Search Results (342)

To address the challenges of inefficient Camellia oleifera fruits harvesting in hilly and mountainous regions due to the difficulty of using large machinery, a handheld vibration harvesting device for Camellia oleifera fruits was designed. Based on the vibration-induced detachment process of Camellia oleifera fruits, a single-pendulum dynamic model of the “fruit-branch” system was established and solved to calculate the tangential acceleration required for fruit detachment. The key factors influencing harvesting efficiency were identified as vibration frequency, amplitude, height, and duration. Using ANSYS, modal response and harmonic response analyses were conducted on a 3D model of the Camellia oleifera tree to determine the operational parameters ensuring branch acceleration meets the fruit detachment. Furthermore, a rigid-flexible coupling simulation system integrating the harvesting device and Camellia oleifera tree was developed on the ADAMS. This analysis revealed the variation patterns of branch acceleration with respect to vibration frequency and clamping height, thereby validating the rationality of the dynamic model and the feasibility of the device. Finally, an orthogonal experiment was designed using Design-Expert 13, and multi-objective optimization analysis was performed on the device's working parameters based on the experimental data. The aforementioned research identified the optimal working parameter combination and actual harvesting performance of the handheld vibration harvesting device: when the vibration frequency is 14 Hz, vibration height is 980 mm, and vibration duration is 13 s, the fruit picking rate reaches 95.22%. The harvesting efficiency of this device is significantly higher than manual picking methods, fully meeting the requirements for efficient Camellia oleifera fruit harvesting. Full article

This dataset presents field observations collected in the municipality of Goiatuba, Goiás State, Brazil, a consolidated and representative agricultural frontier of the Brazilian Cerrado biome. The region presents diverse land use dynamics, including annual cropping systems, irrigated fields with up to three harvests per year, and pasturelands. We conducted a field campaign from 3 to 7 November 2025, corresponding to the beginning of the 2025/2026 Brazilian crop season, when crops were at distinct early phenological stages. To ensure representativeness, we delineated 117 reference fields prior to the field campaign, and an additional 463 plots were surveyed during work. Geographic coordinates, crop types, and photographic records were obtained using the GPX Viewer application, a handheld GPS receiver, and the QField 3.7.9 mobile GIS application running on a tablet uploaded with Sentinel-2 true-color imagery and the municipal road network. Plot boundaries were subsequently digitized in QGIS Desktop 3.34.1 software, following a conservative mapping strategy to minimize edge effects and internal heterogeneity associated with trees and water catchment basins. In total, more than 26,000 hectares of agricultural fields were mapped, along with additional land use and land cover polygons representing water bodies, urban areas, and natural vegetation fragments. All reference fields were labeled based on in situ observations and linked to Sentinel-2 mosaics downloaded via the Google Earth Engine platform. This dataset is well-suited for training, testing, and validation of remote sensing classifiers, benchmarking studies, and agricultural mapping initiatives focused on the beginning of the agricultural season in the Brazilian Cerrado. Full article

In recent years, Polish producers have been increasingly interested in organic fruit production. In this cultivation system, it is very important to choose cultivars that are less susceptible to diseases and pests. In research conducted in the years 2009–2019 in central Poland, the suitability of four sour cherry cultivars (‘Kelleris 16’, ‘Oblacinska’, ‘Debreceni Bötermö’, ‘Pandy 103’) for organic cultivation was assessed. The trees grew in two separate experimental quarters: conventional and organic, about 1 km apart. It was proved that organic sour cherry cultivation is possible, but there are many challenges. In the organic cultivation system, trees were more sensitive to low temperatures and grew and yielded less than those grown using conventional methods. The weaker growth and lower yields were mainly due to the ineffective protection against cherry leaf spot and brown rot. The fruit quality was closely dependent on the weather conditions. The fruit harvested in the organic orchard had a lower weight but tended to be firmer than that harvested in the conventional one. The smallest, but most abundant in soluble solids, were the fruits of the ‘Oblacinska’ cultivar. Unfortunately, they were infested by larvae of cherry fruit flies. Occasionally, the pest larvae lived in organic sour cherries of the ‘Pandy 103’ and ‘Debreceni Bötermö’ cultivars. In years with high rainfall, 20 to 35% of the fruit in the organic quarter was affected by fungal diseases. Full article

The global low-carbon transition is driving the use of renewable energy for ecological monitoring. Traditional power supply for forest monitoring sensor equipment is constrained by high wired costs, frequent battery replacement, and the limitations of low light levels and special spectra under forest canopies on photovoltaic (PV) compatibility. Existing research lacks exploration of the correlation between under-forest spectra and PV performance. This study measured the summer understory light spectra of five tree species in Beijing, evaluated the performance of three types of PV cells—monocrystalline silicon, polycrystalline silicon, and amorphous silicon—and designed a low-light energy harvesting circuit. Results indicate that spectral differences under tree canopies are concentrated from 380–680 nm, exhibiting a distinctive forest-specific spectral feature of “high-band enrichment” above 680 nm. Under low-light conditions, polycrystalline silicon photovoltaics demonstrates optimal performance when adapted to this high-band spectrum. The designed circuit can activate at 5 W/m² irradiance and stably output 4.16 V voltage. This study fills a spectral gap in northern summer tree canopies, providing a comprehensive solution of “material adaptation + circuit customization” for the practical deployment of shaded forest PV systems. Full article

The accurate prediction of plant height is crucial for optimizing soybean cultivar selection and improving yield estimations. In this study, we investigate the potential of machine learning (ML) algorithms to predict soybean plant height (PH) based on a diverse set of agronomic parameters analyzed from forty soybean cultivars evaluated across sequential harvests. Using a comprehensive dataset, the models Elastic Net (EN), Extra Trees (ET), Gaussian Process Regressor (GPR), K-Nearest Neighbors, and XGBoost (XGB) were compared in terms of predictive accuracy, uncertainty, and robustness. Our results demonstrate that ET outperformed other models with an average correlation coefficient of 0.674, $R^2$ of 0.426 and the lowest RMSE of 6.859 cm and MAE of 5.361 cm, while also showing the lowest uncertainty (5.07%). The proposed ML framework includes an extensive model evaluation pipeline that incorporates the Performance Index (PI), ANOVA, and feature importance analysis, providing a multidimensional perspective on model behavior. The most influential features for PH prediction were the number of stems (NS) and insertion of the first pod (IFP). This research highlights the viability of integrating explainable ML techniques into agricultural decision support systems, enabling data-driven strategies for cultivar evaluation and phenotypic trait forecasting. Full article

Mechanical pruning has emerged as a viable alternative to traditional hand pruning in apple orchards in labor-constrained and aging population workforces. While mechanical pruning reduces labor demand and enhances operational efficiency, their effects on tree physiology and fruit development remain poorly understood. In this study, we examined the physiological and transcriptional responses of apple trees to mechanical pruning (MP) and hand pruning (HP), with a focus on hormone metabolism, photosynthetic activity, and stress adaptation. Pruning treatments were applied in an orchard using a tractor-mounted mechanical pruner and manual shears, and distinct metabolic responses after pruning were assessed over multiple time points using transcriptomic analysis. At 168 h after MP, trees exhibited downregulation of MdLhcb genes, indicating a reduction in light harvesting capacity. In addition, MdDFR, a key gene in flavonoid biosynthesis, was also downregulated, suggesting a suppression of secondary metabolism and a distinct physiological response to MP. In addition, stress-responsive genes such as MdNHL3 were rather upregulated, indicating the activation of adaptive signaling networks. Conversely, HP trees showed relatively moderate responses in the same pathways, suggesting pruning method-specific regulatory mechanisms. These findings highlight how pruning methods distinctly influence tree recovery and gene expression dynamics, offering insights into optimizing pruning systems for sustainable and high-quality apple production under labor-constrained conditions. Full article

Careya arborea, commonly known as wild guava, is a deciduous tree native to Asia, including Sri Lanka. Traditionally used to treat various ailments such as skin diseases, tumors, gastrointestinal disorders, and inflammation, it is valued for its notable astringent properties. Rich in phytochemicals, including phenolics, terpenes, sterols, tannins, and saponins, Careya arborea exhibits potent antioxidant, anti-inflammatory, and anticancer activities. Its anticancer effects are primarily attributed to the induction of apoptosis and the inhibition of cancer cell proliferation, with several extracts such as chloroform, ethyl acetate, and methanol demonstrating selective cytotoxicity against cancer cell lines. The high phenolic content of Careya arborea underpins its antioxidant potential, which plays a crucial role in mitigating oxidative stress and associated inflammatory conditions. Despite its medicinal potential, Careya arborea remains an underutilized plant in Sri Lanka. Greater attention should be given to promoting its use in both traditional and modern healthcare systems to harness its therapeutic benefits. Given its therapeutic potential, sustainable harvesting and conservation efforts are essential to protect this plant from overexploitation and habitat loss. Taking all these factors into account, this review emphasizes Careya arborea’s potential as a source of natural therapeutic agent, highlighting the importance of further research and conservation to unlock its full medicinal value for clinical applications. Full article

(1) Background: Austria’s use of fully mechanized harvesting systems has been continuously increasing. Technical developments, such as traction aid winches, have made it possible to drive on increasingly steep terrain. However, this has led to challenges and potential hazards for the operators, resulting in higher stand damage rates and risks of workplace accidents. Since these systems and working environments involve a highly complex interplay of various parameters, the purpose of this protocol is to propose a new set of methodologies that can be used to obtain a holistic interpretation of the psychophysiological interrelationship between the working conditions and stress of harvester and forwarder drivers. (2) Methods: We developed a research protocol to analyse the (a) environmental and (b) machine-related parameters; (c) psychological and psychophysiological responses of the operators; and (d) technical outcome parameters. Within this longitudinal exploratory field study, experienced drivers were monitored for over an hour at the beginning and the end of their workday while operating in varying steep terrains with and without a traction aid winch. The analysis is based on macroscopic (collected using cameras), microscopic (eye-tracking glasses and AI-driven emotion recognition), quantitative (standardized questionnaires), and qualitative (interviews) data. This multimodal research protocol aims to improve the health and safety of forest workers, increase their productivity, and reduce damage to remaining trees. Full article

Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R² = 0.98, error = 11.48%) and canopy area (R² = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R² of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with <10% error. These findings demonstrate that UAV imagery integrated with a user-friendly AI platform can deliver accurate, scalable solutions for biometric monitoring in both perennial and annual specialty crops, supporting applications in harvest planning, orchard management, and market supply forecasting. Full article

The growing implementation of close-to-nature forestry practices in the management of northern forests, characterized by dispersed harvesting operations, has heightened the importance of minimizing damage to residual stands as a key aspect of sustainable forest management. The objective of this study is to examine and compare the resistance of various tree species and diameter classes to wounds incurred during logging operations of differing sizes, intensities, and locations. In addition, the research aims to assess temporal changes in wound characteristics, including healing and closure processes, across species. This long-term, 18-year investigation was conducted in the Kheyrud Forest, located within the Hyrcanian broadleaf forest region of northern Iran, to evaluate the dynamics of wound healing in residual trees following ground-based skidding operations. Through a comprehensive assessment of 272 wounded trees across six species, we demonstrate that species significantly influences healing ratio (Kruskal–Wallis, p < 0.01), with Oriental beech (Fagus orientalis Lipsky) (50.6%) showing superior recovery compared to the Chestnut-leaved oak (Quercus castaneifolia) (37.5%). Healing ratio decreased with larger diameter at breast height (DBH) (R² = 0.114, p < 0.01), while absolute healed area increased. Larger areas (>1000 cm²) reduced healing by 42.3% versus small wounds (<500 cm²) (R² = 0.417, p < 0.01). Severe wounds (deep gouges) showed 19% less healing than superficial injuries (p = 0.003). Circular wounds healed significantly better than rectangular forms (χ² = 24.92, p < 0.001). Healing ratio accelerated after the first decade, reaching 69% by year 17 (R² = 0.469, p < 0.01). Wound height (p = 0.117) and traffic intensity (p = 0.65) showed no statistical impact. Contrary to expectations, stem position had no significant effect on wound recovery, whereas wound geometry proved to be a critical determinant. The findings highlight that appropriate species selection, minimizing wound size (to less than 500 cm²), and adopting extended cutting cycles (exceeding 15 years) are essential for enhancing residual stand recovery in close-to-nature forestry systems. Full article

This study assessed the combined application of poultry manure (Pm), biochar (B), and coenzyme A (CoA) into soils to enhance Moringa oleifera Lam. growth, biomass yield, and nutritional and phytochemical composition. This approach allowed us to cover the gap of knowledge on sustainable, low-cost agronomic management alternatives suitable for smallholder systems. To achieve this objective a field experiment was conducted using three treatments (control (no amendment), Pm + B, and Pm + B + CoA) and four consecutive harvests were monitored. Morphological traits (height, stem diameter, number of branches, and leaf yield) were recorded, and phytochemical analyses of glucosinolates and (poly)phenols were performed via HPLC-DAD-ESI/MSn. Mineral and trace elements were quantified by ICP-OES. The main results retrieved allowed describing the capacity of the combined use of Pm + B + CoA to enhance plant growth and productivity, thus increasing the moringa trees’ height of 226.3 by 39.5%, on average, relative to control plants. ILeaf yield and branch number augmented up to 7.0-fold and 2.5-fold, respectively, under amendment treatments. Petiole girth also increased significantly by >50% (p < 0.01). Phytochemically, Pm + B + CoA significantly elevated total phenolics, vicenin-2, and quercetin acetyl-hexoside in leaves by 2.8-fold, on average, relative to control. The glucosinolate content also augmented as a result of the soil amendments assayed by 51.0%, on average, in stems and petioles, under Pm + B + CoA, compared to control samples. From these results, it can be concluded that the combined use of poultry manure, biochar, and CoA significantly improved M. oleifera growth, biomass yield, and nutritional quality, with a particular efficiency concerning (poly)phenolic accumulation. This low-cost, sustainable amendment strategy provides a viable agronomic solution in regions suffering socioeconomic constraints that hinder access to high-cost agronomic management options. Therefore, this approach effectively links ecological soil management with improved productivity, nutritional value, and potential for food industries. Full article

Early wildfire detection in remote areas remains a critical challenge due to limited connectivity, intermittent solar energy, and the need for autonomous, long-term operation. Existing systems often rely on fixed sensing schedules or cloud connectivity, making them impractical for energy-constrained deployments. We introduce EcoWild, a reinforcement learning-driven cyber-physical system for energy-adaptive wildfire detection on solar-powered edge devices. EcoWild combines a decision tree-based fire risk estimator, lightweight on-device smoke detection, and a reinforcement learning agent that dynamically adjusts sensing and communication strategies based on battery levels, solar input, and estimated fire risk. The system models realistic solar harvesting, battery dynamics, and communication costs to ensure sustainable operation on embedded platforms. We evaluate EcoWild using real-world solar, weather, and fire image datasets in a high-fidelity simulation environment. Results show that EcoWild consistently maintains responsiveness while avoiding battery depletion under diverse conditions. Compared to static baselines, it achieves 2.4× to 7.7× faster detection, maintains moderate energy consumption, and avoids system failure due to battery depletion across 125 deployment scenarios. Full article

The adaptability of several Italian olive cultivars to high-density cultivation was evaluated from 2020 to 2024 in central Italy by assessing their agronomic behavior, with the aim of identifying which Italian olive cultivars can combine high productivity and suitability for intensive mechanization—through high- and very high-density planting systems—allowing biodiversity valorization. The cultivars were Borgiona, Don Carlo, FS17, Gentile di Anghiari, Gentile di Montone, Giulia, Leccio del Corno, Maurino, Moraiolo, Pendolino, Piantone di Falerone, and Piantone di Mogliano. The international cultivar Arbequina was used as a reference. The olive orchard was planted in 2015, at a tree spacing of 5 m × 2 m (1000 trees/ha). Arbequina was found to have limited vigor and high production efficiency, as reported in other works, therefore confirming its suitability for high-density and super-high-density cultivation. Some cultivars, such as Leccio del Corno, Maurino, FS17, Piantone di Mogliano, and Piantone di Falerone, had a production and yield efficiency that was not different from or even higher than Arbequina. Other cultivars found to be promising were Don Carlo and Gentile di Anghiari, which had a slightly lower productive performance than Arbequina. Overall, the results are encouraging and suggest that some of these cultivars may be suitable candidates for high- and super-high-density olive orchards. This suitability is further supported by their favorable fruit characteristics, which appear to facilitate efficient mechanical harvesting. However, additional data is necessary to enable a more comprehensive assessment of these cultivars, particularly their capacity to maintain canopy dimensions compatible with straddle harvester operation, while maintaining a stable vegetative–reproductive balance over time. Full article

Food production systems in Ecuador’s high Andean region are pivotal for food security, rural livelihoods, and agrobiodiversity, yet they are increasingly exposed to climate stress. We assessed four representative crops (tree tomato, quinoa, potato, and maize) across three Andean zones (North, Center, South) in 2015–2022 using monthly NASA POWER (MERRA-2) climate fields. After confirming non-normality, Spearman correlations and multiple linear regressions with leave-one-year-out validation were applied to quantify the influence of maximum/minimum temperature and precipitation on cultivated and harvested area, production, sales, and loss categories. To place monthly signals in a process context, daily extreme-event diagnostics (ETCCDI-style) were also computed: heat days ($\mathrm{TX}90$), ≥5-day dry spells, and the annual maximum consecutive dry days (${\mathrm{CDD}}_{max}$). Models explained a wide range of variability across crops and zones (approx. $R^2\sim 0.55$–0.99), with quinoa showing the most consistent fits (several outcomes $R^2>0.90$). Extremes provide an eye-catching, actionable picture: the Southern zone concentrated dryness hazards, with 1–5 dry spells $\ge 5$ days per year and ${\mathrm{CDD}}_{max}$ up to ∼8 days, while heat-day frequency showed non-significant declines across zones in 2015–2022. Reanalysis frost days were virtually zero—consistent with under-detection of local valley frosts at coarse resolution—so frost risk was interpreted via monthly signals and reported losses. Overall, the results show precipitation-driven vulnerabilities in the South and support quinoa’s role as a resilient option under increasing climate stress, offering concrete guidance for water management and climate-smart planning in mountain agroecosystems. Full article

The rapid development of artificial intelligence technologies has promoted the emergence of Agriculture 4.0, where the machines participating in agricultural activities are made smart with the capacities of self-sensing, self-decision-making, and self-execution. As representative implementations of Agriculture 4.0, intelligent selective fruit harvesting robots demonstrate significant potential to alleviate labor-intensive demands in modern agriculture, where visual detection serves as the foundational component. However, the accurate detection of fruits remains a challenging issue due to the complex and unstructured nature of fruit orchards. This paper comprehensively reviews the recent progress in visual detection methods for selective fruit harvesting robots, covering cameras, traditional detection based on handcrafted feature methods, detection based on deep learning methods, and tree branch detection methods. Furthermore, the potential challenges and future trends of the visual detection system of selective fruit harvesting robots are critically discussed, facilitating a thorough comprehension of contemporary progress in this research area. The primary objective of this work is to highlight the pivotal role of visual perception in intelligent fruit harvesting robots. Full article