Development of Prediction Models for Apple Fruit Diameter and Length Using Unmanned Aerial Vehicle-Based Multispectral Imagery.
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| Title: | Development of Prediction Models for Apple Fruit Diameter and Length Using Unmanned Aerial Vehicle-Based Multispectral Imagery. |
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| Authors: | An, Do Hyun, Kang, Ye Seong, Park, Chang Hyeok, Je, Gang In, Ryu, Chan Seok |
| Source: | AgriEngineering; Nov2025, Vol. 7 Issue 11, p361, 16p |
| Subject Terms: | MULTISPECTRAL imaging, MACHINE learning, REMOTE sensing, AGRICULTURAL drones, ORCHARD management, FRUIT |
| Geographic Terms: | SOUTH Korea |
| Abstract: | In Korea, apple (Malus domestica) is one of the major fruit crops. The area occupied by apple orchards has exhibited a consistent upward trend, increasing from 26,398 hectares in 2003 to 33,313 hectares in 2024, and production reached 460,088 tons in 2024. However, stable apple production is currently threatened by global challenges such as climate change and the decline in rural labor, which hinders timely and efficient orchard management. Under these circumstances, developing automated and data-driven technologies capable of rapidly predicting and responding to apple growth conditions is essential to enhancing management efficiency and ensuring consistent fruit quality and yield stability. In this study, unmanned aerial vehicle (UAV)-based multispectral imagery was acquired and used to analyze time series data. Vegetation indices (VIs) derived from this imagery were then applied to build models predicting fruit diameter and length, which reflect apple size. A total of nine VIs were calculated from the acquired data and utilized as input variables for model development. Based on these variables, four machine learning models—Gaussian process regression (GPR), the K-Nearest Neighbors (KNNs), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB)—were developed to predict the fruit diameter and length. Both RFR and XGB showed tendencies of overfitting, and although the KNNs demonstrated relatively stable performance (diameter: R2 ≥ 0.82, RMSE ≤ 7.61 mm, RE ≤ 12.53%; length: R2 ≥ 0.76, RMSE ≤ 8.85 mm, RE ≤ 15.08%), this model failed to follow the prediction line consistently. In contrast, GPR maintained stable performance in both the validation and calibration stages (diameter: R2 ≥ 0.79, RMSE ≤ 8.23 mm, RE ≤ 13.56%; length: R2 ≥ 0.72, RMSE ≤ 9.48 mm, RE ≤ 16.16%) and followed the prediction line relatively well, indicating that it is the most suitable model for predicting apple size. These results demonstrate that UAV-based multispectral imagery, combined with machine learning techniques, is an effective tool for predicting the size of apples, and it is expected to contribute to orchard management at different growth stages and improve apple productivity in the future. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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