@article{ArvidssonPerezRodriguezMuellerRoeber2011,
  author    = {Arvidsson, Samuel Janne and Perez-Rodriguez, Paulino and M{\"u}ller-R{\"o}ber, Bernd},
  title     = {A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects},
  series = {New phytologist : international journal of plant science},
  volume    = {191},
  journal   = {New phytologist : international journal of plant science},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0028-646X},
  doi       = {10.1111/j.1469-8137.2011.03756.x},
  pages     = {895 -- 907},
  year      = {2011},
  abstract  = {To gain a deeper understanding of the mechanisms behind biomass accumulation, it is important to study plant growth behavior. Manually phenotyping large sets of plants requires important human resources and expertise and is typically not feasible for detection of weak growth phenotypes. Here, we established an automated growth phenotyping pipeline for Arabidopsis thaliana to aid researchers in comparing growth behaviors of different genotypes. The analysis pipeline includes automated image analysis of two-dimensional digital plant images and evaluation of manually annotated information of growth stages. It employs linear mixed-effects models to quantify genotype effects on total rosette area and relative leaf growth rate (RLGR) and ANOVAs to quantify effects on developmental times. Using the system, a single researcher can phenotype up to 7000 plants d(-1). Technical variance is very low (typically < 2\%). We show quantitative results for the growth-impaired starch-excessmutant sex4-3 and the growth-enhancedmutant grf9. We show that recordings of environmental and developmental variables reduce noise levels in the phenotyping datasets significantly and that careful examination of predictor variables (such as d after sowing or germination) is crucial to avoid exaggerations of recorded phenotypes and thus biased conclusions.},
  language  = {en}
}
@article{OPUS4-56760,
  title     = {Plant Hormones},
  series = {Methods in Molecular Biology},
  journal   = {Methods in Molecular Biology},
  number    = {1497},
  editor    = {Kleine-Vehn, J{\"u}rgen and Sauer, Michael},
  publisher = {Springer},
  address   = {New York},
  isbn      = {978-1-4939-6467-3},
  issn      = {1064-3745},
  doi       = {10.1007/978-1-4939-6469-7},
  pages     = {XI, 288},
  year      = {2017},
  abstract  = {This volume aims to present a representative cross-section of modern experimental approaches relevant to Plant Hormone Biology, ranging from relatively simple physiological to highly sophisticated methods. Chapters describe physiological, developmental, microscopy-based techniques, measure hormone contents, and heterologous systems. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.},
  language  = {en}
}
@article{OlasFichtnerApelt2020,
  author    = {Olas, Justyna Jadwiga and Fichtner, Franziska and Apelt, Federico},
  title     = {All roads lead to growth},
  series = {Journal of experimental botany},
  volume    = {71},
  journal   = {Journal of experimental botany},
  number    = {1},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {0022-0957},
  doi       = {10.1093/jxb/erz406},
  pages     = {11 -- 21},
  year      = {2020},
  abstract  = {Plant growth is a highly complex biological process that involves innumerable interconnected biochemical and signalling pathways. Many different techniques have been developed to measure growth, unravel the various processes that contribute to plant growth, and understand how a complex interaction between genotype and environment determines the growth phenotype. Despite this complexity, the term 'growth' is often simplified by researchers; depending on the method used for quantification, growth is viewed as an increase in plant or organ size, a change in cell architecture, or an increase in structural biomass. In this review, we summarise the cellular and molecular mechanisms underlying plant growth, highlight state-of-the-art imaging and non-imaging-based techniques to quantitatively measure growth, including a discussion of their advantages and drawbacks, and suggest a terminology for growth rates depending on the type of technique used.},
  language  = {en}
}
@article{BiermannBachKlaeringetal.2022,
  author    = {Biermann, Robin Tim and Bach, Linh T. and Kl{\"a}ring, Hans-Peter and Baldermann, Susanne and B{\"o}rnke, Frederik and Schwarz, Dietmar},
  title     = {Discovering tolerance-A computational approach to assess abiotic stress tolerance in tomato under greenhouse conditions},
  series = {Frontiers in sustainable food systems},
  volume    = {6},
  journal   = {Frontiers in sustainable food systems},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2571-581X},
  doi       = {10.3389/fsufs.2022.878013},
  pages     = {12},
  year      = {2022},
  abstract  = {Modern plant cultivars often possess superior growth characteristics, but within a limited range of environmental conditions. Due to climate change, crops will be exposed to distressing abiotic conditions more often in the future, out of which heat stress is used as example for this study. To support identification of tolerant germplasm and advance screening techniques by a novel multivariate evaluation method, a diversity panel of 14 tomato genotypes, comprising Mediterranean landraces of Solanum lycopersicum, the cultivar "Moneymaker" and Solanum pennellii LA0716, which served as internal references, was assessed toward their tolerance against long-term heat stress. After 5 weeks of growth, young tomato plants were exposed to either control (22/18 degrees C) or heat stress (35/25 degrees C) conditions for 2 weeks. Within this period, water consumption, leaf angles and leaf color were determined. Additionally, gas exchange and leaf temperature were investigated. Finally, biomass traits were recorded. The resulting multivariate dataset on phenotypic plasticity was evaluated to test the hypothesis, that more tolerant genotypes have less affected phenotypes upon stress adaptation. For this, a cluster-analysis-based approach was developed that involved a principal component analysis (PCA), dimension reduction and determination of Euclidean distances. These distances served as measure for the phenotypic plasticity upon heat stress. Statistical evaluation allowed the identification and classification of homogeneous groups consisting each of four putative more or less heat stress tolerant genotypes. The resulting classification of the internal references as "tolerant" highlights the applicability of our proposed tolerance assessment model. PCA factor analysis on principal components 1-3 which covered 76.7\% of variance within the phenotypic data, suggested that some laborious measure such as the gas exchange might be replaced with the determination of leaf temperature in larger heat stress screenings. Hence, the overall advantage of the presented method is rooted in its suitability of both, planning and executing screenings for abiotic stress tolerance using multivariate phenotypic data to overcome the challenge of identifying abiotic stress tolerant plants from existing germplasms and promote sustainable agriculture for the future.},
  language  = {en}
}