Search Results (94)

This paper explores the optimization of the bullwhip effect (BWE) and inventory costs considering price information symmetry in an omnichannel environment, offering novel insights into managing supply chain dynamics. We examine the pick-up lead time in the “buy online and pick up in store” (BOPS) channel as a critical operational factor, analyzing how the interaction with the ordering lead time affects omnichannel supply chain performance. The research highlights the impacts of the BOPS strategy on demand and inventory information, developing a comparative examination of the BWE and inventory expenses within various supply chain contexts. We discover that the interplay between ordering lead time and pick-up lead time significantly affects both inventory costs and the BWE of omnichannel retailers, with these impacts presenting an inverse relationship. While numerous studies have validated that product returns can restrain the information distortion in supply chains, our findings reveal that this relationship holds true in omnichannel retail only within specific supply chain contexts. This comprehensive approach offers valuable insights for omnichannel supply chain managers seeking to optimize the BOPS strategy and improve overall operational efficiency. Full article

Target detection is the key technology of the apple-picking robot. To overcome the limitations of existing apple target detection methods, including low recognition accuracy of multi-species apples in complex orchard environments and a complex network architecture that occupies large memory, a lightweight apple recognition model based on the improved YOLO11 model was proposed, named YOLO-WAS model. The model aims to achieve efficient and accurate automatic multi-species apple identification while reducing computational resource consumption and facilitating real-time applications on low-power devices. First, the study constructed a high-quality multi-species apple dataset and improved the complexity and diversity of the dataset through various data enhancement techniques. The YOLO-WAS model replaced the ordinary convolution module of YOLO11 with the Adown module proposed in YOLOv9, the backbone C3K2 module combined with Wavelet Transform Convolution (WTConv), and the spatial and channel synergistic attention module Self-Calibrated Spatial Attention (SCSA) combined with the C2PSA attention mechanism to form the C2PSA_SCSA module was also introduced. Through these improvements, the model not only ensured lightweight but also significantly improved performance. Experimental results show that the proposed YOLO-WAS model achieves a precision (P) of 0.958, a recall (R) of 0.921, and mean average precision at IoU threshold of 0.5 (mAP@50) of 0.970 and mean average precision from IoU threshold of 0.5 to 0.95 with step 0.05 (mAP@50:95) of 0.835. Compared to the baseline model, the YOLO-WAS exhibits reduced computational complexity, with the number of parameters and floating-point operations decreased by 22.8% and 20.6%, respectively. These results demonstrate that the model performs competitively in apple detection tasks and holds potential to meet real-time detection requirements in resource-constrained environments, thereby contributing to the advancement of automated orchard management. Full article

With the advancement of agricultural automation technologies, apple-harvesting robots have gradually become a focus of research. As their “perceptual core,” machine vision systems directly determine picking success rates and operational efficiency. However, existing vision systems still exhibit significant shortcomings in target detection and positioning accuracy in complex orchard environments (e.g., uneven illumination, foliage occlusion, and fruit overlap), which hinders practical applications. This study proposes a visual system for apple-harvesting robots based on improved Mask R-CNN and binocular vision to achieve more precise fruit positioning. The binocular camera (ZED2i) carried by the robot acquires dual-channel apple images. An improved Mask R-CNN is employed to implement instance segmentation of apple targets in binocular images, followed by a template-matching algorithm with parallel epipolar constraints for stereo matching. Four pairs of feature points from corresponding apples in binocular images are selected to calculate disparity and depth. Experimental results demonstrate average coefficients of variation and positioning accuracy of 5.09% and 99.61%, respectively, in binocular positioning. During harvesting operations with a self-designed apple-picking robot, the single-image processing time was 0.36 s, the average single harvesting cycle duration reached 7.7 s, and the comprehensive harvesting success rate achieved 94.3%. This work presents a novel high-precision visual positioning method for apple-harvesting robots. Full article

The emergence of the new retail model of community group buying (CGB) has brought a significant impact on the traditional retail of community nanostores while community nanostores, as the leaders of the community, have the natural advantage of becoming the pick-up points of the CGB platform. Therefore, as the two core formats in the new community retail ecosystem, the CGB platform and community nanostore exhibit both competitive and complementary characteristics. Aiming at the community retail market composed of the CGB platform and the community nanostore, this study constructed a Hotelling game model to portray the competition and cooperation between these two channels and explored the impacts of different operational strategies on the equilibrium decisions and optimal profits of community retail market participants through comparative analysis. The research revealed that when retailers adopt the non-cooperation strategy, the community nanostore will occupy a larger market share, and the spillover effect between channels brought by the cooperation strategy is the main factor influencing retailers’ operation strategy. In addition, the type of pick-up point operated by the community nanostore will also affect the cooperation willingness of the CGB platform. Full article

Accurate identification of tomato ripeness and precise detection of picking points is the key to realizing automated picking. Aiming at the problems faced in practical applications, such as low accuracy of tomato ripeness and picking points detection in complex greenhouse environments, which leads to wrong picking, missed picking, and fruit damage by robots, this study proposes the YOLO-TMPPD (Tomato Maturity and Picking Point Detection) model. YOLO-TMPPD is structurally improved and algorithmically optimized based on the YOLOv8 baseline architecture. Firstly, the Depthwise Convolution (DWConv) module is utilized to substitute the C2f module within the backbone network. This substitution not only cuts down the model’s computational load but also simultaneously enhances the detection precision. Secondly, the Content-Aware ReAssembly of FEatures (CARAFE) operator is utilized to enhance the up-sampling operation, enabling precise content-aware processing of tomatoes and picking keypoints to improve accuracy and recall. Finally, the Convolutional Attention Mechanism (CBAM) module is incorporated to enhance the model’s ability to detect tomato-picking key regions in a large field of view in both channel and spatial dimensions. Ablation experiments were conducted to validate the effectiveness of each proposed module (DWConv, CARAFE, CBAM), and the architecture was compared with YOLOv3, v5, v6, v8, v9, and v10. The experimental results reveal that, when juxtaposed with the original network model, the YOLO-TMPPD model brings about remarkable improvements. Specifically, it improves the object detection F1 score by 4.48% and enhances the keypoint detection accuracy by 4.43%. Furthermore, the model’s size is reduced by 8.6%. This study holds substantial theoretical and practical value. In the complex environment of a greenhouse, it contributes significantly to computer-vision-enabled detection of tomato ripening. It can also help robots accurately locate picking points and estimate posture, which is crucial for efficient and precise tomato-picking operations without damage. Full article

Efficient and accurate apple detection is crucial for the operation of apple-picking robots. To improve detection accuracy and speed, we propose a lightweight apple-detection model based on the YOLOv8n framework. The proposed model introduces a novel Self-Calibrated Coordinate (SCC) attention module, which enhances feature extraction, especially for partially occluded apples, by effectively capturing spatial and channel information. Additionally, we replace the C2f module within the YOLOv8n neck with a Partial Convolution Module improved with Reparameterization (PCMR), which accelerates detection, reduces redundant computations, and minimizes both parameter count and memory access during inference. To further optimize the model, we fuse multi-scale features from the second and third pyramid levels of the backbone architecture, achieving a lightweight design suitable for real-time detection. To address missed detections and misclassifications, Polynomial Loss (PolyLoss) is integrated, enhancing class discrimination for different apple subcategories. Compared to the original YOLOv8n, the improved model increases the mAP by 2.90% to 88.90% and improves the detection speed to 220 FPS, which is 30.55% faster. Additionally, it reduces the parameter count by 89.36% and the FLOPs by 2.47%. Experimental results demonstrate that the proposed model outperforms mainstream object-detection algorithms, including Faster R-CNN, RetinaNet, SSD, RT-DETR-R18, RT-DETR-R34, YOLOv5n, YOLOv6-N, YOLOv7-tiny, YOLOv8n, YOLOv9-T and YOLOv11n, in both mAP and detection speed. Notably, the improved model has been used to develop an Android application deployed on the iQOO Neo6 SE smartphone, achieving a 40 FPS detection speed, a 26.93% improvement over the corresponding deployment of YOLOv8n, enabling real-time apple detection. This study provides a valuable reference for designing efficient and lightweight detection models for resource-constrained apple-picking robots. Full article

We propose the HOSA algorithm to pick P-wave arrival times on seismic arrays. HOSA comprises two stages: a single-trace stage (STS) and a multi-channel stage (MCS). STS seeks deviations in higher-order statistics from background noise to identify sets of potential onsets on each trace. STS employs various thresholds and identifies an onset only for solutions that are gently variable with the threshold. Uncertainty is assigned to onsets based on their variation with the threshold. MCS verifies that detected onsets are consistent with the array geometry. It groups onsets within an array by hierarchical agglomerative clustering and selects only groups whose maximum differential times are consistent with the P-wave travel time across the array. HOSA needs a set of P-onsets to be calibrated. These sets may be already available (e.g., preliminary catalogs) or retrieved from picking (manually/automatically) a subset of traces in the target area. We tested HOSA on 226 microearthquakes recorded by 20 temporary arrays of 10 stations each, deployed in the Irpinia region (Southern Italy), which, in 1980, experienced a devastating 6.9 Ms earthquake. HOSA parameters were calibrated using a preliminary catalog of onsets obtained using an automatic template-matching approach. HOSA solutions are more reliable, less prone to false detection, and show higher inter-array consistency than template-matching solutions. Full article

Automated kiwi harvesting hinges on the seamless deployment of a detection model and the accurate detection of kiwifruits. However, practical challenges, such as the limited computational resources on harvesting robots and occlusions among fruits, hinder the effectiveness of automated picking. To address these issues, this paper introduces EDT-YOLOv8n, a lightweight and efficient network architecture based on YOLOv8n. The proposed model integrates the Effective Mobile Inverted Bottleneck Convolution (EMBC) module to replace the C2f modules, mitigating the channel information loss and bolstering generalization. Additionally, the DySample upsampler, an ultra-lightweight and effective dynamic upsampler, improves feature extraction and resource efficiency when compared to traditional nearest-neighbor upsampling. Furthermore, a novel Task Align Dynamic Detection Head (TADDH) is implemented, incorporating group normalization for a more efficient convolutional structure and optimizing the alignment between the classification and localization tasks. The experimental results reveal that the proposed EDT-YOLOv8n model achieves higher precision (86.1%), mAP0.5 (91.5%), and mAP0.5-0.95 (65.9%), while reducing the number of parameters, the number of floating-point operations, and the model size by 15.5%, 12.4%, and 15.0%, respectively. These improvements demonstrate the model’s effectiveness and efficiency in supporting kiwifruit localization and automated harvesting tasks. Full article

We consider a basic joint communication and sensing setup comprising a transmitter, a receiver and a sensor. The transmitter sends a codeword to the receiver through a discrete memoryless channel, and the receiver is interested in decoding the transmitted codeword. At the same time, the sensor picks up a noisy version of the transmitted codeword through one of two possible discrete memoryless channels. The sensor knows the codeword and wishes to discriminate between the two possible channels, i.e., to identify the channel that has generated the output given the input. We study the trade-off between communication and sensing in the asymptotic regime, captured in terms of the channel coding rate against the two types of discrimination error exponents. We characterize the optimal trade-off between the rate and the exponents for general discrete memoryless channels with an input cost constraint. Full article

The detection of tea bud targets is the foundation of automated picking of premium tea. This article proposes a high-performance tea bud detection model to address issues such as complex environments, small target tea buds, and blurry device focus in tea bud detection. During the spring tea-picking stage, we collect tea bud images from mountainous tea gardens and annotate them. YOLOv5 tea is an improvement based on YOLOv5, which uses the efficient Simplified Spatial Pyramid Pooling Fast (SimSPPF) in the backbone for easy deployment on tea bud-picking equipment. The neck network adopts the Bidirectional Feature Pyramid Network (BiFPN) structure. It fully integrates deep and shallow feature information, achieving the effect of fusing features at different scales and improving the detection accuracy of focused fuzzy tea buds. It replaces the independent CBS convolution module in traditional neck networks with Omni-Dimensional Dynamic Convolution (ODConv), processing different weights from spatial size, input channel, output channel, and convolution kernel to improve the detection of small targets and occluded tea buds. The experimental results show that the improved model has improved precision, recall, and mean average precision by 4.4%, 2.3%, and 3.2%, respectively, compared to the initial model, and the inference speed of the model has also been improved. This study has theoretical and practical significance for tea bud harvesting in complex environments. Full article

To address the challenges associated with kiwi fruit detection methods, such as low average accuracy, inaccurate recognition of fruits, and long recognition time, this study proposes a novel kiwi fruit recognition method based on an improved lightweight network S-YOLOv4-tiny detection algorithm. Firstly, the YOLOv4-tiny algorithm utilizes the CSPdarknet53-tiny network as a backbone feature extraction network, replacing the CSPdarknet53 network in the YOLOv4 algorithm to enhance the speed of kiwi fruit recognition. Additionally, a squeeze-and-excitation network has been incorporated into the S-YOLOv4-tiny detection algorithm to improve accurate image extraction of kiwi fruit characteristics. Finally, enhancing dataset pictures using mosaic methods has improved precision in the characteristic recognition of kiwi fruits. The experimental results demonstrate that the recognition and positioning of kiwi fruits have yielded improved outcomes. The mean average precision (mAP) stands at 89.75%, with a detection precision of 93.96% and a single-picture detection time of 8.50 ms. Compared to the YOLOv4-tiny detection algorithm network, the network in this study exhibits a 7.07% increase in mean average precision and a 1.16% acceleration in detection time. Furthermore, an enhancement method based on the Squeeze-and-Excitation Network (SENet) is proposed, as opposed to the convolutional block attention module (CBAM) and efficient channel attention (ECA). This approach effectively addresses issues related to slow training speed and low recognition accuracy of kiwi fruit, offering valuable technical insights for efficient mechanical picking methods. Full article

In this study, a closed multi-channel air-blowing plug seedling pick-up device and a combined plug tray were designed to address the issues of complex structure, high seedling damage rates and low pick-up efficiency in fully automated vegetable transplanter systems. The device operates by sealing the plug seedlings in a seedling cup, where compressed air is channeled into the sealed cavity through multiple passages during the seedling pick-up process. The upper surface of the seedling plug is subjected to uniform force, overcoming the friction and adhesion between the plug seedlings and the tray. This process presses the seedlings into the guide tube, completing the pick-up operation. A mechanical model for the plug seedlings was developed, and the kinetics of the pick-up process were analyzed. The multi-channel high-pressure airflow was simulated and evaluated, identifying three key parameters affecting seedling pick-up performance: water content of the seedling plug, air pressure during pick-up, and air-blowing duration. Using these factors as variables, and with seedling pick-up rate and substrate loss rate as evaluation indicators, single-factor experiments and a three-factor, three-level orthogonal experiment were conducted. The experiments’ results showed that the best seedling pick-up performance was achieved when the water content of the plug was 20%, the air pressure was 0.3 MPa, and the air-blowing time was 30 ms. Under these conditions, the seedling pick-up success rate was 97.22%, and the substrate loss rate was 10.46%. Full article

Wireless soft miniature robots have been studied for biomedical applications. However, the wireless soft miniature robots developed so far are mainly composed of synthetic polymers that do not guarantee biocompatibility and biodegradability. Additionally, current soft robots have limitations in demonstrating mobility in narrow spaces, such as blood vessels within the body, by using their flexible body. This study proposes a wireless hybrid-actuated soft miniature robot for biomedical applications. The proposed soft miniature robot consists of biodegradable chitosan and magnetic nanoparticles (MNPs) and is fabricated into an eight-arm shape by laser micromachining. The soft miniature robot can implement hydrogel swelling and magnetic-actuated shape morphing by using the difference in MNP density and magnetic field responsiveness within the robot body, respectively. Furthermore, the soft miniature robot can be guided by external magnetic fields. As feasibility tests, the soft miniature robot demonstrated on-demand pick-and-place motion, grasping a bead, moving it to a desired location, and releasing it. Furthermore, in an in-channel mobility test, the flexible body of the soft miniature robot passed through a tube smaller in size than the robot itself through magnetically actuated shape morphing. These results indicate that the soft miniature robot with controllable shape change and precise magnetic-driven mobility can be a minimally invasive surgical robot for disease diagnosis and treatment. Full article

Accurately detecting tea bud leaves is crucial for the automation of tea picking robots. However, challenges arise due to tea stem occlusion and overlapping of buds and leaves, presenting varied shapes of one bud–one leaf targets in the field of view, making precise segmentation of tea bud leaves challenging. To improve the segmentation accuracy of one bud–one leaf targets with different shapes and fine granularity, this study proposes a novel semantic segmentation model for tea bud leaves. The method designs a hierarchical Transformer block based on a self-attention mechanism in the encoding network, which is beneficial for capturing long-range dependencies between features and enhancing the representation of common features. Then, a multi-path feature aggregation module is designed to effectively merge the feature outputs of encoder blocks with decoder outputs, thereby alleviating the loss of fine-grained features caused by downsampling. Furthermore, a refined polarized attention mechanism is employed after the aggregation module to perform polarized filtering on features in channel and spatial dimensions, enhancing the output of fine-grained features. The experimental results demonstrate that the proposed Unet-Enhanced model achieves segmentation performance well on one bud–one leaf targets with different shapes, with a mean intersection over union (mIoU) of 91.18% and a mean pixel accuracy (mPA) of 95.10%. The semantic segmentation network can accurately segment tea bud leaves, providing a decision-making basis for the spatial positioning of tea picking robots. Full article

The long-term effects of the centrifugal force of water flow in a curved river channel result in the scouring of the concave bank and the silting of the convex bank. This phenomenon significantly impacts the stability of bank slopes and the surrounding ecological environment. A common hydraulic structure, the spur dike, is extensively employed in river training and bank protection. Focusing on a 180° bend flume as the research subject, this study examines the effects of spur dike placement on the concave bank side of the bend. To this end, a second-order accurate computational format in computational fluid dynamics (CFD) and the RNG k-ε turbulence model were employed. Specifically, the influence mechanism of the pick angle and the river-width-narrowing rate on the flow dynamics and eddy structures within the bend were investigated. The results indicated that both the river-width-narrowing rate and pick angle significantly influence the flow structure of the bend, with the pick angle being the more dominant factor. The vortex scale generated by a positive pick angle of the spur dike is the largest, while upward and downward pick angles produce smaller vortex scales. Both upward and positive pick angles have larger areas of influence, and the maximum value of turbulent kinetic energy occurs at the back of the secondary spur dike. In contrast, the downward pick angle has a smaller area of influence for turbulent kinetic energy, resulting in a smaller vortex at the back of the spur dike and leading to smoother water flow overall. In river-training and bank-protection projects, the selection of the spur dike angle is crucial for controlling scour risk. The findings provide valuable insights for engineering design and construction activities. Full article