Moisture content estimation and senescence phenotyping of novel Miscanthus hybrids combining UAV‐based remote sensing and machine learning. Issue 6 (12th April 2022)

• Record Type: Journal Article
• Authors: Impollonia, Giorgio; Croci, Michele; Martani, Enrico; Ferrarini, Andrea; Kam, Jason; Trindade, Luisa M.; Clifton‐Brown, John; Amaducci, Stefano
• Is Part Of: Global change biology. Volume 14:Issue 6(2022)
• Page Start: 639
• Page End: 656
• Publication Date: 2022-04-12
• Subjects: GAM -- high‐throughput plant phenotyping -- machine learning -- Miscanthus -- moisture content -- multispectral -- remote sensing -- senescence -- transferability -- UAV; Biomass energy -- Periodicals; Biomass energy -- Environmental aspects -- Periodicals; Energy crops -- Periodicals; 662.88
• DOI: 10.1111/gcbb.12930
• Languages: English
• ISSNs: 1757-1693

Abstract

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. Abstract : This study estimated the moisture content of 14 contrasting Miscanthus hybrids combining unmanned aerial vehicle (UAV) remote sensing and machine learning. The random forest (RF) model was trained with moisture content values measured directly from each plot trial, UAV multispectral data (the vegetation indices) and categorical variables of Miscanthus hybrids (material, hybrid code, and genotype). The time series of the moisture content values estimated by RF model from VIs derived from multiple UAV flights were used for phenotyping senescence dynamics and identifying the stay‐green (SG) trait of Miscanthus hybrids using the generalized additive model.