To calyx persistence in Korla fragrant pear (Pyrus sinkiangensis) significantly impacts fruit marketability, with persistent calyx causing up to 40% reduction in premium-grade fruit yield. Investigating the hormonal mechanisms underlying calyx abscission and persistent in Korla Fragrant Pear, we performed comprehensive phytohormone profiling using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS; EXIONLC system coupled with SCIEX 6500 QTRAP+). Flowers from first-position (persistent-calyx) and fourth-position (deciduous-calyx) inflorescences were collected at six developmental stages (0–10 days after flowering). Fourteen endogenous hormones—ACC, ME-IAA, IPA, TZR, SA, IAA, ICA, IP, tZ, DHJA, ABA, JA-ile, cZ, and JA—were identified in the calyx during the flowering stage. The calyx abscission rate was significantly higher in the fourth position (79%) compared to the first position (32%). ACC and ABA are closely linked to abscission, with increased ACC at 0 DAF signaling early abscission and ABA accumulation accelerating late abscission at 8 DAF. Auxin exhibited spatiotemporal specificity, peaking in first-order flowers at 4–6 DAF, potentially inhibiting abscission by maintaining cell activity. Cytokinins generally decreased, while jasmonates significantly increased during the fourth-position anthesis stage 8–10 DAF, suggesting a role in stress-related senescence. By systematic analysis of the flowers at the first order (persistent calyx) and the fourth order (deciduous calyx) from 0 to 10 days after anthesis, we found three key stages of hormone regulation: early prediction stage (0–2 DAF), ACC accumulation at the fourth order was significantly higher than that at the first order at 0 days after anthesis, ACC accumulation at the early stage predicted abscission; During the middle maintenance stage (4–6 DAF), the accumulation of cytokinin decreased significantly, while the accumulation of IAA increased significantly in the first position (persistent calyx); Execution Phase (8–10 DAF), ABA reached its peak at 8 DAF, coinciding with the final separation time. JA played an important role in the late stage. Gibberellin was undetected, implying a weak association with calyx abscission. Venn diagram identified N6-(delta 2-Isopentenyl)-adenine (IP) in first-position flowers, which may influence calyx persistence or abscission. These findings elucidate hormone interactions in calyx abscission, offering a theoretical basis for optimizing exogenous hormone application to enhance fruit quality.

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism.