Search Results (1,136)

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

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

In this study, the association between Neosilba batesi (Diptera: Lonchaeidae) and avocado fruits (Persea americana L.) was investigated. Fruits showing signs of rot and infested with Diptera larvae were collected from commercial orchards in the states of Michoacán and Jalisco, Mexico. N. batesi was identified in association with fruits from both trees and the ground at all sampling sites. Furthermore, a phylogenetic analysis based on the mitochondrial cytochrome c oxidase subunit I (COI) gene supported the morphological identification, showing >99% identity with records from Veracruz, and revealed distinct genetic lineages within the Neosilba genus. In a study within one Michoacán orchard, infested tree-borne fruits averaged 5.40 cm in length and 3.90 cm in width, with a mean of 9.61 larvae emerging per fruit. Females were observed to lay eggs in openings between the pedicel and the fruit, never piercing the exocarp. In contrast, on fallen fruit, they utilized existing wounds with exposed pulp. Infested avocados exhibit characteristic spots indicating the presence of internal larvae and generally detach from the tree. Larvae can feed on avocados in various stages of decomposition and may either emerge through wounds or pupate within the fruit. These findings support the opportunistic and saprophagous behavior associated with this fly species. Full article

Potassium fertilizer management is critical for achieving high yields of Korla fragrant pear, yet current practices often overlook or misuse potassium inputs. In this study, a two-year field experiment (2023–2024) was conducted with 7- to 8-year-old pear trees using four potassium levels (0, 75, 150, and 225 kg/hm²). Metagenomic sequencing was employed to assess the effects on soil microbial communities, sulfur cycle functional genes, and fruit yield. Potassium treatments significantly altered soil physicochemical properties, the abundance of sulfur cycle functional genes, and fruit yield (p < 0.05). Increasing application rates significantly elevated soil-available potassium and organic matter while reducing pH (p < 0.05). Although alpha diversity was unaffected, NMDS analysis revealed differences in microbial community composition under different treatments. Functional gene analysis showed a significant decreasing trend in betB abundance, a peak in hpsO under K150, and variable patterns for soxX and metX across treatments (p < 0.05). All potassium applications significantly increased yield relative to CK, with K150 achieving the highest yield (p < 0.05). PLS-PM analysis indicated significant positive associations between potassium rate, nutrient availability, microbial abundance, sulfur cycling, and yield, and a significant negative association with pH (p < 0.05). These results provide a foundation for optimizing potassium fertilizer strategies in Korla fragrant pear orchards. It is recommended that future studies combine metagenomic and metatranscriptomic approaches to further elucidate the mechanisms linking potassium-driven microbial functional changes to improvements in fruit quality. Full article

Soil compaction is a critical challenge in perennial fruit production, limiting root growth, water infiltration, and nutrient uptake—factors essential for climate-resilient and sustainable orchard systems. In subtropical Asian pear (Pyrus pyrifolia Nakai) orchards under the annual top-working system, intensive machinery traffic exacerbates subsurface hardpan formation and tree performance. This study evaluated the effectiveness of pneumatic subsoiling, a minimally invasive method using high-pressure air injection, in alleviating soil compaction without disturbing orchard surface integrity. Four treatments varying in radial distance from the trunk and pneumatic application were tested in a mature orchard in central Taiwan. Pneumatic subsoiling 120 cm away from the trunk significantly reduced soil penetration resistance by 15.4% at 34 days after treatment (2,302,888 Pa) compared to the control (2,724,423 Pa). However, this reduction was not sustained at later assessment dates, and no significant improvements in vegetative growth, fruit yield, and fruit quality were observed within the first season post-treatment. These results suggest that while pneumatic subsoiling can modify subsurface soil physical conditions with minimal surface disturbance, its agronomic benefits may require longer-term evaluation under varying moisture and management regimes. Overall, this study highlights pneumatic subsoiling may be a potential low-disturbance strategy to contribute to longer-term soil physical resilience. Full article

This paper assesses the complexity and diversity of vegetation in grassy strips with spontaneous plants between tree rows in three fruit orchards (plum, cherry, apple) in Dolj County, Romania, using structural and biodiversity indices. It addresses the lack of data on spontaneous vegetation in Romanian orchards, supporting improved plantation management and native biodiversity conservation. The study found that grassy strips supported high wild herbaceous diversity and a complex, heterogeneous ecological structure, with the apple orchard showing the highest biodiversity. Species diversity, evaluated through species richness, evenness, and diversity indices (Shannon, Simpson, Menhinick, Gleason, etc.), showed species richness ranging from 30 species in the cherry orchard to 40 in the apple orchard. Several species, including Capsella bursa-pastoris, Geranium pusillum, Poa pratensis, Veronica hederifolia, Lolium perenne, and Convolvulus arvensis, were present in 100% of samples, making them constant species from a phytosociological perspective. Their presence indicates relatively stable plant communities in each orchard. From a phytocoenological view, an ecological plant community is defined not only by species composition but also by constancy and co-occurrence in sampling units. Dominance remained low in all orchards, indicating no single plant dominated, while evenness showed a uniform distribution of species. Full article

With the continuous expansion of the orchard planting area, there is an urgent need for autonomous orchard vehicles that can reduce the labor intensity of fruit farmers and improve the efficiency of operations to assist operators in the process of orchard operations. An object detection system that can accurately identify potholes, trees, and other orchard objects is essential to achieve unmanned operation of the orchard vehicle. Aiming to improve upon existing object detection algorithms, which have the problem of low object recognition accuracy in orchard operation scenes, we propose an orchard vehicle object detection algorithm based on Attention-Guided Orchard PointPillars (AGO-PointPillars). Firstly, we use an RGB-D camera as the sensing hardware to collect the orchard road information and convert the depth image data obtained by the RGB-D camera into 3D point cloud data. Then, Efficient Channel Attention (ECA) and Efficient Up-Convolution Block (EUCB) are introduced based on the PointPillars, which can enhance the ability of feature extraction for orchard objects. Finally, we establish an orchard object detection dataset and validate the proposed algorithm. The results show that, compared to the PointPillars, the AGO-PointPillars proposed in this study has an average detection accuracy improvement of 4.64% for typical orchard objects such as potholes and trees, which can prove the reliability of our algorithm. Full article

In recent years, the traditional orchard sprayer has had problems, such as waste of liquid agrochemicals, low target coverage, high manual dependence, and environmental pollution. In this study, an automatic swing-arm sprayer for orchards was developed based on the standardized pear orchard in Pinggu, Beijing. Firstly, the structural principles of a crawler-type traveling system and swing-arm sprayer were simulated using finite element software design. The combination of a diffuse reflection photoelectric sensor and Arduino single-chip microcomputer was used to realize real-time detection and dynamic spray control in the pear canopy, and the sensor delay compensation algorithm was used to optimize target recognition accuracy and improve the utilization rate of liquid agrochemicals. Through the integration of innovative structural design and intelligent control technology, a vertical droplet distribution test was carried out, and the optimal working distance of the spray was determined to be 1 m; the nozzle angle for the upper layer was 45°, that for the lower layer was 15°, and the optimal speed of the swing-arm motor was 75 r/min. Finally, a particle size test and field test of the orchard sprayer were completed, and it was concluded that the swing-arm mode increased the pear tree canopy droplet coverage by 74%, the overall droplet density by 21.4%, and the deposition amount by 23% compared with the non-swing-arm mode, which verified the practicability and reliability of the swing-arm spray and achieved the goal of on-demand pesticide application in pear orchards. Full article

The accurate estimation of evapotranspiration ($ET$) and its components are vital for water resource management and irrigation planning. This study models tree transpiration ($T$) and $ET$ for grapefruit, litchi, and mango orchards using light gradient-boosting machine (LightGBM) optimized using the Bayesian hyperparameter optimization. Grounds $T$ and $ET$ for these crops were measured using the heat ratio method of monitoring sap flow and the eddy covariance technique for quantifying $ET$. The Sentinel 2 satellite was used to compute field leaf area index ($LAI$). The modelled data were used to partition the orchard $ET$ into beneficial ($T$) and non-beneficial water uses (orchard floor evaporation—$E_s$). We adopted the 10-fold cross-validation to test the model robustness and an independent validation to test performance on unseen data. The 10-fold cross-validation and independent validation on $ET$ and $T$ models produced high accuracy with coefficient of determination ($R^2$) $\ge$ 0.88, Kling–Gupta efficiency ($KGE$) $\ge$ 0.91, root mean square error ($RMSE$) $\le$ 0.04 mm/h, and mean absolute error ($MAE$) $\le$ 0.03 mm/h for all the crops. The study demonstrates that LightGBM can accurately model the transpiration and evapotranspiration for subtropical tree crops using Sentinel 2 data. The study found that $E_s$ which combined soil evaporation and understorey vegetation transpiration contributed 35, 32, and 31% to the grapefruit, litchi and mango orchard evapotranspiration, respectively. We conclude that improvements on orchard floor management practices can be utilized to minimize non-beneficial water losses while promoting the productive water use ($T$). Full article

Accurate acquisition of the phenotypic information of trunk-shaped fruit trees plays a crucial role in intelligent orchard management, pruning during dormancy, and improving fruit yield and quality. However, the precise segmentation of trunks and branches remains a significant challenge, limiting the accurate measurement of phenotypic parameters and high-precision pruning of branches. To address this issue, a novel adaptive cuboid regional growth segmentation algorithm is proposed in this study. This method integrates a growth vector that is adaptively adjusted based on the growth trend of branches and a growth cuboid that is dynamically regulated according to branch diameters. Additionally, an innovative reverse growth strategy is introduced to enhance the efficiency of the growth process. Furthermore, the algorithm can automatically and effectively identify the starting and ending points of growth based on the structural characteristics of fruit tree branches, solving the problem of where to start and when to stop. Compared with PointNet++, PointNeXt, and Point Transformer, ACRGS achieved superior performance, with F₁-scores of 95.75% and 96.21% and mIoU values of 0.927 and 0.933 for apple and cherry trees. The results show that the method enables high-precision and efficiency trunk–branch segmentation, providing data support for fruit tree phenotypic parameter extraction and pruning. Full article

Kazakhstan is recognized as one of the primary centers of origin of the wild apple Malus sieversii, concentrated mainly in the mountains like Trans-Ile and Zhongar Alatau, as well as parts of the Tarbagatay, Talas Alatau, and Karatau ranges. As the wild progenitor of Malus domestica, M. sieversii harbors a critical genetic diversity essential for apple breeding and conservation efforts. However, its natural populations are increasingly threatened by latent viral infection, which weakens trees, reduces reproduction, and hinders regeneration. In this study, the spread of apple chlorotic leaf spot virus (ACLSV) and apple stem pitting virus (ASPV) was documented in four wild apple populations, with detection rates of 50.2% and 42.2%, respectively. Mixed infections were observed in 28.8% of sampled trees. Apple stem grooving virus (ASGV) was detected exclusively in cultivated orchards, whereas apple mosaic virus (ApMV) and apple necrotic mosaic virus (ApNMV) were not found in either wild forests or cultivated orchards. Using Geographic Information System (GIS) technology, we developed the first spatial distribution maps of these viruses in wild apple forests in the Tian Shan region, revealing site-specific variation and infection rates. These results underscore the importance of monitoring viral infections in wild M. sieversii populations to preserve genetically valuable, virus-free germplasm critical for apple breeding, crop improvement, and sustainable orchard management. Full article

A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. Full article

Traditional rainfed fig orchards intended for fresh consumption tend to have low yields and cultural practices difficulties due to wide plant spacing and large canopies. This study investigates whether the espalier training system, commonly employed in other fruit species, can be applied to fig cultivation to improve productivity and fruit quality under high-density irrigated plantations. For the first time, four fig varieties (‘San Antonio’, ‘Dalmatie’, ‘Albacor’, and ‘De Rey’) were evaluated in a high-density system (625 trees/ha) using espalier training over four consecutive years (2018–2021) in southwestern Spain. Among the varieties, ‘Dalmatie’ demonstrated the highest suitability to the system, combining low vegetative vigour with superior yield performance, reaching a cumulative yield of 103.15 kg/tree and yield efficiency of 1.94 kg/cm². ‘San Antonio’ was the earliest to ripen and exhibited the longest harvest duration (81 days), enabling early and extended market availability. In terms of fruit quality, ‘Albacor’ stood out for its high total soluble solids content (24.97 °Brix), while ‘De Rey’ exhibited the best sugar–acid balance, with a maturity index of 384.58. The present work demonstrates that intensive fig cultivation on espalier structures offers an innovative alternative to traditional systems, thereby enhancing orchard efficiency, management, and fruit quality. Full article

Air-assisted spraying is the primary method of plant protection in orchards, and precision spraying according to the canopy characteristics of fruit trees can reduce waste and pollution due to pesticide drift. To facilitate targeted pesticide application in the canopy of fruit trees, this study employed a newly developed wind-speed-adjustable orchard sprayer and established a prediction model for deposition based on data from orthogonal trials using a central composite design accounting for the coupling effect of three-dimensional spatial parameters. The experimental design systematically quantified the interaction effects of spray distance (1.5–2.5 m), fan wind speed (10–20 m/s), and deposition height (0.5–3 m) on the spatial distribution of droplets. Model significance was p < 0.0001 and the misfit term was significant (p = 0.2193), supporting its validity. The research found that wind speed and distance significantly interact in influencing deposition. By adjusting fan speed and spray distance, variable applications can be achieved in different canopy zones during plant protection operations. The response surface model developed in this study can be applied to variable-rate spraying control systems, thus providing a quantitative basis for dynamic droplet control guided by canopy characteristics. Validation tests revealed that the model’s accuracy was lower in high canopy regions and upwind spraying scenarios, indicating areas for further research. Full article

Olive trees are generally considered a species well-adapted to drought, but the impact of water shortage is of critical importance on olive production. For this reason, developing tolerant cultivars could be an effective strategy to mitigate the impact of drought in the future. Characterizing drought stress tolerance in olive is a complex task due to the numerous traits involved in this response. In this study, plant growth, pressure–volume curves, gas-exchange and chlorophyll fluorescence traits, and stomata characteristics were monitored in nine cultivars to assess the effects of mild and severe drought stress conditions induced by withholding water for 7 and 21 days, respectively, and were compared to a well-watered control treatment. The plant materials evaluated included traditional cultivars, as well as new developed cultivars suited for high-density hedgerow olive orchards or resistant to verticillium wilt. Significant differences between cultivars were observed for most evaluated traits, with more pronounced differences under severe drought conditions. A multivariate analysis of the complete dataset recorded throughout the evaluation period allowed for the identification of promising cultivars under stress conditions (‘Sikitita’, ‘Sikitita-2’, and ‘Martina’) as well as highly discriminative traits that could serve as key selection parameters in future breeding programs. Full article