Search Results (711)

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

In response to the challenges posed by soil degradation in the arid regions of Xinjiang, China, green and organic management practices have emerged as effective alternatives to conventional agricultural management methods, helping to mitigate soil degradation by promoting natural soil recovery and ecological balance. However, most of the existing studies focus on a single management practice or indicator and lack a systematic assessment of the effects of integrated orchard management in arid zones. This study aims to investigate how different agricultural management practices influence soil physicochemical properties and inter-root microbial communities in apple orchards in Xinjiang and to identify the main physicochemical factors affecting the composition of inter-root microbial communities. Inter-root soil samples were collected from apple orchards under green management (GM), organic management (OM), and conventional management (CM) in major apple-producing regions of Xinjiang. Microbial diversity and community composition of the samples were analyzed using high-throughput amplicon sequencing. The results revealed significant differences (p < 0.05) in soil physicochemical properties across different management practices. Specifically, GM significantly reduced soil pH and C:N compared with OM. Both OM and GM significantly decreased soil available nutrient content compared with CM. Moreover, GM and OM significantly increased bacterial diversity and changed the community composition of bacteria and fungi. Proteobacteria and Ascomycota were identified as the dominant bacteria and fungi, respectively, in all management practices. Linear discriminant analysis (LEfSe) showed that biomarkers were more abundant under OM, suggesting that OM may contribute to ecological functions through specific microbial taxa. Co-occurrence network analysis (building a network of microbial interactions) demonstrated that the topologies of bacteria and fungi varied across different management practices and that OM increased the complexity of microbial co-occurrence networks. Mantel test analysis (analyzing soil factors and microbial community correlations) showed that C:N and available potassium (AK) were significantly and positively correlated with the community composition of bacteria and fungi, and that C:N, soil organic carbon (SOC), and alkaline hydrolyzable nitrogen (AN) were significantly and positively correlated with the diversity of fungi. Redundancy analysis (RDA) further indicated that SOC, C:N, and AK were the primary soil physicochemical factors influencing the composition of microbial communities. This study provides theoretical guidance for the sustainable management of orchards in arid zones. 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 research addresses the enhancement of operational efficiency in apple-picking robots through the design of a bimanual spatial configuration enabling obstacle avoidance in contemporary orchard environments. A parallel coordinated harvesting paradigm for dual-arm systems was introduced, leading to the construction and validation of a six-degree-of-freedom bimanual apple-harvesting robot. Leveraging the kinematic architecture of the AUBO-i5 manipulator, three spatial layout configurations for dual-arm systems were evaluated, culminating in the adoption of a “workspace-overlapping Type B” arrangement. A functional prototype of the bimanual apple-harvesting system was subsequently fabricated. The study further involved developing control architectures for two end-effector types: a compliant gripper and a vacuum-based suction mechanism, with corresponding operational protocols established. A networked communication framework for parallel arm coordination was implemented via Ethernet switching technology, enabling both independent and synchronized bimanual operation. Additionally, an intersystem communication protocol was formulated to integrate the robotic vision system with the dual-arm control architecture, establishing a modular parallel execution model between visual perception and motion control modules. A coordinated bimanual harvesting strategy was formulated, incorporating real-time trajectory and pose monitoring of the manipulators. Kinematic simulations were executed to validate the feasibility of this strategy. Field evaluations in modern Red Fuji apple orchards assessed multidimensional harvesting performance, revealing 85.6% and 80% success rates for the suction and gripper-based arms, respectively. Single-fruit retrieval averaged 7.5 s per arm, yielding an overall system efficiency of 3.75 s per fruit. These findings advance the technological foundation for intelligent apple-harvesting systems, offering methodologies for the evolution of precision agronomic automation. Full article

To address the issue of agricultural robot loss of control caused by GNSS signal degradation or loss in complex agricultural environments such as farmland and orchards, this study proposes a neural network-based SLAM/GNSS fusion localization algorithm aiming to enhance the robot’s localization accuracy and stability in weak or GNSS-denied environments. It achieves multi-sensor observed pose coordinate system unification through coordinate system alignment preprocessing, optimizes SLAM poses via outlier filtering and drift correction, and dynamically adjusts the weights of poses from distinct coordinate systems via a neural network according to the GDOP. Experimental results on the robotic platform demonstrate that, compared to the SLAM algorithm without pose optimization, the proposed SLAM/GNSS fusion localization algorithm reduced the whole process average position deviation by 37%. Compared to the fixed-weight fusion localization algorithm, the proposed SLAM/GNSS fusion localization algorithm achieved a 74% reduction in average position deviation during transitional segments with GNSS signal degradation or recovery. These results validate the superior positioning accuracy and stability of the proposed SLAM/GNSS fusion localization algorithm in weak or GNSS-denied environments. Orchard experimental results demonstrate that, at an average speed of 0.55 m/s, the proposed SLAM/GNSS fusion localization algorithm achieves an overall average position deviation of 0.12 m, with average position deviation of 0.06 m in high GNSS signal quality zones, 0.11 m in transitional sections under signal degradation or recovery, and 0.14 m in fully GNSS-denied environments. These results validate that the proposed SLAM/GNSS fusion localization algorithm maintains high localization accuracy and stability even under conditions of low and highly fluctuating GNSS signal quality, meeting the operational requirements of most agricultural robots. Full article

Sustainable agriculture requires replacing agrochemicals with environmentally friendly products. One alternative is bacterial inoculants with plant-growth-promoting (PGP) activity. Bacterial consortia offer advantages over single-strain inoculants, as they possess more PGP traits and allow the exploitation of bacterial synergies. Synthetic bacterial communities (SynComs) can be used as inoculants that are thoroughly characterized and assessed for efficiency and safety. Here, we describe the construction of a SynCom composed of seven bacterial strains isolated from the rhizosphere of tomato plants and other orchard vegetables. The strains were identified by 16S rDNA sequencing as Pseudomonas spp. (two isolates), Rhizobium sp., Ensifer sp., Microbacterium sp., Agromyces sp., and Chryseobacterium sp. The metagenome of the combined strains was sequenced, allowing the identification of PGP traits and the assembly of their individual genomes. These traits included nutrient mobilization, phytostimulation, and biocontrol. When inoculated into tomato plants in an agricultural soil, the SynCom caused minor effects in soil and rhizosphere bacterial communities. However, it had a high impact on the gene expression pattern of tomato plants. These effects were more significant at the systemic than at the local level, indicating a priming effect in the plant, as signaling through jasmonic acid and ethylene appeared to be altered. Full article

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

Soil compaction from mechanized agriculture is a major concern, as frequent machinery use degrades soil structure, reduces porosity, and ultimately impairs crop productivity. Among potential mitigation strategies to enhance soil resilience to machinery-induced compaction, biochar has shown promise in laboratory settings but remains untested under real field conditions. To address this, we monitored soil in a Tuscan vineyard where biochar was applied at 16 and 32 Mg ha⁻¹, compared to control, on both flat and sloped plots. Soil compaction was induced by 20 passes of a wheeled orchard tractor. Soil bulk density (BD) was measured before, immediately after, and one year following the initial passes, during which 19 additional machine passes occurred as part of the vineyard’s routine agronomic management. Initial results showed a significant BD increase (up to 12.8%) across all treatments, though biochar significantly limited soil compaction, regardless of the applied dose. After one year, in which the soil underwent further compaction, BD further increased across all treatments (up to 20.2%), with the steepest increase observed on the sloped terrain. At this stage, the mitigating effect of biochar on soil compaction was no longer evident. Our findings suggest that biochar may offer some short-term relief from compaction, but further investigations are needed to clarify its long-term effectiveness under field conditions. Full article

To address the limitations of conventional single-track rail systems in challenging hilly and mountainous terrains, which are ill-suited for transporting heavy agricultural machinery, there is a critical need to develop a specialized the double-track rail transportation system optimized for orchard equipment. Recognizing this requirement, our research team designed and implemented a double-track rail transportation system. In this innovative system, the rail functions as the pivotal component, with its structural properties significantly impacting the machine’s overall stability and operational performance. In this study, resistance strain gauges were employed to analyze the stress–strain distribution of the track under a full load of 750 kg, a critical factor in the system’s design. To further investigate the structural performance of the double-track rail, the impact hammer method was utilized in conjunction with triaxial acceleration sensors to conduct experimental modal analysis (EMA) under actual support conditions. By integrating the Eigensystem Realization Algorithm (ERA), the first 20 natural modes and their corresponding parameters were successfully identified with high precision. A comparative analysis between finite element simulation results and experimental measurements was performed, revealing the double-track rail’s inherent vibration characteristics under constrained modal conditions versus actual boundary constraints. These valuable findings serve as a theoretical foundation for the dynamic optimization of rail structures and the mitigation of resonance issues. The advancement of hilly and mountainous rail transportation systems holds significant promise for enhancing productivity and transportation efficiency in agricultural operations. Full article

In alignment with the United Nations’ Sustainable Development Goals (SDGs) 12 (Responsible Consumption and Production) and 13 (Climate Action), this research explores the sustainable valorization of mangosteen peels into mangosteen peel powder (MPP), a value-added product with pharmaceutical properties. Mangosteen peels are an abundant agricultural waste in Thailand. This study evaluates six MPP production schemes, each employing different drying methods. Life Cycle Assessment (LCA) is utilized to assess the global warming potential (GWP) of these schemes, and the quality of the MPP produced is also compared. The results show that a combination of frozen storage and freeze-drying (scheme 4) has the highest GWP (1091.897 kgCO₂eq) due to substantial electricity usage, whereas a combination of frozen storage and sun-drying (scheme 5) has the lowest GWP (0.031 kgCO₂eq) but is prone to microbial contamination. Frozen storage without coarse grinding, combined with hot-air drying (scheme 6), is identified as the optimal scheme in terms of GWP (11.236 kgCO₂eq) and product quality. Due to the lack of an onsite hot-air-drying facility, two transportation strategies are integrated into scheme 6 for scenarios A and B. These transportation strategies include transporting mangosteen peels from orchards to a facility in another province or transporting a mobile hot-air-drying unit to the orchards. The analysis indicates that scenario B is more favorable both operationally and environmentally, due to its lower emissions. This research is the first to comparatively assess the GWP of different MPP production schemes using LCA. Furthermore, it aligns with the growing trend in international trade which places greater emphasis on environmentally friendly production processes. Full article

Urbanization is causing a decrease in agricultural land. This leads to changes in cropping patterns, irrigation water availability, and water allowance. Therefore, change in cropping pattern, irrigation water availability, and water allowance were investigated in the Multan region of Pakistan using remote sensing and GIS techniques. The multi-temporal Landsat images with 30 m resolution were acquired for both Rabi (winter) and Kharif (summer) seasons for the years of 1988, 1999 and 2020. The image processing tasks including layer stacking, sub-setting, land use/land cover (LULC) classification, and accuracy assessment were performed using ERDAS Imagine (2015) software. The LULC maps showed a considerable shift of orchard area to urban settlements and other crops. About 82% of orchard areas have shifted to urban settlements and other crops from 1988 to 2020. The LULC maps for Kharif season indicated that cropped areas for cotton have decreased by 42.5% and the cropped areas for rice have increased by 718% in the last 32 years (1988–2020). During the rabi season, the cropped areas for wheat (Triticum aestivum L.) have increased by 27% from 1988 to 2020. The irrigation water availability and water allowance have increased up to 125 and 110% due to decrease in agricultural land, respectively. The overall average accuracies were found as 87 and 89% for Rabi and Kharif crops, respectively. The LULC mapping technique may be used to develop a decision support system for evaluating the changes in cropping pattern and their impacts on net water availability and water allowances. 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

The event scale method, employed for assessing changes in nitrogen (N) dynamics pre- and post-rain, provides insights into its transport to surface water systems. However, the relationships between N discharge in catchments dominated by different land uses and water quality remain unclear. This study quantified variations in key N components in ponds across forest, paddy field, and orchard catchments before and after six rainfall events. The results showed that nitrate (NO₃⁻-N) was the main N component in the ponds. Post-rainfall, N concentrations increased, with ammonium (NH₄⁺-N) and particulate nitrogen (PN) exhibiting significant elevations in agricultural ponds. Orchard catchments contributed the highest N load to the ponds, while forest catchments contributed the lowest. Following a heavy rainstorm event, total nitrogen (TN) loads in the ponds within forest, paddy field, and orchard catchments reached 6.68, 20.93, and 34.62 kg/ha, respectively. These loads were approximately three times higher than those observed after heavy rain events. The partial least squares structural equation model (PLS-SEM) identified that rainfall amount and changes in water volume were the dominant factors influencing N dynamics. Furthermore, the greater slopes of forest and orchard catchments promoted more N loss to the ponds post-rain. In paddy field catchments, larger catchment areas were associated with decreased N flux into the ponds, while larger pond surface areas minimized the variability in N concentration after rainfall events. In orchard catchment ponds, pond area was positively correlated with N concentrations and loads. This study elucidates the effects of rainfall characteristics and catchment heterogeneity on N dynamics in surface waters, offering valuable insights for developing pollution management strategies to mitigate rainfall-induced alterations. Full article

Accurate estimation of fruit quantity is essential for efficient harvest management, storage, transportation, and marketing in the agricultural industry. To address the limited generalizability of single-species models, this study presents a comprehensive deep learning-based framework for multi-species fruit-load estimation, leveraging the MetaFruit dataset, which contains images of five fruit species collected under diverse orchard conditions. Four representative object detection and regression models—YOLOv8, RT-DETR, Faster R-CNN, and a U-Net-based heatmap regression model—were trained and compared as part of the proposed multi-species learning strategy. The models were evaluated on both the internal MetaFruit dataset and two external datasets, NIHS-JBNU and Peach, to assess their generalization performance. Among them, YOLOv8 and the RGBH heatmap regression model achieved F1-scores of 0.7124 and 0.7015, respectively, on the NIHS-JBNU dataset. These results indicate that a deep learning-based multi-species training strategy can significantly enhance the generalizability of fruit-load estimation across diverse field conditions. Full article

With the advancement of agricultural automation technologies, apple-harvesting robots have gradually become a focus of research. As their “perceptual core,” machine vision systems directly determine picking success rates and operational efficiency. However, existing vision systems still exhibit significant shortcomings in target detection and positioning accuracy in complex orchard environments (e.g., uneven illumination, foliage occlusion, and fruit overlap), which hinders practical applications. This study proposes a visual system for apple-harvesting robots based on improved Mask R-CNN and binocular vision to achieve more precise fruit positioning. The binocular camera (ZED2i) carried by the robot acquires dual-channel apple images. An improved Mask R-CNN is employed to implement instance segmentation of apple targets in binocular images, followed by a template-matching algorithm with parallel epipolar constraints for stereo matching. Four pairs of feature points from corresponding apples in binocular images are selected to calculate disparity and depth. Experimental results demonstrate average coefficients of variation and positioning accuracy of 5.09% and 99.61%, respectively, in binocular positioning. During harvesting operations with a self-designed apple-picking robot, the single-image processing time was 0.36 s, the average single harvesting cycle duration reached 7.7 s, and the comprehensive harvesting success rate achieved 94.3%. This work presents a novel high-precision visual positioning method for apple-harvesting robots. Full article