Prediction Model of Jujube Yield and First-Order Fruit Rate Based on BP Neural Network and SHAP Analysis

To achieve accurate prediction of jujube tree yield and quality, this study developed a prediction model based on a backpropagation (BP) neural network. By measuring the diurnal variation of photosynthetic rate and tree structural parameters at different phenological stages, and screening input variables through correlation analysis, two prediction models were established: one for yield er plant (with an 11-10-1 network structure) and another for the rate of first-grade fruits (with a 7-8-1 structure). After optimization, both models demonstrated excellent performance, with validation set R2 values reaching 0.87556 and 0.94406, respectively. SHAP (SHapley Additive exPlanations) analysis was applied to interpret key influencing factors, revealing that features X11 and X10 contributed the most to the yield-per-plant model, whereas features X7 and X8 were the most critical in the first-grade fruit rate model. Response surface analysis further identified optimal parameter intervals for high yield and quality: the yield per plant was maximized when photosynthetically active radiation in the evening during the fruiting period was between 0.7–0.8 μmol·m−2·s−1 and tree height was 2.5–3.5 m; the rate of first-grade fruits was optimized when tree height ranged from 1 to 4 m and the number of first-grade branches was between 1 and 13. This study provides a reliable prediction tool and a theoretical foundation for precision management in jujube cultivation.