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2026 Vol.8, Issue 3 Preview Page

Research Article

Pre-harvest prediction of apple soluble solids content using UAV based multispectral imagery
Chang-Hyeok Park1
,
Chan-Seok Ryu1*
,
Gang-In Je1
,
Ho-Jun Kwon1
,
Ye-Seong Kang2
,
Do-Hyun An2
,
Ji-An Kim2
,
Gang-Jin Lee3
,
Si-Hyeong Jang4
1Department of Bio-System Engineering, Institute of Agriculture & Life Science, GyeongSang National University, Jinju 52828, Republic of Korea
2Department of Smart Agro-industry, Institute of Agriculture & Life Science, GyeongSang National University, Jinju 52828, Republic of Korea
3Institute of Agriculture & Life Science, GyeongSang National University, Jinju 52828, Republic of Korea
4Fruit Research Division, National Institute of Horticultural & Herbal Science, Wanju 55365, Republic of Korea
*Corresponding Author
30 September 2026. pp. 291-302
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Abstract

This study developed and validated machine learning models for predicting the pre-harvest soluble solids content (SSC) of apples using only vegetation indices derived from UAV-based multispectral imagery. A total of 39 'Fuji' apple trees across 10 orchards in Andong, Gyeongsangbuk-do, Republic of Korea, were surveyed over two growing seasons (2024–2025). Imagery was acquired at noon on 17 September 2024 and 16 September 2025 using a Trinity F90+ fixed-wing UAV equipped with an Altum-PT multispectral sensor, flown at an altitude of 150 m with 75% overlap, yielding a ground sample distance (GSD) of 3.17 cm/pixel. Following radiometric calibration, mosaicking, and pansharpening, vegetation and background were separated using NDVI, and ARI, GLI, and NDVI rededge were extracted at the individual tree level. Using SSC measured with a handheld refractometer as the dependent variable, multiple linear regression (MLR) and k-nearest neighbor regression (KNR) models were constructed and evaluated by leave-one-out cross-validation (LOOCV) in terms of R2, RMSE, and MAPE. At the validation stage, MLR yielded an R2 of 0.55, a MAPE of 9.21%, and an RMSE of 1.34 °Brix. These results confirm that individual-tree SSC can be estimated non-destructively and without contact from canopy-level spectral information alone, and provide evidence supporting the potential use of this approach for orchard-scale maturity assessment and harvest timing decisions in precision agriculture.

Keywords
Apple
Unmanned Aerial Vehicle
Vegetation index
Soluble Solids Content
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Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 8
  • No :3
  • Pages :291-302
  • Received Date : 2026-09-08
  • Revised Date : 2026-09-16
  • Accepted Date : 2026-09-17
  • DOI :https://doi.org/10.22765/pastj.20260021
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