UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

Giorgio Impollonia*, Michele Croci, Andrea Ferrarini, Jason Brook, Enrico Martani, Henri Blandinières, Andrea Marcone, Danny Awty-Carroll, Chris Ashman, Jason Kam, Andreas Kiesel, Luisa M. Trindade, Mirco Boschetti, John Clifton-Brown, Stefano Amaducci

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)


Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using a VIs time series, and predicted yield using a peak descriptor derived from a VIs time series with 2.3 Mg DM ha−1 of the root mean square error (RMSE). The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production.

Original languageEnglish
Article number2927
JournalRemote Sensing
Issue number12
Publication statusPublished - 1 Jun 2022


  • high-throughput phenotyping
  • machine learning
  • Miscanthus
  • multi-sensor interoperability
  • multispectral images
  • remote sensing
  • trait estimation
  • UAV
  • yield prediction


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