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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #417073
Research Project: Enhancing Cropping System and Grassland Sustainability in the Texas Gulf Coast Region by Managing Systems for Productivity and Resilience

Location: Grassland Soil and Water Research Laboratory

Title: LiDAR-based estimation of corn (Zea Mays L.) plant height and leaf area index across multiple planting dates

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

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/29/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Advancements among unmanned aerial vehicles (UAVs) and potential remote sensing sensors, such as LiDAR, provide ample opportunity for information gathering among crops for precision agriculture. This study employed a UAV mounted LiDAR sensor to estimate corn height and leaf area index (LAI) among a multi-planting date study. This research used a diverse dataset of height and LAI data collected from corn experimental plots treated with seven planting dates across multiple field locations from 2022-2023 at the Grassland, Soil and Water Research Laboratory of Temple, Texas. Our findings suggest that direct use of LiDAR in precision agriculture applications could enhance biophysical information gathering of dense canopy crops, such as corn.

Technical Abstract: Advancements among unmanned aerial vehicles (UAVs) and potential remote sensing sensors, such as LiDAR, provide ample opportunity for information gathering among crops for precision agriculture. This study employed a UAV mounted LiDAR sensor to estimate corn (Zea Mays L.) height and leaf area index (LAI) among a multi-planting date study. This research used a diverse dataset of height and LAI data collected from corn experimental plots treated with seven planting dates across multiple field locations from 2022-2023 at the Grassland, Soil and Water Research Laboratory of Temple, Texas. We focused our analytics on LiDAR-based returns and indices that utilize LiDAR returns (e.g. first, last, single) and LiDAR return intensities (e.g. first, last, single) to correlate to the in-situ height and LAI measurements of the corn. Results are promising with coefficients of determination (R2) reaching as high as 0.97. Moreover, it was found that return-based indices generally had higher coefficients of determination and lower error terms (RMSE) than return intensity-based indices. Our findings suggest that direct use of LiDAR in precision agriculture applications could enhance biophysical information gathering of dense canopy crops, such as corn.