ArticlePlant physiology2020
Spectral Phenotyping of Physiological and Anatomical Leaf Traits Related with Maize Water Status.
Article in Plant physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed, 70 citations in OpenAlex.
- VIS-NIR-SWIR Proximal Spectroradiometry Coupled with Machine Learning and Deep Learning for Ornamental Plant Identification and Classification.ACS omega · 2026Article
- Methodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.Plants (Basel, Switzerland) · 2026Article
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Generalizability and transferability of machine learning models using hyperspectral reflectance data for maize traits.Scientific reports · 2026Article
- Article
- Hyperspectral Leaf Reflectance Detects Interactive Genetic and Environmental Effects on Tree Phenotypes, Enabling Large-Scale Monitoring and Restoration Planning Under Climate Change.Plant, cell & environment · 2025Article
- Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery.Plant methods · 2024Article
- Fruit Water Stress Index of Apple Measured by Means of Temperature-Annotated 3D Point Cloud.Plant phenomics (Washington, D.C.) · 2024Article
- Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field.Nature communications · 2023Article
- Physiological and Biochemical Responses Induced by Plum Pox Virus and Plum Bark Necrosis Steam Pitting Associated Virus in Tuscany Autochthonous Plum cv. Coscia di Monaca.Plants (Basel, Switzerland) · 2023Article
- A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.Journal of experimental botany · 2023Article
- Spectroradiometer Calibration for Radiance Transfer Measurements.Sensors (Basel, Switzerland) · 2023Article
- Time for a drought experiment: Do you know your plants' water status?The Plant cell · 2023Article
- Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean.Plant phenomics (Washington, D.C.) · 2023Article
- Quantifying physiological trait variation with automated hyperspectral imaging in rice.Frontiers in plant science · 2023Article
- Article
- Advances in field-based high-throughput photosynthetic phenotyping.Journal of experimental botany · 2022Review
- Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding?Plant physiology · 2022Article
- Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges.Plant communications · 2021Review
- Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance.Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in phenotyping techniques capable of rapidly and nondestructively detecting impacts of drought on crops are necessary to meet the 21st-century challenge of food security. Here, we describe the use of hyperspectral reflectance to predict variation in physiological and anatomical leaf traits related with water status under varying water availability in six maize (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.