Mechanism and Prediction of Gray Jujube Fruit Quality Using Explainable ANN

Abstract

Gray jujube (Ziziphus jujuba Mill) is an important economic fruit crop in Xinjiang, China, whose fruit quality is regulated by complex interactions among tree architecture, physiological functions, and environmental factors. Based on 2 years of field experiments, we developed an interpretable artificial neural network model integrating 13 structural and physiological indicators to predict four quality parameters: vitamin C (VC), soluble sugar, titratable acid, and sugar-acid ratio. The model architecture was optimized through Bayesian optimization, resulting in a 13–4–1/13–5–1 network structure with high prediction accuracy (R² = 0.89–0.98). Biological interpretation of the connection weights revealed that the elongation of bearing shoots (1.2–3.1 cm/month) and SPAD values (33–41.5) were key drivers of VC accumulation, reflecting their roles in photosynthate transport and light-harvesting efficiency. Canopy structural characteristics, particularly leaf inclination angles of 26°–34° combined with a direct beam transmittance of 0.32–0.43, were found to synergistically enhance sugar accumulation by optimizing light distribution while maintaining sufficient gas exchange. Furthermore, net photosynthetic rates exceeding 12 μmol·m⁻²·s⁻¹ significantly reduced organic acid content, indicating a shift in carbon partitioning toward sugar synthesis. These findings demonstrate that the model successfully bridges computational analysis with biological processes, providing both a predictive tool and mechanistic insights for gray jujube quality management. The integration of architectural, physiological, and environmental parameters in this framework offers a comprehensive approach for precision cultivation of this important crop.