Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R-2) and root mean squared error for predictive models ranged from similar to 0.4 in the early season to similar to 0.6 for sorghum and similar to 0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

Automated summary

This study investigates the effectiveness of using LiDAR data combined with statistical and plant structure features, along with ground reference values, to estimate LAI for sorghum and maize at different times using wheeled vehicles and drones. Predictive models show R-2 results ranging from around 0.4 in the early season to 0.6 to 0.80 for sorghum and maize in more mature growth stages.