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Article http://dx.doi.org/10.26855/ijfsa.2025.09.012

Kiwifruit Health Assessment Model Based on Spectral Analysis and UAV Imagery

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Haoyang Qin*, Haiyang He, Xiaoyu Liu, Xu Sun

Xi'an Mineral Resources Survey Center, China Geological Survey, Xi'an 710000, Shaanxi, China.

*Corresponding author: Haoyang Qin

This research was funded by “Study on Kiwifruit Monitoring Model in the Western Guanzhong Basin Based on Hyperspectral Remote Sensing Technology (No.2024JC-YBQN-0318)”.
Published: October 27,2025

Abstract

The health condition of kiwifruit leaves can reflect the growth status of the kiwifruit plant. Due to the growth characteristics of kiwifruit, remote sensing imagery has difficulty penetrating its leaf canopy for direct monitoring of the fruit. Therefore, extracting physiological parameter indicators of kiwifruit leaves through spectral analysis and remote sensing technology is crucial for monitoring kiwifruit health. This study collected leaf spectral characteristics and physiological parameters of mature kiwifruit under different health conditions, including disease, drought, and normal growth, and established evaluation indicators for leaf health status. The backward interval partial least squares (BiPLS) method was used to screen bands sensitive to leaf health. The selected band variables were used as input spectral feature parameters for the modeling algorithm, and an Endmember Mixture Model (EMMA) was employed to construct a remote sensing monitoring model suitable for assessing leaf health status. Drone imagery was collected from three large-scale kiwifruit orchards to monitor and assess the growth and health status of kiwifruit within the orchards. The results show that there are spectral features sensitive to health status within the visible, near-infrared, and short-wave infrared bands, and the coefficient of determination (R²) of the developed inversion model can reach 0.82. The inversion results from the drone imagery are largely consistent with the field sampling survey results within the orchards. The research findings can provide a technical basis for rapid, non-destructive remote sensing monitoring of kiwifruit health status.

Keywords

Kiwifruit; Spectral analysis; UAV imagery; Health assessment model

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How to cite this paper

Kiwifruit Health Assessment Model Based on Spectral Analysis and UAV Imagery

How to cite this paper: Haoyang Qin, Haiyang He, Xiaoyu Liu, Xu Sun. (2025) Kiwifruit Health Assessment Model Based on Spectral Analysis and UAV Imagery. International Journal of Food Science and Agriculture9(3), 235-243.

DOI: http://dx.doi.org/10.26855/ijfsa.2025.09.012