ArticleFrontiers in plant science2023
Estimating yield-contributing physiological parameters of cotton using UAV-based imagery.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series.Plant phenomics (Washington, D.C.) · 2026Article
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- UAV-based multitier feature selection improves nitrogen content estimation in arid-region cotton.Frontiers in plant science · 2025Article
- Using Machine Learning Methods Combined with Vegetation Indices and Growth Indicators to Predict Seed Yield ofPlants (Basel, Switzerland) · 2024Article
- YOLO SSPD: a small target cotton boll detection model during the boll-spitting period based on space-to-depth convolution.Frontiers in plant science · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPAR
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