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This paper introduces the core enterprise into the traditional accounts receivable financing model, which only includes the bank and the small and medium-sized enterprise (SME), and further analyses the strategic decisions of the bank, the SME, and the core enterprise by applying game theory. The stability of strategic decisions is also analysed. Finally, we prove the usability of the pledged financing mode of accounts receivable based on supply chain finance through simulation analysis and evolutionary game theory. Research results show that adding the core enterprise to the traditional accounts receivable financing model can reduce the bank's loan risk to a certain extent, enhance the credibility of the accounts receivable, which will be beneficial to the realisation of accounts receivable financing. The successful operation of accounts receivable financing mode will help the three parties to achieve three-win results. In addition, the lending rate of the bank and the reputation loss suffered by the core enterprise due to default will affect the evolutionary equilibrium of the tripartite evolutionary game. [ABSTRACT FROM AUTHOR]

This study presents the Fast Fruit 3D Detector (FF3D), a novel framework that contains a 3D neural network for fruit detection and an anisotropic Gaussian-based next-best view estimator. The proposed one-stage 3D detector, which utilizes an end-to-end 3D detection network, shows superior accuracy and robustness compared to traditional 2D methods. The core of the FF3D is a 3D object detection network based on a 3D convolutional neural network (3D CNN) followed by an anisotropic Gaussian-based next-best view estimation module. The innovative architecture combines point cloud feature extraction and object detection tasks, achieving accurate real-time fruit localization. The model is trained on a large-scale 3D fruit dataset and contains data collected from an apple orchard. Additionally, the proposed next-best view estimator improves accuracy and lowers the collision risk for grasping. Thorough assessments on the test set and in a simulated environment validate the efficacy of our FF3D. The experimental results show an AP of 76.3%, an AR of 92.3%, and an average Euclidean distance error of less than 6.2 mm, highlighting the framework's potential to overcome challenges in orchard environments. [ABSTRACT FROM AUTHOR]

Environmental mapping and robot navigation are the basis for realizing robot automation in modern agricultural production. This study proposes a new autonomous mapping and navigation method for gardening scene robots. First, a new LiDAR slam-based semantic mapping algorithm is proposed to enable the robots to analyze structural information from point cloud images and generate roadmaps from them. Secondly, a general robot navigation framework is proposed to enable the robot to generate the shortest global path according to the road map, and consider the local terrain information to find the optimal local path to achieve safe and efficient trajectory tracking; this method is equipped in apple orchards. The LiDAR was evaluated on a differential drive robotic platform. Experimental results show that this method can effectively process orchard environmental information. Compared with vnf and pointnet++, the semantic information extraction efficiency and time are greatly improved. The map feature extraction time can be reduced to 0.1681 s, and its MIoU is 0.812. The resulting global path planning achieved a 100% success rate, with an average run time of 4ms. At the same time, the local path planning algorithm can effectively generate safe and smooth trajectories to execute the global path, with an average running time of 36 ms. [ABSTRACT FROM AUTHOR]

In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps. [ABSTRACT FROM AUTHOR]

Digital modelling stands as a pivotal step in the realm of Digital Twinning. The future trend of Digital Twinning involves automated exploration and environmental modelling in complex scenes. In our study, we propose an innovative solution for robot odometry, path planning, and exploration in unknown outdoor environments, with a focus on Digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation, with emphasis on full coverage of unknown maps based on feasible boundaries of interest. The approach allows for dynamic changes to expected targets and behaviours. The evaluation is conducted on a robotic platform with a lightweight 3D LiDAR sensor model. The robustness of different types of odometry is compared, and the impact of parameters on motion planning is explored. The consistency and efficiency of exploring completely unknown areas are assessed in both indoor and outdoor scenarios. The experiment shows that the method proposed in this article can complete autonomous exploration and environmental modelling tasks in complex indoor and outdoor scenes. Finally, the study concludes by summarizing the reasons for exploration failures and outlining future focuses in this domain. [ABSTRACT FROM AUTHOR]

Compared to traditional rigid robotics, soft robotics has attracted increasing attention due to its advantages in compliance, safety, and low cost. As an essential part of soft robotics, the soft robotic gripper also shows its superior while grasping objects with irregular shapes. Recent research has been conducted to improve grasping performance by adjusting the variable effective length (VEL). However, the existing VEL function achieved by mechanisms such as multi-chamber design or tunable stiffness shape memory material requires a complex pneumatic circuit design or a time-consuming phase-changing process. This work proposes a fold-based soft robotic finger with VEL function from 3D printing. It is experimentally tested and modeled by the hyperelastic material property. Mathematic and finite element modeling is conducted to study the bending behaviour of the proposed soft actuator. Most importantly, an antagonistic constraint mechanism is proposed to achieve the VEL, and the experiments demonstrate that better conformity is achieved. Finally, dual-mode grippers are designed and evaluated to demonstrate the advances of VEL on grasping performance. [ABSTRACT FROM AUTHOR]

In the dynamic and unstructured environment where horticultural crops grow, obstacles and interference frequently occur but are rarely addressed, which poses significant challenges for robotic harvesting. This work proposed a tactile-enabled robotic grasping method that combines deep learning, tactile sensing, and soft robots. By integrating fin-ray fingers with embedded tactile sensing arrays and customized perception algorithms, the robot gains the ability to sense and handle branch interference during the harvesting process and thus reduce potential mechanical fruit damage. Through experimental validations, an overall 83.3–87.0% grasping status detection success rate, and a promising interference handling method have been demonstrated. The proposed grasping method can also be extended to broader robotic grasping applications wherever undesirable foreign object intrusion needs to be addressed. [ABSTRACT FROM AUTHOR]

Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large shock force will cause flowers to fall and affect the yield. As a result, accurate positioning becomes a difficult problem for robot picking. Therefore, studying target recognition and localization of Camellia oleifera fruits in complex environments has many difficulties. In this paper, a fusion method of deep learning based on visual perception and image processing is proposed to adaptively and actively locate fruit recognition and picking points for Camellia oleifera fruits. First, to adapt to the target classification and recognition of complex scenes in the field, the parameters of the You Only Live Once v7 (YOLOv7) model were optimized and selected to achieve Camellia oleifera fruits' detection and determine the center point of the fruit recognition frame. Then, image processing and a geometric algorithm are used to process the image, segment, and determine the morphology of the fruit, extract the centroid of the outline of Camellia oleifera fruit, and then analyze the position deviation of its centroid point and the center point in the YOLO recognition frame. The frontlighting, backlight, partial occlusion, and other test conditions for the perceptual recognition processing were validated with several experiments. The results demonstrate that the precision of YOLOv7 is close to that of YOLOv5s, and the mean average precision of YOLOv7 is higher than that of YOLOv5s. For some occluded Camellia oleifera fruits, the YOLOv7 algorithm is better than the YOLOv5s algorithm, which improves the detection accuracy of Camellia oleifera fruits. The contour of Camellia oleifera fruits can be extracted entirely via image processing. The average position deviation between the centroid point of the image extraction and the center point of the YOLO recognition frame is 2.86 pixels; thus, the center point of the YOLO recognition frame is approximately considered to be consistent with the centroid point of the image extraction. [ABSTRACT FROM AUTHOR]

In recent years, the relationship between agricultural commodities and crude oil has become increasingly close with the promotion of biofuel policies. This study examines the dynamic correlation between global crude oil futures and seven agricultural commodity futures by applying the consistent dynamic conditional correlation and dynamic equicorrelation models. The empirical results show that the dynamic correlation between the global crude oil futures market and China's agricultural futures market is weak compared to the global agricultural futures market. In particular, soybean oil has the strongest correlation with crude oil, while Dalian Commodity Exchange (DCE) corn and Zhengzhou Commodity Exchange wheat have the weakest correlation with crude oil. There is an indirect linkage between crude oil futures and DCE soybean meal and DCE soybean oil. Moreover, the dynamic correlation between crude oil and agricultural commodities increased during the financial crisis, the novel coronavirus (COVID‐19) epidemic, and the crude oil crash crisis. Brent crude oil has a stronger co‐movement with China's agricultural commodities than West Texas Intermediate crude oil and can better hedge the risk of agricultural commodities. The findings of this study provide some insights into the contagion risk management of crude oil futures and agricultural futures markets. [ABSTRACT FROM AUTHOR]

Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the grasping process has severely reduced harvest success rate and limited the overall performance of robotic harvesting. This work focuses on leaf interference caused slip detection and handling, where solutions to robotic grasping in an unstructured environment are proposed. Through analysis of the motion and force of fruit grasping under leaf interference, the connection between object slip caused by leaf interference and inadequate harvest performance is identified for the first time in the literature. A learning-based perception and manipulation method is proposed to detect slip that causes problematic grasps of objects, allowing the robot to implement timely reaction. Our results indicate that the proposed algorithm detects grasp slip with an accuracy of 94%. The proposed sensing-based manipulation demonstrated great potential in robotic fruit harvesting, and could be extended to other pick-place applications. [ABSTRACT FROM AUTHOR]

How to non-destructively and quickly estimate the storage time of citrus fruit is necessary and urgent for freshness control in the fruit market. As a feasibility study, we present a non-destructive method for storage time prediction of Newhall navel oranges by investigating the characteristics of the rind oil glands in this paper. Through the observation using a digital microscope, the oil glands were divided into three types and the change of their proportions could indicate the rind status as well as the storage time. Images of the rind of the oranges were taken in intervals of 10 days for 40 days, and they were used to train and test the proposed prediction models based on K-Nearest Neighbors (KNN) and deep learning algorithms, respectively. The KNN-based model demonstrated explicit features for storage time prediction based on the gland characteristics and reached a high accuracy of 93.0%, and the deep learning-based model attained an even higher accuracy of 96.0% due to its strong adaptability and robustness. The workflow presented can be readily replicated to develop non-destructive methods to predict the storage time of other types of citrus fruit with similar oil gland characteristics in different storage conditions featuring high efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Soft bending actuators, as one of the most important components of soft robotics, have attracted significantly increasing attention due to their robustness, compliance, inherent safety, and ease of manufacture. However, the key disadvantages can be the low output force, slow response speed, large deformation and vibration, which can potentially be addressed by introducing a bistable mechanism enabled by a prestressed steel shell. This work proposes a novel soft actuator with bistable property, which can maintain the predefined initial state and enhance bending motion at the corresponding stable state. A novel dual-actuation mechanism, which utilises pneumatic pressure for closing and tendon-driven for opening process, is proposed for autonomous transition between both states, and for a fast response. Mathematical model is proposed and compared with the experimental result for triggering pressure, which serves as a threshold to activate the transition of the stable state. Experimental results also indicate that closing and opening speeds are enhanced by 9.82% and more than ten times, respectively, as compared with the existing pneumatic bistable reinforced actuator design. Mathematical and experimental results suggest that a programmable bending angle at the second stable state can also be achieved by adjusting the preset tendon extension. The tendon arrangement also acts as a passive damping mechanism to reduce the oscillation while closing. The damping ratio is increased by more than four times, indicating that the oscillation decay is significantly accelerated for quick stabilization. Finally, a three-finger soft gripper is developed based on the proposed actuator design, which demonstrates promising performance in grasping objects with various shapes and sizes. The experimental results also show that the proposed bistable gripper can grasp the object with a weight up to 2067 g, which is more than 17 times heavier than that of three actuators. [ABSTRACT FROM AUTHOR]

Defective citrus fruits are manually sorted at the moment, which is a time-consuming and cost-expensive process with unsatisfactory accuracy. In this paper, we introduce a deep learning-based vision system implemented on a citrus processing line for fast on-line sorting. For the citrus fruits rotating randomly on the conveyor, a convolutional neural network-based detector was developed to detect and temporarily classify the defective ones, and a SORT algorithm-based tracker was adopted to record the classification information along their paths. The true categories of the citrus fruits were identified through the tracked historical information, resulting in high detection precision of 93.6%. Moreover, the linear Kalman filter model was applied to predict the future path of the fruits, which can be used to guide the robot arms to pick out the defective ones. Ultimately, this research presents a practical solution to realize on-line citrus sorting featuring low costs, high efficiency, and accuracy. [ABSTRACT FROM AUTHOR]

Carbon dioxide enhanced oil recovery is an interim solution as the world transitions to a cleaner energy future, extending oil production from existing fields whilst also sequestering carbon dioxide. To make this process efficient, the gas and oil need to develop miscibility over a period of time through the exchange of chemical components between the two phases, termed multiple-contact miscibility. Currently, measurements to infer the development of multiple-contact miscibility are limited to macroscopic visualization. We present a "rock-on-a-chip" measurement system that offers several potential measurements for different wetting conditions to infer the onset of multiple-contact miscibility. Here, a two-dimensional microfluidic porous medium with a stochastic distribution of pillars was created, and an analogue ternary system was used to mimic the real oil and gas multiple-contact miscibility process. Experiments were performed in two directions, imbibition and drainage, permitting study of different wetting properties of the host rock. The distinct behavior of trapped non-wetting ganglia during imbibition and the evolution of phase interfaces during drainage were observed and analyzed as the system developed miscibility. We show how these observations can be converted into rapid measurements for identifying the development of miscibility. [ABSTRACT FROM AUTHOR]

BACKGROUND: Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist when applying CNN to segment prostate, such as data distribution issue caused by insubstantial and inconsistent intensity levels and vague boundaries in MRI. OBJECTIVE: To segment prostate gland from a MRI dataset including different prostate images with limited resolution and quality. METHODS: We propose and apply a global histogram matching approach to make intensity distribution of the MRI dataset closer to uniformity. To capture the real boundaries and improve segmentation accuracy, we employ a module of variational models to help improve performance. RESULTS: Using seven evaluation metrics to quantify improvements of our proposed fusion approach compared with the state of art V-net model resulted in increase in the Dice Coefficient (11.2%), Jaccard Coefficient (13.7%), Volumetric Similarity (12.3%), Adjusted Rand Index (11.1%), Area under ROC Curve (11.6%), and reduction of the Mean Hausdorff Distance (16.1%) and Mahalanobis Distance (2.8%). The 3D reconstruction also validates the advantages of our proposed framework, especially in terms of smoothness, uniformity, and accuracy. In addition, observations from the selected examples of 2D visualization show that our segmentation results are closer to the real boundaries of the prostate, and better represent the prostate shapes. CONCLUSIONS: Our proposed approach achieves significant performance improvements compared with the existing methods based on the original CNN or pure variational models. [ABSTRACT FROM AUTHOR]

Robotic harvesting shows a promising aspect in future development of agricultural industry. However, there are many challenges which are still presented in the development of a fully functional robotic harvesting system. Vision is one of the most important keys among these challenges. Traditional vision methods always suffer from defects in accuracy, robustness, and efficiency in real implementation environments. In this work, a fully deep learning-based vision method for autonomous apple harvesting is developed and evaluated. The developed method includes a light-weight one-stage detection and segmentation network for fruit recognition and a PointNet to process the point clouds and estimate a proper approach pose for each fruit before grasping. Fruit recognition network takes raw inputs from RGB-D camera and performs fruit detection and instance segmentation on RGB images. The PointNet grasping network combines depth information and results from the fruit recognition as input and outputs the approach pose of each fruit for robotic arm execution. The developed vision method is evaluated on RGB-D image data which are collected from both laboratory and orchard environments. Robotic harvesting experiments in both indoor and outdoor conditions are also included to validate the performance of the developed harvesting system. Experimental results show that the developed vision method can perform highly efficient and accurate to guide robotic harvesting. Overall, the developed robotic harvesting system achieves 0.8 on harvesting success rate and cycle time is 6.5 s. [ABSTRACT FROM AUTHOR]

The range of motion and force output of soft actuators are important characteristics that determine the capacities of soft robots. Pouch motors are a family of soft fluidic actuators featuring a simple mechanical structure and easy fabrication process, but their relatively small contraction ratio constrains their potential applications. Inspired by the origami membrane in the lobster leg joint, we propose two types of origamic pouch motors in which origami structures are integrated into the longitudinal edges of traditional pouches. As a uniform and large degree of transverse expansion can be facilitated by unfolding the origami structures, a significantly larger contraction ratio and enhanced force output can be achieved. The complete workflow of origamic pouch motors is presented including the design principles, fabrication process, theoretical modeling, and experimental characterization, and multiple pouches can be conveniently assembled to realize different motion profiles with a larger range of motion as well. For application demonstrations, we develop different grippers based on origamic pouch motors for safe and forceful grasping. The results of this study can provide guidance for the development of robotic systems featuring easy fabrication, safe actuation, lightweight, a large motion range, and high force output. • Bio-inspired designs of two types of origamic pouch motors. • A large contraction ratio and enhanced force output. • Theoretical modeling to illustrate the working principles. • Application demonstrations for safe and forceful grasping. [ABSTRACT FROM AUTHOR]

Autonomous harvesting shows a promising prospect in the future development of theagriculture industry, while the vision system is one of the most challenging components in theautonomous harvesting technologies. This work proposes a multi-function network to perform thereal-time detection and semantic segmentation of apples and branches in orchard environments byusing the visual sensor. The developed detection and segmentation network utilises the atrous spatialpyramid pooling and the gate feature pyramid network to enhance feature extraction ability of thenetwork. To improve the real-time computation performance of the network model, a lightweightbackbone network based on the residual network architecture is developed. From the experimentalresults, the detection and segmentation network with ResNet-101 backbone outperformed on thedetection and segmentation tasks, achieving an F 1 score of 0.832 on the detection of apples and 87.6%and 77.2% on the semantic segmentation of apples and branches, respectively. The network modelwith lightweight backbone showed the best computation efficiency in the results. It achieved an F 1score of 0.827 on the detection of apples and 86.5% and 75.7% on the segmentation of apples andbranches, respectively. The weights size and computation time of the network model with lightweightbackbone were 12.8 M and 32 ms, respectively. The experimental results show that the detection andsegmentation network can effectively perform the real-time detection and segmentation of applesand branches in orchards.