A Review of Imaging Techniques for Plant Phenotyping

By Lei Li, Qin Zhang, Danfeng Huang. Italian National Conference on Sensors, 2014

1,004 citations recorded by the source corpus.

Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/a0aaedd253249059252937b53d98b2c958a7ac3c

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Citations in the corpus

High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “The advantage of the stereo camera is that it provides 3D structures at a relatively low cost, but the spatial resolution is limited and sensitive to variable outdoor illumination conditions [247].”
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (2017, background): “In some circumstances, hand-tuned parameters can be invalidated by variation in the scene including issues like lighting, contrast, and exposure (Li et al., 2014).”
Seed priming: state of the art and new perspectives (2015, background): “The observed changes in size and shape occurring and any possible differences within the same or different seed populations can be analysed by means of mathematical models (Fiorani and Schurr 2013; Lei et al. 2014).”