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

Frontiers in Plant Science

Melba M. Crawford¹  Mitchell R. Tuinstra¹  Behrokh Nazeri² 

[1] Department of Agronomy, Purdue University, West Lafayette, IN, United States;Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States;

关键词: high-throughput phenotyping;    remote sensing;    LiDAR;    leaf area index;    machine learning;    row crops;   

DOI  :  10.3389/fpls.2021.740322

来源: DOAJ

【 摘 要 】

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 (R2) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

【 授权许可】

Unknown