Ai-driven maize yield forecasting using unmanned aerial vehicle-based hyperspectral and lidar data fusion

K. Dilmurat, V. Sagan, S. Moose

Research output: Contribution to journalConference articlepeer-review

Abstract

The increased availability of remote sensing data combined with the wide-ranging applicability of artificial intelligence has enabled agriculture stakeholders to monitor changes in crops and their environment frequently and accurately. Applying cutting-edge technology in precision agriculture also enabled the prediction of pre-harvest yield from standing crop signals. Forecasting grain yield from standing crops benefits high-Throughput plant phenotyping and agriculture policymaking with information on where crop production is likely to decline. Advanced developments in the Unmanned Aerial Vehicle (UAV) platform and sensor technologies aided high-resolution spatial, spectral, and structural data collection processes at a relatively lower cost and shorter time. In this study, UAV-based LiDAR and hyperspectral images were collected during the growing season of 2020 over a cornfield near Urbana Champaign, Illinois, USA. Hyperspectral imagery-based canopy spectral & texture features and LiDAR point cloud-based canopy structure features were extracted and, along with their combination, were used as inputs for maize yield prediction under the H2O Automated Machine Learning framework (H2O-AutoML). The research results are (1) UAV Hyperspectral imagery can successfully predict maize yield with relatively decent accuracies; additionally, LiDAR point cloud-based canopy structure features are found to be significant indicators for maize yield prediction, which produced slightly poorer, yet comparable results to hyperspectral data; (2) regardless of machine learning methods, integration of hyperspectral imagery-based canopy spectral and texture information with LiDAR-based canopy structure features outperformed the predictions when using a single sensor alone; (3)the H2O-AutoML framework presented to be an efficient strategy for machine learning-based data-driven model building.

Original languageEnglish (US)
Pages (from-to)193-199
Number of pages7
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume5
Issue number3
DOIs
StatePublished - May 17 2022
Event2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III - Nice, France
Duration: Jun 6 2022Jun 11 2022

Keywords

  • Data Fusion
  • Hyperspectral
  • LiDAR
  • Machine Learning
  • Remote Sensing
  • Unmanned Aerial Vehicle (UAV)

ASJC Scopus subject areas

  • Instrumentation
  • Environmental Science (miscellaneous)
  • Earth and Planetary Sciences (miscellaneous)

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