ArticleFrontiers in plant science2022
Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- UAV remote sensing for yield prediction in staple crops: a review.Frontiers in plant science · 2026Pooled it
- Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles.Frontiers in plant science · 2024Pooled it
- Remote evaluation of rice nitrogen utilization efficiency using chlorophyll-related spectral indices derived from unmanned aerial vehicle imagery.Frontiers in plant science · 2026Article
- Article
- Improving the estimation accuracy of rice leaf protein nitrogen using data augmentation, explainable machine learning, and UAV hyperspectral imagery.Frontiers in plant science · 2026Article
- A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions.Scientific reports · 2025Article
- Characterizing stay-green in barley across diverse environments: unveiling novel haplotypes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.Frontiers in plant science · 2024Article
- Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs.Sensors (Basel, Switzerland) · 2023Article
- Article
- Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R
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