Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

By David Deery, Jose A. Jiménez-Berni, H. Jones, Xavier Sirault, Robert Furbank. 2014

360 citations recorded by the source corpus.

The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the future. This review evaluates the role of proximal remote sensing buggies for field-based phenotyping with a particular focus on the application of currently available sensor technology for large-scale field phenotyping. To illustrate the potential for the development of high throughput phenotyping techniques, a case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer. The development of such techniques for routine deployment in commercial-scale breeding and pre-breeding operations will require a multidisciplinary approach to leverage the recent technological advances realised in computer science, image analysis, proximal remote sensing and robotics.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/d77c1ecfac4f5049400c75734a82ee4f87d73dee

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Citations in the corpus

A Review of Imaging Techniques for Plant Phenotyping (2014, background): “The ground station controls the unmanned aerial platforms by radio link, transmitting the position, altitude and status [40,41].”
Plant phenotyping: from bean weighing to image analysis (2015, background): “Moreover, these setups are continuously being refined and their image-processing capabilities form the basis for a next generation of pheno-typing platforms that are operating in the field from different carriers such as tractors [18,19], blimps [20,21] or unmanned aerial vehicles [22].”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, background): “However, as the canopy temperature is sensitive to the environmental conditions, eliminating the influence of background temperature, including the incoming solar radiation, the ambient air temperature and the wind speed, is required (Sugiura et al., 2007; Deery et al., 2014).”
Unmanned Aircraft System (UAS) Technology and Applications in Agriculture (2019, methodology): “Traditionally, phenotyping for breeding trials has been conducted through time-intensive and laborious means [66]; therefore, Chapman and his colleagues conducted a study using an UAS to help alleviate this time-consuming and costly procedure in 2014.”
Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. (2017, background): “Several ground-based phenotyping vehicles equipped with a range of different sensors have previously been developed (Andrade-Sanchez et al. 2014; Deery et al. 2014).”
Translating High-Throughput Phenotyping into Genetic Gain (2018, background): “The images of the phenomobile correspond to (from left to right) a proximal remote sensing buggy [21,104], the phenomobile lite (https://www.”