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20 pages, 7127 KiB  
Article
An Improved Hierarchical Leaf Density Model for Spatio-Temporal Distribution Characteristic Analysis of UAV Downwash Air-Flow in a Fruit Tree Canopy
by Shenghui Fu, Naixu Ren, Shuangxi Liu, Mingxi Shao, Yuanmao Jiang, Yuefeng Du, Hongjian Zhang, Linlin Sun and Wen Zhang
Agronomy 2025, 15(8), 1867; https://doi.org/10.3390/agronomy15081867 (registering DOI) - 1 Aug 2025
Abstract
In the process of plant protection for fruit trees using rotary-wing UAVs, challenges such as droplet drift, insufficient canopy penetration, and low agrochemical utilization efficiency remain prominent. Among these, the uncertainty in the spatio-temporal distribution of downwash airflow is a key factor contributing [...] Read more.
In the process of plant protection for fruit trees using rotary-wing UAVs, challenges such as droplet drift, insufficient canopy penetration, and low agrochemical utilization efficiency remain prominent. Among these, the uncertainty in the spatio-temporal distribution of downwash airflow is a key factor contributing to non-uniform droplet deposition and increased drift. To address this issue, we developed a wind field numerical simulation model based on an improved hierarchical leaf density model to clarify the spatio-temporal characteristics of downwash airflow, the scale of turbulence regions, and their effects on internal canopy airflow under varying flight altitudes and different rotor speeds. Field experiments were conducted in orchards to validate the accuracy of the model. Simulation results showed that the average error between the simulated and measured wind speeds inside the canopy was 8.4%, representing a 42.11% reduction compared to the non-hierarchical model and significantly improving the prediction accuracy. The coefficient of variation (CV) was 0.26 in the middle canopy layer and 0.29 in the lower layer, indicating a decreasing trend with an increasing canopy height. We systematically analyzed the variation in turbulence region scales under different flight conditions. This study provides theoretical support for optimizing UAV operation parameters to improve droplet deposition uniformity and enhance agrochemical utilization efficiency. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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30 pages, 8037 KiB  
Review
A Review of Multiscale Interaction Mechanisms of Wind–Leaf–Droplet Systems in Orchard Spraying
by Yunfei Wang, Zhenlei Zhang, Ruohan Shi, Shiqun Dai, Weidong Jia, Mingxiong Ou, Xiang Dong and Mingde Yan
Sensors 2025, 25(15), 4729; https://doi.org/10.3390/s25154729 (registering DOI) - 31 Jul 2025
Abstract
The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent [...] Read more.
The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article
(This article belongs to the Special Issue Application of Sensors Technologies in Agricultural Engineering)
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21 pages, 8731 KiB  
Article
Individual Segmentation of Intertwined Apple Trees in a Row via Prompt Engineering
by Herearii Metuarea, François Laurens, Walter Guerra, Lidia Lozano, Andrea Patocchi, Shauny Van Hoye, Helin Dutagaci, Jeremy Labrosse, Pejman Rasti and David Rousseau
Sensors 2025, 25(15), 4721; https://doi.org/10.3390/s25154721 (registering DOI) - 31 Jul 2025
Abstract
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 [...] Read more.
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
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17 pages, 2601 KiB  
Article
Tree Selection of Vernicia montana in a Representative Orchard Cluster Within Southern Hunan Province, China: A Comprehensive Evaluation Approach
by Juntao Liu, Zhexiu Yu, Xihui Li, Ling Zhou, Ruihui Wang and Weihua Zhang
Plants 2025, 14(15), 2351; https://doi.org/10.3390/plants14152351 - 30 Jul 2025
Abstract
With the objective of identifying superior Vernicia montana trees grounded in phenotypic and agronomic traits, this study sought to develop and implement a comprehensive evaluation method which would provide a practical foundation for future clonal breeding initiatives. Using the Vernicia montana propagated from [...] Read more.
With the objective of identifying superior Vernicia montana trees grounded in phenotypic and agronomic traits, this study sought to develop and implement a comprehensive evaluation method which would provide a practical foundation for future clonal breeding initiatives. Using the Vernicia montana propagated from seedling forests grown in the Suxian District of Chenzhou City in southern Hunan Province, we conducted pre-selection, primary selection, and re-selection of Vernicia montana forest stands and took the nine trait indices of single-plant fruiting quantity, single-plant fruit yield, disease and pest resistance, fruit ripening consistency, fruit aggregation, fresh fruit single-fruit weight, fresh fruit seed rate, dry seed kernel rate, and seed kernel oil content rate as the optimal evaluation indexes and carried out cluster analysis and a comprehensive evaluation in order to establish a comprehensive evaluation system for superior Vernicia montana trees. The results demonstrated that a three-stage selection process—consisting of pre-selection, primary selection, and re-selection—was conducted using a comprehensive analytical approach. The pre-selection phase relied primarily on sensory evaluation criteria, including fruit count per plant, tree size, tree morphology, and fruit clustering characteristics. Through this rigorous screening process, 60 elite plants were selected. The primary selection was based on phenotypic traits, including single-plant fruit yield, pest and disease resistance, and uniformity of fruit ripening. From this stage, 36 plants were selected. Twenty plants were then selected for re-selection based on key performance indicators, such as fresh fruit weight, fresh fruit seed yield, dry seed kernel yield, and oil content of the seed kernel. Then the re-selected optimal trees were clustered and analyzed into three classes, with 10 plants in class I, 7 plants in class II, and 3 plants in class III. In class I, the top three superior plants exhibited outstanding performance across key traits: their fresh fruit weight per fruit, fresh fruit seed yield, dry seed yield, and seed kernel oil content reached 41.61 g, 42.80%, 62.42%, and 57.72%, respectively. Compared with other groups, these figures showed significant advantages: 1.17, 1.09, 1.12, and 1.02 times the average values of the 20 reselected superior trees; 1.22, 1.19, 1.20, and 1.08 times those of the 36 primary-selected superior trees; and 1.24, 1.25, 1.26, and 1.19 times those of the 60 pre-selected trees. Fruits counts per plant and the number of fruits produced per plant of the best three plants in class I were 885 and 23.38 kg, respectively, which were 1.13 and 1.18 times higher than the average of 20 re-selected superior trees, 1.25 and 1.30 times higher than the average of 36 first-selected superior trees, and 1.51 and 1.58 times higher than the average of 60 pre-selected superior trees. Class I superior trees, especially the top three genotypes, are suitable for use as mother trees for scion collection in grafting. The findings of this study provide a crucial foundation for developing superior clonal varieties of Vernicia montana through selective breeding. Full article
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16 pages, 2280 KiB  
Article
Mechanical Properties of Korla Fragrant Pear Fruiting Branches and Pedicels: Implications for Non-Destructive Harvesting
by Yanwu Jiang, Jun Chen, Zhiwei Wang, Jianguo Zhou and Guangrui Hu
Horticulturae 2025, 11(8), 880; https://doi.org/10.3390/horticulturae11080880 - 29 Jul 2025
Viewed by 173
Abstract
The Korla fragrant pear is a highly valued economic fruit in China’s Xinjiang region. However, biomechanical data on the fruit-bearing branches and pedicels of this species remain incomplete, which to some extent hinders the advancement of harvesting equipment and techniques. Therefore, refining these [...] Read more.
The Korla fragrant pear is a highly valued economic fruit in China’s Xinjiang region. However, biomechanical data on the fruit-bearing branches and pedicels of this species remain incomplete, which to some extent hinders the advancement of harvesting equipment and techniques. Therefore, refining these data is of great significance for the development of efficient and non-destructive harvesting strategies. This study aims to elucidate the mechanical properties of the fruiting branches and peduncles of Korla fragrant pears, thereby establishing a theoretical foundation for the future development of intelligent harvesting technology for this variety. The research utilized axial and radial compression tests, along with three-point bending test methods, to quantitatively analyze the elastic modulus and shear modulus of the branches and peduncles. The test results reveal that the elastic modulus of the fruiting branches under axial compression is 263.51 ± 76.51 MPa, while under radial compression, it measures 135.53 ± 73.73 MPa (where ± represents the standard deviation). In comparison, the elastic modulus of the peduncles is recorded at 152.96 ± 119.95 MPa. Additionally, the three-point bending test yielded a shear modulus of 75.48 ± 32.84 MPa for the branches and 30.23 ± 8.50 MPa for the peduncles. Using finite element static structural analysis, the simulation results aligned closely with the experimental data, falling within an acceptable error range, thus validating the reliability of the testing methods and outcomes. The mechanical parameters obtained in this study are critical for modeling the stress and deformation behaviors of pear-bearing structures during mechanical harvesting. These findings provide valuable theoretical support for the optimization of harvesting device design and operational strategies, with the aim of reducing fruit damage and improving harvesting efficiency in pear orchards. Full article
(This article belongs to the Section Postharvest Biology, Quality, Safety, and Technology)
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31 pages, 3855 KiB  
Article
Exploring Sidewalk Built Environment Design Strategies to Promote Walkability in Tropical Humid Climates
by Pakin Anuntavachakorn, Purinat Pawarana, Tarid Wongvorachan, Chaniporn Thampanichwat and Suphat Bunyarittikit
Buildings 2025, 15(15), 2659; https://doi.org/10.3390/buildings15152659 - 28 Jul 2025
Viewed by 293
Abstract
The world is facing a state of “global boiling,” causing damage to various sectors. Developing pedestrian systems is a key to mitigating it, especially in tropical and humid cities where the climate discourages walking and increases the need for shaded walkways. Recent research [...] Read more.
The world is facing a state of “global boiling,” causing damage to various sectors. Developing pedestrian systems is a key to mitigating it, especially in tropical and humid cities where the climate discourages walking and increases the need for shaded walkways. Recent research shows a lack of data and in-depth studies on the built environment promoting walkability in such climates, creating a research gap this study aims to fill. Using Singapore as a case study, four locations—Marina Bay, Orchard Road, Boat Quay, and Chinatown—were surveyed and analyzed through visual decoding and questionnaires. Results show that natural light is the most frequently observed and important element in pedestrian pathway design in tropical and humid areas. Trees and sidewalks are also important in creating a walk-friendly environment. Green spaces significantly influence the desire to walk, though no clear positive outcomes were found. Additionally, “Other Emotions” negatively affect the decision to walk, suggesting these should be avoided in future pedestrian pathway designs to encourage walking. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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17 pages, 1728 KiB  
Article
The Impact of Colony Deployment Timing on Tetragonula carbonaria Crop Fidelity and Resource Use in Macadamia Orchards
by Claire E. Allison, James C. Makinson, Robert N. Spooner-Hart and James M. Cook
Plants 2025, 14(15), 2313; https://doi.org/10.3390/plants14152313 - 26 Jul 2025
Viewed by 236
Abstract
Crop fidelity is a desirable trait for managed pollinators and is influenced by factors like competing forage sources and colony knowledge of the surrounding environment. In European honey bees (Apis mellifera L.), colonies deployed when the crop is flowering display the highest [...] Read more.
Crop fidelity is a desirable trait for managed pollinators and is influenced by factors like competing forage sources and colony knowledge of the surrounding environment. In European honey bees (Apis mellifera L.), colonies deployed when the crop is flowering display the highest fidelity. We tested for a similar outcome using a stingless bee species that is being increasingly used as a managed pollinator in Australian macadamia orchards. We observed Tetragonula carbonaria (Smith) colonies deployed in macadamia orchards at three time points: (1) before crop flowering (“permanent”), (2) early flowering (“early”), and (3) later in the flowering period (“later”). We captured returning pollen foragers weekly and estimated crop fidelity from the proportion of macadamia pollen they collected, using light microscopy. Pollen foraging activity was also assessed via weekly hive entrance filming. The early and later introduced colonies initially exhibited high fidelity, collecting more macadamia pollen than the permanent colonies. In most cases, the permanent colonies were already collecting diverse pollen species from the local environment and took longer to shift over to macadamia. Pollen diversity increased over time in all colonies, which was associated with an increase in the proportion of pollen foragers. Our results indicate that stingless bees can initially prioritize a mass-flowering crop, even when flowering levels are low, but that they subsequently reduce fidelity over time. Our findings will inform pollinator management strategies to help growers maximize returns from pollinator-dependent crops like macadamia. Full article
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30 pages, 92065 KiB  
Article
A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies
by Jin Lu, Zhongji Cao, Jin Wang, Zhao Wang, Jia Zhao and Minjie Zhang
Agriculture 2025, 15(15), 1622; https://doi.org/10.3390/agriculture15151622 - 26 Jul 2025
Viewed by 237
Abstract
During the automated picking of table grapes, the automatic recognition and segmentation of grape pedicels, along with the positioning of picking points, are vital components for all the following operations of the harvesting robot. In the actual scene of a grape plantation, however, [...] Read more.
During the automated picking of table grapes, the automatic recognition and segmentation of grape pedicels, along with the positioning of picking points, are vital components for all the following operations of the harvesting robot. In the actual scene of a grape plantation, however, it is extremely difficult to accurately and efficiently identify and segment grape pedicels and then reliably locate the picking points. This is attributable to the low distinguishability between grape pedicels and the surrounding environment such as branches, as well as the impacts of other conditions like weather, lighting, and occlusion, which are coupled with the requirements for model deployment on edge devices with limited computing resources. To address these issues, this study proposes a novel picking point localization method for table grapes based on an instance segmentation network called Progressive Global-Local Structure-Sensitive Segmentation (PGSS-YOLOv11s) and a simple combination strategy of morphological operators. More specifically, the network PGSS-YOLOv11s is composed of an original backbone of the YOLOv11s-seg, a spatial feature aggregation module (SFAM), an adaptive feature fusion module (AFFM), and a detail-enhanced convolutional shared detection head (DE-SCSH). And the PGSS-YOLOv11s have been trained with a new grape segmentation dataset called Grape-⊥, which includes 4455 grape pixel-level instances with the annotation of ⊥-shaped regions. After the PGSS-YOLOv11s segments the ⊥-shaped regions of grapes, some morphological operations such as erosion, dilation, and skeletonization are combined to effectively extract grape pedicels and locate picking points. Finally, several experiments have been conducted to confirm the validity, effectiveness, and superiority of the proposed method. Compared with the other state-of-the-art models, the main metrics F1 score and mask mAP@0.5 of the PGSS-YOLOv11s reached 94.6% and 95.2% on the Grape-⊥ dataset, as well as 85.4% and 90.0% on the Winegrape dataset. Multi-scenario tests indicated that the success rate of positioning the picking points reached up to 89.44%. In orchards, real-time tests on the edge device demonstrated the practical performance of our method. Nevertheless, for grapes with short pedicels or occluded pedicels, the designed morphological algorithm exhibited the loss of picking point calculations. In future work, we will enrich the grape dataset by collecting images under different lighting conditions, from various shooting angles, and including more grape varieties to improve the method’s generalization performance. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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20 pages, 2181 KiB  
Article
Metabarcoding Analysis Reveals Microbial Diversity and Potential Soilborne Pathogens Associated with Almond Dieback and Decline
by André Albuquerque, Mariana Patanita, Joana Amaro Ribeiro, Maria Doroteia Campos, Filipa Santos, Tomás Monteiro, Margarida Basaloco and Maria do Rosário Félix
Plants 2025, 14(15), 2309; https://doi.org/10.3390/plants14152309 - 26 Jul 2025
Viewed by 306
Abstract
Almond decline and dieback have become significant challenges in newly established orchards, with symptoms including internal necrosis, canker, and external gummosis. This work aims to explore the potential fungal and bacterial causative agents through metabarcoding and traditional culture plate isolation across six almond [...] Read more.
Almond decline and dieback have become significant challenges in newly established orchards, with symptoms including internal necrosis, canker, and external gummosis. This work aims to explore the potential fungal and bacterial causative agents through metabarcoding and traditional culture plate isolation across six almond cultivars. Our results emphasize the multifactorial nature of almond decline and dieback, with possible co-infections by opportunistic fungi and bacteria playing a central role. Classical isolation identified 47 fungal species or genera, including Diaporthe amygdali, Diplodia corticola, Phytophthora sp., and several Fusarium species. Almond metabarcoding revealed a more diverse microbial community, highlighting the prevalence of soilborne pathogens such as Neocosmospora rubicola, Dactylonectria estremocensis, and Plectosphaerella niemeijerarum. Soil metabarcoding suggested that these pathogens likely originate from nursery substrates or soils shared with other crops, such as olives and vineyards, that serve as a source of inoculum. ‘Soleta’ generally presented lower richness when compared to the other tested cultivars, suggesting a higher degree of biotic stress and decreased plant resilience. This study highlights the value of integrating NGS approaches to comprehensively study complex diseases and the need for further research on pathogen interactions and cultivar susceptibility for the future development of new sustainable, targeted management strategies in almond orchards. Full article
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31 pages, 11649 KiB  
Article
Development of Shunt Connection Communication and Bimanual Coordination-Based Smart Orchard Robot
by Bin Yan and Xiameng Li
Agronomy 2025, 15(8), 1801; https://doi.org/10.3390/agronomy15081801 - 25 Jul 2025
Viewed by 155
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Smart Farming: Advancing Techniques for High-Value Crops)
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18 pages, 2469 KiB  
Article
Neural Network-Based SLAM/GNSS Fusion Localization Algorithm for Agricultural Robots in Orchard GNSS-Degraded or Denied Environments
by Huixiang Zhou, Jingting Wang, Yuqi Chen, Lian Hu, Zihao Li, Fuming Xie, Jie He and Pei Wang
Agriculture 2025, 15(15), 1612; https://doi.org/10.3390/agriculture15151612 - 25 Jul 2025
Viewed by 173
Abstract
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 [...] Read more.
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
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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21 pages, 1420 KiB  
Article
Functional Characterization of a Synthetic Bacterial Community (SynCom) and Its Impact on Gene Expression and Growth Promotion in Tomato
by Mónica Montoya, David Durán-Wendt, Daniel Garrido-Sanz, Laura Carrera-Ruiz, David Vázquez-Arias, Miguel Redondo-Nieto, Marta Martín and Rafael Rivilla
Agronomy 2025, 15(8), 1794; https://doi.org/10.3390/agronomy15081794 - 25 Jul 2025
Viewed by 300
Abstract
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 [...] Read more.
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
(This article belongs to the Section Farming Sustainability)
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21 pages, 727 KiB  
Article
Seasonal and Cultivar-Dependent Phenolic Dynamics in Tuscan Olive Leaves: A Two-Year Study by HPLC-DAD-MS for Food By-Product Valorization
by Tommaso Ugolini, Lorenzo Cecchi, Graziano Sani, Irene Digiglio, Barbara Adinolfi, Leonardo Ciaccheri, Bruno Zanoni, Fabrizio Melani and Nadia Mulinacci
Separations 2025, 12(8), 192; https://doi.org/10.3390/separations12080192 - 24 Jul 2025
Viewed by 144
Abstract
Olive tree leaf is a phenol-rich, high-potential-value biomass that can be used to formulate food additives and supplements. Leaf phenolic content varies depending on numerous factors, like cultivar, geographical origin, year, and season of harvest. The aim of this research was to evaluate [...] Read more.
Olive tree leaf is a phenol-rich, high-potential-value biomass that can be used to formulate food additives and supplements. Leaf phenolic content varies depending on numerous factors, like cultivar, geographical origin, year, and season of harvest. The aim of this research was to evaluate the variations in phenolic profile of four major Tuscan cultivars (Frantoio, Leccio del Corno, Leccino, and Moraiolo) over four different phenological phases and across two years. All 96 olive leaf samples were harvested from trees grown in the same orchard located in Florence. After drying, their phenolic profile was characterized using HPLC-DAD-MS, and the obtained data were processed by ANOVA, GA-LDA, and RF methods. A total of 25 phenolic derivatives were analyzed, with total contents ranging 16,674.0–50,594.3 mg/kg and oleuropein (4570.0–27,547.7 mg/kg) being the predominant compound regardless of cultivar, year, and season of harvest. Oleuropein and hydroxytyrosol glucoside showed inverse proportionality and similar behavior across years in all cultivars, and therefore were highlighted as main phenolic compounds correlated with the seasonal variability in studied cultivars. Interesting behavior was also pointed out for apigenin rutinoside. Application of GA-LDA and RF methods allowed pointing out the excellent performance of leaf phenols in discriminating samples based on cultivar, harvest year, and harvesting season. Full article
(This article belongs to the Special Issue Extraction and Isolation of Nutraceuticals from Plant Foods)
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15 pages, 4180 KiB  
Article
Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field Disturbances
by Yunfei Wang, Xiang Dong, Weidong Jia, Mingxiong Ou, Shiqun Dai, Zhenlei Zhang and Ruohan Shi
Agriculture 2025, 15(15), 1597; https://doi.org/10.3390/agriculture15151597 - 24 Jul 2025
Viewed by 228
Abstract
In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial [...] Read more.
In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies. Full article
(This article belongs to the Section Agricultural Technology)
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25 pages, 8282 KiB  
Article
Performance Evaluation of Robotic Harvester with Integrated Real-Time Perception and Path Planning for Dwarf Hedge-Planted Apple Orchard
by Tantan Jin, Xiongzhe Han, Pingan Wang, Yang Lyu, Eunha Chang, Haetnim Jeong and Lirong Xiang
Agriculture 2025, 15(15), 1593; https://doi.org/10.3390/agriculture15151593 - 24 Jul 2025
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Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Orchard Management)
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