Jujube Orchards Research Articles

SwinLabNet: Jujube Orchard Drivable Area Segmentation Based on Lightweight CNN-Transformer Architecture

Identifying drivable areas between orchard rows is crucial for intelligent agricultural equipment. However, challenges remain in this field’s accuracy, real-time performance, and generalization of deep learning models. This study proposed the SwinLabNet model in the context of jujube orchards, an innovative network model that utilized a lightweight CNN-transformer hybrid architecture. This approach optimized feature extraction and contextual information capture, effectively addressing long-range dependencies, global information acquisition, and detailed boundary processing. After training on the jujube orchard dataset, the SwinLabNet model demonstrated significant performance advantages: training accuracy reached 97.24%, the mean Intersection over Union (IoU) was 95.73%, and the recall rate was as high as 98.36%. Furthermore, the model performed exceptionally well on vegetable datasets, highlighting its generalization capability across different crop environments. This study successfully applied the SwinLabNet model in orchard environments, providing essential support for developing intelligent agricultural equipment, advancing the identification of drivable areas between rows, and laying a solid foundation for promoting and applying intelligent agrarian technologies.

Full-progress crop management and harvesting scheme with integrated space information: A case of jujube orchard

Space information integration can better obtain the environmental information, crop information, climate information and other key factors of the farmland, which is more helpful for crop management and harvesting. In the traditional crop management and harvesting process, crop management and harvesting are two relatively independent processes, lacking a complete full-process scheme. In this regard, this paper proposes a full-process crop management and harvesting scheme with integrated space information, which takes the space information of the crop planting area as the core, fully associates and closely integrates the crop management process and harvesting process, and carries out scientific field management, accurate yield estimation, and reasonable harvesting path planning for crops, to improve the intelligence and precision of the crop management and harvesting process. By using the jujube orchard as a case study, it was verified that the full-process crop management and harvesting scheme with integrated space information can improve the management level and economic benefits of the jujube orchard.

Effect of jujube orchard abandonment time on soil properties and enzyme activities at soil profile in the Loess Plateau

In the Loess Plateau, the impact of abandoned farmland on soil properties and enzyme activity, along with its temporal variations and potential driving factors, remains a mystery. This study was designed to systematically and comprehensively examine the variations in soil enzyme activities, particle size distribution, and stability of soil aggregates at different stages of ecological recovery in the Loess Plateau. Our findings reveal a nuanced temporal pattern: with the progression of cropland abandonment, there is a notable decrease in soil bulk density. Concurrently, a dynamic trend in enzyme activities is observed—initially exhibiting a decline, followed by an increase over extended periods of recovery. Notably, prolonged abandonment leads to marked enhancements in soil structure. Parameters such as the mean weight diameter (MWD) and geometric mean diameter (GMD) of soil aggregates show an overall increasing trend. In terms of the Relative Dissipation Index (RSI), our data indicate a sequence of control > 2 years of abandonment > 4 years > 6 years > 14 years. From this, it can be seen that fallowing may be an effective natural restoration strategy for improving the physical structure of soils in the Loess Plateau and restoring soil nutrients. However, positive changes may take a long time to become evident.

YOLOv7-Branch: A Jujube Leaf Branch Detection Model for Agricultural Robot.

The intelligent harvesting technology for jujube leaf branches presents a novel avenue for enhancing both the quantity and quality of jujube leaf tea, whereas the precise detection technology for jujube leaf branches emerges as a pivotal factor constraining its development. The precise identification and localization of jujube leaf branches using real-time object detection technology are crucial steps toward achieving intelligent harvesting. When integrated into real-world scenarios, issues such as the background noise introduced by tags, occlusions, and variations in jujube leaf morphology constrain the accuracy of detection and the precision of localization. To address these issues, we describe a jujube leaf branch object detection network based on YOLOv7. First, the Polarized Self-Attention module is embedded into the convolutional layer, and the Gather-Excite module is embedded into the concat layer to incorporate spatial information, thus achieving the suppression of irrelevant information such as background noise. Second, we incorporate implicit knowledge into the Efficient Decoupled Head and replace the original detection head, enhancing the network's capability to extract deep features. Third, to address the issue of imbalanced jujube leaf samples, we employ Focal-EIoU as the bounding box loss function to expedite the regression prediction and enhance the localization accuracy of the model's bounding boxes. Experiments show that the precision of our model is 85%, which is increased by 3.5% compared to that of YOLOv7-tiny. The mAP@0.5 value is 83.7%. Our model's recognition rate, recall and mean average precision are superior to those of other models. Our method could provide technical support for yield estimation in the intelligent management of jujube orchards.

Cropping with Vicia villosa and native grass improves soil's bacterial structure and ecological network in a jujube orchard.

In a jujube orchard, cropping withgrass may influence bacterial diversity and ecological networks due to changes of physicochemical properties in soil, which has a serious effect on the stability of soil ecosystems. The aim of this study was to analyze the effects of different cultivation methods (CK: cleaning tillage; NG: cropping with native grass; VV: cropping with Vicia villosa) on the soil's bacterial structure and its co-occurrence network in a jujube orchard. The results showed that the highest moisture content, total nitrogen, and organic matter in the rhizosphere soil of a jujube orchard was found in the VV group. The soil's moisture content, total nitrogen, and organic matter in the VV group were 2.66%, 0.87 gkg-1, and 5.55 mgkg-1 higher than that found in the CK group. Compared to the CK group, the number of unique species in the rhizosphere soil in the NG and the VV groups increased by 7.33% and 21.44%. The PICRUSt and FAPROTAX analysis showed that sown grass had a greater influence on the ecological function of the soil's bacteria. Cropping with Vicia villosa and native grass significantly increased aerobic chemoheterotrophy, nitrogen respiration, nitrate reduction related to biochemical cycles, and the relative abundance of genes related to carbohydrate metabolism and the biodegradation of xenobiotics. The bacterial network complexity in the NG group was higher than that in the CK and VV groups and was greatest in the hub nodes (OTU42, Bacteroidota; OTU541, Nitrospiraceae). In this study, the ecological benefit seen in the soil's microbial function provides support to the theory that cropping with grass (Vicia villosa) increases the sustainable development of a jujube orchard.

Inversion of Soil Moisture Content in Cotton Fields Using GBR-RF Algorithm Combined with Sentinel-2 Satellite Spectral Data

Soil moisture content plays a vital role in agricultural production, significantly influencing crop growth, development, and yield. Thoroughly understanding the specific soil moisture content in cotton fields is crucial for enhancing agricultural efficiency and driving sustainable agricultural development. This study utilized the gradient-boosting regression–random forest (GBR-RF) algorithm and the GBR and RF algorithms separately, in conjunction with Sentinel-2 satellite images, to estimate cotton soil moisture content, focusing on the B1–B8 bands and in particular the sensitive B6, B7, and B8 bands. The soil data in the jujube orchard of the study area were collected using soil augers at a depth of 30 cm, with soil data collected from a depth of 20 to 30 cm. The findings revealed that the integrated learning algorithm GBR-RF demonstrated high accuracy, with R2, MAE, and MSE results of 0.8838, 1.0121, and 1.6168, respectively. In comparison, the results using just the GBR algorithm yielded R2, MAE, and MSE values of 0.8158, 1.1327, and 1.9645, respectively, while those obtained from the RF algorithm were 0.8415, 1.0680, and 1.8331, respectively. These results indicate that the algorithms exhibited strong generalization, robustness, and accuracy, with GBR-RF outperforming GBR and RF by 8.34% and 5.03%, respectively, in combination with using the B1–B8 bands for inversion. Furthermore, utilizing the full-band data resulted in R2 values that were up to 24.27% higher than those of the individual bands, affirming the efficacy of band combinations for improved accuracy. This study’s demonstration of the positive impact of integrated learning algorithms on estimating cotton soil moisture content underscores the advantages of multi-band data combinations over single-band data, highlighting their ability to enhance accuracy without significantly impacting errors. Importantly, this study’s findings, while not limited to a single experimental field, have broad applicability in cotton precision agriculture, offering valuable insights for research on yield enhancement and agricultural efficiency.

Efficiency and economic feasibility of using Neonicotinoids against Flat-headed borer Chrysobothris affinis (Fabricius, 1794) (Buprestidae: Coleoptera) infesting Jujube orchards in Basra Province, Iraq

A field study evaluated the efficiency and economic feasibility of using three Neonicotinoids to control Flat-headed borers infesting Jujube orchards in Basra Province during 2020 and 2021. The insecticides tested were Acetamiprid, Imidacloprid and Thiamethoxam, with recommended field application rates. This study showed that Acetamiprid and Imidacloprid provided the best protection to jujube trees, reflected in the high average efficiency (88.70% and 93.40%, respectively). Acetamiprid and Thiamethoxam enhanced the production, with an average of 60.10 and 60.00 kg/tree, respectively, compared to 39.50 kg of the check trees. Thus, the study showed the highest economic return from controlling the flat-headed borers using Acetamiprid and Thiamethoxam (5714900 and 5676500 dinars/ dunum, respectively). In contrast, the economic returns of Imidacloprid were 5082900 Iraqi dinars/ dunum. These results confirmed that the high economic feasibility was related to the efficacy of the low-cost insecticides in reducing the fruit infestation rate and increasing the yield. Keywords: Buprestidae; Insecticides; flat-headed borers; Economic visibility; Neonicotinoids.

Autonomous navigation method of jujube catch-and-shake harvesting robot based on convolutional neural networks

Autonomous navigation of agricultural devices plays an indispensable step for the realization of various field intelligent operation tasks. However, the accurate extraction of navigation path is still a challenging task due to the complex orchard environment with many interference factors. To cope with the challenge, a low-cost vision system based on convolutional neural network and machine learning algorithm is proposed to obtain accurate navigation path for autonomous path detection of catch-and-shake harvesting robot under winter jujube orchard. In the vision system, firstly, an improved light-weight YOLOX-Nano architecture with some tricks including CSP attention block, SPPCSPC-F and ASFF is proposed to detect root points of jujube tree. Results show that the mAP of our model is 84.08 % and model size is only 12.30 MB, which can meet the requirements of embedded deployment. Secondly, a K-means clustering algorithm is applied to divide root points into two classes which belong to left and right tree row. Finally, the navigation line can be determined by geometric relations and least squares. The experimental results showed that the navigation centerline mean pixel errors and average heading angle deviation are 4.49 pixels and 2.55°, respectively. Therefore, this method can provide a new theoretical basis and technical reference for autonomous navigation of winter jujube catch-and-shake harvesting robots.

Cost–Benefit Evaluation of Walnut and Jujube Orchards under Fruit Tree–Crop Intercropping Conditions in Southern Xinjiang

Fruit planting plays an essential role in achieving zero hunger, ensuring food security, and increasing the income of local people. As one of China’s principal fruit-planting areas, southern Xinjiang possesses more than 80% of the total fruit-planting areas of Xinjiang. However, the spatial distribution, extent, and types of fruit trees remain unknown, generating uncertainty in calculating the economic benefits of orchards in this area. In this paper, we used walnut and jujube orchards under fruit tree–crop intercropping conditions in the Hotan Oasis in southern Xinjiang as the research object. Walnut and jujube orchards were precisely characterized using object-oriented and decision tree classification methods. Then, the economic benefits for farmers were estimated based on field surveys and statistical data. From 2003 to 2020, the area of jujube orchards rapidly increased from 1.91 × 103 ha to 33.59 × 103 ha, while that of walnut orchards steadily increased from 18.05 × 103 ha to 40.24 × 103 ha. The expansion areas of walnut orchards were mainly transformed from other orchards in the oasis, while the expansion areas of jujube orchards mainly originated from newly cultivated land in the desert. The increases in areas and yields largely offset the increase in planting costs and the decrease in purchase prices, resulting in an increasing trend in the total net income of the orchards. From 2003 to 2020, the total net income of walnut orchards increased by 68.96%, and the total net income of jujube orchards significantly increased by 23.37 times. However, the output/input ratios of walnut orchards under intercropping conditions were approximately two times higher than those of jujube orchards under monocropping conditions. The increase in investment slightly offset the decreases in yields and purchase prices, resulting in increases of 5.24% and 18.64% in the output/input ratios of walnuts and jujubes, respectively, in orchards exhibiting standardized cultivation. In the future, it is necessary to significantly expand the area of orchards involving standardized cultivation and improve the yield and quality of fruits, thereby increasing the yields and income levels of local farmers.

Impact of Mulching on Soil Moisture and Sap Flow Characteristics of Jujube Trees

The main purpose of this study was to assess the influence of grass planting and jujube branch mulching on soil moisture levels and jujube tree transpiration rates, with the ultimate goal of improving jujube tree production in rain-fed orchards. The study encompassed four treatments: jujube branch mulching (JBM), jujube branch mulching with white clover planting (JBM + WCP), white clover planting (WCP), and clean cultivation (CC). During a two-year experiment, it was observed that the JBM treatment exhibited the highest capacity for moisture conservation. Specifically, it resulted in an average increase of 2.69% (in 2013) and 2.23% (in 2014) in soil moisture content compared with the CC treatment. The application of statistical analysis revealed significant differences (p < 0.05) between JBM and JBM + WCP, as well as highly significant differences (p < 0.01) between JBM and WCP in the year 2013. In 2014, JBM exhibited significant differences (p < 0.01) from both JBM + WCP and WCP. Between April and August, JBM exhibited the highest soil moisture content, followed by CC, with WCP showing the lowest levels. From September to October, JBM retained its status as the treatment with the highest soil moisture content, JBM + WCP ranked second, and CC experienced a decline and recorded the lowest soil moisture content. Under sunny conditions, all treatments showed a broad peak curve in the daily variation of sap flow velocity. In cloudy weather, a multi-peak wave-like curve was observed with similar trends across treatments. Between April and August, the monthly average sap flow velocity of JBM ranked the highest, followed by CC, while WCP showed the lowest velocity. During the period of September to October, JBM maintained its lead in sap flow velocity, while JBM + WCP rose to the second position, and CC’s sap flow velocity dropped to the lowest level. JBM and WCP treatments showed significant differences (p < 0.01), and in 2014, JBM also had significant differences (p < 0.05) compared with JBM + WCP. The sap flow velocity was positively correlated with air temperature, vapor pressure deficit, wind velocity, photosynthetically active radiation, and soil temperature. Photosynthetically active radiation was identified as the main driving factor influencing jujube tree transpiration. In conclusion, the findings of this study demonstrate the effectiveness of using pruned jujube branches for coverage in rain-fed jujube orchards. This approach not only conserves mulching materials and diminishes the expenses associated with transporting pruned jujube tree branches away from the jujube orchard but also achieves multiple objectives, including increasing soil moisture, promoting jujube tree transpiration, and enhancing soil water utilization. These results have significant implications for the efficient utilization of rainwater resources in rain-fed jujube orchards and provide valuable insights for practical applications in orchard management.

Incorporating dynamic schemes of canopy light extinction coefficient improves transpiration model performance for fruit plantations

The light extinction coefficient (C) describes the efficiency of light interception by a plant canopy, and is a key parameter commonly used to partition radiant energy between a vegetation canopy and the soil surface in several important transpiration models. The C has been observed to vary considerably in the field, but is often parametrized as a fixed constant value, resulting in large potential uncertainty in transpiration estimation. We hypothesized that C changes with leaf area index (LAI) and environments, and that accurate characterization of the C variation pattern would improve transpiration modelling. To test these hypotheses, the relationships between measured C and potential influencing factors were explored for three different fruit plantations in different climate zones in China. Based on these relationships, a new method was proposed to capture C dynamics that considered both LAI and environmental factors. Additionally, 16 C schemes (15 dynamic C schemes and one fixed C scheme) were individually embedded in the Penman-Monteith model, and comprehensive comparisons were made to determine the most effective C scheme for simulating transpiration based on a Bayesian framework. Results showed that C displayed clear variations across all sites, and that there were significant relationships of C with LAI and environmental factors. Compared with the model using a fixed C scheme, the model using the best dynamic C scheme significantly improved model performance by increasing estimation accuracy and decreasing model uncertainty. Specifically, the average coefficients of determination (R2) increased from 0.35 to 0.76, 0.82 to 0.85, and 0.90 to 0.93 for the orange orchard, the jujube orchard, and the grape greenhouse, respectively. Additionally, the average mean relative errors (MRE) decreased by 54.99%, 26.62%, and 13.95%, respectively, and the average uncertainty band widths (B) decreased by 46.81%, 21.74%, and 11.43%, respectively. Model performance improvement was more significant at sites with more serious environmental stresses. The results of our study emphasize the necessity for considering dynamic C patterns in water consumption estimation, particularly for regions facing severe abiotic stresses that are likely to occur in many regions under future climate conditions.

Optimizing Soil Moisture Conservation and Temperature Regulation in Rainfed Jujube Orchards of China’s Loess Hilly Areas Using Straw and Branch Mulching

The implementation of the “Returning Farmland to Forest” project in the loess hilly region of China has led to the establishment of large-scale economic forests, which have become the dominant industry driving local economic development. However, the region faces challenges such as drought, water shortages, and an uneven distribution of precipitation, which have a severe impact on the growth of economic forests, including jujube trees. Water stress significantly reduces yield and efficiency, posing a threat to the sustainable and healthy development of jujube ecological and economic forests. Therefore, this study aimed to address these issues by implementing straw mulching (SM) and jujube branch mulching (BM) measures in the mountainous jujube economic forests. Through long-term monitoring and statistical analysis, the study investigated the effects of different mulching treatments on soil moisture and soil temperature. The research findings reveal that both SM and BM significantly increased soil moisture in the 0–280 cm soil layer during the jujube growing season (p < 0.05). In both normal precipitation (2014) and drought (2015) years, SM increased average soil moisture content by 5.10% and 4.60%, respectively, compared to the uncovered treatment (CK). SM also had a positive impact on the soil moisture content in each layer of the soil profile. However, BM only increased soil moisture content in the 40–100 cm and 220–280 cm soil layers. Additionally, SM and BM reduced the variation of soil moisture, with SM showing a more significant effect in regulating soil moisture and achieving more stable moisture levels. During the jujube growing seasons in 2014 and 2015, SM and BM decreased soil temperature in the 0–10 cm soil layer. The temperature difference compared to CK decreased with increasing soil depth. SM had an overcooling effect, while BM reduced the temperature before the fruit expansion period and maintained warmth afterward. Both SM and BM also reduced the daily range and variation range of soil temperature, with SM having a more pronounced effect. The temperature of the 0–20 cm soil layer exhibited the strongest correlation with air temperature, and SM showed the weakest response. In conclusion, adopting straw mulching and jujube branch mulching in rain-fed jujube orchards in the loess hilly region not only saves materials and reduces costs but also contributes to water retention and temperature regulation. Straw mulching, in particular, plays a more significant role in moisture retention and temperature regulation and is advantageous for soil management in rain-fed jujube orchards. These research findings provide a scientific basis for optimizing water and heat management in orchards with limited water resources.

Pan trapping is an effective method to trap adults of the jujube gall midge, Dasineura jujubifolia (Diptera: Cecidomyiidae).

Jujube gall midge (Dasineura jujubifolia Jiao & Bu) (Diptera: Cecidomyiidae) is an important pest in jujube (Ziziphus jujuba Mill.) orchards in Aksu, Xinjiang, China. Yellow sticky traps are the main device used for monitoring jujube gall midge adults, but their efficacy is low. Here, we compared the effectiveness of yellow sticky traps with water pan traps (are commonly used for trapping Diptera insects) to monitor jujube gall midge adults. Yellow sticky traps and pan traps were deployed for 2 consecutive years in jujube orchards in Aksu, Xinjiang, China. The midge's population dynamics as revealed by these 2 trap types were consistent, but the effectiveness of pan traps was about 5 times greater than that of the yellow sticky traps. In addition, pan traps captured fewer non-target species (e.g., parasitic wasps, lacewings, and lady beetles) than yellow sticky traps. Our study suggests that pan trap is an effective device to monitor jujube gall midge adults with minimal harm to natural enemies.

Surfactants Improving the Wetting Behavior and Adhesion Mechanism of Pesticide Dilution Droplets on Jujube Leaf Surfaces.

Fruit tree leaves have different chemical compositions and diverse wax layer structures that result in different patterns of wetting and pesticide solution spreading on their surface. Fruit development is a time when pests and diseases occur, during which a large number of pesticides are needed. The wetting and diffusion properties of pesticide droplets on fruit tree leaves were relatively poor. To solve this problem, the wetting characteristics of leaf surfaces with different surfactants were studied. The contact angle, surface tension, adhesive tension, adhesion work, and solid-liquid interfacial tension of five surfactant solution droplets on jujube leaf surfaces during fruit growth were studied by the sessile drop method. C12E5 and Triton X-100 have the best wetting effects. Two surfactants were added to a 3% beta-cyfluthrin emulsion in water, and field efficacy tests were carried out on peach fruit moths in a jujube orchard at different dilutions. The control effect is as high as 90%. During the initial stage when the concentration is low, due to the surface roughness of the leaves, the surfactant molecules adsorbed at the gas-liquid and solid-liquid interfaces reach an equilibrium, and the contact angle on the leaf surface changes slightly. With increasing surfactant concentration, the pinning effect in the spatial structure on the leaf surface is overcome by liquid droplets, thereby significantly decreasing the contact angle. When the concentration is further increased, the surfactant molecules form a saturated adsorption layer on the leaf surface. Due to the existence of a precursor water film in the droplets, surfactant molecules on the interface continuously move to the water film on the surface of jujube tree leaves, thus causing interactions between the droplets and the leaves. The conclusion of this study provides theoretical guidance for the wettability and adhesion of pesticides on jujube leaves, so as to achieve the purpose of reducing pesticide use and improving pesticide efficacy.

AGHRNet: An attention ghost-HRNet for confirmation of catch‐and‐shake locations in jujube fruits vibration harvesting

The development of an intelligent jujube fruit harvesting device is a critical step in achieving the whole mechanization process. Catch‐and‐shake harvesting, as an efficient and stable vibration harvesting method, has widely been used to save labor and improve harvesting efficiency in large-scale jujube orchards. However, existing catch‐and‐shake harvesters still rely heavily on the operator's naked eyes to determine the shaking position, which is subjective, highly inefficient and highly labor intensive. To address this problem, this study proposes a computer vision system including truck and branches segmentation, skeleton extraction and vibration position selection to automatically identify appropriate shaking positions. A lightweight attention Ghost-HRNet (AGHRNet) based on deep learning is designed to separate the truck and branches from the complex orchard background. In AGHRNet, an attention Ghost block is proposed to lighten the model and improve segmentation accuracy. Moreover, we also construct a hybrid loss function to solve the pixel-level category imbalance of the trunks, branches and background. To verify the effectiveness of the AGHRNet, ten state-of-the-art semantic segmentation methods, including the FCN8s, SegNet, U-Net, PSPNet, DeepLabv3+, BiSeNet, DDRNet, DANet, SegFormer and HRNet, are compared. Comparative experimental results show that compared with the other state-of-the-art methods, the proposed AGHRNet has higher segmentation accuracy (77.79 % mIoU, 89.46 % mPA) and a smaller model size (9.53 MB) on our datasets. Finally, mask medial axis extraction and the minimum inscribed circle operation are applied to obtain the appropriate vibration location. The computer vision system can provide technical references for catch‐and‐shake harvesting and developing jujube fruit-harvesting robots.

Variations of deep soil moisture under different vegetation restoration types in a watershed of the Loess Plateau, China

The soil water content and water consumption of deep layer (200–1000 cm) of sloping farmland, grassland and Jujube orchard in Yuanzegou small watershed in the loess hilly region were studied. The results showed that (1) the soil moisture content of sloping farmland, grassland and Jujube orchard increased at first and then decreased at 0–200 cm, with mean values of 11.91%, 11.23% and 9.99% respectively; From 200 to 1000 cm, the soil moisture content decreased slowly and tended to be stable, with mean values of 11.77%, 11.62% and 9.96% respectively. (2) 200–1000 cm, the soil water storage ranged in the order of sloping farmland > grassland > Jujube orchard, with mean values of 148.78, 145.28 and 121.11 mm respectively. (3) In the 200–1000 cm soil layer, the water consumption of the Jujube orchard ranged from 21.67 to 32.97 mm, and that of grassland ranged from − 4.47 to 10.32 mm, the water consumption of deep soil in Jujube orchard was significantly higher than that in grassland (p < 0.05)., Although the Jujube orchard had obvious deep soil moisture consumption, it was insufficient to cause serious soil drying and increased farmers' income, so it can be planted locally, but the planting density should be reasonable and water-saving engineering technology should be adopted.

Skeleton extraction and pruning point identification of jujube tree for dormant pruning using space colonization algorithm.

The dormant pruning of jujube is a labor-intensive and time-consuming activity in the production and management of jujube orchards, which mainly depends on manual operation. Automatic pruning using robots could be a better way to solve the shortage of skilled labor and improve efficiency. In order to realize automatic pruning of jujube trees, a method of pruning point identification based on skeleton information is presented. This study used an RGB-D camera to collect multi-view information on jujube trees and built a complete point cloud information model of jujube trees. The space colonization algorithm acts on the global point cloud to generate the skeleton of jujube trees. The iterative relationship between skeleton points was represented by constructing a directed graph. The proposed skeleton analysis algorithm marked the skeleton as the trunk, the primary branches, and the lateral branches and identified the pruning points under the guidance of pruning rules. Finally, the visual model of the pruned jujube tree was established through the skeleton information. The results showed that the registration errors of individual jujube trees were less than 0.91cm, and the average registration error was 0.66cm, which provided a favorable database for skeleton extraction. The skeleton structure extracted by the space colonization algorithm had a high degree of coincidence with jujube trees, and the identified pruning points were all located on the primary branches of jujube trees. The study provides a method to identify the pruning points of jujube trees and successfully verifies the validity of the pruning points, which can provide a reference for the location of the pruning points and visual research basis for automatic pruning.

南疆骏枣园土壤养分与果实品质的相关性分析——以和田地区枣园为例

南疆骏枣园土壤养分与果实品质的相关性分析——以和田地区枣园为例

Effects of Different Land Use Patterns on Soil Water in Loess Hilly and Gully Regions of China.

Soil water is a major barrier to ecological restoration and sustainable land use in China’s Loess Hilly Region. For the restoration of local vegetation and the optimal use of the region’s land resources, both theoretically and practically, it is essential to comprehend the soil water regimes under various land use types. The soil water content in the 0−160 cm soil profile of slope cropland, terraced field, jujube orchard, and grassland was continuously measured using EC-5 soil moisture sensors during the growing season (May−October) in the Yuanzegou catchment in the Loess Hilly Region to characterize the changes in soil water in these four typical land use types. The results showed that in both years of normal precipitation and drought, land use patterns varied in seasonal variability, water storage characteristics, and vertical distribution of soil water. In the dry year of 2015, the terraced field effectively held water. During the growing season, the 0−60 cm soil layer’s average soil water content was 2.6%, 4.2%, and 1.8% higher than the slope cropland, jujube orchard, and grassland, respectively (p < 0.05), and the 0−160 cm soil layer’s water storage was 43.90, 32.08, and 18.69 mm higher than the slope cropland, jujube orchard, and grassland, respectively. The average soil water content of the 0−60 cm soil layer in the jujube orchard was 2.9%, 3.8%, and 4.5% lower than that of slope cropland, terraced field, and grassland, respectively, during the normal precipitation year (2014) (p < 0.05). Only 35.0% of the total soil water storage was effectively stored in the 0−160 cm soil layer of the jujube orchard during the drought year. There was a significant difference in the grey relational grade between the soil water in the top layer (0−20 cm) and the soil water in the middle layer (20−100 cm) under different land use types, with the terraced field having the highest similarity degree of soil water variation trend, followed by grassland, slope cropland, and jujube orchard. Slope croplands in the study region may be converted into terraced fields to enhance the effective use of rainfall resources and encourage the expansion of ecological agriculture. Proper water management practices must be employed to reduce jujube tree water consumption and other wasteful water usage in order to guarantee the jujube orchard’s ability to expand sustainably. This would address the issue of the acute water deficit in the rain-fed jujube orchards in the Loess Hilly Region.

Monitoring of the Fruit Flies (Bactrocera spp.) Infesting Jujube Orchard using Static Spinosad Traps

Fruit flies (Bactrocera spp.) are regarded as serious insect pests of fruits and vegetables in the world. The goal of this study was to examine the effect of spinosad traps on Bactrocera spp. at different heights 0, 1, 2, and 3 m on jujube tree during 2020-2021. Flies’ populations were counted weekly. The results revealed that the highest population of B. zonata (225.6 flies) were recorded at 2 m height on (22 October, 2020) and the lowest ones (21.6 flies) were recorded at the ground level (0 m height) during (4 February, 2021). However, the overall maximum catches were 158.95 at 2 m height and minimum was 68.72 at the ground level. Similarly, the maximum population of B. dorsalis was (50.5 flies) at 2 m height during (9 October, 2020), but the minimum (2.5 flies) was in the ground level during (4 February, 2021). The overall highest B. dorsalis catches were (43.50 flies) at 2 m height and the lowest was (3.55 flies) at ground level. The population of B. zonata correlated positively (r= 0.2939**) with temperature, but negatively (r= -0.0223NS) with relative humidity. However, B. dorsalis populations was positive correlated with both of the temperature and relative humidity (r= 0.0261** and r= 0.0091NS, respectively). Ultimately, pheromone traps (Spinosad+Methyl eugenol) at 2 m height are highly recommended to catches both fruit flies (B. zonata and B. dorsalis) in Jujube Orchards.