High-throughput profiling and analysis of plant responses over time to abiotic stress

Veley, Kira M.; Berry, Jeffrey C.; Fentress, Sarah J.; Schachtman, Daniel P.; Baxter, Ivan; Bart, Rebecca

doi:10.1101/132787

Cited by 2 publications

(2 citation statements)

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“…Because these micro-treatments are often unknown, and therefore not accounted for, they show up as experimental noise and may lead to false positives. For instance, nitrogen is known to affect plant growth (size, color, yield, etc) (Chapin et al 1987, Veley et al 2017) . If nitrogen is unevenly distributed across a field experiment aimed at characterizing biomass among diverse genotypes, the variability in nitrogen may confound the experiment.…”

Section: Discussionmentioning

confidence: 99%

Increased signal to noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components

et al. 2021

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Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create micro-treatments throughout the field. To make matters worse, the variation within different soil properties is often non-randomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a tool with which sources of environmental variation in field data can be identified and removed, improving our ability to resolve effects of interest and to quantify subtle phenotypes.

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Section: Discussionmentioning

confidence: 99%

Increased signal to noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components

et al. 2021

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“…Among the most related data sets, some provide multiple images of plants that would allow 3D reconstruction; however, they do not include complete 3D plant models [6][7][8][9]. Uchiyama et al [7] provided a data set containing multiple RGB and depth images of Komatsuna plant together with the manually annotated leaf labels.…”

Section: Introductionmentioning

confidence: 99%

ROSE-X: an annotated data set for evaluation of 3D plant organ segmentation methods

et al. 2020

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Background: The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods. The need for complete 3D models of real plants with organ-level labeling is even more pronounced due to the advances in 3D vision-based phenotyping techniques and the difficulty of full annotation of the intricate 3D plant structure. Results: We introduce the ROSE-X data set of 11 annotated 3D models of real rosebush plants acquired through X-ray tomography and presented both in volumetric form and as point clouds. The annotation is performed manually to provide ground truth data in the form of organ labels for the voxels corresponding to the plant shoot. This data set is constructed to serve both as training data for supervised learning methods performing organ-level segmentation and as a benchmark to evaluate their performance. The rosebush models in the data set are of high quality and complex architecture with organs frequently touching each other posing a challenge for the current plant organ segmentation methods. We report leaf/stem segmentation results obtained using four baseline methods. The best performance is achieved by the volumetric approach where local features are trained with a random forest classifier, giving Intersection of Union (IoU) values of 97.93% and 86.23% for leaf and stem classes, respectively. Conclusion: We provided an annotated 3D data set of 11 rosebush plants for training and evaluation of organ segmentation methods. We also reported leaf/stem segmentation results of baseline methods, which are open to improvement. The data set, together with the baseline results, has the potential of becoming a significant resource for future studies on automatic plant phenotyping.

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High-throughput profiling and analysis of plant responses over time to abiotic stress

Abstract: Sorghum (Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized 1 7

Cited by 2 publications

References 57 publications

Increased signal to noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components

Increased signal to noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components

ROSE-X: an annotated data set for evaluation of 3D plant organ segmentation methods

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