Dataset Information

GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton.

ABSTRACT: Imaging sensors can extend phenotyping capability, but they require a system to handle high-volume data. The overall goal of this study was to develop and evaluate a field-based high throughput phenotyping system accommodating high-resolution imagers. The system consisted of a high-clearance tractor and sensing and electrical systems. The sensing system was based on a distributed structure, integrating environmental sensors, real-time kinematic GPS, and multiple imaging sensors including RGB-D, thermal, and hyperspectral cameras. Custom software was developed with a multilayered architecture for system control and data collection. The system was evaluated by scanning a cotton field with 23 genotypes for quantification of canopy growth and development. A data processing pipeline was developed to extract phenotypes at the canopy level, including height, width, projected leaf area, and volume from RGB-D data and temperature from thermal images. Growth rates of morphological traits were accordingly calculated. The traits had strong correlations (r = 0.54-0.74) with fiber yield and good broad sense heritability (H2 = 0.27-0.72), suggesting the potential for conducting quantitative genetic analysis and contributing to yield prediction models. The developed system is a useful tool for a wide range of breeding/genetic, agronomic/physiological, and economic studies.

SUBMITTER: Jiang Y

PROVIDER: S-EPMC5775337 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Publications

GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton.

Jiang Yu Y Li Changying C Robertson Jon S JS Sun Shangpeng S Xu Rui R Paterson Andrew H AH

Scientific reports 20180119 1

Imaging sensors can extend phenotyping capability, but they require a system to handle high-volume data. The overall goal of this study was to develop and evaluate a field-based high throughput phenotyping system accommodating high-resolution imagers. The system consisted of a high-clearance tractor and sensing and electrical systems. The sensing system was based on a distributed structure, integrating environmental sensors, real-time kinematic GPS, and multiple imaging sensors including RGB-D, ...[more]

PMID: 29352136

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Project description:Canopy temperature (CT) has been related to water-use and yield formation in crops. However, constantly (e.g., sun illumination angle, ambient temperature) as well as rapidly (e.g., clouds) changing environmental conditions make it difficult to compare measurements taken even at short time intervals. This poses a great challenge for high-throughput field phenotyping (HTFP). The aim of this study was to i) set up a workflow for unmanned aerial vehicles (UAV) based HTFP of CT, ii) investigate different data processing procedures to combine information from multiple images into orthomosaics, iii) investigate the repeatability of the resulting CT by means of heritability, and iv) investigate the optimal timing for thermography measurements. Additionally, the approach was v) compared with other methods for HTFP of CT. The study was carried out in a winter wheat field trial with 354 genotypes planted in two replications in a temperate climate, where a UAV captured CT in a time series of 24 flights during 6 weeks of the grain-filling phase. Custom-made thermal ground control points enabled accurate georeferencing of the data. The generated thermal orthomosaics had a high spatial accuracy (mean ground sampling distance of 5.03 cm/pixel) and position accuracy [mean root-mean-square deviation (RMSE) = 4.79 cm] over all time points. An analysis on the impact of the measurement geometry revealed a gradient of apparent CT in parallel to the principle plane of the sun and a hotspot around nadir. Averaging information from all available images (and all measurement geometries) for an area of interest provided the best results by means of heritability. Correcting for spatial in-field heterogeneity as well as slight environmental changes during the measurements were performed with the R package SpATS. CT heritability ranged from 0.36 to 0.74. Highest heritability values were found in the early afternoon. Since senescence was found to influence the results, it is recommended to measure CT in wheat after flowering and before the onset of senescence. Overall, low-altitude and high-resolution remote sensing proved suitable to assess the CT of crop genotypes in a large number of small field plots as is required in crop breeding and variety testing experiments.

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Dataset Information

GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton.

Publications

GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton.

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