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

Abstract

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 ∼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.

Plant phenotyping relevance

LiDARデータから作物キャノピーのLAIを推定するセンサー・解析手法を、車載およびUAVプラットフォームで評価しており、植物形質取得が研究の中心です。

Code and data availability

The supplied blocks contain no data availability statement, public dataset link, or author code repository. The only URLs are the article DOI and Velodyne LiDAR sensor datasheets, which are generic hardware documentation, not paper-specific phenotyping data or analysis assets.

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