scholarly journals Functional Modeling of Plant Growth Dynamics

2017 ◽  
Author(s):  
Yuhang Xu ◽  
Yumou Qiu ◽  
James C. Schnable
Keyword(s):  
Time Series ◽  
Plant Growth ◽  
Growth Curves ◽  
Growth Patterns ◽  
Estimation Accuracy ◽  
Plant Phenotyping ◽  
Continuous Growth ◽  
Wide Range ◽  
Functional Anova ◽  
Over Time

ABSTRACTRecent advances in automated plant phenotyping have enabled the collection time series measurements from the same plants of a wide range of traits over different developmental time scales. The availability of time series phenotypic datasets has increased interest in statistical approaches for comparing patterns of change between different plant genotypes and different treatment conditions. Two widely used methods of modeling growth over time are point-wise analysis of variance (ANOVA) and parametric sigmoidal curve fitting. Point-wise ANOVA yields discontinuous growth curves, which do not reflect the true dynamics of growth patterns in plants. In contrast, fitting a parametric model to a time series of observations does capture the trend of growth, however these models require assumptions regarding the true pattern of plant growth. Depending on the species, treatment regime, and subset of the plant lifecycle sampled this assumptions will not always hold true. Here we introduce a different approach – functional ANOVA – which yields continuous growth curves without requiring assumptions regarding patterns of plant growth. We compare and validate this approach using data from an experiment measuring growth of two maize (Zea mays ssp. mays) genotypes under two water availability treatments over a 21-day period. Functional ANOVA enables a nonparametric estimation of the dynamics of changes in plant traits over time without assumptions regarding curve shape. In addition to estimating smooth curves of trait values over time, functional ANOVA also estimates the the derivatives of these curves – e.g. growth rates – simultaneously. Using two different subsampling strategies, we demonstrate that this functional ANOVA method enables the comparison of growth curves between plants phenotyped on non-overlapping days with little reduction in estimation accuracy. This means functional ANOVA based approaches can allow larger numbers of samples and biological replicates to be scored in a single experiment given fixed amounts of phenotyping infrastructure and personnel.

PATTERNS OF GROWTH IN HEIGHT AND WEIGHT FROM BIRTH TO EIGHTEEN YEARS OF AGE

PEDIATRICS ◽  
1959 ◽  
Vol 24 (5) ◽  
pp. 904-921
Author(s):  
Robert B. Reed ◽  
Harold C. Stuart
Keyword(s):  
Growth Curves ◽  
Growth Patterns ◽  
Individual Growth ◽  
Growth Spurt ◽  
Adolescent Growth Spurt ◽  
Wide Range ◽  
Basic Facts ◽  
The Individual ◽  
Rates Of Growth ◽  
Mature Size

In this report is displayed the range of variation observed in the growth curves of height and weight in a series of 134 children observed from birth to 18 years. For purposes of simplification the individuals have been classified on the basis of their rates of growth during three successive 6-year intervals. Even in terms of this crude classification several basic facts about individual growth patterns of height and weight are apparent. The wide range of differences between individuals applies not only to facts about size at specific ages but also to the pattern of change followed from age period to age period. The rate of growth during early childhood, i.e. before 6 years of age, is associated with, but not specifically predictive of, size at maturity and timing of the adolescent growth spurt. Individuals with rapid growth before 6 years of age tend to have large mature size and early adolescent growth spurt. It will be the objective of future reports from this research project to determine the manner in which the individual differences in growth demonstrated and classified here are related to aspects of physical development, to environmental influences such as dietary intake and to the level of health of the child.

Using tree-ring crossdating techniques to validate annual growth increments in long-lived fishes

2005 ◽  
Vol 62 (10) ◽  
pp. 2277-2284 ◽  
Author(s):  
Bryan A Black ◽  
George W Boehlert ◽  
Mary M Yoklavich
Keyword(s):  
Time Series ◽  
Tree Ring ◽  
Growth Increment ◽  
Growth Patterns ◽  
Freshwater Species ◽  
Age Validation ◽  
Average Growth ◽  
Growth Increments ◽  
Wide Range ◽  
Ring Analysis

We applied crossdating, a dendrochronology (tree-ring analysis) age validation technique, to growth increment widths of 50 Sebastes diploproa otoliths ranging from 30 to 84 years in age. Synchronous growth patterns were matched by the following: (i) checking the dates of conspicuously narrow growth increments for agreement among samples and (ii) statistically verifying that growth patterns correlated among samples. To statistically verify pattern matching, we fit each time series of otolith measurements with a spline, and all measurements were divided by the values predicted by the curve. This standardized each time series to a mean of 1, removing the effects of age on growth and homogenizing variance. Each time series was then correlated with the average growth patterns of all other series, yielding an average correlation coefficient (r) of 0.53. Average growth of all 50 samples was significantly correlated with an upwelling index (r = 0.40, p = 0.002), the Pacific Decadal Oscillation (r = –0.29, p = 0.007), and the Northern Oscillation Index (r = 0.51, p = 0.0001), corroborating accuracy. We believe this approach to age validation will be applicable to a wide range of long-lived marine and freshwater species.

scholarly journals Predicting Plant Growth from Time-Series Data Using Deep Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 331
Author(s):  
Robail Yasrab ◽  
Jincheng Zhang ◽  
Polina Smyth ◽  
Michael P. Pound
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Plant Growth ◽  
Time Series Data ◽  
Plant Traits ◽  
Domain Adaptation ◽  
Series Data ◽  
Plant Phenotyping ◽  
Research Issues ◽  
Public Datasets

Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.

scholarly journals Learning Where to Look for COVID-19 Growth: Multivariate Analysis of COVID-19 Cases Over Time using Explainable Convolution-LSTM

2021 ◽  
Author(s):  
Novanto Yudistira ◽  
Sutiman Bambang Sumitro ◽  
Alberth Nahas ◽  
Nelly Florida Riama
Keyword(s):  
Health Economic ◽  
Time Series ◽  
Multivariate Analysis ◽  
Environmental Factor ◽  
Contextual Information ◽  
Descriptive Analysis ◽  
Growth Patterns ◽  
Contributing Factors ◽  
Coarse To Fine ◽  
Over Time

AbstractDeterminant factors which contribute to the prediction should take into account multivariate analysis for capturing coarse-to-fine contextual information. From the preliminary descriptive analysis, it shows that environmental factor such as UV (ultraviolet) is one of the essential factors that should be considered to observe the COVID-19 epidemic drivers, During summer, UV can inactivate viruses that live in the air and on the surface of the objects especially at noon in tropical or subtropical countries. However, it may not be significant in closed spaces like workspace and areas with the intensive human-to-human transmission, especially in densely populated areas. Different COVID-19 pandemic growth patterns in northern subtropical, southern subtropical and tropical countries occur over time. Moreover, there are education, government, morphological, health, economic, and behavioral factors contributing to the growth of COVID-19. Multivariate analysis via visual attribution of explainable Convolution-LSTM is utilized to see high contributing factors responsible for the growth of daily COVID-19 cases. For future works, data to be analyzed should be more detailed in terms of the region and the period where the time-series sample is acquired. The explainable Convolution-LSTM code is available here: https://github.com/cbasemaster/time-series-attribution

Vegetation indices as a proxy for spatio-temporal variations in water availability in the semi-arid Rio Santa valley (Callejón de Huaylas, Peru)

2021 ◽  
Author(s):  
Lorenz Hänchen ◽  
Cornelia Klein ◽  
Fabien Maussion ◽  
Wolfgang Gurgiser ◽  
Georg Wohlfahrt
Keyword(s):  
Time Series ◽  
Plant Growth ◽  
Water Availability ◽  
Vegetation Indices ◽  
Growing Season ◽  
Rainfall Data ◽  
Season Length ◽  
Wet Season ◽  
Semi Arid ◽  
Over Time

<p>In the semi-arid Peruvian Andes, the agricultural growing season is mostly determined by the timing of the onset and cessation of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa valley (Callejón de Huaylas) bordered by the glaciated Coordillera Blanca to the east and the unglaciated Coordillera Negra to the west, reported increasing challenges in the predictability of the onset, more frequent dry spells and extreme precipitation events during the wet season. Previous studies based on time-series of local rain gauges however did not show any significant changes in either timing or intensity of the wet season. Both in-situ and satellite rainfall data for the region lack the necessary spatial resolution to capture the highly variable rainfall distribution typical for complex terrain, and are often of questionable quality and temporal consistency. As in other Andean valleys, there remains considerable uncertainty in the Rio Santa basin regarding hydrological changes over the last decades. These changes are of a great concern for the local society and the lacking knowledge about changes in water availability (i.e. rainfall) and water demand (i.e. land use practices) hinder the assessment of relevant factors for the development of adaption strategies.</p><p>The over-archiving goal of this study was to better understand variability and recent changes of plant growth and rainfall seasonality and the interactions between them in the Rio Santa basin. Specifically, we aimed to illustrate how satellite-derived information on vegetation greenness can be exploited to infer a robust and highly resolved picture of recent changes in rainfall and vegetation across the region: As the semi-arid climate causes water availability (i.e. precipitation) to be the key limiting factor for plant growth, patterns of precipitation occurrence and the seasonality of vegetation indices (VIs) are tightly coupled. Therefore, these indices can serve as an integrated proxy of rainfall. By combining a 20 year time series of MODIS Aqua and Terra VIs (from 2000 to today) and datasets of precipitation (both remote-sensing and observations) we explore recent spatial and temporal changes in vegetation and water availability by combining VIs timeseries and derived land surface phenology (LSP) with measures of wet season onset and cessation from rainfall data. Furthermore, we analyse the interaction of El Niño Southern Oscillation (ENSO) and the wet and growing season.</p><p>We find spatially variable but significant greening over the majority of the Rio Santa valley domain. This greening is particularly pronounced during the the dry season (Austral winter) and indicates an overall increase of plant available water over time. The start of the growing season (SOS) is temporally highly variable and dominates the variability of growing season length over time. Peak and end of season (POS, EOS) are significantly delayed in the 20 year analysis. By partitioning the results into periods of three stages of ENSO (neutral, Niño, Niña) we find an earlier onset of the rainy and growing season and an overall increased season length in years associated with El Niño.</p>

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

2020 ◽  
Vol 11 ◽  
Author(s):  
Soumyashree Kar ◽  
Vincent Garin ◽  
Jana Kholová ◽  
Vincent Vadez ◽  
Surya S. Durbha ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area ◽  
High Throughput ◽  
Missing Values ◽  
Time Series Data ◽  
Series Data ◽  
Genotypic Differences ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
Over Time

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.

scholarly journals Functional principal component based time-series genome-wide association in sorghum

2020 ◽  
Author(s):  
Chenyong Miao ◽  
Yuhang Xu ◽  
Sanzhen Liu ◽  
Patrick S. Schnable ◽  
James C. Schnable
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Genome Wide Association ◽  
Phenotypic Change ◽  
Genome Wide Association Studies ◽  
Functional Principal Component ◽  
Genome Wide ◽  
Wide Range ◽  
Over Time

ABSTRACTThe phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track phenotypic change over time. Identifying genetic loci regulating differences in the pattern of phenotypic change remains challenging. In this study we used functional principal component analysis (FPCA) to achieve this aim. Time-series phenotype data was collected from a sorghum diversity panel using a number of technologies including RGB and hyperspectral imaging. Imaging lasted for thirty-seven days centered on reproductive transition. A new higher density SNP set was generated for the same population. Several genes known to controlling trait variation in sorghum have been cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotype. Partitioning was consistent with the known molecular function of the individual cloned genes. FPCA-based genome-wide association studies can enable robust time-series mapping analyses in a wide range of contexts. Time-series analysis can increase the accuracy and power of quantitative genetic analyses.

Episodic whole plant growth patterns in Ligustrum

1993 ◽  
Vol 89 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Jeff S. Kuehny ◽  
Mary C. Halbrooks
Keyword(s):  
Plant Growth ◽  
Growth Patterns ◽  
Whole Plant

Anti-corruption in History

2017 ◽  
Keyword(s):  
Financial Institutions ◽  
Global Media ◽  
Policy Makers ◽  
The Past ◽  
Human Wellbeing ◽  
Modern Period ◽  
Legitimate Power ◽  
Wide Range ◽  
Scant Attention ◽  
Over Time

Anticorruption in History is the first major collection of case studies on how past societies and polities, in and beyond Europe, defined legitimate power in terms of fighting corruption and designed specific mechanisms to pursue that agenda. It is a timely book: corruption is widely seen today as a major problem, undermining trust in government, financial institutions, economic efficiency, the principle of equality before the law and human wellbeing in general. Corruption, in short, is a major hurdle on the “path to Denmark”—a feted blueprint for stable and successful statebuilding. The resonance of this view explains why efforts to promote anticorruption policies have proliferated in recent years. But while the subjects of corruption and anticorruption have captured the attention of politicians, scholars, NGOs and the global media, scant attention has been paid to the link between corruption and the change of anticorruption policies over time and place. Such a historical approach could help explain major moments of change in the past as well as reasons for the success and failure of specific anticorruption policies and their relation to a country’s image (of itself or as construed from outside) as being more or less corrupt. It is precisely this scholarly lacuna that the present volume intends to begin to fill. A wide range of historical contexts are addressed, ranging from the ancient to the modern period, with specific insights for policy makers offered throughout.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

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