Search Results (50)

Mechanical damage reduces the marketability of Korla fragrant pears, severely restricting industry development. To enhance the commercial value of pears, this study investigated the effects of impact, compressive, and combined impact-compressive damage types on the weight loss rate, L*, a*, and b* of pears, and constructed a multi-output prediction model for the weight loss rate, L*, a*, and b* of damaged pears during storage by integrating partial least squares regression (PLSR), support vector regression (SVR), and long short-term memory (LSTM), from which the optimal prediction model was selected to achieve synchronous detection of the physical quality of damaged pears during storage. The results indicated that during storage, the weight loss rate, a*, and b* of pears subjected to different damage types gradually increased with prolonged storage time, while L* gradually decreased. Under the same damage volume situation, pears subjected to impact-static pressure combined action exhibited the fastest storage quality change speed, followed by impact action, static pressure action. The SVR multi-output model demonstrated optimal performance in predicting the weight loss rate, L*, a*, and b* of damaged pears during storage, achieving mean coefficient of determination R², root mean square error (RMSE), and residual prediction deviation (RPD) values of 0.988, 0.513, and 10.072, respectively, for these four quality indicators. These results establish a theoretical foundation for the development of simultaneous monitoring techniques for fruit storage quality. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

The Korla fragrant pear is a highly valued economic fruit in China’s Xinjiang region. However, biomechanical data on the fruit-bearing branches and pedicels of this species remain incomplete, which to some extent hinders the advancement of harvesting equipment and techniques. Therefore, refining these data is of great significance for the development of efficient and non-destructive harvesting strategies. This study aims to elucidate the mechanical properties of the fruiting branches and peduncles of Korla fragrant pears, thereby establishing a theoretical foundation for the future development of intelligent harvesting technology for this variety. The research utilized axial and radial compression tests, along with three-point bending test methods, to quantitatively analyze the elastic modulus and shear modulus of the branches and peduncles. The test results reveal that the elastic modulus of the fruiting branches under axial compression is 263.51 ± 76.51 MPa, while under radial compression, it measures 135.53 ± 73.73 MPa (where ± represents the standard deviation). In comparison, the elastic modulus of the peduncles is recorded at 152.96 ± 119.95 MPa. Additionally, the three-point bending test yielded a shear modulus of 75.48 ± 32.84 MPa for the branches and 30.23 ± 8.50 MPa for the peduncles. Using finite element static structural analysis, the simulation results aligned closely with the experimental data, falling within an acceptable error range, thus validating the reliability of the testing methods and outcomes. The mechanical parameters obtained in this study are critical for modeling the stress and deformation behaviors of pear-bearing structures during mechanical harvesting. These findings provide valuable theoretical support for the optimization of harvesting device design and operational strategies, with the aim of reducing fruit damage and improving harvesting efficiency in pear orchards. Full article

The maturity of Korla fragrant pears directly influences their harvesting, packaging, transportation, and storage. Investigating the mechanical properties of fragrant pears at various maturity stages can help minimize damage during postharvest handling. This study employs micro-CT technology combined with reverse model scanning to develop a numerical model for force damage across different maturity stages, supported by experimental validation. The results demonstrate that both rupture force and rupture strain progressively decrease as the maturity of Korla fragrant pears increases, exhibiting a sudden transition. Simultaneously, the fruit’s microstructure shifts from distinct cellular organization to an irregular, collapsed state. The proposed numerical model, which accounts for this abrupt change, provides a better fit than models based on a single physical parameter, with the R² value improving from 0.7922 to 0.9665. Furthermore, this model accurately quantifies the mechanical properties of fragrant pears at all stages of maturity. These findings offer technical support for reducing postharvest losses and serve as a reference for developing damage prediction models for other fruits and vegetables. Full article

Potassium fertilizer management is critical for achieving high yields of Korla fragrant pear, yet current practices often overlook or misuse potassium inputs. In this study, a two-year field experiment (2023–2024) was conducted with 7- to 8-year-old pear trees using four potassium levels (0, 75, 150, and 225 kg/hm²). Metagenomic sequencing was employed to assess the effects on soil microbial communities, sulfur cycle functional genes, and fruit yield. Potassium treatments significantly altered soil physicochemical properties, the abundance of sulfur cycle functional genes, and fruit yield (p < 0.05). Increasing application rates significantly elevated soil-available potassium and organic matter while reducing pH (p < 0.05). Although alpha diversity was unaffected, NMDS analysis revealed differences in microbial community composition under different treatments. Functional gene analysis showed a significant decreasing trend in betB abundance, a peak in hpsO under K150, and variable patterns for soxX and metX across treatments (p < 0.05). All potassium applications significantly increased yield relative to CK, with K150 achieving the highest yield (p < 0.05). PLS-PM analysis indicated significant positive associations between potassium rate, nutrient availability, microbial abundance, sulfur cycling, and yield, and a significant negative association with pH (p < 0.05). These results provide a foundation for optimizing potassium fertilizer strategies in Korla fragrant pear orchards. It is recommended that future studies combine metagenomic and metatranscriptomic approaches to further elucidate the mechanisms linking potassium-driven microbial functional changes to improvements in fruit quality. Full article

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. Full article

Pear black spot disease seriously threatens the pear industry. Currently, its control mainly relies on chemical fungicides while biological control using antagonistic microorganisms represents a promising alternative approach. This study identified and characterized Bacillus velezensis TRMB57782 as a biocontrol strain through whole-genome sequencing. AntiSMASH analysis predicted the strain’s potential to produce secondary metabolites such as surfactin, difficidin, and bacilysin. In vitro experiments demonstrated that TRMB57782 inhibited the growth of Alternaria gaisen. In vivo experiments using excised branches and pear fruits at two different stages also showed significant control effects. A preliminary exploration of the metabolic substances of TRMB57782 was carried out. The strain can produce siderophores and three biocontrol enzymes. Crude extracts obtained by the hydrochloric acid precipitation and ammonium sulfate saturation precipitation of the bacterial liquid exhibited significant activity and volatile organic compounds showed biocontrol activity. Meanwhile, the effects of strain TRMB57782 on the hyphae of pathogenic fungi were studied, leading to hyphal atrophy and spore shrinkage. This paper provides an effective biocontrol strategy for fragrant pear black spot disease, reveals the antibacterial mechanism of Bacillus velezensis TRMB57782, and offers a new option for the green control of pear black spot disease. Full article

Pyrus bretschneideri ‘Xinli No.7’, a progeny of Pyrus sinkiangensis ‘Korla Fragrant Pear’, is an early-maturing, high-quality pear (Pyrus spp.) cultivar. As a dominant variety in China’s pear-producing regions, it holds significant agricultural importance. Investigating its male sterility (MS) mechanisms is critical for hybrid breeding and large-scale cultivation. Integrated cytological, physiological, and transcriptomic analyses were conducted to compare dynamic differences between male sterility (MS, ‘Xinli No.7’) and male-fertile (MF, ‘Korla Fragrant Pear’) plants during anther development. Cytological observations revealed that, compared with ‘Korla Fragrant Pear’, the tapetum of ‘Xinli No.7’ exhibited delayed degradation and abnormal thickening during the uninucleate microspore stage. This pathological alteration compressed the microspores, ultimately leading to their abortion. Physiological assays demonstrated excessive reactive oxygen species (ROS) accumulation, lower proline content, higher malondialdehyde (MDA) levels, and reduced activities of antioxidant enzymes (peroxidase and catalase) in MS plants. Comparative transcriptomics identified 283 co-expressed differentially expressed genes (DEGs). Functional enrichment linked these DEGs to ROS-scavenging pathways: galactose metabolism, ascorbate and aldarate metabolism, arginine and proline metabolism, fatty acid degradation, pyruvate metabolism, and flavonoid biosynthesis. qRT-PCR validated the expression patterns of key DEGs in these pathways. A core transcriptome-mediated MS network was proposed, implicating accelerated ROS generation and dysregulated tapetal programmed cell death. These findings provide theoretical insights into the molecular mechanisms of male sterility in ‘Xinli No.7’, supporting future genetic and breeding applications. Full article

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. Full article

Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in the east, south, west, and north positions of peripheral canopy leaves. The leaf soil plant analysis development (SPAD) method was implemented using a SPAD-502 laser chlorophyll meter. The instrument measures the relative chlorophyll content as the SPAD value. Leaf spectra were acquired using a portable field spectrometer, ASD FieldSpec4. ViewSpecPro 6.2 software was employed to smooth the ground spectral data. Traditional mathematical transformations and the discrete wavelet transform were used to process the spectral data, then correlation analysis was employed to extract the sensitive bands, and partial least squares regression (PLS) was used to establish a model for estimating the chlorophyll content of pear tree leaves. The findings indicate that (1) the models developed using the discrete wavelet transform had coefficients of determination (R²) exceeding 0.65, and their predictive performance surpassed that of other models employing various mathematical transformations, and (2) the model constructed using the L1 scale for the discrete wavelet transform had greater estimation accuracy and stability than models established through traditional mathematical transformations or the high-frequency scale for discrete wavelet transform, with an R² value of 0.742 and a root mean square error (RMSE) of 0.936. The prediction model for relative chlorophyll content established in this study was more accurate for chlorophyll monitoring in pear trees, and thus, it provided a new method for rapid estimation. Moreover, the model provides an important theoretical basis for the efficient management of pear trees. Full article

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. Full article

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. Full article

In modern agricultural production, accurately estimating the leaf water content (LWC) of Korla fragrant pear is crucial for achieving scientific irrigation and ensuring fruit quality. However, constructing accurate and effective LWC prediction models remains challenging due to limitations in sample selection, spectral feature analysis, and model applicability. To address these issues, this study was conducted to systematically optimize the process. During sample collection, a random split method was employed to divide the dataset into modeling and testing sets at a ratio of 75%:25%. This approach ensures computational efficiency, avoids data leakage, and balances training and evaluation needs, particularly for small- to medium-sized datasets. Specifically, in stage S1, 352 samples were allocated to the modeling set and 108 to the testing set, while in stage S2, 137 and 58 samples were assigned, respectively. The analysis revealed slight differences in LWC distribution and standard deviation between the modeling and testing sets, validating the scientific rigor of dataset division. For instance, the LWC distribution in the S1 modeling set ranged from 4.88% to 83.45%, with a standard deviation of 11.33%. The spectral acquisition process within the range of 4000 cm⁻¹ to 10,000 cm⁻¹ exhibited complex absorbance variation trends, showing distinct characteristics across different intervals. Preprocessing techniques such as SG convolution smoothing, MSC, and SNV significantly reduced the absorbance variability and enhanced spectral features. Notably, the selection of LWC feature bands differed markedly between stages S1 and S2. For example, in S1, SNV-SPA (successive projections algorithm) feature bands were concentrated around 5000 cm⁻¹, 6000 cm⁻¹, and 7000 cm⁻¹, whereas their positions shifted significantly in S2, reflecting the growth dynamics of the Korla fragrant pear. During the model-building phase, various algorithms, including Random Forest Regression (RFR), Backpropagation Neural Network (BP), and Support Vector Regression (SVR), were compared. Under different feature selections, the RFR model demonstrated strong predictive ability with determination coefficients (R²) exceeding 0.75 and root mean square errors (RMSE) below 0.7%. Specifically, the SNV-CARS-BP model achieved an R² of 0.81594 in S1, while the SNV-SPA-RFR model reached an R² of 0.817756 in S2, with relative deviations between the predicted and actual values of less than 5%. These results provide robust support for the precise LWC monitoring of Korla fragrant pear and offer valuable insights for subsequent research. Full article

Pear Valsa cankers were found in various Korla fragrant pear orchards in Alaer, Xinjiang. Disease samples underwent tissue isolation, resulting in six isolates. Pathogenicity tests revealed that the XLFL-6 isolate was the most virulent, demonstrating typical Valsa canker symptoms. Research on its biological characteristics indicated that the optimal growth conditions for XLFL-6 were a temperature of 28 °C and a pH of five. Under these conditions, the colonies of XLFL-6 exhibited the largest growth diameter, and adding glucose and peptone separately to the Czapek medium was most conducive to the growth of its mycelium. Based on morphological observations and multigene sequence analyses (ITS+TEF+TUB), the pathogenic fungus was identified as C. pyri. The infection process of C. pyri was elucidated through tissue observations using both light and electron microscopy. The conidia displayed a similar germination pattern on both wounded and intact twigs. However, the infection process was delayed in the case of intact bark. By 8 h post-inoculation, the conidia achieved a germination rate of 15%. Although germination had occurred, the infection process had not yet commenced. In contrast, for wounded bark tissue, it was observed that 24 h post-inoculation, the fungal hyphae from the conidia directly invaded the wounded tissue. These hyphae penetrate the cell walls, proliferate within the host tissue, and spread throughout the phloem and xylem. After 20 d, numerous pycnidia had breached the bark surface, and yellow waxy gums filled with conidia flowed abundantly from the pycnidia ostioles, with the host tissue being nearly totally disintegrated. Regarding enzyme activity, the polygalacturonase (PG) activity, the primary cell wall-degrading enzyme in the treatment group, was seven times greater than that of the control group. The carboxymethyl cellulose (Cx) activity within the treatment group continued to increase. Xylanase activity rose swiftly to its peak between days 1 and 4, then decreased from days 5 to 10, although it remained higher than that of the control group. Overall, this study is the first to provide a detailed report on the characteristics and proliferation of C. pyri and further elucidates its modes and pathways of invasion. Full article

The difficulty in controlling the quality of Korla pears is the main factor limiting their market value. The key to solving this problem is to detect the color of Korla pears quickly and accurately. This study employed near-infrared spectroscopy (NIRS) technology to measure the absorbance of Korla fragrant pears. The full-spectrum data were pre-processed using six methods: Savitzky–Golay convolution smoothing (SGCS), Savitzky–Golay convolution derivative (SGCD), multiplicative scatter correction (MSC), vector normalization (VN), min–max normalization (MMN), and standard normal variate transformation (SNV). The pre-processed spectral data were subjected to characteristic band extraction using the successive projections algorithm (SPA) and uninformative variable elimination (UVE) methods. Subsequently, detection models for the color indices L*, a*, and b* of Korla fragrant pears were established using the partial least squares regression (PLSR) with full-spectrum and characteristic extracted spectral data. The optimal detection models were determined. The results indicated that pre-processing and characteristic extraction improved the accuracy of the PLSR model. The optimal detection model for the color index L* was SGCD-UVE-PLSR (correlation coefficient (R) = 0.80, Root Mean Square Error (RMSE) = 1.19); for the color index a*, it was VN-SPA-PLSR (R = 0.84 and RMSE = 1.28), and for the color index b*, it was MSC-UVE-PLSR (R = 0.84 and RMSE = 1.25). This research provides a theoretical reference for developing color detection instruments for Korla fragrant pears. Full article