<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data"^^ . "The rapid development of phenotyping technologies over the last years gave the\r\nopportunity to study plant development over time. The treatment of the massive\r\namount of data collected by high-throughput phenotyping (HTP) platforms is however\r\nan important challenge for the plant science community. An important issue is to\r\naccurately estimate, over time, the genotypic component of plant phenotype. In outdoor\r\nand field-based HTP platforms, phenotype measurements can be substantially affected\r\nby data-generation inaccuracies or failures, leading to erroneous or missing data. To\r\nsolve that problem, we developed an analytical pipeline composed of three modules:\r\ndetection of outliers, imputation of missing values, and mixed-model genotype adjusted\r\nmeans computation with spatial adjustment. The pipeline was tested on three different\r\ntraits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea,\r\nsorghum), measured during two seasons. Using real-data analyses and simulations,\r\nwe showed that the sequential application of the three pipeline steps was particularly\r\nuseful to estimate smooth genotype growth curves from raw data containing a large\r\namount of noise, a situation that is potentially frequent in data generated on outdoor\r\nHTP platforms. The procedure we propose can handle up to 50% of missing values. It\r\nis also robust to data contamination rates between 20 and 30% of the data. The pipeline\r\nwas further extended to model the genotype time series data. A change-point analysis\r\nallowed the determination of growth phases and the optimal timing where genotypic\r\ndifferences were the largest. The estimated genotypic values were used to cluster the\r\ngenotypes during the optimal growth phase. Through a two-way analysis of variance\r\n(ANOVA), clusters were found to be consistently defined throughout the growth duration.\r\nTherefore, we could show, on a wide range of scenarios, that the pipeline facilitated\r\nefficient extraction of useful information from outdoor HTP platform data. High-quality\r\nplant growth time series data is also provided to support breeding decisions. The R\r\ncode of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP."^^ . "2020-11" . . . "11" . "552509" . . "Frontiers Media"^^ . . . "Frontiers in Plant Science (TSI)"^^ . . . "1664462X" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "S"^^ . "Kar"^^ . "S Kar"^^ . . "M O"^^ . "Urban"^^ . "M O Urban"^^ . . "R"^^ . "Tanaka"^^ . "R Tanaka"^^ . . "H"^^ . "Iwata"^^ . "H Iwata"^^ . . "V"^^ . "Garin"^^ . "V Garin"^^ . . "V"^^ . "Vadez"^^ . "V Vadez"^^ . . "J"^^ . "Kholová"^^ . "J Kholová"^^ . . "S S"^^ . "Durbha"^^ . "S S Durbha"^^ . . "J"^^ . "Adinarayana"^^ . "J Adinarayana"^^ . . . . . . "SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data (PDF)"^^ . . . . . "fpls-11-552509.pdf"^^ . . . "SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data (Other)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #11757 \n\nSpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data\n\n" . "text/html" . . . "Crop Physiology"@en . .