Moisture content estimation and senescence phenotyping of novel Miscanthus hybrids combining UAV‐based remote sensing and machine learning

Autor: Giorgio Impollonia, Michele Croci, Enrico Martani, Andrea Ferrarini, Jason Kam, Luisa M. Trindade, John Clifton‐Brown, Stefano Amaducci
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: GCB Bioenergy, Vol 14, Iss 6, Pp 639-656 (2022)
Druh dokumentu: article
ISSN: 1757-1707
1757-1693
DOI: 10.1111/gcbb.12930
Popis: 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.
Databáze: Directory of Open Access Journals
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