ArticleSensors (Basel, Switzerland)2022
Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 78 citations in OpenAlex.
- Uncovering the genetic architecture of biomass yield and related traits in Southern US oat germplasm using genome-wide association study.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Nitrogen fertilizer application for improving the biomass, quality, and nitrogen fixation of alfalfa (PeerJ · 2025Article
- OBM-RFEcv: An adaptive ensemble model for monitoring key growth indicators of Gerbera using multi-spectral image fusion features.PloS one · 2025Article
- Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa.G3 (Bethesda, Md.) · 2024Article
- Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning.Plants (Basel, Switzerland) · 2024Article
- Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location.Plant methods · 2024Article
- Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Aboveground biomass estimation of wetland vegetation at the species level using unoccupied aerial vehicle RGB imagery.Frontiers in plant science · 2023Article
- Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models.Frontiers in plant science · 2023Article
- Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture.Sensors (Basel, Switzerland) · 2022Article
- Unearthing soil-plant-microbiota crosstalk: Looking back to move forward.Frontiers in plant science · 2022Review
- Construction of Remote Sensing Model of Fresh Corn Biomass Based on Neural Network.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (
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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.