Search Results (2,967)

Labor shortages and uneven terrain in orchards present significant challenges to autonomous navigation. This study proposes a navigation system that integrates Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) data to enhance localization accuracy and map stability through Simultaneous Localization and Mapping (SLAM). To minimize distortions in LiDAR scans caused by ground irregularities, real-time tilt correction was implemented based on IMU feedback. Furthermore, the path planning module was improved by modifying the Rapidly-Exploring Random Tree (RRT) algorithm. The enhanced RRT generated smoother and more efficient trajectories with quantifiable improvements: the average shortest path length was 2.26 m, compared to 2.59 m with conventional RRT and 2.71 m with A* algorithm. Tracking performance also improved, achieving a root mean square error of 0.890 m and a maximum lateral deviation of 0.423 m. In addition, yaw stability was strengthened, as heading fluctuations decreased by approximately 7% relative to the standard RRT. Field results validated the robustness and adaptability of the proposed system under real-world agricultural conditions. These findings highlight the potential of LiDAR–IMU sensor fusion and optimized path planning to enable scalable and reliable autonomous navigation for precision agriculture. Full article

To address suboptimal throughput performance in conventional intelligent apple harvesting systems predominantly employing single manipulators, a dual-arm harvesting robot prototype was engineered. Leveraging the AUBO-i5 manipulator framework and kinematic characteristics, a coordinated workspace arrangement was established. Subsequently, the dual-manipulator harvesting platform was fabricated. A dynamic task allocation methodology and intelligent fruit sequencing approach were formulated, grounded in U-tube optimization principles. This framework achieved parallel operation ratios between 82.1% and 99%, with combined trajectory lengths spanning 9.24–11.90 m. Building upon established apple harvesting knowledge, a sequencing strategy incorporating dynamic manipulator zoning was developed. Validation was conducted through V-REP kinematic simulations where end-effector poses were continuously tracked, confirming zero limb interference during coordinated motion. Field assessments yielded parallel operation rates of 85.7–93.3%, total harvest durations of 17.8–22.3 s, and inter-manipulator path differentials of 267–541 mm. Throughout testing, collision-free operation was maintained while successfully harvesting all target fruits according to planned sequences. These outcomes validate the efficacy of U-tube-based dynamic zoning and sequencing methodologies for dual-manipulator fruit harvesting in intelligent orchard applications. Full article

Apricot (Prunus armeniaca L.) exhibits a gametophytic self-incompatibility (GSI) system. To identify the S-genotypes of the main apricot cultivars, including 133 native Chinese cultivars and 35 foreign accessions, PCR was performed using a combination of five primers based on the conserved regions of Prunus S-RNase genes. After cloning and sequencing the PCR products, the S-genotypes of all 168 apricot cultivars were determined. A total of 46 different S-RNase alleles, with 15 new alleles, were identified. For all 168 accessions, the top five most frequent S-alleles were S₈, S₁₁, S₉, S₁₆, and S₅₃. S₁₁, S₈, and S₁₆ were the most frequent in Chinese cultivars, and S₉, S₈, and S₂ were mostly found in European accessions. For Chinese apricot cultivars, the distribution of S-alleles among five geographic regions was also investigated. In Northwest China, S₁₆ was the most frequent S-allele. In the Xinjiang region, S₆₆, S₄₉, and S₁₄ were the top three most frequent S-alleles. In North China, S₈, S₁₁, and S₅₃ were the top three most frequent S-alleles. In addition, the self-compatible type, S_C, was not detected in these 133 Chinese accessions. Finally, the phylogenetic tree of apricot S-alleles indicated that there are four groups of S-RNase genes (S₉₇/S₁₀₆, S₁₄/S_14a/S₆₆, S₉/S₁₇/S₄₄, and S₂₃/S₅₃) presenting a very close relation. These results provide more data on the S-genotypes of apricot accessions, which can support future breeding programs by aiding in the selection of the appropriate parents and contributing to efficient orchard design by combining cultivars with suitable pollinizers. Full article

The present research aimed to evaluate the effectiveness of new organo-mineral biostimulants in an apple orchard, including their relevance to spring frosts and to enhancing yield. The study evaluated the effects of foliar sprays with organo-mineral fertilizers on apple yield, comparing three treatments: 1—control (no treatment); 2—foliar spray with a 1% blend of “WPU” Antifreeze and 1% “WP Drip Ca + Mg”; 3—foliar application using a 3% solution of both “WPU” Antifreeze and “WP Drip Ca + Mg”. The NPC “White Pearl” foliar sprays exhibited cryoprotective properties to spring frosts through multiple mechanisms, i.e., prevention of cellular dehydration via elevated bound water content and accumulation of osmoprotective compounds including proline and soluble sugars. This research shows that the applied treatments improved carbohydrate metabolism by enhancing the biosynthesis of glucose and starch, as well as changing the donor–acceptor relationships between the leaf apparatus and the fruit toward the forming apple, promoting a better outflow of assimilates into ripening fruits. The 1% solution treatment enhanced apple yield by 70% (1.7-fold) relative to the untreated control. These findings indicate that the “White Pearl” organo-mineral fertilizer NPC (especially at 1% concentration) could serve as an effective supplement to conventional apple farming practices, boosting overall productivity. Full article

Proper management of water resources in agriculture is of utmost importance for sustainable productivity, especially under the current context of climate change. However, many smart agriculture systems, including for managing irrigation, involve costly, complex tools for most farmers, especially small/medium-scale producers, despite the availability of user-friendly and community-accessible tools supported by well-established providers (e.g., Google). Hence, this paper proposes an irrigation management system integrating low-cost Internet of Things (IoT) sensors with community-accessible cloud-based data management tools. Specifically, it resorts to sensors managed by an ESP32 development board to monitor several agroclimatic parameters and employs Google Sheets for data handling, visualization, and decision support, assisting operators in carrying out proper irrigation procedures. To ensure reproducibility for both digital experts but mainly non-technical professionals, a comprehensive set of guidelines is provided for the assembly and configuration of the proposed irrigation management system, aiming to promote a democratized dissemination of key technical knowledge within a do-it-yourself (DIY) paradigm. As part of this contribution, a market survey identified numerous e-commerce platforms that offer the required components at competitive prices, enabling the system to be affordably replicated. Furthermore, an irrigation management prototype was tested in a real production environment, consisting of a 2.4-hectare yellow kiwi orchard managed by an association of producers from July to September 2021. Significant resource reductions were achieved by using low-cost IoT devices for data acquisition and the capabilities of accessible online tools like Google Sheets. Specifically, for this study, irrigation periods were reduced by 62.50% without causing water deficits detrimental to the crops’ development. Full article

To address the problems of insufficient trajectory tracking accuracy, pronounced jitter over undulating terrain, and limited disturbance rejection in orchard mobile robots, this paper proposes a trajectory tracking control strategy based on a double-loop adaptive sliding mode. Firstly, a kinematic model of the orchard robot is constructed and a time-varying integral terminal sliding surface is designed to achieve global fast finite-time convergence. Secondly, a sinusoidal saturation switching function with a variable boundary is employed to suppress the high-frequency chattering inherent in sliding mode control. Thirdly, an improved double-power reaching law (Improved DPRL) is introduced to enhance disturbance rejection in the inner loop while ensuring continuity of the outer-loop output. Finally, Lyapunov stability theory is used to prove the asymptotic stability of the double-loop system. The experimental results show that attitude angle error settles within 0.01 rad after 0.144 s, while the position errors in both the x-axis and y-axis directions settle within 0.01 m after 0.966 s and 0.753 s, respectively. Regarding position error convergence, the Integral of Absolute Error (IAE)/Integral of Squared Error (ISE)/Integral of Time-Weighted Absolute Error (ITAE) are 0.7629 m, 0.7698 m, and 0.2754 m, respectively; for the attitude angle error, the IAE/ISE/ITAE are 0.0484 rad, 0.0229 rad, and 0.1545 rad, respectively. These results indicate faster convergence of both position and attitude errors, smoother control inputs, and markedly reduced chattering. Overall, the findings satisfy the real-time and accuracy requirements of fast trajectory tracking for orchard mobile robots. Full article

Avocado orchards are among the most profitable and fastest-growing commodity crops in Mexico, especially in the area known as the “Avocado Belt”. Several efforts have been made to monitor their expansion; however, there is currently no method that can be easily updated to track this expansion. The main objective of this study was to monitor the expansion of avocado orchards from 1993 to 2024, using the Continuous Change Detection and Classification (CCDC) algorithm and Landsat 5, 7, 8, and 9 imagery. Presence/absence maps of avocado orchards corresponding to 1 January of each year were used to perform a trajectory analysis, identifying eight possible change trajectories. Finally, maps from 2020 to 2023 were verified using reference data and very-high-resolution images. The maps showed a level of agreement = 0.97, while the intersection over union for the avocado orchard class was 0.62. The main results indicate that the area occupied by avocado orchards more than tripled from 1993 to 2024, from 64,304.28 ha to 200,938.32 ha, with the highest expansion occurring between 2014 and 2024. The trajectory analysis confirmed that land conversion to avocado orchards is generally permanent and happens only once (i.e., gain without alternation). The method proved to be a robust approach for monitoring avocado orchard expansion and could be an attractive alternative for regularly updating this information. Full article

To address the challenges of layered fertilization in orchards and the lack of dedicated equipment, this study proposes a layered fertilization technique based on the three-dimensional distribution characteristics of jujube root systems and develops an orchard layered fertilizer applicator. First, the agronomic advantages of layered fertilization were systematically elucidated by analyzing the spatial distribution patterns of jujube roots, as well as the mechanisms of fertilizer nutrient transport and uptake. Second, parametric design was conducted for key components (e.g., trenching–fertilizing unit), with emphasis on the structural design of the fertilizer-dividing box and the augerless spiral conveying mechanism. A three-factor, three-level experiment based on response surface methodology was implemented, where the coefficient of variation (CV) of fertilization uniformity and row consistency were selected as evaluation indices to optimize key parameters (forward speed, augerless spiral speed, and fertilizer gate opening). The optimal operational combination was determined as follows: forward speed of 2.62 km/h, augerless spiral speed of 29.87 r/min, and fertilizer gate opening of 3.49 cm. Field tests demonstrated that the CVs of fertilization uniformity and row consistency reached 7.77% and 8.46%, respectively, meeting the agronomic requirements for orchard fertilization. This study provides a reference for the development of orchard fertilization technologies and machinery. Full article

Soil samples of different tree ages from the core area and expansion area of Korla City were selected to determine their nutrients and yield, and the analysis was combined with a Principal Component Analysis (PCA) biplot. The soil fertility and yield in the core area were superior to those in the expansion area. PCA biplot analysis showed that the cumulative variance contribution rate of the principal components of the orchard with a tree age of 10–20 years was 80.60%. PC1 had strong positive loadings for calcium, available phosphorus, organic matter, total nitrogen, and yield, and a strong negative loading for pH. PC2 had strong loadings for manganese, zinc, copper, selenium, and iron, as well as for magnesium, boron, available nitrogen, and electrical conductivity. For the core area, soil conditions need to be maintained. For the expansion area, salinization should be addressed; the input of Mg and B should be controlled; and the application of calcium, phosphorus fertilizers, and organic fertilizers should be increased to improve production and quality. Full article

To address the insufficient accuracy of traditional single-sensor navigation methods in dense planting environments of pomegranate orchards, this paper proposes a vision and LiDAR fusion-based navigation line extraction method for orchard environments. The proposed method integrates a YOLOv8-ResCBAM trunk detection model, a reverse ray projection fusion algorithm, and geometric constraint-based navigation line fitting techniques. The object detection model enables high-precision real-time detection of pomegranate tree trunks. A reverse ray projection algorithm is proposed to convert pixel coordinates from visual detection into three-dimensional rays and compute their intersections with LiDAR scanning planes, achieving effective association between visual and LiDAR data. Finally, geometric constraints are introduced to improve the RANSAC algorithm for navigation line fitting, combined with Kalman filtering techniques to reduce navigation line fluctuations. Field experiments demonstrate that the proposed fusion-based navigation method improves navigation accuracy over single-sensor methods and semantic-segmentation methods, reducing the average lateral error to 5.2 cm, yielding an average lateral error RMS of 6.6 cm, and achieving a navigation success rate of 95.4%. These results validate the effectiveness of the vision and 2D LiDAR fusion-based approach in complex orchard environments and provide a viable route toward autonomous navigation for orchard robots. Full article

Crataegus harbisonii Beadle (Harbison’s or Harbison hawthorn) is a Tennessee (USA) endemic tree of the Rosaceae family, currently considered “critically imperiled” at the state, national, and global levels. It is known from only two extant wild locations, one in Davidson County, Tennessee consisting of a single living individual and a population of less than 100 individuals in Obion County, Tennessee. Key ex situ conservation efforts undertaken over the last three years with this critically imperiled species are reported here. The Obion County population was intensively surveyed and all C. harbisonii individuals documented. Over three seasons, seeds were collected and propagated, and clones were generated via chip-budding and grafting. Conservation seed orchards were planned and established to provide a stable, long-term source of genetically robust seed for reforestation and research. To date, 19 sources from the Obion County location as well as the single Davidson County genotype have been successfully preserved through clonal propagation, and open-pollinated seedlings produced from 12 unique mother trees. Additional material is being added annually. We report lessons learned as well as key future research directions, now enabled through the establishment of germplasm resources. Full article

The objective of this study was to evaluate the impact of two nitrogen doses in combination with strong creeping fescue (Festuca rubra L. ssp. rubra Gaudin) and Chewing’s red fescue (Festuca rubra L. ssp. commutata Gaudin) used as living mulches on the weed community in an apple tree (Malus domestica Borkh.) orchard. The cover grasses were sown in the tree rows, and herbicide fallow served as the control. Grass living mulches effectively reduced the number and share of annual weed cover and limited the spread of perennial plants compared with herbicide fallow. Use of F. rubra L. subspecies did not favor the biodiversity of the orchard agroecosystem flora, due to the effective soil surface coverage by sod in the tree rows. Living mulch sod was characterized by lower variability in weed taxa compared with the abundant weed composition in the herbicide fallow, which also exhibited the highest number of weed taxa each year. Dominant species in the orchard across all treatments included Trifolium repens L. and Taraxacum spp. Doubling the nitrogen fertilization rate, while limiting the application area to the tree canopy, did not increase the perennial weed population in the living mulch sod. Both subspecies are useful as living mulch in a young apple orchard, but from the perspective of sod durability and weed control, strong creeping red fescue offers better prospects. Full article

Accurate target recognition and localization remain significant challenges for robotic fruit harvesting in unstructured orchard environments characterized by branch occlusion and leaf clutter. To address the difficulty in identifying and locating apples under such visually complex conditions, this paper proposes an improved YOLOv5-based visual recognition algorithm incorporating an efficient channel attention (ECA) module. The ECA module is strategically integrated into specific C3 layers (C3-3, C3-6, C3-9) of the YOLOv5 network architecture to enhance feature representation for occluded targets. During operation, the system simultaneously acquires apple pose information and achieves precise spatial localization through coordinate transformation matrices. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed system. The custom-designed six-degree-of-freedom (6-DOF) robotic arm exhibits a wide operational range with a maximum working angle of 120°. The ECA-enhanced YOLOv5 model achieves a confidence level of 90% and an impressive in-range apple recognition rate of 98%, representing a 2.5% improvement in the mean Average Precision (mAP) compared to the baseline YOLOv5s algorithm. The end-effector positioning error is consistently controlled within 1.5 mm. The motion planning success rate reaches 92%, with the picking completed within 23 s per apple. This work provides a novel and effective vision recognition solution for future development of harvesting robots. Full article

This study aims to develop a multi-dimensional framework to systematically identify optimal adjustment zones for converting orchard land into cultivated land, thereby providing a reference for spatial optimization of cultivated land within the context of integrating diverse land occupation activities into the requisition–compensation balance system. The research incorporates land quality evaluation, land-use conversion cost assessment, ecological loss analysis, and scenario-based simulations. The study demonstrates that (1) compared to the common practice of directly converting orchard land to cultivated land by only considering the slope, our multi-scenario optimization model for cultivated land reduces both economic and ecological losses. (2) For cities prioritizing ecological or economic development, selecting strategies under corresponding priority scenarios can maximize the protection of local ecological environments or maintain economic levels, thereby providing reserve resources for cultivated land optimization and adjustment. (3) Under the MMEG (EG: Ecological priority scenario) and MMEM (EM: Economic priority scenario) scenarios (MM: conversion of medium-low-grade orchard land to medium-high-grade cultivated land), the area of cultivated land optimal adjustment zones is the largest. The method of comprehensively identifying cultivated land optimal adjustment zones through multi-dimensional scenario settings is more comprehensive than the conventional approach that only considers slope. This method enhances cultivated land quality more effectively and protects both the ecosystem and the economy. Full article