ArticleSensors (Basel, Switzerland)2019
Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A Portable High-Resolution Snapshot Multispectral Imaging Device Leveraging Spatial and Spectral Features for Non-Invasive Corn Nitrogen Treatment Classification.Sensors (Basel, Switzerland) · 2025Article
- Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies.Plants (Basel, Switzerland) · 2024Review
- Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.G3 (Bethesda, Md.) · 2024Article
- Impact of irrigation, nitrogen fertilization, and plant density on stay-green and its effects on agronomic traits in maize.Frontiers in plant science · 2024Article
- On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies.Open life sciences · 2023Article
- Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics.Frontiers in plant science · 2023Article
- Field phenotyping for African crops: overview and perspectives.Frontiers in plant science · 2023Review
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- Genetic analysis and yield assessment of maize hybrids under low and optimal nitrogen environments.Heliyon · 2022Article
- Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding.Frontiers in plant science · 2022Article
- Scaling up high-throughput phenotyping for abiotic stress selection in the field.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models.Frontiers in plant science · 2021Article
- How does nitrogen shape plant architecture?Journal of experimental botany · 2020Review
- High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field.Frontiers in bioengineering and biotechnology · 2020Review
- Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest.Frontiers in artificial intelligence · 2020Article
- Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UAV-Based Agrochemical Spraying.Sensors (Basel, Switzerland) · 2019Article
- A Robust Automated Image-Based Phenotyping Method for Rapid Vegetative Screening of Wheat Germplasm for Nitrogen Use Efficiency.Frontiers in plant science · 2019Article
- PredictingFrontiers in plant science · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red-green-blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R
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