collection
Plant Phenomics and Precision Agriculture
Advances in plant phenomics and precision agriculture rely on research that spans disciplines and spatiotemporal scales. From cell to field, from second to season, investigators seek to understand plant processes and environmental interactions, and to develop interventions for maximal resilience and yield. These efforts are fundamental to guaranteeing food security and sustainable crop production in the context of rising demand and increasingly challenging environments.
In this Collection, we bring together research that develops and tests -- in controlled environments or in the field -- new methods and technologies for phenotypic measurements and agricultural surveillance and intervention.
Image Credit: DJI-Agras, on CC BY 4.0
Guest Editors
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Image creditMalia Gehan by PLOS ONE, CC BY 4.0Guest Editor, PLOS ONE Malia Gehan
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Image creditGuillaume Lobet by PLOS ONE, CC BY 4.0Guest Editor, PLOS ONE Guillaume Lobet
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Image creditSierra Young by PLOS ONE, CC BY 4.0Guest Editor, PLOS ONE Sierra Young
Introducing the Plant Phenomics & Precision Agriculture Collection
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Introducing the Plant Phenomics & Precision Agriculture Collection Introducing the Plant Phenomics & Precision Agriculture Collection
PLOS ONE Collection
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Image creditPhoto by Teemu Paananen, on CC BY 4.0PLOS ONE Registration of spatio-temporal point clouds of plants for phenotyping
Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and…
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Image creditPhoto by Nguyen Dang Hoang Nhu, on CC BY 4.0PLOS ONE Internal defect scanning of sweetpotatoes using interactance spectroscopy
While standard visible-light imaging offers a fast and inexpensive means of quality analysis of horticultural products, it is generally…
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Image creditPhoto by Katherine Volkovski, on CC BY 4.0PLOS ONE High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network
Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of…
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Image creditPhoto by Hello I'm Nik , on CC BY 4.0PLOS ONE The search for yield predictors for mature field-grown plants from juvenile pot-grown cassava (Manihot esculenta Crantz)
Cassava is the 6th most important source of dietary energy in the world but its root system architecture (RSA) had seldom been quantified.…
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Image creditPhoto by Markus Spiske, on CC BY 4.0PLOS ONE Production location of the gelling agent Phytagel has a significant impact on Arabidopsis thaliana seedling phenotypic analysis
Background: Recently, it was found that 1% Phytagel plates used to conduct Arabidopsis thaliana seedling phenotypic analysis no longer…
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Image creditPhoto by Juan Manuel Núñez Méndez, on CC BY 4.0PLOS ONE The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures
Organic farmers, who rely on legumes as an external nitrogen (N) source, need a fast and easy on-the-go measurement technique to determine…
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Image creditPhoto by Alice Karolina, on CC BY 4.0PLOS ONE Changes in reflectance of rice seedlings during planthopper feeding as detected by digital camera: Potential applications for high-throughput phenotyping
Damage to grasses and cereals by phloem-feeding herbivores is manifest as nutrient and chlorophyll loss, desiccation, and a gradual…
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Image creditPhoto by Sergio Camalich, on CC BY 4.0PLOS ONE Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing
Chlorophyll content is an important indicator of the growth status of japonica rice. The objective of this paper is to develop an…
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Image creditPhoto by Sergio Camalich, on CC BY 4.0PLOS ONE A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops
Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this…
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Image creditPhoto by Bharath Raj, on CC BY 4.0PLOS ONE High-throughput, image-based phenotyping reveals nutrient-dependent growth facilitation in a grass-legume mixture
This study used high throughput, image-based phenotyping (HTP) to distinguish growth patterns, detect facilitation and interpret…
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Image creditPhoto by Bill Oxford, on CC BY 4.0PLOS ONE Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning
Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and…
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PLOS ONE An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning…