Plant phenotyping: from bean weighing to image analysis

By Achim Walter, Frank Liebisch, Andreas Hund. Plant Methods, 2015

393 citations recorded by the source corpus.

Plant phenotyping refers to a quantitative description of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Today, rapid developments are taking place in the field of non-destructive, image-analysis -based phenotyping that allow for a characterization of plant traits in high-throughput. During the last decade, ‘the field of image-based phenotyping has broadened its focus from the initial characterization of single-plant traits in controlled conditions towards ‘real-life’ applications of robust field techniques in plant plots and canopies. An important component of successful phenotyping approaches is the holistic characterization of plant performance that can be achieved with several methodologies, ranging from multispectral image analyses via thermographical analyses to growth measurements, also taking root phenotypes into account.

Subfields: Ecology, Food Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/a059418ba24f3038f9e2d62c87a7e7a3db548a9c

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Citations in the corpus

Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach (2017, background): “At the same time, research has been done in the ﬁeld of crop phenotyping in deﬁning traits for plant performance (White et al. 2012; Walter et al. 2012, 2015).”
Crop Phenomics: Current Status and Perspectives (2019, background): “Under highly controlled conditions, the classic image processing pipeline can provide acceptable phenotyping results, such as biomass (Leister et al., 1999), NDVI (Walter et al., 2015), chlorophyll responses (Walter et al., 2015), and compactness (Vylder et al., 2012) etc.”
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (2016, neutral): “Image analysis has become a key component in these work-flows [1], where automated measurement and counting have allowed for increased throughput and unbiased, consistent measurement systems.”
Multi-modality imagery database for plant phenotyping (2016, background): “For this approach to be of effective, plants must be monitored continuously, over developmental time scales and under conditions that approximate the native environment (dynamic ‘field’ conditions) [12,34].”
Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies (2021, background): “[252] proposed a solution to address the limited energy availability problem by using solar energy harvesting technologies to charge the batteries of the WSN nodes.”
Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, background): “31 Moreover, high-throughput, automated phenotyping approaches for traits such as leaf area index (LAI), 32 canopy cover (CC) or plant height are urgently needed to facilitate the assessment of large breeding 33 populations or precision agriculture approaches (Fiorani and Schurr, 2013; Walter et al.”
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (2017, background): “Likewise, accurate measurement of Normalized Diﬀerence Vegetation Index (NDVI) (Walter et al., 2015), chlorophyll responses (Campbell et al., 2015), and other simple features have been demonstrated.”
Seaweed production: overview of the global state of exploitation, farming and emerging research activity (2017, methodology): “for robust, cost-effective, downstream processing (Cabrera-Bosquet et al., 2012; Li et al., 2014; Mutka & Bart, 2015; Walter et al., 2015) and have also been used”
Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! (2019, methodology): “Here UAVs, tractor-based or stationary platforms are often used [27,28,29 ,30,31].”
Learning to Count Leaves in Rosette Plants (2015, background): “High throughput plant phenotyping allows large-scale plant analysis [13, 17, 39], in the hope of reducing the bottleneck”