This study, leveraging near-infrared spectroscopy technology and integrating vegetation index analysis, aims to develop a hyperspectral imaging-based non-destructive inspection technique for swift monitoring of crop chlorophyll content by rapidly predicting leaf SPAD. To this end, a high-precision spectral prediction model was first established under laboratory conditions using ex situ lyophilized Leaf samples. This model provides a core algorithmic foundation for future non-destructive field applications. A systematic study was conducted to develop prediction models for leaf SPAD values of Korla fragrant pear at different growth stages (fruit-setting period, fruit swelling period and Maturity period). This involved comparing various spectral preprocessing algorithms (AirPLS, Savitzky–Golay, Multiplicative Scatter Correction, FD, etc.) and CARS Feature Selection methods for the screening of optimal spectral feature band. Subsequently, models were constructed using BP Neural Network and Support Vector Regression algorithms. The results showed that leaf samples at different growth stages exhibited significant differences in their spectral features within the 5000–7000 cm⁻¹ (effective features for predicting chlorophyll (SPAD)) and 7000–8000 cm⁻¹ (moisture absorption valley) bands. The Savitzky–Golay+FD (Savitzky–Golay smoothing combined with first-order derivative (FD)) preprocessing algorithm performed optimally in feature extraction. Growth period specificity models significantly outperformed whole growth period models, with the optimal models for the fruit-setting period and fruit swelling period being FD-CARS-BP (Coefficient of determination (R²) > 0.86), and the optimal model for the Maturity period being Savitzky–Golay-FD+Savitzky–Golay-CARS-BP (Coefficient_of_determination (R²) = 0.862). Furthermore, joint modeling of characteristic spectra and vegetation indices further improved prediction performance (Coefficient of determination (R²) > 0.85, Root Mean Square Error (RMSE) 2.5). This study presents a reliable method for non-destructive monitoring of chlorophyll content in Korla fragrant pears, offering significant value for nutrient management and stress early warning in precision agriculture.

This study investigated the soil–leaf–fruit relationship in Korla fragrant pears (Pyrus sinkiangensis Yu) to establish a scientific cultivation framework by analyzing soil nutrients (alkali-hydrolyzable nitrogen, available phosphorus, available potassium, and pH at 0–60 cm depth) across key phenological stages (fruit setting, expansion, and maturation), combined with leaf and fruit quality indicators. Artificial neural network modeling demonstrated strong predictive capability (R² > 0.85), while sensitivity analysis quantified the relative contributions of different factors, revealing that titratable acidity was optimized when available potassium (30–47 mg/kg) in 40–60 cm soil during fruit setting coincided with pH 7.4–7.8 in 20–40 cm, or when pH 7.3–7.7 in 40–60 cm at fruit setting interacted with alkali-hydrolyzable nitrogen (33.0–53.2 mg/kg) in 40–60 cm during maturation. Fruit shape index improvement required available potassium (40–60 mg/kg) in 40–60 cm at maturation combined with leaf total nitrogen (2.0–6.5 mg/kg) at fruit setting, or specific maturation-stage alkali-hydrolyzable nitrogen levels paired with fruit setting SPAD (Soil and Plant Analysis Development) values (30–41). Furthermore, synergistic effects between expansion stage available phosphorus in 40–60 cm soil and leaf SPAD (Soil and Plant Analysis Development) values simultaneously enhanced the soluble solids content while reducing peel thickness. These findings provide precise nutrient management thresholds for quality optimization, offering practical guidance for orchard management to enhance Korla fragrant pears quality through targeted agricultural practices.

In this study, the effects of natural grass cultivation and clear cultivation on the physiological characteristics of Korla fragrant pear during fruit development and storage were investigated, providing a scientific basis for high-quality fragrant pear cultivation. Sugar components, enzyme activities, and gene expression levels in the pulp and peel were comprehensively analyzed during fruit development and storage. A classification model was constructed using machine learning algorithms (RF, KNN, SVM), and particle swarm optimization (PSO) was employed to identify key factors. The results showed that natural grass cultivation significantly enhanced sugar accumulation in the fruits, particularly at 120 and 150 days after flowering, with fructose content increasing by 9.09 mg·g⁻¹ and 12.59 mg·g⁻¹, respectively, and glucose content also rising significantly. Additionally, natural grass cultivation promoted the relative expression levels of GK, PFK, and FK genes in the pulp. During fruit storage, enzyme activities in the natural grass cultivation group were consistently higher than those in the clear cultivation group across different periods, with PFK activity being 23.73 U/L higher at 150 days of storage. The model identified the activities of glyceraldehyde kinase, phosphofructokinase, and fructokinase as key factors influencing sugar content. A significant negative correlation was observed between peel phosphofructokinase activity and fruit fructose content, though this relationship requires further investigation. This study elucidates the regulatory mechanism by which cultivation methods affect fruit quality through enzyme activity and photosynthetic product distribution. Our findings provide a critical scientific foundation for the high-quality cultivation of Korla fragrant pear and are expected to advance the efficient development of the fragrant pear industry, helping farmers improve both fruit quality and income.

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.

This study performed genome-wide identification and functional characterization of BAHD genes in Korla fragrant pear and investigated their roles in the de