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Moisture content estimation and senescence phenotyping of novel Miscanthus hybrids combining UAV‐based remote sensing and machine learning
- Source :
- GCB Bioenergy, 14(6), 639-656, GCB Bioenergy, GCB Bioenergy 14 (2022) 6
- Publication Year :
- 2022
- Publisher :
- Wiley, 2022.
-
Abstract
- Miscanthus is a leading perennial biomass crop that can produce high yields on marginal lands. Moisture content is a highly relevant biomass quality trait with multiple impacts on efficiencies of harvest, transport, and storage. The dynamics of moisture content during senescence and overwinter ripening are determined by genotype × environment interactions. In this paper, unmanned aerial vehicle (UAV)-based remote sensing was used for high-throughput plant phenotyping (HTPP) of the moisture content dynamics during autumn and winter senescence of 14 contrasting hybrid types (progeny of M. sinensis x M. sinensis [M. sin x M. sin, eight types] and M. sinensis x M. sacchariflorus [M. sin x M. sac, six types]). The time series of moisture content was estimated using machine learning (ML) models and a range of vegetation indices (VIs) derived from UAV-based remote sensing. The most important VIs for moisture content estimation were selected by the recursive feature elimination (RFE) algorithm and were BNDVI, GDVI, and PSRI. The ML model transferability was high only when the moisture content was above 30%. The best ML model accuracy was achieved by combining VIs and categorical variables (5.6% of RMSE). This model was used for phenotyping senescence dynamics and identifying the stay-green (SG) trait of Miscanthus hybrids using the generalized additive model (GAM). Combining ML and GAM modeling, applied to time series of moisture content values estimated from VIs derived from multiple UAV flights, proved to be a powerful tool for HTPP.
- Subjects :
- senescence
Renewable Energy, Sustainability and the Environment
UAV
multispectral
transferability
Forestry
Miscanthus
GAM
Plant Breeding
remote sensing
machine learning
Laboratorium voor Plantenveredeling
high-throughput plant phenotyping
EPS
Waste Management and Disposal
Agronomy and Crop Science
Settore AGR/02 - AGRONOMIA E COLTIVAZIONI ERBACEE
moisture content
Subjects
Details
- ISSN :
- 17571707 and 17571693
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- GCB Bioenergy
- Accession number :
- edsair.doi.dedup.....adf33de58ab883aa68be264e241f5406