Machine learning-based prediction of sorghum biomass from UAV multispectral imagery data
Author:
Affiliation:
1. Donald Danforth Plant Science Center,Saint Louis,MO,USA,63132
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10127223/10127224/10127516.pdf?arnumber=10127516
Reference28 articles.
1. Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States
2. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass
3. FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images
4. Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat
5. Prediction of sorghum biomass based on image based features derived from time series of UAV images
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1. Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review;The Plant Phenome Journal;2024-04-05
2. A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning;IEEE Access;2023
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