Special issue on computer vision and image analysis in plant phenotyping

Abstract

Plant phenotyping is the identification of effects on the phenotype (i.e., the plant appearance and behavior) as a result of genotype differences (i.e., differences in the genetic code) and the environment. Previously, the process of taking phenotypic measurements has been laborious, costly, and time-consuming. In recent years, noninvasive, imagingbased methods have become more common. These images are recorded by a range of capture devices from small embedded camera systems to multi-million Euro smart greenhouses, at scales ranging from microscopic images of cells, to entire fields captured by UAV imaging.These images need to be analyzed in a high-throughput, robust, and accurate manner. UN-FAO statistics show that according to current population predictions we will need to achieve a 70 % increase in food productivity by 2050, simply to maintain current global agricultural demands. Phenomics-large-scale measurement of plant traits-is the B Hanno Scharr

Detection of leaf structures in close-range hyperspectral images using morphological fusion

“…Close-range hyperspectral (HS) imaging is a novel research tool for biologists (Scharr et al 2016). Several works have reported the design and implementation of HS imaging systems that capture reflectance information from plant leaves at close range (Mahlein et al 2010;Rumpf et al 2010).…”

Sharing the Right Data Right: A Symbiosis with Machine Learning

“…To make this publication avenue as attractive as possible we organized the workshop together with an internationally renowned and high-ranking computer vision conference to maximise visibility. In addition, fulllength papers were published together with main conference proceedings [4] -a great value for CV researchers-and extended versions were bundled in a special issue [5] of a computer vision journal.…”

Developmental normalization of phenomics data generated by high throughput plant phenotyping systems

“…The performance depends heavily on the complexity of images, which frequently include interference, light reflection, leaf overlap, and foreign objects that must be removed (for example, pots, and soil) [ 5 ]. Furthermore, the identification of multiple leaves at the same time (multi-instance segmentation) is difficult due to their similarity in shape and appearance [ 6 ]. Without algorithms that extract accurate measurements, it is more complicated to scale the principal environmental variables influencing the phenotype and underlying physiological processes [ 7 ].…”