Search Results (207)

Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article

Lightweight steel-framing (LSF) systems have become increasingly prominent in modern construction due to their structural efficiency, design flexibility, and sustainability. However, traditional facade materials such as stone are often cost-prohibitive, and brick veneers—despite their popularity—pose seismic performance concerns. This study introduces an innovative porcelain sheathing system for cold-formed steel (CFS) shear walls. Porcelain has no veins thus it offers integrated and reliable strength unlike granite. Four full-scale CFS shear walls incorporating screwed porcelain sheathing (SPS) were tested under combined cyclic lateral and constant gravity loading. The experimental program investigated key seismic characteristics, including lateral stiffness and strength, deformation capacity, failure modes, and energy dissipation, to calculate the system response modification factor (R). The test results showed that configurations with horizontal sheathing, double mid-studs, and three blocking rows improved performance, achieving up to 21.1 kN lateral resistance and 2.5% drift capacity. The average R-factor was 4.2, which exceeds the current design code values (AISI S213: R = 3; AS/NZS 4600: R = 2), suggesting the enhanced seismic resilience of the SPS-CFS system. This study also proposes design improvements to reduce the risk of brittle failure and enhance inelastic behavior. In addition, the results inform discussions on permissible building heights and contribute to the advancement of CFS design codes for seismic regions. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

This study investigated the impact of incorporating hemp fibers into composites for manufacturing industrial parts. The Global Warming Potential (GWP) of producing a traditional polymer matrix composite containing glass fibers was compared to that of producing a counterpart from natural hemp fibers. The investigation concluded that the partial replacement of synthetic fibers with biomass reduced the GWP of the product by up to 25% without compromising its mechanical properties. This study also quantified and discussed the GWP of intermediate products obtained from alternative routes, such as the manufacture of hemp stalks and pellets. In these cases, the findings showed that the amount of CO₂ absorbed during plant growth exceeded the emissions related to soil preparation, farming, and processing of hemp stalks by up to 15 times, and the processing of row hemp bales into pellets could result in an even “greener” product. This study highlights the importance of using bio-based inputs in reducing greenhouse gas emissions in the materials manufacturing industry and concludes that even partial substitutions of synthetic inputs with natural fibers can show significant reductions in this type of environmental impact. Full article

To address the problems of low crushing efficiency and uneven distribution in traditional straw crushing and returning machines for cotton stalk return operations in Xinjiang, a vertical straw crushing and returning machine with large and small dual-blade discs was designed, adapted to Xinjiang’s cotton planting model. The machine employs a differentiated configuration of large and small blade discs corresponding to four and two rows of cotton stalks, respectively, effectively reducing tool workload while significantly improving operational efficiency. A simulation model of the crushing and returning machine was developed using the discrete element method (DEM), and a flexible cotton stalk model was established to systematically investigate the effects of machine forward speed, crushing blade rotational speed, and knife tip-to-ground clearance on operational performance. Single-factor simulation experiments were conducted using crushing qualification rate and broken stalk drop rate as evaluation indicators. Subsequently, a multi-factor orthogonal field experiment was designed with Design-Expert software (13.0.1.0, Stat-Ease Inc, Minneapolis, MN, USA). The optimal working parameters were determined to be machine forward speed of 3.5 m/s, crushing blade shaft speed of 1500 r/min, and blade tip ground clearance of 60 mm. Verification tests demonstrated that under these optimal parameters, the straw crushing qualification rate reached 95.9% with a broken stalk drop rate of 15.5%. The relative errors were less than 5% compared to theoretical optimization values, confirming the reliability of parameter optimization. This study provides valuable references for the design optimization and engineering application of straw return machinery. Full article

Traditional three-row roller YRT turntable bearings exhibit high friction torque during operation, which limits their performance in high-precision and high-response applications. To address this issue, a novel ball–roller composite turntable bearing is proposed that effectively reduces friction torque while maintaining a high load capacity. A mechanical model based on statics is established, and the Newton–Raphson method is employed to calculate the contact load. The formation mechanism of friction torque is analyzed, and a corresponding computational model is developed and validated using experimental data. The effects of axial load, eccentricity, overturning moment, rotational speed, and axial clearance on friction torque are systematically studied. Results indicate that friction torque increases with these parameters. Axial clearance has a significant influence, and an optimal clearance value between the balls and rollers is determined. Additionally, a reasonable range for the raceway curvature radius coefficient is proposed. When the numerical ratio of balls to rollers is 1, the bearing exhibits optimal friction performance. Among various roller crowning strategies, logarithmic crowning yields the best results. This study provides a theoretical basis and technical support for the optimized design of ball–roller composite turntable bearings. Full article

Given the problem that traditional garlic cultivation patterns in China have difficulty in achieving comprehensive mechanized production, an experimental investigation on mechanization-friendly cultivation agronomy was conducted. In this study, an orthogonal experimental method was used to conduct continuous tracking experiments for three years in three major garlic production regions of China. All the experiments were used to verify the impacts of sprout orientation, planting mode, planting density, and row spacing on garlic bulb yield per hectare. For every impact, nine experiments were processed. The results indicated the following: (1) planting density influenced the garlic bulb yield per hectare extremely significantly, followed by row spacing, planting pattern, and sprout orientation; (2) the combination of sprout orientation (1–45°), planting pattern (large ridge), a planting density (42.75)/10,000 plants per hectare, and row spacing (26 + 10) led to the largest garlic bulb yield per hectare, which means this combination was the best form of cultivation agronomy. This study will provide a valuable reference for China’s farmland suitability for agricultural machinery operation (FSAM) production program. Full article

Straw row grouping is vital in conservation tillage for precision seeding, and accurate centerline extraction of the seedling bands enhances agricultural spraying efficiency. However, the traditional single-threshold Otsu segmentation struggles with adaptability and accuracy under complex field conditions. To overcome these issues, this study proposes an adaptive multi-threshold Otsu algorithm optimized by a Simulated Annealing-Enhanced Differential Evolution–Whale Optimization Algorithm (SADE-WOA). The method avoids premature convergence and improves population diversity by embedding the crossover mechanism of Differential Evolution (DE) into the Whale Optimization Algorithm (WOA) and introducing a vector disturbance strategy. It adaptively selects thresholds based on straw-covered image features. Combined with least-squares fitting, it suppresses noise and improves centerline continuity. The experimental results show that SADE-WOA accurately separates soil regions while preserving straw texture, achieving higher between-class variance and significantly faster convergence than the other tested algorithms. It runs at just one-tenth of the time of the Grey Wolf Optimizer and one-ninth of that of DE and requires only one-sixth to one-seventh of the time needed by DE-GWO. During centerline fitting, the mean yaw angle error (MEA) ranged from 0.34° to 0.67°, remaining well within the 5° tolerance required for agricultural navigation. The root-mean-square error (RMSE) fell between 0.37° and 0.73°, while the mean relative error (MRE) stayed below 0.2%, effectively reducing the influence of noise and improving both accuracy and robustness. Full article

Accurate extraction of table borders in scanned road engineering drawings is crucial for the digital transformation of engineering archives, which is an essential step in the development of intelligent infrastructure systems. However, challenges such as degraded borders, image blur, and character adjoining often hinder the precise delineation of table structures, making automated parsing difficult. Existing solutions, including traditional OCR tools and deep learning methods, struggle to consistently delineate table borders in the presence of these visual distortions and fail to perform well without extensive annotated datasets, which limits their effectiveness in real-world applications. We propose TableBorderNet, a semantic segmentation framework designed for precise border extraction under complex visual conditions. The framework captures structural context by guiding convolutional feature extraction along explicit row and column directions, enabling more accurate delineation of table borders. To ensure topological consistency in complex or degraded inputs, a topology-aware loss function is introduced, which explicitly penalizes structural discontinuities during training. Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. Experiments demonstrate that the method achieves an Intersection-over-Union of 94.2% and a topological error of 1.07%, outperforming existing approaches. These results underscore its practicality and scalability for accelerating the digitization of engineering drawings in support of data-driven road asset management. Full article

Precision spraying technology has attracted increasing attention in orchard production management. Traditional chemical pesticide application relies on subjective judgment, leading to fluctuations in pesticide usage, low application efficiency, and environmental pollution. This study proposes a machine vision-based precision spraying control system for orchards. First, a canopy leaf wall area calculation method was developed based on a multi-iteration GrabCut image segmentation algorithm, and a spray volume calculation model was established. Next, a fuzzy adaptive control algorithm based on an extended state observer (ESO) was proposed, along with the design of flow and pressure controllers. Finally, the precision spraying system’s performance tests were conducted in laboratory and field environments. The indoor experiments consisted of three test sets, each involving six citrus trees, totaling eighteen trees arranged in two staggered rows, with an interrow spacing of 3.4 m and an intra-row spacing of 2.5 m; the nozzle was positioned approximately 1.3 m from the canopy surface. Similarly, the field experiments included three test sets, each selecting eight citrus trees, totaling twenty-four trees, with an average height of approximately 1.5 m and a row spacing of 3 m, representing a typical orchard environment for performance validation. Experimental results demonstrated that the system reduced spray volume by 59.73% compared to continuous spraying, by 30.24% compared to PID control, and by 19.19% compared to traditional fuzzy control; meanwhile, the pesticide utilization efficiency increased by 61.42%, 26.8%, and 19.54%, respectively. The findings of this study provide a novel technical approach to improving agricultural production efficiency, enhancing fruit quality, reducing pesticide use, and promoting environmental protection, demonstrating significant application value. Full article

The provision of food in pre-Inca/Inca cultures (1000 BC–≈1532 AD) in environments near Lake Titikaka (approximately 4000 m above sea level) was possible through an agricultural technique called “Waru-Waru”, which consists of filling the space (volume) between rows of land containing plants that are cultivated (a series of earth platforms surrounded by water canals) with water, using water as thermal energy storage to store energy during the day and to regulate the temperature of the soil and crop atmosphere at night. The problem is that these cultures left no evidence in written documents that have been preserved to this day indicating the mathematical models, the physics involved, and the experimental part they performed for the research, development, and innovation of the “Waru-Waru” technique. From a review of the existing literature, there is (1) bibliography that is devoted to descriptive research (about the geometry, dimensions, and shapes of the crop fields (and more based on archaeological remains that have survived to the present day) and (2) studies presenting complex mathematical models with many physical parameters measured only with recently developed instrumentation. The research objectives of this paper are as follows: (1) develop a mathematical model that uses finite differences in fluid mechanics, thermodynamics, and heat transfer to explain the experimental and theory principles of this pre-Inca/Inca technique; (2) the proposed mathematical model must be in accordance with the mathematical calculation tools available in pre-Inca/Inca cultures (yupana and quipu), which are mainly based on arithmetic operations such as addition, subtraction, and multiplication; (3) develop a mathematical model in a sequence of steps aimed at determining the best geometric form for thermal energy storage and plant cultivation and that has a simple design (easy to transmit between farmers); (4) consider the assumptions necessary for the development of the mathematical model from the point of view of research on the geometry of earth platforms and water channels and their implantation in each cultivation area; (5) transmit knowledge of the construction and maintenance of “Waru-Waru” agricultural technology to farmers who have cultivated these fields since pre-Hispanic times. The main conclusion is that, in the mathematical model developed, algebraic mathematical expressions based on addition and multiplication are obtained to predict and explain the evolution of soil and water temperatures in a specific crop field using crop field characterization parameters for which their values are experimentally determined in the crop area where a “Waru-Waru” is to be built. Therefore, the storage of thermal energy in water allows crops to survive nights with low temperatures, and indirectly, it allows the interpretation that the Inca culture possessed knowledge of mathematics (addition, subtraction, multiplication, finite differences, approximation methods, and the like), physics (fluids, thermodynamics, and heat transfer), and experimentation, with priority given to agricultural techniques (and in general, as observed in all archaeological evidence) that are in-depth, exact, practical, lasting, and easy to transmit. Understanding this sustainable energy storage technique can be useful in the current circumstances of global warming and climate change within the same growing areas and/or in similar climatic and environmental scenarios. This technique can help in reducing the use of fossil or traditional fuels and infrastructure (greenhouses) that generate heat, expanding the agricultural frontier. Full article

Current deformation modeling theories for aircraft thin-walled components in riveting assembly typically assume uniform rivet interference. However, engineering practice shows that rivet interference is non-uniform, and such interference directly affects the magnitude of thin-walled component deformation during riveting assembly. Therefore, this paper investigates the deformation of aircraft thin-walled components caused by press riveting, models the non-uniform rivet interference for thin-walled components in riveting assembly, and conducts deformation prediction modeling. This paper performs stress analysis on the rivet shank to obtain the non-uniform distribution of riveting interference. Further, the non-uniform radial stress of the rivet shank and the bending moment of thin-walled components are derived. Using the thin plate theory, the deformation of aircraft thin-walled components in riveting assembly is calculated. The prediction model is applied to thin-walled component models with single-row and double-row riveting assemblies. Results show that the proposed prediction model is more accurate. Specifically, compared with traditional methods, the prediction accuracy of each index from this model is improved by over 29%. Full article

This paper presents the identification of the functional requirements and development of a preliminary concept of the AgriRover, a low-cost, modular autonomous vehicle intended to support sustainable practices in traditional vineyards in developing countries, focusing on the Ica region of Peru. Viticulture in this region faces acute challenges such as soil salinity, climate variability, labour shortages, and low technological readiness. Rather than offering a ready-made technological integration, this study adopts a step-by-step design approach grounded in the realities of smallholder farmers. The authors mapped the phenological stages of grapevines using the BBCH scale and systematically reviewed available sensing and monitoring technologies to determine the most context-appropriate solutions. Virtual modelling and preliminary analysis validate AgriRover’s geometric configuration and path-following capabilities within narrow vineyard rows. The proposed platform is meant to be adaptable, scalable, and maintainable using locally available material and human resources. AgriRover offers a practical and affordable foundation for precision agriculture in resource-constrained settings by aligning viticultural challenges with sensor deployment strategies and sustainability criteria. The sustainability analysis of the initial AgriRover concept was evaluated using the CML methodology, accounting for local waste processing rates and energy mixes to reflect environmental realities in Peru. Full article

To meet the demands of real-time and high-definition (HD) image processing applications, denoising methods must be both computationally efficient and hardware friendly. Traditional image denoising techniques are typically simple, fast, and resource-efficient but often fall short in terms of denoising performance and adaptability. This paper proposes an adjustable-threshold denoising method along with a corresponding hardware implementation designed to support the real-time processing of large-array images commonly used in image signal processors (ISPs). The proposed technique employs a LeGall 5/3 wavelet with a row-transform structure and multilevel decomposition. A 2D Pyramid VisuShrink thresholding algorithm is introduced, where the threshold is derived from the median value of the HH sub-band using a multi-stage segmentation approach. To further optimize performance, a quantization strategy with fixed-point parameter design is applied to minimize storage requirements and computational errors. A specialized hardware architecture is developed to enable the real-time denoising of 4K images while adhering to constraints on speed and resource utilization. The architecture incorporates a finite state machine (FSM) and a reusable median calculation unit to efficiently share threshold-related storage and computational resources. The system is implemented and verified on an FPGA, achieving real-time performance at a maximum frequency of 230 MHz. It supports flexible input data formats with resolutions up to 4096×4096 pixels and 16-bit depth. Comprehensive comparisons with other real-time denoising methods demonstrate that the proposed approach consistently achieves better PSNR and SSIM across various noise levels and image sizes. In addition to delivering improved denoising accuracy, the hardware implementation offers advantages in processing speed and resource efficiency while supporting a wide range of large-array images. Full article

Cumin (Cuminum cyminum L.) is a globally important spice crop, particularly significant in Xinjiang, China, where it is extensively cultivated in cotton–cumin intercropping systems. This review concentrates on the serious bottleneck hindering the development of the cumin industry: the low level of harvesting mechanization. Traditional manual harvesting methods are labor-intensive, inefficient, and result in high yield losses. This paper fully explores the prospects and challenges of mechanizing cumin harvesting in accordance with the particular biological characteristics of cumin plants and the complexity of intercropping systems. We review the current status of research in the following domains: (1) cumin biological traits and intercropping models; (2) grain loss and stalk damage patterns in stripper harvesting of similar crops; (3) factors influencing root–soil interaction during mechanical extraction; (4) uprooting–conveying harvesting techniques and row division/plant singulation methods applicable to root and tuber crops; and (5) cumin-threshing and -cleaning technologies. This review highlights the inadequacy of current grain-harvesting machinery for cumin and underscores the urgent need for specialized, low-loss harvesting technologies tailored to cumin’s delicate nature and intercropping context. Finally, we propose future research directions to overcome these mechanization challenges and promote the sustainable development of the cumin industry. Full article