Search Results (134)

Jujubes have a beautiful taste, and high nutritional and economic value. The planting area of dwarf and densely planted jujubes is large and shows an increasing trend; however, the mechanization level and efficiency of fresh jujube harvesting are low. For this reason, our research group conducted a study on mechanical harvesting technology for fresh jujubes. A pneumatic jujube harvester was designed. This harvester is composed of a self-regulating picking mechanism, a telescopic conveying pipe, a negative pressure generator, a cleaning mechanism, a double-chamber collection box, a single-door shell, a control assembly, a generator, a towing mobile chassis, etc. During the harvest, the fresh jujubes on the branches are picked under the combined effect of the flexible squeezing of the picking roller and the suction force of the negative pressure air flow. They then enter the cleaning mechanism through the telescopic conveying pipe. Under the combined effect of the upper and lower baffles of the cleaning mechanism and the negative-pressure air flow, the fresh jujubes are separated from impurities such as jujube leaves and branches. The clean fresh jujubes fall into the collection box. We considered the damage rate of fresh jujubes, impurity rate, leakage rate, and harvesting efficiency as the indexes, and the negative-pressure suction wind speed, picking roller rotational speed, and the inclination angle of the upper and lower baffles of the cleaning and selection machinery as the test factors, and carried out the harvesting test of fresh jujubes. The test results show that when the negative-pressure suction wind speed was 25 m/s, the picking roller rotational speed was 31 r/min, and the inclination angles of the upper and lower baffle plates for cleaning and selecting were −19° and 19.5°, respectively, the breakage rate of fresh jujube harvesting was 0.90%, the rate of impurity was 1.54%, the rate of leakage was 2.59%, and the efficiency of harvesting was 73.37 kg/h, realizing the high-efficiency and low-loss harvesting of fresh jujubes. This study provides a reference for the research and development of fresh jujube mechanical harvesting technology and equipment. Full article

Recent years have seen significant interest among agricultural researchers in using robotics and machine vision to enhance intelligent orchard harvesting efficiency. This study proposes an improved hybrid framework integrating YOLO VX deep learning, 3D object recognition, and SLAM-based navigation for harvesting ripe fruits in greenhouse environments, achieving servo control of robotic arms with flexible end-effectors. The method comprises three key components: First, a fruit sample database containing varying maturity levels and morphological features is established, interfaced with an optimized YOLO VX model for target fruit identification. Second, a 3D camera acquires the target fruit’s spatial position and orientation data in real time, and these data are stored in the collaborative robot’s microcontroller. Finally, employing binocular calibration and triangulation, the SLAM navigation module guides the robotic arm to the designated picking location via unobstructed target positioning. Comprehensive comparative experiments between the improved YOLO v12n model and earlier versions were conducted to validate its performance. The results demonstrate that the optimized model surpasses traditional recognition and harvesting methods, offering superior target fruit identification response (minimum 30.9ms) and significantly higher accuracy (91.14%). Full article

Warehouses are vital in linking production to consumption, often using a forward–reserve layout to balance picking efficiency and bulk storage. However, replenishing the forward area from reserve storage is prone to delays and congestion, especially during high-demand periods. This study investigates the strategic use of buffer areas—intermediate zones between forward and reserve locations—to enhance flexibility and reduce bottlenecks. Although buffer zones are common in practice, they often lack a structured decision-making framework. We address this gap by developing an optimization model that integrates demand forecasts to guide daily replenishment decisions. To handle the computational complexity arising from large state and action spaces, we implement an approximate dynamic programming (ADP) approach using certainty-equivalent control within a rolling-horizon framework. A real-world case study from an automotive spare parts warehouse demonstrates the model’s effectiveness. Results show that strategically integrating buffer zones with an ADP model significantly improves replenishment timing, reduces direct picking by up to 90%, minimizes congestion, and enhances overall flow of intra-warehouse inventory management. Full article

The pick-up gripper, as a core component of automatic transplanting systems, presents challenges in reliably grasping seedlings. In this study, a large-opening flexible seedling pick-up gripper was designed based on standard trays and seedling characteristics. Structural design and force analysis of the grasping mechanism were conducted to develop a functional prototype. As this represented the first prototype of this new gripper, multi-factor orthogonal tests and performance tests under local conditions were performed to evaluate its grasping effectiveness. It was found that the end diameter of the pick-up pin and the extraction speed for lifting plug seedlings vertically had the most significant effects, followed by the penetration depth and grasping force. The optimum grasping effectiveness was achieved when the end diameter of the pick-up pin was 1.2 mm, the penetration depth in the top straight line of the pick-up pin was 40 mm, the grasping force for squeezing root lumps was 0.4 MPa, and the extraction speed for lifting plug seedlings in a vertical direction was 900 mm/s. For typical vegetable seedlings, the average success rate in transplanting was up to 95%. Under the combined actions of penetrating, squeezing, and extracting operations, plug seedlings could be efficiently picked out for efficient transplanting. Full article

Innovative demand-responsive transport services are spreading in most urban areas, allowing dynamic matching between demand and supply and enabling travellers to request shared rides in real-time via mobile applications. They are used both as an alternative to public transport and as an access/egress leg to mass transit stations, i.e., acting as a feeder service. In low-demand areas and small-sized cities, it is often difficult to provide effective and cost-efficient public transport, thus resulting in an extensive use of private vehicles. Using an agent-based modelling approach, this study compares the performance of fixed-route transit (FRT) and demand-responsive transit (DRT), where optional stops can be activated on demand. The aim is to identify the conditions allowing DRT to become more advantageous than FRT in small-sized cities, both for travellers and the transport operator. A real-time matching algorithm identifies optimal trip chains (i.e., public transport lines; pick-up, drop-off and transfer stops; and time windows) for travel requests, dynamically updating vehicles’ routes and schedules. The model is applied to the city of Caltanissetta, Italy, where a transit service with six FRT urban lines is currently operating. Travel patterns were reconstructed from thousands of travel requests collected by a Mobility-as-a-Service platform within one-year. The main findings demonstrate the benefits of DRT in providing a higher quality of service, reducing riding times for passengers, and enhancing service efficiency without burdening operating costs. The DRT reduced the vehicle-kilometres travelled by up to 5% compared to FRT while decreasing passenger ride times by approximately 10%. An economic analysis showed reductions in operator unit costs of up to 3.4% for low-demand rates, confirming the advantages of flexible operations in small-sized cities. Full article

Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences in key quality indicators, and the resulting specific harvesting needs. It then reviews recent progress in intelligent perception, recognition, and automation within the chili pepper industry. For perception and recognition, the review covers the evolution from traditional image processing to deep learning-based methods (e.g., YOLO and Mask R-CNN achieving a mAP > 90% in specific studies) for pepper detection, segmentation, and fine-grained cultivar identification, analyzing the performance and optimization in complex environments. In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. The main current research challenges are listed including the environmental adaptability/robustness, efficiency/real-time performance, multi-cultivar adaptability/flexibility, system integration, and cost-effectiveness. Finally, future directions are given (e.g., multimodal sensor fusion, lightweight models, and edge computing applications) in the hope of guiding the intelligent growth of the chili pepper industry. Full article

To determine an optimal plan for complex tasks, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with optimal control, which relies on the optimization of cost functions; instead, a recent biologically motivated proposal casts planning and control as an inference process. Active inference assumes that action and perception are two complementary aspects of life whereby the role of the former is to fulfill the predictions inferred by the latter. Here, we present an active inference approach that exploits discrete and continuous processing, based on three features: the representation of potential body configurations in relation to the objects of interest; the use of hierarchical relationships that enable the agent to easily interpret and flexibly expand its body schema for tool use; the definition of potential trajectories related to the agent’s intentions, used to infer and plan with dynamic elements at different temporal scales. We evaluate this deep hybrid model on a habitual task: reaching a moving object after having picked a moving tool. We show that the model can tackle the presented task under different conditions. This study extends past work on planning as inference and advances an alternative direction to optimal control. Full article

What if you could identify someone’s right palmprint just by looking at their left—and vice versa? That is exactly what I set out to do. I built a specially adapted Siamese network that only needs one palm to reliably recognize the other, making biometric systems far more flexible in everyday settings. My solution rests on two simple but powerful ideas. First, Anchor Embedding through Feature Aggregation (AnchorEFA) creates a “super-anchor” by averaging four palmprint samples from the same person. This pooled anchor smooths out noise and highlights the consistent patterns shared between left and right palms. Second, I use a Concatenated Similarity Measurement—combining Euclidean distance with Element-wise Absolute Difference (EAD)—so the model can pick up both big structural similarities and tiny textural differences. I tested this approach on three public datasets (POLYU_Left_Right, TongjiS1_Left_Right, and CASIA_Left_Right) and saw a clear jump in accuracy compared to traditional methods. In fact, my four-sample AnchorEFA plus hybrid similarity metric did not just beat the baseline—it set a new benchmark for cross-palmprint recognition. In short, recognizing a palmprint from its opposite pair is not just feasible—it is practical, accurate, and ready for real-world use. This work opens the door to more secure, user-friendly biometric systems that still work even when only one palmprint is available. Full article

Under the strategic objectives of carbon peaking and carbon neutrality, it is inevitable for large-scale integration of renewable energy into thermal power units. Nevertheless, improving the capacity of these units for flexible peak shaving is necessary on account of the intermittent and instability of renewable energy. As a novel combustion technology, self-preheating combustion technology offers enormous merits in this aspect, with increasing combustion efficiency (η) and controlling NO_x emissions simultaneously. Considering production and operation cost, installation difficulty and environmental pollution, this study innovatively proposed a compact fluidized modification device (FMD) on the basis of this technology and explored the influences of buffer tank and operation load on operation stability, fuel modification, combustion characteristics and NO_x emissions on an MW grade pilot-scale test platform. Afterwards, the comparative analysis on performance disparities was further launched between FMD and traditional self-preheating burner (TSB). Adding the buffer tank enhanced operation stability of FMD and improved its modification conditions, and thus promoted NO_x emission control. Optimal modification efficiency was realized at medium and high loads, respectively, for high-volatile and low-volatile coals. As load increased, η increased for high-volatile coal, but with NO_x emissions increasing. In comparison, this condition reduced NO_x emissions with high η for low-volatile coal. Compared to TSB, FMD demonstrated more conspicuous advantages in stable operation and fuel modification. Simultaneously, FMD was more conducive to realizing clean and efficient combustion at high temperatures. In industrial applications, appropriate FMD or TSB should be picked out grounded in diverse application requirements. By optimizing burner structure and operational parameters, original NO_x emissions decreased to a minimum of 77.93 mg/m³ with high η of 98.59% at low load of 30%. Full article

To address the challenges of manual apple harvesting and the limitations of existing devices—such as constrained workspace, low efficiency, and limited flexibility—a two-stage, sub-region harvesting device was developed. The design, informed by the fruit distribution characteristics in densely planted dwarf apple orchards, integrates a positioning mechanism and a fruit-picking mechanism, enabling multiple pickings within a single positioning operation to enhance workspace coverage. A forward kinematics model was established using the Denavit–Hartenberg (D–H) parameter method. An improved Monte Carlo simulation based on a hybrid Beta distribution estimated the maximum reachable distances of the fruit-picking reference point in the X, Y, and Z directions as 2146 mm, 2169 mm, and 2165 mm, respectively—adequately covering the target harvesting domain. Incorporating a translational axis structure further expanded the harvesting volume by 1.165 m³, a 42.40% improvement over the conventional 3R configuration. To support adaptive control, a random point–geometry fusion method was proposed to solve for joint variables based on harvesting postures, and an automatic fruit-picking control system was implemented. Experimental validation, including reference point tracking and harvesting tests, demonstrated maximum positioning errors of 1.5 mm and 2.2 mm, a fruit-picking success rate of 76.53%, and an average picking time of 7.24 s per fruit—marking a 4.6% improvement compared to existing devices reported in previous studies. This study provides a comprehensive technical framework and practical reference for advancing mechanized apple harvesting. Full article

This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking procedure based on an interactive multi-criteria decision-making method (the so-called TODIM method). More precisely, the best-performing assets from the investable universe are identified using three financial criteria. The first criterion is based on mutual information, and it is employed to capture the microstructure of the stock market. The second one is the momentum, and the third is the upside-to-downside beta ratio. To calculate the preference weights used in the chosen multi-criteria decision-making procedure, two methods are compared, namely equal and entropy weighting. In the second stage, this work considers a portfolio optimization model where the objective function is a modified version of the Sharpe ratio, consistent with the choices of a rational agent even when faced with negative risk premiums. Additionally, the portfolio design incorporates a set of bound, budget, and cardinality constraints, together with a set of risk budgeting restrictions. To solve the resulting non-smooth programming problem with non-convex constraints, this paper proposes a variant of the distance-based parameter adaptation for success-history-based differential evolution with double crossover (DISH-XX) algorithm equipped with a hybrid constraint-handling approach. Numerical experiments on the US and European stock markets over the past ten years are conducted, and the results show that the flexibility of the proposed portfolio model allows the better control of losses, particularly during market downturns, thereby providing superior or at least comparable ex post performance with respect to several benchmark investment strategies. Full article

Brown mushrooms are widely consumed globally due to their low calorie content, high nutritional value, and suitability for periodic growth in industrial mushroom houses, offering significant commercial value. Most robotic grippers pick mushrooms based on precise force control, which requires a high-precision force sensor, increasing production costs and potential failure rates. This study presents a fully soft gripper, as the body made of silicon rubber and driven by cable. Its inherent softness, offering a more natural solution for safely picking mushrooms by relying only on simple position control of the servo. Finite element analysis was employed to optimize the cable-driven displacement. Additionally, the gripper can measure mushroom diameters during picking using rough thin-film force sensors and bending sensors attached to the fingers, based on mathematical derivation. Field experiments were conducted with the proposed gripper mounted on a homemade mushroom-harvesting robot to pick medium-sized and large-sized mushrooms. The results demonstrated non-destructive harvesting, an average measurement accuracy of 96.6% for medium mushrooms and 96.1% for large mushrooms, and an average harvesting time of 7.5 s per mushroom. Compared to force-controlled grippers, the proposed cable-driven gripper features a simpler structural design and more efficient control logic, making it highly suitable for industrial applications. Full article

Although articulated robots with flexible automation systems are essential for implementing smart factories, their high initial investment costs make them difficult for small and medium-sized enterprises to implement. This study proposes a federated learning-based articulated robot control framework to improve the task completion of multiple articulated robots used in automated systems under limited computing resources. The proposed framework consists of two modules: (1) a federated learning module for the cooperative training of multiple joint robots on a part-picking task and (2) an articulated robot control module to balance the efficiency of limited resources. The proposed framework is applied to cases with different numbers of joint robots, and its performance is evaluated in terms of training completion time, resource share ratio, network traffic, and completion time of a picking task. Under the devised framework, the experiment demonstrates object recognition by three joint robots with an accuracy of approximately 80% at a minimum number of learning rounds of 76 and with a network traffic intensity of 2303.5 MB. As a result, this study contributes to the expansion of federated learning use for articulated robot control in limited environments, such as small and medium-sized enterprises. Full article

Tomato is one of the most popular and widely cultivated fruits and vegetables in the world. In large-scale cultivation, manual picking is inefficient and labor-intensive, which is likely to lead to a decline in the quality of the fruits. Although mechanical picking can improve efficiency, it is affected by factors such as leaf occlusion and changes in light conditions in the tomato growth environment, resulting in poor detection and recognition results. To address these challenges, this study proposes a tomato detection method based on Graph-CenterNet. The method employs Vision Graph Convolution (ViG) to replace traditional convolutions, thereby enhancing the flexibility of feature extraction, while reducing one downsampling layer to strengthen global information capture. Furthermore, the Coordinate Attention (CA) module is introduced to optimize the processing of key information through correlation computation and weight allocation mechanisms. Experiments conducted on the Tomato Detection dataset demonstrate that the proposed method achieves average precision improvements of 7.94%, 10.58%, and 1.24% compared to Faster R-CNN, CenterNet, and YOLOv8, respectively. The results indicate that the improved Graph-CenterNet method significantly enhances the accuracy and robustness of tomato detection in complex environments. Full article

Addressing the issue of complex terrain and small field plots in hilly and mountainous regions where large combine harvesters are not suitable, this paper presented the design and development of a semi-feed self-propelled peanut combine harvester. This harvester is characterized by its small size and flexible steering. Theoretical calculations were used to determine the structural parameters of the main working components. A three-factor, three-level orthogonal experimental design was implemented, focusing on forward speed, vibration frequency and picking roller rotational speed, as these parameters significantly influence operational performance. Through this experiment, regression models were established between the total loss rate, the broken pod rate, and these three above mentioned factors. Through multi-objective optimization, it was found that when the forward speed is 0.44 m/s, the picking roller rotational speed is 350 rpm, and the vibration frequency is 6.4 Hz, the total loss rate and broken pods rate of the harvester are the lowest. Validation experiments were conducted under this parameter combination, with the total loss rate and broken pods rate being effectively reduced to 3.21% and 0.85%, respectively. The experiments proved that this harvester meets the requirements for mechanized peanut harvesting in hilly and mountainous regions. Full article