Journal of The Korean Society of Grassland and Forage Science (한국초지조사료학회지)

The Korean Society of Grassland and Forage Science (한국초지조사료학회)
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Predicting Italian Ryegrass Productivity Using UAV-Derived GLI Vegetation Indices

  • Seung Hak Yang (National Institute of Animal Science, RDA) ;
  • Jeong Sung Jung (National Institute of Animal Science, RDA) ;
  • Ki Choon Choi (National Institute of Animal Science, RDA)
  • Received : 2024.09.20
  • Accepted : 2024.09.27
  • Published : 2024.09.30
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Abstract

Italian ryegrass (IRG) has become a vital forage crop due to its increasing cultivation area and its role in enhancing forage self-sufficiency. However, its production is susceptible to environmental factors such as climate change and drought, necessitating precise yield prediction technologies. This study aimed to assess the growth characteristics of IRG and predict dry matter yield (DMY) using vegetation indices derived from unmanned aerial vehicle (UAV)-based remote sensing. The Green Leaf Index (GLI), normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), and optimized soil-adjusted vegetation index (OSAVI) were employed to develop DMY estimation models. Among the indices, GLI demonstrated the highest correlation with DMY (R2 = 0.971). The results revealed that GLI-based UAV observations can serve as reliable tools for estimating forage yield under varying environmental conditions. Additionally, post-winter vegetation coverage in the study area was assessed using GLI, and 54% coverage was observed in March 2023. This study assesses that UAV-based remote sensing can provide high-precision predictions of crop yield, thus contributing to the stabilization of forage production under climate variability.

Keywords

Acknowledgement

The work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ017291)" Rural Development Administration, Republic of Korea.

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