Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis

Das, Mita; Goswami, Saptarsi; Bashyam, Srinidhi; Awada, Tala; Samal, Ashok

doi:10.1109/iccvw.2017.237

Cited by 24 publications

(19 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Structural and physiological phenotypes are further divided into two groups: holistic and component. The holistic phenotypes consider the whole plant as a single object and compute its basic geometrical properties, e.g., height of the bounding rectangle to account for plant height, area of the convex-hull to account for plant size (Das Choudhury et al, 2017, 2018). Component phenotypes are computed by considering individual components of the plants, i.e., leaves, stem, flower, and fruit.…”

Section: A Taxonomy For Plant Phenotypesmentioning

confidence: 99%

“…The method accepts plant image sequence as the input and produces a leaf status report containing the phenotypic information, i.e., the emergence timing, total number of leaves present at any point of time, total number of leaves emerged, the day on which a particular leaf stopped growing or lost, and the length and relative growth rate of individual leaves. The method in Das Choudhury et al (2017) introduces an algorithm to compute stem angle, a potential measure for plants' susceptibility to lodging, based on graph-based plant architecture determination. A time series clustering analysis is used to summarize the temporal patterns of the stem angles into different groups to provide further insight into genotype specific behavior of the plants.…”

Section: Recent Advancements In Image-based Plant Phenotypingmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Image Analysis for High-Throughput Plant Phenotyping

2019

Self Cite

View full text Add to dashboard Cite

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field.

show abstract

Section: A Taxonomy For Plant Phenotypesmentioning

confidence: 99%

Section: Recent Advancements In Image-based Plant Phenotypingmentioning

confidence: 99%

Leveraging Image Analysis for High-Throughput Plant Phenotyping

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…van der Heijden et al (2012 have discussed the need to exploit temporal information for improving phenotyping efficiency of traits like total leaf area. A few studies have considered temporal plant phenotyping for assessing genotypes based on temporal patterns of phenotypic development (van Dusschoten et al, 2016;Das Choudhury et al, 2017). However, to the extent of our knowledge, a systematic approach to determining an OTW where h 2 and genetic diversity are maximized, has not yet been proposed.…”

Section: Temporal Analysis Of G-bluesmentioning

confidence: 99%

SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

et al. 2020

View full text Add to dashboard Cite

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

show abstract

“…Extracting meaningful numerical phenotypes based on image analysis remains a critical bottleneck in plant science. Current image-based plant phenotyping methods have mainly focused on the computation of phenotypes, e.g., morphological, architectural, textural and color-based, from 2D plant image sequences for vegetative and reproductive stages (Dellen et al, 2015 ; Brichet et al, 2017 ; Das Choudhury et al, 2017 , 2018 ; Pound et al, 2017 ; Zhang et al, 2017 ; Yin et al, 2018 ). However, plants, 3D in nature, exhibit increasing architectural complexity over time due to self-occlusions and phyllotaxy (i.e., arrangements of leaves around the stem), which pose significant challenges in the attempt to accurately estimating phenotypes from a 2D image and linking these phenotypes to genetic expression.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Image Analysis to Compute 3D Plant Phenotypes Based on Voxel-Grid Plant Reconstruction

et al. 2020

Self Cite

View full text Add to dashboard Cite

High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.

show abstract

Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis

Cited by 24 publications

References 8 publications

Leveraging Image Analysis for High-Throughput Plant Phenotyping

Leveraging Image Analysis for High-Throughput Plant Phenotyping

SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

Leveraging Image Analysis to Compute 3D Plant Phenotypes Based on Voxel-Grid Plant Reconstruction

Contact Info

Product

Resources

About