LiDAR-based indices and machine learning efforts to model biophysical estimations of corn (Zea mays L.)

Light Detection and Ranging (LiDAR) in precision agriculture is gaining traction as the technology becomes both accessible and affordable, particularly for assessing biophysical characteristics of vegetation. This study investigates the potential of unmanned aerial vehicle (UAV)-based LiDAR data for modeling Leaf Area Index (LAI), a key indicator of crop health and productivity. We explore laser penetration indices to model LAI and compare these results with machine learning models using various LiDAR return types (e.g., ground, vegetation, first, last). In both approaches, in-situ LAI measurements obtained with a LiCOR LAI-2000 were used as ground truth. The study was conducted over two years with a multi-date planting of corn (Zea mays L.) in Temple, TX. Our findings indicate that LiDAR-based methods, both through penetration indices and machine learning, hold promise for accurately modeling LAI and other biophysical crop traits in precision agriculture.