AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLE-BASED HYPERSPECTRAL AND LIDAR DATA FUSION
- 1Geospatial Institute, Saint Louis University, St. Louis, MO 63108, USA
- 2Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA
- 3Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA
Keywords: Unmanned Aerial Vehicle (UAV), Machine Learning, Remote Sensing, LiDAR, Hyperspectral, Data Fusion
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.