Search Results (156)

The sustainability of Korla fragrant pear orchards has been increasingly threatened by prolonged intensive agricultural practices. In response, biofertilizers and green manures have gained attention due to their potential to enhance soil structure, activate microbial functions, and improve nutrient uptake. However, the dynamic changes in soil bacterial communities under such interventions remain inadequately understood. This study was conducted from 2022 to 2023 in 7- to 8-year-old Korla fragrant pear orchards in Bayin’guoleng Mongol Autonomous Prefecture, Xinjiang. The treatments included: conventional fertilization (CK), biofertilizer (JF), oil sunflowers (DK1) with 25 cm row spacing and a seeding rate of 27 kg·hm⁻², oil sunflowers (DK2) with 25 cm row spacing and a seeding rate of 33 kg·hm⁻², sweet clover (CM1) with 20 cm row spacing and a seeding rate of 21 kg·hm⁻², and sweet clover (CM2) with 20 cm row spacing and a seeding rate of 27 kg·hm⁻². During the 2023 pear season, soil samples from the 0–20 cm layer were collected at the fruit setting, expansion, and maturity stages. Their physical and chemical properties were analyzed, and the structure and diversity of the soil bacterial community were examined using 16S rRNA gene high-throughput sequencing. Fruit yield was assessed at the maturity stage. Compared to CK, the relative abundance of Actinobacteria increased by 101.00%, 38.99%, and 50.38% in the JF, DK2, and CM1 treatments, respectively. DK1 and CM1 treatments resulted in a 152.28% and 145.70% increase in the relative abundance of the taxon Subgroup_7, while JF and DK2 treatments enhanced the relative abundance of the taxon Gitt-GS-136 by 318.91% and 324.04%, respectively. The Chao1 index for CM2 was 18.76% higher than CK. LEfSe analysis showed that the DK2 and CM2 treatments had a more significant regulatory effect on bacterial community structure. All treatments led to higher fruit numbers and yield compared to CK, with JF showing the largest yield increase. Fertilizer type, soil nutrients, and bacterial community structure all significantly positively influenced pear yield. In conclusion, high-density oil sunflower planting is the most effective approach for maintaining soil microbial community stability, followed by low-density sweet clover. This study provides a systematic evaluation of the dynamic effects of bio-fertilizers and different green manure planting patterns on soil microbial communities in Korla fragrant pear orchards, presenting practical, microbe-based strategies for sustainable orchard management. Full article

This paper presents a comprehensive pipeline for automated prediction of branches to be pruned, integrating 3D reconstruction of fruit trees, automatic branch labeling, and pruning prediction. The workflow begins with capturing multi-view RGB-D images in orchard settings, followed by generating and preprocessing point clouds to reconstruct partial 3D models of pear trees using the TEASER++ algorithm. Differences between pre- and post-pruning models are used to automatically label branches to be pruned, creating a valuable dataset for both reconstruction methods and training machine learning models. A neural network based on PointNet++ is trained to predict branches to be pruned directly on point clouds, with performance evaluated through quantitative metrics and visual inspections. The pipeline demonstrates promising results, enabling real-time prediction suitable for robotic implementation. While some inaccuracies remain, this work lays a solid foundation for future advancements in autonomous orchard management, aiming to improve precision, speed, and practicality of robotic pruning systems. Full article

In response to the global labor shortage in the pear industry, the use of robots for harvesting has become an inevitable trend. Developing pear harvesting robots for orchard operations is of significant importance. This paper systematically reviews the progress of three key technologies in pear harvesting robotics: Firstly, in the field of recognition technology, traditional methods are limited by sensitivity to lighting conditions and occlusion errors. In contrast, deep learning models, such as the optimized YOLO series and two-stage architectures, significantly enhance robustness in complex scenes and improve handling of overlapping fruits. Secondly, positioning technology has advanced from 2D pixel coordinate acquisition to 3D spatial reconstruction, with the integration of posture estimation (binocular vision + IMU) addressing occlusion issues. Finally, the end effector is categorized based on harvesting mechanisms: gripping–twisting, shearing, and adsorption (vacuum negative pressure). However, challenges such as fruit skin damage and positioning bottlenecks remain. The current technologies still face three major challenges: low harvesting efficiency, high fruit damage rates, and high equipment costs. In the future, breakthroughs are expected through the integration of agricultural machinery and agronomy (standardized planting), multi-arm collaborative operation, lightweight algorithms, and 5G cloud computing. Full article

Soil samples of different tree ages from the core area and expansion area of Korla City were selected to determine their nutrients and yield, and the analysis was combined with a Principal Component Analysis (PCA) biplot. The soil fertility and yield in the core area were superior to those in the expansion area. PCA biplot analysis showed that the cumulative variance contribution rate of the principal components of the orchard with a tree age of 10–20 years was 80.60%. PC1 had strong positive loadings for calcium, available phosphorus, organic matter, total nitrogen, and yield, and a strong negative loading for pH. PC2 had strong loadings for manganese, zinc, copper, selenium, and iron, as well as for magnesium, boron, available nitrogen, and electrical conductivity. For the core area, soil conditions need to be maintained. For the expansion area, salinization should be addressed; the input of Mg and B should be controlled; and the application of calcium, phosphorus fertilizers, and organic fertilizers should be increased to improve production and quality. Full article

Since the late 1970s, brown spot of pear (BSP), a fungal disease caused by Stemphylium vesicarium (Wallr.) Simmons, has been one of the most important pear fungal diseases in Italy. To protect orchards from BSP, frequent fungicide application is essential throughout the period spanning petal fall to the onset of fruit maturation. In Italy, boscalid was the first succinate dehydrogenase inhibitor (SDHIs) fungicide authorised against BSP; subsequently, penthiopyrad and fluxapyroxad were authorised against the disease. In 2016 and 2017, SDHI compounds were applied against BSP as solo products at the University of Bologna’s experimental farm, showing a reduction in efficacy. Stemphylium vesicarium strains were isolated from leaves and fruit, and sensitivity assays and molecular analyses were performed. In vitro tests confirmed resistance to SDHIs, and two specific single-nucleotide polymorphisms were discovered, SDHB P230L and SDHC H134R, both leading to amino acid substitutions in succinate dehydrogenase subunits and confirming the resistant phenotype. Full article

Abnormally warm winters in recent years have accelerated flowering in fruit trees, increasing their vulnerability to late frost damage. To address this challenge, this study aimed to evaluate and compare the performance of three phenology models—the development rate (DVR), modified DVR (mDVR), and Chill Days (CD) models—for predicting full bloom dates of ‘Niitaka’ pear, using image-derived phenological observations. The goal was to identify the most reliable and regionally transferable model for nationwide application in South Korea. A key strength of this study lies in the integration of real-time orchard imagery with automated weather station (AWS) data, enabling standardized and objective phenological monitoring across multiple regions. Using five years of temperature data from seven orchard sites, chill and heat unit accumulations were calculated and compared with observed full bloom dates obtained from orchard imagery and field records. Correlation analysis revealed a strong negative relationship between cumulative heat units and bloom timing, with correlation coefficients ranging from –0.88 (DVR) to –0.94 (mDVR). Among the models, the mDVR model demonstrated the highest stability in chill unit estimation (CV = 6.3%), the lowest root-mean-square error (RMSE = 2.9 days), and the highest model efficiency (EF = 0.74), indicating superior predictive performance across diverse climatic conditions. In contrast, the DVR model showed limited generalizability beyond its original calibration zone. These findings suggest that the mDVR model, when supported by image-based phenological data, provides a robust and scalable tool for forecasting full bloom dates of temperate fruit trees and enhancing grower preparedness against late frost risks under changing climate conditions. 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

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

Soil compaction is a critical challenge in perennial fruit production, limiting root growth, water infiltration, and nutrient uptake—factors essential for climate-resilient and sustainable orchard systems. In subtropical Asian pear (Pyrus pyrifolia Nakai) orchards under the annual top-working system, intensive machinery traffic exacerbates subsurface hardpan formation and tree performance. This study evaluated the effectiveness of pneumatic subsoiling, a minimally invasive method using high-pressure air injection, in alleviating soil compaction without disturbing orchard surface integrity. Four treatments varying in radial distance from the trunk and pneumatic application were tested in a mature orchard in central Taiwan. Pneumatic subsoiling 120 cm away from the trunk significantly reduced soil penetration resistance by 15.4% at 34 days after treatment (2,302,888 Pa) compared to the control (2,724,423 Pa). However, this reduction was not sustained at later assessment dates, and no significant improvements in vegetative growth, fruit yield, and fruit quality were observed within the first season post-treatment. These results suggest that while pneumatic subsoiling can modify subsurface soil physical conditions with minimal surface disturbance, its agronomic benefits may require longer-term evaluation under varying moisture and management regimes. Overall, this study highlights pneumatic subsoiling may be a potential low-disturbance strategy to contribute to longer-term soil physical resilience. Full article

In recent years, the traditional orchard sprayer has had problems, such as waste of liquid agrochemicals, low target coverage, high manual dependence, and environmental pollution. In this study, an automatic swing-arm sprayer for orchards was developed based on the standardized pear orchard in Pinggu, Beijing. Firstly, the structural principles of a crawler-type traveling system and swing-arm sprayer were simulated using finite element software design. The combination of a diffuse reflection photoelectric sensor and Arduino single-chip microcomputer was used to realize real-time detection and dynamic spray control in the pear canopy, and the sensor delay compensation algorithm was used to optimize target recognition accuracy and improve the utilization rate of liquid agrochemicals. Through the integration of innovative structural design and intelligent control technology, a vertical droplet distribution test was carried out, and the optimal working distance of the spray was determined to be 1 m; the nozzle angle for the upper layer was 45°, that for the lower layer was 15°, and the optimal speed of the swing-arm motor was 75 r/min. Finally, a particle size test and field test of the orchard sprayer were completed, and it was concluded that the swing-arm mode increased the pear tree canopy droplet coverage by 74%, the overall droplet density by 21.4%, and the deposition amount by 23% compared with the non-swing-arm mode, which verified the practicability and reliability of the swing-arm spray and achieved the goal of on-demand pesticide application in pear orchards. 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

This paper proposes a cluster-based flight path construction method for automated drone-assisted pear pollination systems in orchard environments. The approach uses RGB-D (Red-Green-Blue-Depth) sensing through an observation drone equipped with RGB and depth cameras to detect blooming pear flowers. Flower detection is performed using a YOLO (You Only Look Once)-based object detection algorithm, and three-dimensional flower positions are estimated by integrating depth information with the drone’s positional and orientation data in the east-north-up coordinate system. To enhance pollination efficiency, the method applies the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm to group detected flowers based on spatial proximity that correspond to branch-level distributions. The cluster centroids then construct a collision-free flight path, with offset vectors ensuring safe navigation and appropriate nozzle orientation for effective pollen spraying. Field experiments conducted using RTK-GNSS-based flight control confirmed the accuracy and stability of generated flight trajectories. The drone hovered in front of each flower cluster and performed uniform spraying along the planned path. The method achieved a fruit set rate of 62.1%, exceeding natural pollination at 53.6% and compared to the 61.9% of manual pollination. These results demonstrate the effectiveness and practicability of the method for real-world deployment in pear orchards. Full article

The ecological region and training system are critical in determining an orchard’s microclimate and, ultimately, the quality and yield of the fruit produced. However, few studies have addressed the effects of their interactions on the commodity properties preferred by consumers, including appearance, flavor, and nutritional components. This study was conducted in distinct ecological regions at the county scale, with two classic cultivation modes (a traditional freestanding system with natural grassing and fruit without bagging and a flat-type trellis system with floor covering and fruit bagging) used for investigation and testing in 2020 and 2024, respectively. Significant differences in internal and external quality attributes were observed between the two groups. A sensory analysis showed that an increase in the soluble solid content and a better fruit appearance were strongly associated with higher purchase intentions. By integrating meteorological parameters, it was also found that temperature and air humidity during the month before harvest were associated with the pear phytochemical and metabolomic profiles. Planting site had a particularly notable effect on quality attributes and sensory experience, with low-latitude-harvested samples under cultivation mode 1 clustering together and showing higher overall scores, while cultivation mode 2 may be more suitable for high-latitude areas. Our results pave the way for making precise recommendations for the selection of suitable planting sites and optimum cultivation modes in Jiangxi Province to achieve high-quality ‘Cuiguan’ pears and fully exploit their planting potential. Full article

Chlorophyll content is a critical indicator of the physiological status and fruit quality of pear trees, with Soil Plant Analysis Development (SPAD) values serving as an effective proxy due to their advantages in rapid and non-destructive acquisition. However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. The results demonstrated that (1) both spectral reflectance and vegetation indices exhibited significant correlations with SPAD values, indicating strong retrieval potential; (2) the OIA model consistently outperformed individual algorithms, achieving the highest accuracy when using the combined feature scheme; (3) among the phenological stages, the fruit-enlargement stage yielded the best retrieval performance, with R² values of 0.740 and 0.724 for the training and validation sets, respectively. This study establishes a robust SPAD retrieval framework that integrates multi-source features and multiple models, enhancing prediction accuracy across different growth stages and providing technical support for intelligent orchard monitoring and precision management. Full article

In specialized orchards, approximately 6–10 tons/hectare of cactus pear pruning waste and 60 million tons of spent coffee grounds are estimated to be produced each year worldwide. Composting is a process that produces stable organic matter useful in agriculture. The aim of this work was to explore the potential of Opuntia ficus-indica (OFI) cladodes and spent coffee ground (SCG) mixtures for compost production and to assess their benefits for agricultural applications. Three composting campaigns were carried out using rotating composters. Feedstock for these campaigns was formulated with different ratios of OFI and SCGs, and the compost obtained were characterized by their chemical and physical proprieties. To assess these composts, basil was grown in plots using growing substrate as a blank and comparing it with substrate mixed with 10% of each compost. All plants sprouted and grew up. While no significant differences were detected in polyphenol content among the grown plants, the yields with compost at OFI–SCG (3.3:1) were differentiated for longer shoots and there was greater biomass compared to the control. Compost obtained from cladode mixed with spent coffee grounds proved to be a good soil improver with the characteristics of being able to ameliorate soil fertility and plant growth. Full article