Development of a Predictive Model Linking Electrical Characteristics to Semi-Lethal Temperature in Potted Apple Trees with Validation on Mature Specimens

In the context of increasingly frequent extreme low-temperature events, developing a rapid and non-destructive method to evaluate plant cold tolerance is of great scientific significance for accurately assessing the cold hardiness of fruit trees. In this study, the correlations between 23 electrophysiological features—including electrical signal and impedance parameters—and the cold tolerance indicator semi-lethal temperature (LT50) were analyzed. Principal component analysis (PCA) was used to identify the optimal electrical parameters reflecting cold resistance in apples. A multiple linear regression model was then constructed based on these parameters, and its accuracy was validated using 13-year-old field-grown apple trees. The results showed the following: (1) Six electrical parameters (r1, re, r, Min, Std, and Peak) were significantly correlated with LT50 (p < 0.05) and made major contributions to the first principal component (PC1), confirming their status as optimal indicators of cold tolerance. (2) A regression model for predicting LT50 was established using these parameters, achieving a coefficient of determination (R2) of 0.9187, indicating excellent model fit. (3) Model validation yielded R2 values of 0.9323 and 0.9999, MAE values of 1.243 and 0.900, MAPE values of 6.64% and 4.02%, and RMSE values of 1.29 and 1.12, respectively. The predicted LT50 values closely matched measured values in an overall trend, confirming the high accuracy of the model. These findings demonstrate that electrophysiological parameters can effectively reflect the dynamic changes in cold tolerance of apple trees during overwintering and provide a theoretical and methodological foundation for rapid and convenient cold hardiness assessment in other deciduous fruit species.