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2025 Vol.7, Issue 1 Preview Page

Research Article

Estimation of napa cabbage fresh weight using uav-based multispectral images and accumulated temperature
Chang-Hyeok Park1
,
Chan-Seok Ryu1*
,
Ye-Seong Kang2
,
Gang-In Je1
,
Ho-Jun Kwon1
1Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science), Jinju 52828, Republic of Korea
2Department of Smart Agro-industry, GyeongSang National University (Institute of Agriculture & Life Science), Jinju 52725, Republic of Korea
*Corresponding Author
31 March 2025. pp. 56-67
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Abstract

This study aimed to develop a regression model to accurately estimate napa cabbage fresh weight using UAV-based multispectral imagery, incorporating accumulated temperature (AT) to improve prediction accuracy under varying environmental conditions. Growth data and multispectral images were collected for two cultivars, Cheongmyeonggael and Bulam No.3, during the 2022 and 2023 growing seasons, and ten vegetation indices (VIs) were calculated. Both linear regression models (Multiple Linear Regression, Ridge, Lasso) and nonlinear models (Support Vector Regression, K-Nearest Neighbors) were applied, and their performance was evaluated using K-Fold Cross Validation. As a result, Ridge Regression showed the highest prediction accuracy in cultivar-specific models, while Multiple Linear Regression performed best in the integrated model. NDRE and TCARI were the most influential variables selected in the Ridge Regression models of Cheongmyeonggael and Bulam No.3, respectively. Furthermore, the inclusion of accumulated temperature significantly improved model performance, confirming its potential to reflect environmental growth conditions. This study presents the potential of integrating remote sensing imagery with climate data to enhance crop biomass estimation and suggests the feasibility of applying this precision agriculture-based yield prediction model under diverse environmental conditions.

Keywords
Napa Cabbage
Accumulated Temperature
Multispectral Image
UAV
Machine Learning
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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 : 7
  • No :1
  • Pages :56-67
  • Received Date : 2025-03-26
  • Revised Date : 2025-04-02
  • Accepted Date : 2025-04-02
  • DOI :https://doi.org/10.22765/pastj.20250005
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