Search Results (38)

The mechanized transplanting of sweet potato slips onto mulched raised beds in China’s Huang-Huai-Hai region faces significant challenges due to fragmented smallholder farms and the specific agronomic requirement of “boat-shaped” horizontal planting. To address this gap, this study aimed to develop a compact, cost-effective transplanter that meets the “boat-shaped” planting agronomy and adapts to small plots. We designed the 2CGX-1 mini wheel-driven transplanter coupled with a tractor. This machine features a compact chassis (<1.5 m length) for enhanced maneuverability on small plots, a novel five-bar taking-planting mechanism optimized for boat-shaped placement (achieving a stem-soil angle of 56.2° and planting depth of 110 mm), and an integrated spring buffer system. Transmission design ensures precise synchronization between the dual-chain seedling feeding mechanism and planting actions, allowing plant spacing adjustment from 18 to 30 cm. Coupled Adams–EDEM simulations demonstrated that the buffer system reduces maximum resistance on the clip fingers by 37.8% when encountering obstacles. Field validation under optimal parameters (0.55 km/h operating speed, 30 plants/min transplanting frequency) showed high consistency: average planting depth 101.3 mm (SD 1.38), plant spacing 330.3 mm (SD 11.24), seedling length under the film 185 mm (SD 3.65), and stem-soil angle 47.9° (SD 3.41), with qualification rates exceeding 91.9% for all key parameters except submerged length (82.5%). Compared with manual planting (≤0.1 ha/day per person, labor cost > ¥800/ha), this transplanter achieves a daily operational efficiency of ~0.35 ha/day (calculated by 0.55 km/h speed × 0.8 m working width × 8 h daily working time). Meanwhile, the consistency of its key planting indicators and the planting qualification rate are significantly superior to those of manual planting, while improving operational quality and significantly reducing labor cost input. Deviations in individual indicators mainly stem from planting positioning deviations induced by terrain undulations in hilly test areas, and sweet potato seedlings’ tendency to fall off during clamping due to mechanical vibration. However, these errors are within the acceptable agricultural operation range and do not compromise the machine’s overall compliance with agronomic requirements. The transplanter effectively meets agronomic requirements while offering a cost-effective, adapted solution for small-scale sweet potato production systems, significantly advancing mechanization capabilities for mulched cultivation. Full article

This study examines the determinants of financial sustainability in Turkish maritime industry by analyzing firm-level panel data from 190 ship and boat maintenance firms and 208 coastal shipping companies for the 2010–2022 period, comprising 5174 firm-year observations. Fixed-effects models with Driscoll–Kraay robust standard errors are employed to evaluate how asset structure, liquidity, and energy efficiency jointly affect firm profitability across subsectors, using the Operating Return on Assets (OROA) as the principal indicator of operational performance. The empirical results indicate substantial heterogeneity between maintenance and shipping firms. For maintenance firms, OROA shows a positive association with the Non-Current Assets to Total Assets ratio (NCATA) and the Economic Efficiency Ratio (EER) but a negative association with the Current Ratio (CR), suggesting that capital deepening and operational efficiency tend to correlate with stronger performance, whereas excess liquidity is associated with weaker outcomes. For shipping firms, OROA is positively associated with EER and Total Asset Turnover (TATR) but negatively associated with Fixed Asset Turnover (FATR) and CR, indicating relationships consistent with efficiency gains from energy management and asset utilization but linkages suggesting challenges from fleet aging and liquidity mismanagement. Overall, the findings suggest that the drivers of financial sustainability are associated with different structural conditions across maritime subsectors, highlighting the importance of targeted modernization, port efficiency, and energy-transition investment strategies. Full article

The unique hollowed-out catamaran hulls and complex environmental conditions in lake areas hinder traditional ranging algorithms (combining target detection and stereo matching) from accurately obtaining depth information near the center of ships. This not only impairs the navigation of electric tourist boats but also leads to high computing resource consumption. To address this issue, this study proposes a ranging method integrating improved ORB (Oriented FAST and Rotated BRIEF) with stereo vision technology. Combined with traditional optimization techniques, the proposed method calculates target distance and angle based on the triangulation principle, providing a rough alternative solution for the “gap period” of stereo matching-based ranging. The method proceeds as follows: first, it acquires ORB feature points with relatively uniform global distribution from preprocessed binocular images via a local feature weighting approach; second, it further refines feature points within the ROI (Region of Interest) using a quadtree structure; third, it enhances matching accuracy by integrating the FLANN (Fast Library for Approximate Nearest Neighbors) and PROSAC (Progressive Sample Consensus) algorithms; finally, it applies the screened matching point pairs to the triangulation method to obtain the position and distance of the target ship. Experimental results show that the proposed algorithm improves processing speed by 6.5% compared with the ORB-PROSAC algorithm. Under ideal conditions, the ranging errors at 10m and 20m are 2.25% and 5.56%, respectively. This method can partially compensate for the shortcomings of stereo matching in ranging under the specified lake area scenario. Full article

Boat collisions pose severe threats to maritime safety, economic activity, and environmental sustainability. Conventional risk assessment methods—such as Failure Mode and Effects Analysis, and Fault Tree Analysis—are widely applied but remain inadequate for addressing the uncertainty, subjectivity, and interdependency of risk factors in complex maritime environments. This study proposes a fuzzy Multi-Criteria Decision-Making framework for the risk assessment of boat collisions. The model integrates fuzzy logic with Analytic Hierarchy Process for criterion weighting and the Technique for Order Preference by Similarity to the Ideal Solution for risk ranking. Fuzzy logic is employed to capture linguistic expert judgments and to manage vague or incomplete data, which are common challenges in marine operations. Key collision risk factors—human error, boat engine system failure, environmental conditions, and intentional threats—are identified through literature review, incident data analysis, and expert consultation. A comparative analysis with a baseline non-fuzzy model demonstrates the added value of the fuzzy-integrated framework, showing improved capacity to handle imprecision and uncertainty. The model outputs not only prioritise risk rankings but also support the identification of critical control actions and effective safety measures. A case study of Nigerian waters illustrates the practicality of the framework in guiding risk mitigation strategies and informing policy decisions under uncertainty. Full article

Sea anchors for fishing operations are essential equipment to enhance catch efficiency and ensure operational stability at sea. However, previous studies have mainly focused on theoretical modeling or experiments under restricted conditions, which have not sufficiently reflected the complex operating environments and practical needs of real-world fisheries. To address this gap, this study derived key factors to improve the design and operation of sea anchors and quantitatively analyze the relative importance and rank of these factors. An expert panel was formed from 25 participants, including jigging vessel captains, recreational fishing boat captains, sea anchor manufacturers, and research institute workers. Using a three-round Delphi process followed by Analytic Hierarchy Process (AHP) analysis, we distilled an initial list of 52 improvement suggestions into 15 prioritized items, quantitatively ranked by relative importance based on expert consensus. The highest-ranked factor was ‘Enhancement of fabric drying performance’, followed by ‘Application of low-cost, high-efficiency materials’, ‘Improvement of recovery’, ‘Enhancement of UV resistance’, and ‘Product quality certification’. The highest-weighted metric was ‘Improvement of usability’, followed by ‘Enhanced durability’ and ‘Improvement of functionality’. The consistency ratio (CR) of the pairwise-comparison matrix was 0.0014 (AHP acceptability criterion: CR ≤ 0.1), confirming the reliability and consistency of the analysis. By reflecting real-world priorities through a robust and systematic analytical process, this study offers a foundation for evidence-based improvements in sea anchor design and operation, overcoming the limitations of earlier approaches rooted in subjective judgment or trial-and-error experience. Full article

Computer Vision (CV) has proven to be a powerful tool for automation in agri-food industrial processes, offering high-precision solutions tailored to specific working conditions. Recent advancements in Artificial Neural Networks (ANNs) have revolutionized CV applications, enabling systems to autonomously learn and optimize tasks. However, ANN-based approaches often require complex development and lengthy training periods, making their implementation a challenge. In this study, we explore the use of the Segment Anything Model (SAM), a pre-trained neural network developed by META AI in 2023, as an alternative for industrial segmentation tasks in the table olive (Olea europaea L.) processing industry. SAM’s ability to segment objects regardless of scene composition makes it a promising tool to improve the efficiency of olive pitting machines (DRRs). These machines, widely employed in industrial processing, frequently experience mechanical inefficiencies, including the “boat error,” which arises when olives are improperly oriented, leading to defective pitting and pit splinter contamination. Our approach integrates SAM into n CV workflow to diagnose and quantify boat errors without designing or training an additional task-specific ANN. By analyzing the segmented images, we can determine both the percentage of boat errors and the size distribution of olives during transport. The results validate SAM as a feasible option for industrial segmentation, offering a simpler and more accessible solution compared to traditional ANN-based methods. Moreover, our statistical analysis reveals that improper calibration—manifested as size deviations from the nominal value—does not significantly increase boat error rates. This finding supports the adoption of complementary CV technologies to enhance olive pitting efficiency. Future work could investigate real-time integration and the combination of CV with electromechanical correction systems to fully automate and optimize the pitting process. Full article

Unmanned boat distance detection is an important foundation for autonomous navigation tasks of unmanned boats. Monocular vision ranging has the advantages of low hardware equipment requirements, simple deployment, and high efficiency of distance detection. Unmanned boats can sense the real-time navigational situation of waters through monocular vision ranging, providing data support for their autonomous navigation. This paper establishes a framework for unmanned boat distance detection. The framework extracts and recognizes the features of an unmanned boat through Yolov11m-pose and selects the key points of the ship for physical distance mapping. Using the camera calibration to obtain the pixel focal length, the main point coordinates and other parameters are obtained. The number of pixel points in the image key point to the image center pixel and the actual distance of the camera from the horizontal plane are combined with the focal length of the camera for triangular similarity conversion. These data are fused with the camera pitch angle and other parameters to obtain the final distance. At the same time, experimental verification of the key point detection model demonstrates that it fully meets the requirements for unmanned boat ranging tasks, as assessed by Precision, Recall, mAP50, mAP50-95 and other indicators. These indicators show that Yolov11m-pose has a better accuracy in the key point detection task with an unmanned boat. The verification experiments also illustrate the accuracy of the key point-based physical distance mapping compared with the traditional detection frame-based physical distance mapping, which was assessed by the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The metrics show that key point-based unmanned boat distance mapping has greater accuracy in a variety of environmental situations, which verifies the effectiveness of this approach. Full article

Table olive pitting machines (DRRs) are essential in the agri-food industry but face significant limitations that constrain their performance and compromise process reliability. The main defect, known as the “boat error”, results from improper olive orientation during pitting, leading to bone fragmentation, pulp damage, and potential risks to consumer safety. Traditional quality control methods, such as the use of flotation tanks and expert sensory evaluation, rely on destructive sampling, are time-consuming, and reduce overall productivity. To address these challenges, this study presents a novel computer vision (CV) system integrated into a commercial DRR machine. The system captures high-speed images of Gordal olives (Olea europaea regalis) just before pitting; these are later analyzed offline using a custom MATLAB application that applies HSV-based segmentation and morphological analysis to quantify the olive size and orientation. The method accurately identifies boat error cases based on angular thresholds, without interrupting the production flow or damaging the product. The results show that 97% of olives were correctly aligned, with only 1.1% presenting critical misorientation. Additionally, for the first time, the system allowed a detailed evaluation of the olive size distribution at the machine inlet, revealing an unexpected proportion of off-caliber olives. This contamination in sizing suggests a possible link between calibration deviations and the occurrence of boat errors, introducing a new hypothesis for future investigation. While the current implementation is limited to offline analysis, it represents a non-destructive, low-cost, and highly precise diagnostic tool. This work lays the foundation for a deeper understanding of DRR machine behavior and provides a framework for future developments aimed at optimizing their performance through targeted correction strategies. Full article

Standardizing data collection, management, and analysis processes can improve the reliability and efficiency of fisheries monitoring programs, yet few studies have examined the operationalization of these tasks within agency settings. We reviewed the Mississippi Department of Wildlife, Fisheries, and Parks, Fisheries Bureau’s inland recreational fisheries monitoring program—a 30+-year effort to standardize field protocols, data handling procedures, and automated analyses through a custom-built computer application, the Fisheries Resources Analysis System (FRAS). Drawing on quantitative summaries of sampling trends and qualitative interviews with fisheries managers, we identified key benefits, challenges, and opportunities associated with the Bureau’s standardization efforts. Standardized procedures improved sampling consistency, data reliability, and operational efficiency, enabling the long-term tracking of fish population and angler metrics across more than 270 managed waterbodies. However, challenges related to analytical transparency and spatiotemporal comparisons persist. Simulations indicated that under current conditions, 5.8, 22.9, and 37.1 years would be required to sample (boat electrofishing) 50%, 75%, and 95% of the Bureau’s waterbodies at least once, respectively; these figures should translate to other agencies, assuming similar resource availability per waterbody. The monitoring program has reduced manual processing effort and enhanced staff capacity for waterbody-specific management, yet several opportunities remain to improve efficiency and utility. These include expanding FRAS functionalities for trend visualization, integrating mobile field data entry to reduce transcription errors, linking monitoring results with management objectives, and enhancing automated report generation for management support. Strengthening these elements could not only streamline workflows but better position agencies to apply standardized data in adaptive management embedded into the monitoring program. Full article

The draping of textile semi-finished products for complex geometries is still prone to errors, e.g., wrinkles, gaps, and fiber undulations, leading to reduced mechanical properties of the composite. Reinforcing textiles made from carbon fiber (CF) rovings (i.e., endless continuous fibers) can be draped mainly based on their ability to deform under in-plane shearing. However, CF rovings are hardly stretchable in the fiber direction. These limited degrees of freedom make the production of complex shell-shaped geometries from standard CF-roving fabrics challenging. Contrary to continuous rovings, this paper investigates the processing of spun yarns made of recycled carbon fibers (rCFs), which are discontinuous staple fibers with defined lengths. rCFs are obtained from end-of-life composites or production waste, making them a sustainable alternative to virgin carbon fibers in the high-performance components of, e.g., automobiles, boats, or sporting goods. These staple fiber-spun yarns are considerably more stretchable, which is due to the ability of the individual fibers to slide against each other when deformed, resulting in improved formability of fabrics made from rCF yarns, enabling the draping of much more complex structures. This study aims to develop and characterize woven fabrics based on previous studies of rCF yarns for thermoset composites. In order to investigate staple fiber-spun yarns, a previous micro-scale modeling approach is extended. The formability of fabrics made from those rCF yarns is investigated through experimental forming tests and meso-scale simulations. Full article

Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes. The current state-of-the-art methodologies for rowing performance analysis involve the installation of dedicated instrumented equipment, with the most commonly employed systems being PowerLine and BioRow. This procedure can be both expensive and time-consuming, thus limiting trainers’ ability to monitor athletes. In this study, we developed an easier-to-install and cheaper method for estimating rowers’ forces and powers based only on cable position sensors for ergometer rowing and inertial measurement units (IMUs) and GPS for scull rowing. We used data from 12 and 11 rowers on ergometer and on boat, respectively, to train a long short-term memory (LSTM) network. The LSTM was able to reconstruct the forces and power at the gate with an overall mean absolute error of less than 5%. The reconstructed forces and power were able to reveal inter-subject differences in technique, with an accuracy of 93%. Performing leave-one-out validation showed a significant increase in error, confirming that more subjects are needed in order to develop a tool that could be generalizable to external athletes. Full article

Mapping shallow-water bathymetry and morphology represents a technical challenge. In fact, acoustic surveys are limited by water depths reachable by boat, and airborne surveys have high costs. Photogrammetric approaches (either via drone or from the sea surface) have opened up the possibility to perform shallow-water surveys easily and at accessible costs. This work presents a simple, low-cost, and highly portable platform that allows gathering sequential photos and echosounder depth values of shallow-water sites (up to 5 m depth). The photos are then analysed in conjunction with photogrammetric techniques to obtain digital bathymetric models and orthomosaics of the seafloor. The workflow was tested on four repeated surveys of the same area in the Western Mediterranean and allowed obtaining digital bathymetric models with centimetric average accuracy and precision and root mean square errors within a few decimetres. The platform presented in this work can be employed to obtain first-order bathymetric products, enabling the contextual establishment of the depth accuracy of the final products. Full article

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that integrates data from the Inertial Measurement Unit (IMU) with LiDAR sensors to correct tilt-induced distortions in LiDAR point cloud data and reduce ripple effects around objects. The adjusted data are grouped using clustering algorithms and bounding boxes for precise object localization. Additionally, a specialized Kalman filter tailored for maritime environments mitigates object discontinuities between successive frames and addresses data sparsity caused by boat tilting. The methodology was evaluated using the VRX simulator, with experiments conducted on the Volga River using real USVs. The preprocessing effectiveness was assessed using the Root Mean Square Error (RMSE) and tracking accuracy was evaluated through detection rate metrics. The results demonstrate a 25% to 30% improvement in detection accuracy and show that the pipeline can aid industry even with sparse object representation across different frames. This study highlights the potential of integrating sensor fusion with specialized tracking for accurate dynamic object detection in maritime settings, establishing a new benchmark for USV navigation systems’ accuracy and reliability. Full article

In this paper, we introduce a model designed to predict human error probability (HEP) in the context of fishing boat operations utilizing the cognitive reliability and error analysis method (CREAM). We conducted an analysis of potential accidents on fishing boats and calculated the cognitive failure probability (CFP) for each identified accident. The common performance conditions (CPCs) from the original CREAM were adapted to better reflect the conditions on fishing boats, with the adapted CPCs’ validity confirmed through expert consultations. To apply CREAM, data were gathered via a survey of fishermen, with the uncertainty in the collected data addressed through the application of fuzzy set theory (FST). We then established a Bayesian network (BN) model to elucidate the relationship between the fuzzy data and HEP, utilizing a weighted sum algorithm to determine conditional probabilities within the BN. Both basic and extended versions of CREAM were applied to analyze the most common accidents among fishermen, calculating the CFP for each type of accident. According to our analysis, the poorer the dynamic CPC, the higher the probability that a fall accident will occur inside the boat due to human error, necessitating a countermeasure. The paper proposes safety enhancements for small fishing boats and illustrates the increased precision of human reliability analysis (HRA) models in forecasting human error by incorporating quantitative methods. It calls for further data collection and refinement of the model for more accurate operational risk assessments. Full article

Mangroves offer vital ecological advantages including air and water filtration, coastal and estuarine habitat provision, sediment stabilization, and wave energy dissipation. Their intricate root systems play a key role in safeguarding shorelines from tsunamis and erosive storms by dissipating wave energy. Moreover, mangroves shield against boat wakes and wind-waves, thus naturally bolstering shoreline defense. Wave dissipation is a function of forest width, tree diameter, and forest density. Restoration efforts of juvenile mangroves in Florida’s Indian River Lagoon (IRL) aim to reduce wave energy in areas vulnerable to erosion. Physical model testing of wave dissipation through mangroves is limited due to the complexity in representing the mangrove structure, where prop roots are non-uniform in both diameter and location. Previous studies have quantified wave-dissipating effects through the use of scaled and parameterized mangrove structures. This study measures the dissipation effects of live mangroves in a wave flume, forced by conditions representative of the IRL. These measurements are used to validate a parameterized dowel model. Error between wave attenuation factors for the live mangrove and dowel system was on average 2.5%. Validation of the modularized dowel system allowed for further parameterized testing to understand forest structure effects, such as sediment stabilization and wave attenuation. Maximum wave attenuation achieved in this study was 27–35% corresponding to a 40–60% reduction in wave energy depending on the configuration of the system. The wave reduction resulted in a 50–70% decrease in sediment erosion from the berm. The dowel tests indicate a target minimum thickness for mangrove root systems of 0.6 m for shoreline stabilization and restoration in the IRL. Full article