A deep survival analysis approach for citrus huanglongbing prognosis

• Analyze the key factors influencing Huanglongbing progression and validate the effectiveness of chemical interventions. • First application of survival analysis for prognostic study of citrus Huanglongbing. • Combined KAN with survival analysis to propose the REK-Surv model for efficient and accurate survival prediction. Precise interventions in the management of fruit tree diseases and pests are of great importance. However, most existing studies have focused primarily on disease detection and identification, with relatively little attention given to the progression of affected trees and the effectiveness of subsequent interventions. In this study, we developed a deep survival analysis model—REK-Surv—based on the Kernel Attention Network framework to predict the prognosis of citrus trees infected with huanglongbing. During a 15-month follow-up, we applied three different intervention strategies to 56 trees and collected a range of potential prognostic factors, including the intrinsic health status of the trees, surrounding ecological conditions, and environmental variables such as climate and soil properties, in order to evaluate their effects on survival outcomes. Experimental results demonstrated that the REK-Surv model effectively captured the risk function of diseased trees, achieving a high prediction accuracy of 99%. Analysis of survival curves, CT values, and SHAP values for the different interventions revealed that chemical treatment had a certain inhibitory effect on huanglongbing progression, whereas pruning showed no significant effect. Contribution analysis of prognostic factors identified light intensity, wind direction, soil electrical conductivity, and wind speed as key determinants of tree survival. Furthermore, June and November were identified as high-risk periods associated with sharp increases in mortality rates. This study provides quantitative evidence supporting precise interventions for huanglongbing-infected citrus trees, expands the application of survival models in plant disease prognosis, and contributes to more scientific and forward-looking pest and disease management in orchards.