Search Results (86)

Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. Our system first employs a machine vision system to identify and locate mature cherries, distinguishing them from unripe fruits, leaves, and branches, which are treated as obstacles. Based on this visual data, the MSI-RRTCHP algorithm generates an optimal picking trajectory. Its core innovation is a synergistic strategy that enables intelligent navigation by combining probability-guided exploration, goal-oriented sampling, and adaptive step size adjustments based on the obstacle’s density. To optimize the picking sequence for multiple targets, we introduce an enhanced traversal algorithm ($\sigma$-TSP) that accounts for obstacle interference. Field experiments demonstrate that our integrated system achieved a 90% picking success rate. Compared with established algorithms, the MSI-RRTCHP algorithm reduced the path length by up to 25.47% and the planning time by up to 39.06%. This work provides a practical and efficient framework for robotic cherry harvesting, showcasing a significant step toward intelligent agricultural automation. Full article

This paper examines the effect of digital innovation on cost stickiness in manufacturing firms, focusing on the underlying mechanisms and contextual factors. Using data from Chinese A-share listed manufacturing firms from 2012 to 2023, we find that, first, for each one-unit increase in the level of digital technology, the cost stickiness index of enterprises decreases by an average of 0.4315 units, primarily through digital process innovation and digital business model innovation, whereas digital product innovation does not exhibit a statistically significant impact. Second, manufacturing servitization and the optimization of human capital structure are identified as key mediating mechanisms. Digital innovation promotes servitization by transitioning firms from product-centric to service-oriented business models, thereby reducing fixed costs and improving resource flexibility. It also optimizes human capital by increasing the proportion of high-skilled employees and reducing labor adjustment costs. Third, the effect of digital innovation on cost stickiness is found to be heterogeneous. Firms with high financing constraints benefit more from the cost-reducing effects of digital innovation due to improved resource allocation efficiency. Additionally, mid-tenure executives are more effective in leveraging digital innovation to mitigate cost stickiness, as they balance short-term performance pressures with long-term strategic investments. These findings contribute to the understanding of how digital transformation reshapes cost behavior in manufacturing and provide insights for policymakers and firms seeking to achieve sustainable development through digital innovation. Full article

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article

Drones can improve the efficiency of point-to-point logistics and distribution and reduce labor costs; however, the complex three-dimensional airspace environment poses significant challenges for flight paths. To address this demand, this paper proposes a hybrid algorithm that integrates the Sparrow Search Algorithm (SSA) with the African Vulture Optimization Algorithm (AVOA). Firstly, the algorithm introduces Sobol sequences at the population initialization stage to optimize the initial population; then, we incorporate SSA’s discoverer and vigilant mechanisms to balance exploration and exploitation and enhance global exploration capabilities; finally, multi-guide differencing and dynamic rotation transformation strategies are introduced in the first exploitation phase to enhance the direction of local exploitation by fusing multiple pieces of information; the second exploitation phase achieved a dynamic balance between elite guidance and population diversity through adaptive weight adjustment and enhanced Lévy flight strategy. In this paper, a three-dimensional model is built under a variety of constraints, and SAVOA (Sparrow-Enhanced African Vulture Optimization Algorithm) is compared with a variety of popular algorithms in simulation experiments. SAVOA achieves the optimal path in all scenarios, verifying the efficiency and superiority of the algorithm in UAV logistics path planning. Full article

Water resources have become a critical factor limiting agricultural development and ecological health in arid regions. The ecological efficiency of agricultural water use (EEAWU) serves as an indicator of the sustainable utilization of agricultural water resources, taking into account both economic output and environmental impact. This paper, grounded in the social–ecological system (SES) framework, integrates multidimensional variables related to social behavior, economic decision-making, and ecological constraints to construct an analytical system that examines the impact mechanism of farmers’ part-time employment on the EEAWU. Utilizing survey data from 448 farmers in the western Tarim River Basin, and employing the super-efficiency SBM model alongside Tobit regression for empirical analysis, the study reveals the following findings: (1) the degree of farmers’ part-time employment is significantly negatively correlated with EEAWU (β = −0.041, p < 0.05); (2) as the extent of part-time employment increases, farmers adversely affect EEAWU by altering agricultural labor allocation, adjusting crop structures, and inadequately adopting water-saving measures; (3) farm size plays a negative moderating role in the relationship between farmers’ part-time engagement and the EEAWU, where scale expansion can alleviate the EEAWU losses associated with part-time employment through cost-sharing and factor substitution mechanisms. Based on these findings, it is recommended to enhance the land transfer mechanism, promote agricultural social services, implement tiered water pricing and water-saving subsidy policies, optimize crop structures, and strengthen environmental regulations to improve EEAWU in arid regions. Full article

Accurate fish population estimation is crucial for fisheries management, ecological monitoring, and aquaculture optimization. Traditional manual counting methods are labor-intensive and error-prone, while existing automated approaches struggle with occlusions, small-object detection, and identity switches. To address these challenges, this paper proposes an improved fish counting framework integrating YOLOv8-DT for detection and Byte-OCSORT for tracking. YOLOv8-DT incorporates the Deformable Large Kernel Attention Cross Stage Partial (DLKA CSP) module for adaptive receptive field adjustment and the Triple Detail Feature Infusion (TDFI) module for enhanced multi-scale feature fusion, improving small-object detection and occlusion robustness. Byte-OCSORT extends OC-SORT by integrating ByteTrack’s two-stage matching and a Class-Aware Cost Matrix (CCM), reducing ID switches and improving multi-species tracking stability. Experimental results on real-world underwater datasets demonstrate that YOLOv8-DT achieves a mAP₅₀ of 0.971 and mAP_50:95 of 0.742, while Byte-OCSORT reaches a MOTA of 72.3 and IDF1 of 69.4, significantly outperforming existing methods, confirming the effectiveness of the proposed framework for robust and accurate fish counting in complex aquatic environments. Full article

During long-span arch bridge construction, repeated adjustments of large cantilevered segments and nonuniform cable tensions can lead to deviations from the desired arch profile, reducing structural efficiency and increasing labor and material costs. To precisely control the process of cable-stayed buckle construction in long-span arch bridges and achieve an optimal arch formation state, a constrained optimization for the buckle and anchor cable forces under one-time tension is developed in this paper. First, by considering the coupling effect of the cable-stayed buckle system with the buckle tower and arch rib structure, the control equations between the node displacement and cable force after tensioning are derived based on the influence matrix method. Then, taking the cable force size, arch rib closure joint alignment, upstream and downstream side arch rib alignment deviation, tower deviation, and the arch formation alignment displacement after loosening the cable as the constraint conditions, the residual sum of squares between the arch rib alignment and the target alignment during the construction stage is regarded as the optimization objective function, to solve the cable force of the buckle and anchor cables that satisfy the requirements of the expected alignment. Applied to a 310 m asymmetric steel truss arch bridge, the calculation of arch formation alignment is consistent with the ideal arch alignment, with the largest vertical displacement difference below 5 mm; the maximum error between the measured and theoretical cable forces during construction is 4.81%, the maximum difference between the measured and theoretical arch rib alignments after tensioning is 3.4 cm, and the maximum axial deviation of the arch rib is 5 cm. The results showed the following: the proposed optimization method can effectively control fluctuations of arch rib alignment, tower deviation, and cable force during construction to maintain the optimal arch shape and calculate the buckle and anchor cable forces at the same time, avoiding iterative calculations and simplifying the analysis process. Full article

Pruning wood mass is crucial for grapevine management, as it reflects the vine’s vigor and balance. However, traditional manual measurement methods are time-consuming and labor-intensive. Recent advances in digital imaging offer non-invasive techniques, but limited research has explored pruning wood weight estimation, especially regarding the use of artificial backgrounds and lighting. This study assesses the use of image analysis for estimating wood weight, focusing on image acquisition conditions. This research aimed to (i) evaluate the necessity of artificial backgrounds and (ii) identify optimal daylight conditions for accurate image capture. Results demonstrated that estimation accuracy strongly depends on the sun’s position relative to the camera. The highest accuracy was achieved when the camera faced direct sunlight (morning on the northwest canopy side and afternoon on the southeast side), with R² values reaching 0.90 and 0.93, and RMSE as low as 44.24 g. Artificial backgrounds did not significantly enhance performance, suggesting that the method is applicable under field conditions. Leave-One-Group-Out Cross-Validation (LOGOCV) confirmed the model’s robustness when applied to Catarratto cv. (LOGOCV R² = 0.86 in NB and 0.84 in WB), though performance varied across other cultivars. These findings highlight the potential of automated image-based assessment for efficient vineyard management, using minimal effort adjustments to image collection that can be incorporated into low-cost setups for pruning wood weight estimation. Full article

Finding appropriate shaking parameters is crucial in designing effective mechanical harvesters. The maximum fruit removal can be achieved when the machine operator properly adjusts the amplitude and frequency for shaking each tree. This review covers the progress in research and development over the past decades on using mechanical harvesters for nut trees, such as almonds, pistachios, walnuts, and hickories, with a specific focus on the natural frequency of individual trees. Furthermore, the reported values of shaking frequency and amplitude from previous studies were discussed and compared, along with frequency calculation approaches based on various shaking mechanisms. Additionally, other parameters, such as clamping force, height, and shaking amplitude, were investigated to determine optimal values for minimizing tree damage. This review emphasizes that the tree’s diameter, height, and canopy morphology should be the primary factors considered when estimating the optimal shaking frequency for nut trees. It also highlights that, to date, the shaking amplitude, frequency, and duration set by field managers or machine operators tend to remain consistent for all trees, which can limit harvesting efficiency. The findings suggest that selecting these parameters uniformly across all trees may not result in efficient fruit removal for individual trees. However, with the assistance of modern computing technology and its adaptation for in-field applications, it is feasible to determine the optimal shaking frequency for each tree mathematically. This approach can maximize fruit removal rates while minimizing tree damage. Finally, the review suggests that improving existing harvesting machines by incorporating better vibratory patterns could offer benefits such as enhanced productivity, reduced labor costs, and decreased permanent tree damage. Full article

In China, potatoes are predominantly cultivated using the tuber piece planting method. During the cutting process, it is essential to divide seed potatoes into tuber pieces based on the distribution of their bud eyes, ensuring that each tuber piece contains one to two bud eyes. These tuber pieces are subsequently sown into the soil. Currently, the preparation of potato tuber pieces relies heavily on manual labor, which presents challenges such as inefficiency and high operational costs. To address these issues, a seed potato cutting machine capable of posture adjustment, cutting, and spraying was designed. Three types of seed potato cutters were developed based on the distribution patterns of bud eyes. The movement mechanism of the posture adjustment process was analyzed, and a mathematical model was established. The key factors influencing the posture adjustment effectiveness were identified through discrete element simulation analysis. Using the qualified rate of potato cutting and the blind eye rate as evaluation metrics, a three-factor, three-level, orthogonal experimental design was implemented. The experimental factors included the rotational speed of the conical roller, the number of conical rollers, and the cutting angle. For the straight-shaped cutter, the optimal combination was determined as follows: a conical roller speed of 12 r/min, 44 conical rollers, and a cutting angle of 0°, yielding a qualified rate of 90.3% and a blind eye rate of 1.86%. For the Y-shaped cutter, the optimal parameters were 14 r/min, 44 conical rollers, and a 5° cutting angle, achieving a qualified rate of 87.9% and a blind eye rate of 2.86%. The cross-shaped cutter performed best at 14 r/min, 44 conical rollers, and a 0° cutting angle, with a qualified rate of 87.1% and a blind eye rate of 3.80%. All optimal configurations met agronomic requirements, demonstrating the efficacy of the designed machine and cutters. Full article

The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations. This system utilizes a Multi-Model Super Ensemble (MMSE) forecasting framework to generate accurate hourly gridded weather forecasts. Building upon these forecasts, combined with real-time in-greenhouse meteorological data, the AI-GECS employs a hybrid deep learning model, CLSTM-CNN-BP, to project the greenhouse’s microclimate on an hourly basis. This predictive capability allows for the assessment of crop physiological indicators within the anticipated microclimate, thereby enabling preemptive adjustments to cooling systems to mitigate adverse conditions. All processes run on a cloud-based platform, automating operations for enhanced environmental control. The AI-GECS was tested in an experimental greenhouse at the Taiwan Agricultural Research Institute, showing strong alignment with greenhouse management needs. This system offers a resource-efficient, labor-saving solution, fusing microclimate forecasts with crop models to support sustainable agriculture. This study represents critical advancements in greenhouse automation, addressing the agricultural challenges of climate variability. Full article

Many traditional fruit vendors still rely on manual sorting to pick out high-quality apples. This process is not only time-consuming but can also damage the apples. Meanwhile, automated detection technology is still in its early stage and lacks full reliability. To improve this technology, we propose a novel method, which incorporates a learnable scaling factor and residual connection to enhance the Multi-Head Attention mechanism. In our approach, a learnable scaling factor is first applied to adjust the attention weights dynamically, and then a residual connection combines the scaled attention output with the original input to preserve essential features from the initial data. By integrating Multi-Head Attention with Convolutional Neural Networks (CNNs) using this method, we propose a lightweight deep learning model called “Learnable Residual Multi-Head Attention Networks Fusion with CNNs” to detect defective apples. Compared to existing models, our proposed model has lower memory usage, shorter training time, and higher detection precision. On the test set, the model achieves an accuracy of 97.5%, a recall of 98%, and a specificity of 97%, along with the lowest detection time of 46 ms. Experimental results show that the proposed model using our method is highly promising for commercial sorting, as it reduces labor costs, increases the supply of high-quality apples, and boosts consumer satisfaction. Full article

In the context of urban space constraints, subway and underground projects have become crucial strategies to alleviate urban congestion and enhance residents’ quality of life. However, pit engineering, a frequent accident area in geotechnical engineering, urgently requires innovative safety monitoring technologies. Traditional monitoring methods face challenges such as high labor costs, lengthy monitoring cycles, high-risk working environments, and over-reliance on human judgment. To address these issues, this paper introduces an innovative monitoring system integrating Fiber Bragg Grating (FBG) sensing technology based on a subway pit project in Guangzhou. This system not only achieves fully automated data acquisition but also includes an intelligent monitoring cloud platform, providing unprecedented automated and intelligent monitoring solutions for support structures and the surrounding environment during mechanical shaft construction. The key findings of this paper include the following: (1) The breakthrough application of distributed optical fiber monitoring technology, including successfully deploying this advanced technology in complex pit engineering environments, enabling the precise and continuous monitoring of support structures and surrounding changes, and demonstrating its high effectiveness and intelligence in practical engineering. (2) The innovative design of an intelligent safety monitoring system. By integrating sensors and wireless communication technology, an efficient data networking architecture is constructed, supporting remote configuration and flexible adjustment of monitoring equipment, significantly enhancing data collection‘s real-time performance and continuity while greatly reducing safety risks for field staff, achieving an intelligent upgrade of monitoring work. (3) Comprehensive and accurate empirical analysis. During shaft excavation, the monitoring data collected by the system were stable and reliable, with all indicators maintained within reasonable ranges and closely matching expected changes caused by construction activities, validating the system’s practical application effectiveness in complex construction environments and providing a scientific basis for pit engineering safety management. Full article

This study presents the design and implementation of a wire-driven, multi-joint robotic arm equipped with a cutting and gripping mechanism for harvesting delicate strawberries, with the goal of reducing labor and costs. The arm is mounted on a lifting mechanism and linked to a laterally movable module, which is affixed to the tube cultivation shelf. The trained deep learning model can instantly detect strawberries, identify optimal picking points, and estimate the contour area of fruit while the mobile platform is in motion. A two-stage fuzzy logic control (2s-FLC) method is employed to adjust the length of the arm and bending angle, enabling the end of the arm to approach the fruit picking position. The experimental results indicate a 90% accuracy in fruit detection, an 82% success rate in harvesting, and an average picking time of 6.5 s per strawberry, reduced to 5 s without arm recovery time. The performance of the proposed system in harvesting strawberries of different sizes under varying lighting conditions is also statistically analyzed and evaluated in this paper. Full article

Achieving precise synchronization is critical for multi-camera systems in various applications. Traditional methods rely on hardware-triggered synchronization, necessitating significant manual effort to connect and adjust synchronization cables, especially with multiple cameras involved. This not only increases labor costs but also restricts scene layout and incurs high setup expenses. To address these challenges, we propose a novel subframe synchronization technique for multi-camera systems that operates without the need for additional hardware triggers. Our approach leverages a time-calibrated video featuring specific markers and a uniformly moving ball to accurately extract the temporal relationship between local and global time systems across cameras. This allows for the calculation of new timestamps and precise frame-level alignment. By employing interpolation algorithms, we further refine synchronization to the subframe level. Experimental results validate the robustness and high temporal precision of our method, demonstrating its adaptability and potential for use in demanding multi-camera setups. Full article