Search Results (2,778)

As vehicle intelligence and automotive electrification advance, the extensive deployment of distributed sensing nodes for comprehensive monitoring has grown rapidly. This poses severe challenges, such as rising onboard power consumption and the inability of conventional centralized power supply systems to sustain these sensors. Triboelectric nanogenerators (TENGs), an emerging technology for energy harvesting and self-powered sensing, exhibit great potential to address the above challenges. This review systematically summarizes research on TENGs for vehicle energy harvesting and intelligent sensing, covering their fundamental working principles and applications in diverse vehicle scenarios. First, the basic principle and working modes of TENGs are described, and their suitability for complex and variable vehicle environments is evaluated. Subsequently, existing applications are categorized into three domains: vehicle vibration systems, wheel–road systems, and intelligent vehicle systems. Studies on various topics are reviewed, including vibration energy harvesting and sensing, vehicle collision monitoring, tire energy harvesting, road sensing, smart cockpits, human–machine interaction, and vehicle fluid monitoring. Emphasis is placed on their technical approaches and application prospects. Finally, the state-of-the-art research and prevailing technical bottlenecks are summarized, and potential solutions and future research perspectives are discussed. This review aims to support the reliable practical deployment of TENG technology in vehicle engineering and to provide a technical basis for energy-saving strategies and in situ sensing technologies for future intelligent vehicles. Full article

Organic fertilization is a key strategy for improving soil structure and fertility in China’s Vertisols, yet the trade-offs among soil quality enhancement, grain yield performance, and nutrient use efficiency under different organic amendment regimes remain insufficiently elucidated. Based on a unique 43-year field fertilization experiment, this study systematically evaluated the effects of long-term chemical fertilization (NPK) alone, low-dose (NPKLS) and high-dose straw incorporation (NPKHS), combined chemical fertilizer with cattle manure (NPKCM), and pig manure (NPKPM) fertilization on soil physical, chemical properties, crop yields and plant nutrient utilization efficiency. The results showed that NPKCM and NPKPM significantly improved soil physical properties by reducing soil bulk density, improving soil pore structure, and enhancing soil water retention capacity and saturated hydraulic conductivity. Although long-term manure application led to slight soil salt accumulation, the rate of accumulation remained substantially lower than that associated with commercial organic fertilizers and did not approach the crop salinity damage threshold, suggesting low ecological risk. Compared with NPK treatment, manure amendment effectively counteracted soil acidification induced by prolonged chemical fertilization, while also significantly increasing soil total phosphorus and available phosphorus content, and elevated the proportion of active phosphorus (PAC). The improved soil phosphorus activation capacity and comprehensive soil quality further contributed to substantial increases in wheat grain yield under NPKCM and NPKPM treatments. Despite these agronomic benefits, the additional nitrogen and phosphorus inputs from manure resulted in soil nutrient surpluses, which considerably reduced nitrogen and phosphorus partial factor productivity as well as agronomic efficiency. In contrast, straw incorporation treatments (NPKLS, NPKHS) sustained stable crop yield without notable declines in nutrient efficiency, positioning them as a greener and more sustainable approach to balancing grain production with resource use efficiency. These findings highlight the need to integrate nutrient credits from manure into fertilization program. Given the 43-year evidence, fertilization strategy should consider not only the nutrients supplied by manure but also the quantities exported through harvested products, with adjustments based on annual soil fertility analyses. Such nutrient budgeting is essential to maximize fertilizer use efficiency, prevent excessive phosphorus accumulation, and maintain balanced soil fertility over time. Full article

During forest exploitation, large amounts of wood biomass are left as residues after harvesting, even though this material could be valuable in reducing greenhouse gas emissions by replacing fossil fuels and serving as a source of bioactive compounds. Within the context of the European Union’s circular bioeconomy strategy, enhancing the efficacy and rational application of forest logging residues constitutes a critical future objective. To evaluate the possible use of the stumps as forest residue-derived woody biomass for the first time, the chemical composition and presence of bioactive compounds in the xylem and bark of F. angustifolia Vahl. ssp. pannonica Soó & Simon stumps were analyzed, including the content of cellulose, acid-insoluble lignin, acid-soluble lignin, ash, and extractives soluble in toluene–ethanol and hot water. In hot water extracts, total phenolic content, the antioxidant activity determined by scavenging capacity toward DPPH and HPTLC, the content of elements by ICP-OES, and the identification of bioactive compounds by UHPLC-DAD-ESI MS/MS were assessed. Considering the chemical composition, the quality of the xylem of the F. angustifolia (cellulose: 42.46 ± 0.45%; lignin: 25.05 ± 0.02%; hemicelluloses: 19.74 ± 0.46%) was similar to that of the stem. Hot water extracts of the bark of F. angustifolia stumps showed high total phenolic content (33.94 ± 1.43 mg GAE/g DW) and strong antioxidant potential (198.48 ± 3.33 μmol TE/g DW), while toxic metals were not detected. UHPLC-DAD-ESI-MS/MS analysis indicated phenylethanoid glycosides as the dominant compounds in the extracts. The analyzed stumps of Fraxinus angustifolia Vahl. in the “Morović” Forest Administration represent a valuable biomass with potential for further valorization. Full article

Phytochemicals with antifungal activity against Fusarium graminearum (F. graminearum) are considered potential in-feed anti-mycotoxigenic agents that may complement conventional post-harvest control strategies. Therefore, we screened fourteen phytochemicals, including seven quinolizidine alkaloids (QAs), for anti-F. graminearum activity, evaluated their individual and combined efficacy in inhibiting mycelial growth, spore germination, and deoxynivalenol (DON) production, and explored the underlying mechanisms via transcriptomic analysis. Matrine (MT), sophoridine (SR), and sophocarpine (SC) exhibited antifungal activity against F. graminearum, with minimum inhibitory concentrations (MICs) of 5, 4, and 4 mg/mL, respectively. The three QAs at their MICs completely inhibited mycelial growth and DON production in liquid cultures, but were less effective on potato dextrose agar plates and in pig feed. Particularly pronounced antifungal activity and inhibitory effects on DON production were observed when the three QAs were applied in combination with berberine hydrochloride (BBR). The observed inhibition appears to be attributed to dysregulated expression of the TRI cluster genes and ERG biosynthesis genes, coupled with significant modulation of steroid biosynthesis (ko00100) and central carbon/lipid metabolism pathways. These findings provide preliminary experimental evidence supporting the further exploration of QA–BBR combinations to control fungal proliferation and DON accumulation in pig feed. Full article

This study investigates active geometric modulation in a variable-length pendulum energy harvester combining radial electromagnetic, rotational electromagnetic, and piezoelectric transduction. A reciprocal seven-state electromechanical model is formulated and analysed using phase-aligned continuation, transverse Floquet stability, Lyapunov diagnostics, physical load variation, and paired control-on/control-off energy accounting. A minimal threshold-based shift of the radial spring equilibrium serves to reveal the branch-support mechanism rather than to provide a final control strategy. The results show that an established finite-amplitude branch may persist below the local instability boundary of the inactive response, while piezoelectric loading can modify this boundary through electromechanical back-action. All three transduction channels contribute to gross electrical output, and physical load matching increases that output. Nevertheless, the continuous modulation remains energetically unfavourable after actuator, power-conditioning, and auxiliary demands are included and does not robustly retain the branch under the tested nonstationary excitations. The results therefore define requirements for phase-aware, adaptive, latching, or regenerative implementations with substantially lower actuation work. Full article

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints. Full article

Forage conservation is a strategic component of Mediterranean livestock systems, where seasonal drought, irregular rainfall, and high summer temperatures limit the availability of fresh forage. In these environments, hay and haylage are widely used as preservation strategies, yet their environmental performance remains insufficiently quantified under semi-arid conditions. It is hypothesized that the higher material and energy inputs required for haylage could be compensated for by the combined effects of biomass preservation efficiency and forage productivity, resulting in comparable or lower impacts per unit of conserved forage under Mediterranean farm conditions. To test this hypothesis, a life cycle assessment was conducted to compare hay and haylage production in a forage farm located in Sicily, Italy, considering the 2024–2025 season. The analysis adopted a cradle-to-farm-gate system boundary, and two complementary impact assessment methods, namely the CML-IA baseline method and the ReCiPe 2016 method, were applied. Environmental burdens were calculated per hectare of cultivated land and per ton of dry matter produced to capture both land-based and product-based performance. The choice of functional unit strongly influenced the interpretation of the environmental impact results. Per hectare, hay and haylage displayed comparable overall burdens, with haylage showing higher impacts in impact categories linked to plastic use and wrapping operations. Per ton of dry matter, however, haylage showed consistently lower impacts, attributed to greater biomass recovery and higher dry matter output, reflecting the combined effect of forage composition, crop productivity, and conservation efficiency. Hay systems showed greater burdens per unit of product under the evaluated farm conditions due to prolonged drying periods, repeated field operations, and higher biomass losses during harvesting and storage. The results suggest that, within the specific Mediterranean farm context analyzed, the combined effects of biomass preservation efficiency and crop productivity were important factors influencing environmental performance when impacts were expressed on an output basis. This case study contributes to current knowledge by identifying biomass recovery, rather than input minimization alone, as a key driver of sustainable forage conservation. Full article

To address the visual perception challenges in automated harvesting of the ‘Sucui No.1 Pear’, this study proposes a fast detection and 3D localization method based on an improved YOLOv11 architecture and an RGB-D camera. First, a multi-scene ‘Sucui No.1 Pear’ dataset containing 5842 RGB-D image pairs and 31,559 labeled instances was constructed. Second, a lightweight YOLOv11-pear detection model optimized for pear fruit was developed; by reconstructing the feature pyramid network and introducing a global attention mechanism, using K-means++ clustering to optimize prior anchor boxes, and designing a composite loss function (Varifocal Loss + CIoU Loss + DFL), the detection accuracy was improved while maintaining lightweight design. The model adopts a “detect first, then fuse” strategy, achieving robust 3D coordinate calculation based on the median depth of the bottom region of the detection box. Experimental results show that YOLOv11-pear achieves 93.8% mAP@0.5 and 65.5% mAP@0.5:0.95 on the independent test set, with a precision of 94.2% and a recall of 91.5%. The model has only 5.8 M parameters and achieves real-time inference at 38.7 FPS on the Jetson Orin NX edge platform, with a mean absolute error of 9.9 mm for 3D localization. In severely occluded and complex lighting scenarios, the mAP@0.5 reaches 80.9% and 87.5%, respectively. After integrating the vision system into the harvesting robot platform, end-to-end closed-loop testing in a real orchard achieved an 88% harvesting success rate and 5% fruit damage rate. The average time for the visual perception stage was 1.3 s, accounting for 9.4% of the harvesting cycle. This research provides a high-precision, lightweight, and deployable vision solution for automated harvesting of the ‘Sucui No.1 Pear’, and has important reference value for promoting the development of intelligent fruit harvesting technology. Full article

Elevated CO₂ (eCO₂) is expected to enhance crop productivity but may simultaneously reduce nutritional quality through nutrient dilution. However, responses among crop species with contrasting functional traits and the associated plant–soil responses remain insufficiently understood. Using a multi-crop Free-Air CO₂ Enrichment (FACE) platform, we investigated the effects of eCO₂ on per-plant biomass and yield-component traits, nutritional quality, soil properties, and mineral element dynamics in four representative crops with contrasting functional traits: soybean, peanut, maize, and sweet potato. Elevated CO₂ significantly increased several per-plant biomass and yield-component traits, with the magnitude of these responses varying among crop species and soybean showing pronounced increases in multiple measured traits. Protein concentration decreased significantly by 11.47% in maize and also declined in sweet potato, whereas it remained stable in soybean and increased in peanut, demonstrating crop-specific differences in nutritional responses to eCO₂. Bulk soil responses also differed among crop types, with soybean and sweet potato showing significant decreases in soil pH and crop-specific changes in the total concentrations of Zn and Cu; however, these changes were not consistently accompanied by corresponding increases in elemental concentrations in the harvested plant tissues. These findings indicate that changes in total soil elemental concentrations do not necessarily correspond to higher elemental concentrations in edible organs. Overall, the four crop species exhibited distinct responses to eCO₂, highlighting the importance of integrating crop nutritional quality, nutrient dynamics, and plant–soil interactions when developing adaptive agricultural management strategies under future climate change. Full article

Lardizabala biternata is an endemic vine and the sole species of the monotypic genus Lardizabala, distributed across central–southern Chile and producing edible fruits traditionally harvested from wild populations. Despite its cultural relevance, potential agronomic value, and evolutionary singularity, its conservation status remains poorly understood because of incomplete distributional knowledge and historical nomenclatural inconsistencies. Here, we conducted a preliminary record-based spatial conservation assessment of L. biternata using a taxonomically and geographically curated occurrence database integrated with Kernel Density Estimation (KDE), Extent of Occurrence (EOO), Area of Occupancy (AOO), overlap analyses with Chile’s protected-area network (SNASPE) and the Chilean Winter Rainfall–Valdivian Forests biodiversity hotspot, and ecological representativeness assessments based on mapped vegetation-class units. Our analyses revealed substantial spatial and ecological conservation gaps. Most documented occurrence records were located outside protected areas, while KDE-derived occurrence-record density patterns were concentrated within the biodiversity hotspot, a region characterized by high endemism and intense anthropogenic pressure. Although EOO indicated a relatively broad geographic extent, AOO indicated restricted occupancy based on documented records. In addition, several mapped vegetation-class units intersecting the 95% KDE-derived occurrence-record density area lacked formal conservation coverage entirely. Together, these findings suggest that L. biternata may warrant greater conservation attention than previously recognized. This study provides the first integrated record-based spatial conservation assessment of the species and establishes a baseline framework for future conservation evaluations, ecological studies, and ex situ conservation strategies. Full article

This study investigated the population dynamics of Cynoglossus gracilis in the offshore waters of Liaoning Province, China, to support sustainable fisheries management. Biological samples were collected from five major fishing ports between March and November 2024. Population dynamics were assessed based on growth, mortality, and exploitation parameters, and four seasonal fishing closure scenarios were evaluated using egg production per recruit (EPR) and spawning biomass per recruit (SBR) models. The estimated exploitation rate (E = 0.53) indicated that the stock was experiencing relatively high fishing pressure but remained close to the sustainable exploitation reference level. Yield-per-recruit analysis suggested that increasing the minimum catchable size could improve resource utilization efficiency. Among the evaluated closure scenarios, the April–September closure provided the greatest conservation benefit by enhancing reproductive potential and protecting spawning stock biomass. These findings provide valuable insights for optimizing harvest regulations and seasonal closure strategies, contributing to the sustainable management of Cynoglossus gracilis resources in the offshore waters of Liaoning Province. Full article

Resveratrol is a plant-derived stilbene polyphenol that has attracted interest as a potential dietary additive for improving edible fish muscle quality. This review summarizes current evidence on pre-harvest dietary resveratrol supplementation and its possible effects on nutritional composition, flavor-related biochemical traits, and physicochemical quality of fish muscle or fillets. We distinguish nutritional quality, including proximate composition, amino-acid profiles, and fatty-acid composition, from flavor-related and sensory-relevant proxies, such as free amino acids, inosine monophosphate, volatile precursors, color, pH, water-holding capacity, and texture. Direct evidence from edible fish muscle remains limited and fragmented, and most available studies rely on biochemical or instrumental endpoints rather than trained-panel sensory evaluation or consumer acceptance testing. Reported effects suggest species- and dose-dependent changes in muscle protein deposition, amino-acid accumulation, fatty-acid composition, pH, cooking or drip loss, and texture-related traits. Putative mechanisms may involve antioxidant protection, muscle fiber remodeling, lipid metabolism, endoplasmic-reticulum stress, and delivery-dependent bioavailability; however, many mechanistic links are still inferred from hepatic tissues, mammalian models, or in vitro systems. Future studies should combine standardized dose–response feeding trials with gas chromatography–mass spectrometry (GC–MS)-based volatile profiling, lipid and protein oxidation indices, instrumental texture analysis, sensory evaluation, and shelf-life testing before resveratrol can be recommended as a robust feed-based strategy for improving edible fish muscle quality. Full article

Lasiodiplodia theobromae is an important postharvest fruit rot pathogen that reduces the shelf life and marketability of durian. This study developed an ultrasound-assisted submicron curcumin-removed turmeric oleoresin and chitosan coating and evaluated its antifungal activity against L. theobromae in ‘Ri 6’ durian. An optimum formulation of 250 g L⁻¹ CRTO and 11.18 g L⁻¹ chitosan was obtained using response surface optimisation and prepared with ultrasound energy input of 384 J mL⁻¹. The optimised dispersion showed a hydrodynamic droplet size of 514 nm, a low polydispersity index of 0.16, a positive ζ-potential of +38 mV, and an emulsification efficiency of 86.16%; it retained one of the highest degrees of visual homogeneity among the 15 design formulations after 7 months of storage at 25 °C. In vitro assays showed strong dose-dependent inhibition of L. theobromae, with mycelial growth suppressed by 98.4–100% at 0.10 C₀ and 0.20 C₀; the two concentrations did not differ significantly (p > 0.05). Lesion development on wounded-inoculated fruit was reduced by 84.8–88.6% in CRTO–chitosan-coated fruit compared with the untreated control. Ultrasound-assisted submicron CRTO–chitosan coating is therefore a promising natural strategy for controlling postharvest fruit rot caused by L. theobromae in ‘Ri 6’ durian. The study was limited to one cultivar and one harvest batch, and postharvest quality, sensory attributes, and long-term dispersion stability were not assessed. Full article

The genus Agastache comprises aromatic and medicinal plants of increasing interest for the diversification of high-value crops. Among them, Agastache foeniculum (Pursh) Kuntze and Agastache mexicana Lint. et Epling represent two contrasting yet complementary models of multipurpose medicinal and aromatic species. This review critically compares both species with respect to taxonomy, origin, domestication, agronomic performance, phytochemical diversity, biological activities, quality standardization, safety, regulatory status, and industrial applications. Current evidence indicates that A. foeniculum is well adapted to temperate cultivation systems and represents a valuable source of essential oils and polyphenol-rich biomass, whereas A. mexicana offers considerable potential for the development of standardized nutraceutical, cosmetic, and phytopharmaceutical products. Nevertheless, the broader commercialization of both species remains constrained by pronounced chemotype variability, limited multi-site agronomic validation, insufficient clinical evidence, safety concerns associated with certain volatile constituents, and an evolving regulatory framework. The available evidence underscores the need to move beyond descriptive studies toward integrated crop development strategies that combine metabolomics-based chemotype classification, genomic and transcriptomic analyses, breeding for stable and safe phytochemical profiles, polyploidy-assisted improvement, climate-smart cultivation practices, and standardized post-harvest processing. Collectively, these approaches will facilitate the sustainable exploitation of both species and support their transition from promising medicinal and aromatic plants to commercially viable, high-value multipurpose crops for sustainable agriculture. Full article

Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture. Full article