Evaluation of Phenology Models for Predicting Full Bloom Dates of ‘Niitaka’ Pear Using Orchard Image-Based Observations in South Korea

Abnormally warm winters in recent years have accelerated flowering in fruit trees, increasing their vulnerability to late frost damage. To address this challenge, this study aimed to evaluate and compare the performance of three phenology models—the development rate (DVR), modified DVR (mDVR), and Chill Days (CD) models—for predicting full bloom dates of ‘Niitaka’ pear, using image-derived phenological observations. The goal was to identify the most reliable and regionally transferable model for nationwide application in South Korea. A key strength of this study lies in the integration of real-time orchard imagery with automated weather station (AWS) data, enabling standardized and objective phenological monitoring across multiple regions. Using five years of temperature data from seven orchard sites, chill and heat unit accumulations were calculated and compared with observed full bloom dates obtained from orchard imagery and field records. Correlation analysis revealed a strong negative relationship between cumulative heat units and bloom timing, with correlation coefficients ranging from –0.88 (DVR) to –0.94 (mDVR). Among the models, the mDVR model demonstrated the highest stability in chill unit estimation (CV = 6.3%), the lowest root-mean-square error (RMSE = 2.9 days), and the highest model efficiency (EF = 0.74), indicating superior predictive performance across diverse climatic conditions. In contrast, the DVR model showed limited generalizability beyond its original calibration zone. These findings suggest that the mDVR model, when supported by image-based phenological data, provides a robust and scalable tool for forecasting full bloom dates of temperate fruit trees and enhancing grower preparedness against late frost risks under changing climate conditions.