement, plant image analysis is a non-trivial computer vision task [Minervini et al., 2015].”
Leaf segmentation in plant phenotyping: a collation study (2015, background): “In fact, most experts now agree that lack of reliable and automated algorithms to extract fine-grained information from these vast datasets forms a new bottleneck in our understanding of plant biology and function [39].”