ArticleFrontiers in plant science2021
Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Predicting sugar beet leaf area index: evaluating performance of double sigmoid functions under different irrigation and plant density scenarios.Scientific reports · 2025Article
- A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers.Frontiers in plant science · 2023Article
- Accelerating leaf area measurement using a volumetric approach.Plant methods · 2022Article
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Authors and funding
3 authors.
Funding
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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 (
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