ArticleScientific reports2021
Aerial high-throughput phenotyping of peanut leaf area index and lateral growth.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancing crop improvement through GWAS and beyond in mung bean.Frontiers in plant science · 2024Pooled it
- Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods.Sensors (Basel, Switzerland) · 2026Article
- Next-Generation Precision Breeding in Peanut (Insects · 2026Review
- Growth analysis of cotton using UAS derived multi temporal canopy features.Scientific reports · 2025Article
- Aerial imagery and Segment Anything Model for architectural trait phenotyping to support genetic analysis in peanut breeding.Plant phenomics (Washington, D.C.) · 2025Article
- Integrative genomics and genetics from evolutionary insights to precision breeding in peanuts (Arachis Hypogaea L.).Functional & integrative genomics · 2025Review
- Water status and plant traits of dry bean assessment using integrated spectral reflectance and RGB image indices with artificial intelligence.Scientific reports · 2025Article
- Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US.Sensors (Basel, Switzerland) · 2025Article
- Article
- Understanding the impacts of drought on peanutsFrontiers in genetics · 2024Review
- Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field.Nature communications · 2023Article
- Soybean leaf estimation based on RGB images and machine learning methods.Plant methods · 2023Article
- Low-Cost Hyperspectral Imaging to Detect Drought Stress in High-Throughput Phenotyping.Plants (Basel, Switzerland) · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features.Frontiers in plant science · 2023Article
- Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance.Frontiers in genetics · 2023Review
- Accelerating leaf area measurement using a volumetric approach.Plant methods · 2022Article
- Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut (Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Leaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models' suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.
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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.