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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #418882
Research Project: Development of Enhanced Tools and Management Strategies to Support Sustainable Agricultural Systems and Water Quality

Location: Grassland Soil and Water Research Laboratory

Title: Laser penetration and intensity LiDAR-based plant height and LAI estimations of corn

Author
item Flynn, Kyle
item BAATH, GURJINDER - Texas Agrilife Research
item RAM SAPKOTA, BALA - Texas Agrilife Research
item Smith, Douglas

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/15/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: n/a - abstract only.

Technical Abstract: Light Detection and Ranging (LiDAR) provides ample opportunity to establish near-real time crop measurements for improved precision agriculture management. When mounted to an unmanned aerial vehicle (UAV) platform, LiDAR has great potential to offer accurate measures of crop height and leaf area index (LAI), both important for management decision making for corn (Zea mays L.) crop production across its many stages of growth. Thus, this research aimed to assess the accuracy of height and LAI measurements of multi-planting date (seven different days) experiments conducted at the Grassland, Soil and Water Research Laboratory of Temple, Texas, USA across two years (2022 and 2023) and three field experiments. The crop height measures relied on the laser penetration capabilities of the LiDAR system to determine the ground layer through utilization of the cloth simulation filter and the maximum height of the vegetation. The LAI analytics were more complex as they incorporated the use of laser penetration- or intensity-based indices that were further divided into various return types (e.g. first, last, single). Results for both crop height and LAI measurements utilizing the UAV mounted LiDAR system are promising. Crop height coefficients of determination (R2) were as high at 0.92, while LAI coefficients of determination (R2) were as high as 0.78. Consequently, the time and resource efficient crop height and LAI LiDAR-based measurement methods presented here have precision agriculture implications for field-based systems such as corn.