Search Results (276)

The search for sustainable, economically viable, and effective plant protection strategies against pathogenic bacteria, fungi, and viruses is a major challenge in modern agricultural practices. Chitosan (CS) is an abundant cationic natural biopolymer known for its biocompatibility, low toxicity, and antimicrobial properties. Its potential use in agriculture for pathogen control is a promising alternative to traditional chemical fertilisers and pesticides, which raise concerns regarding public health, environmental protection, and pesticide resistance. This study focused on the preparation of chitosan nanoparticles (CS-NPs) through cross-linking with organic molecules, such as tannic acid (TA). Various formulations were explored for the development of stable nanoscale particles having encapsulation capabilities towards low compounds of varying polarity and with potential agricultural applications relevant to plant health and growth. The solution properties of the NPs were assessed using dynamic and electrophoretic light scattering (DLS and ELS); their morphology was observed through atomic force microscopy (AFM), while analytical ultracentrifugation (AUC) measurements provided insights into their molar mass. Their properties proved to be primarily influenced by the concentration of CS, which significantly affected its intrinsic conformation. Additional structural insights were obtained via infrared and UV–Vis spectroscopic measurements, while detailed fluorescence analysis with the use of three different probes, as model cargo molecules, provided information regarding the hydrophobic and hydrophilic microdomains within the particles. Full article

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

Human milk has been used for over 70 years to monitor pollutants such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). Despite the growing body of data, our understanding of the pollutant exposome, particularly co-exposure patterns and their interactions, remains limited. Artificial intelligence (AI) offers considerable potential to enhance biomonitoring efforts through advanced data modelling, yet its application to pollutant dynamics in complex biological matrices such as human milk remains underutilized. This study applied an AI-based framework, integrating machine learning, metaheuristic hyperparameter optimization, explainable AI, and postprocessing, to analyze PCB-170 levels in breast milk samples from 186 mothers in Zadar, Croatia. Among 24 analyzed POPs, the most influential predictors of PCB-170 concentrations were hexa- and hepta-chlorinated PCBs (PCB-180, -153, and -138), alongside p,p’-DDE. Maternal age and other POPs exhibited negligible global influence. SHAP-based interaction analysis revealed pronounced co-behavior among highly chlorinated congeners, especially PCB-138–PCB-153, PCB-138–PCB-180, and PCB-180–PCB-153. These findings highlight the importance of examining pollutant interactions rather than individual contributions alone. They also advocate for the revision of current monitoring strategies to prioritize multi-pollutant assessment and focus on toxicologically relevant PCB groups, improving risk evaluation in real-world exposure scenarios. Full article

Aptamers are single-stranded DNA or RNA oligonucleotides screened by systematic evolution of ligands by exponential enrichment (SELEX) methods, which are widely used in food analysis. Aptamers have the advantages of low molecular weight, ease of preparation, simplicity of chemical modification, and structural stability. Aptamers generated by SELEX are typically 80–100 bases in length, and the affinity of the aptamer can be improved by sequence optimization. Methods of aptamer optimization commonly include truncation, mutation, and chemical modification, and molecular docking, molecular dynamics, circular dichroism, and isothermal titration to assess often the binding performance of the aptamer to the target. Optimized aptamers usually enhance the affinity of the aptamer for the target and increase its sensitivity in the detection of pesticides, heavy metals, fungal toxins, pathogenic bacteria, and other objects. This paper focuses on truncation, mutation, chemical modification, the introduction of rare nucleotides, and computer-aided design. It provides an overview of non-immobilized optimization metrics. Full article

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

Air pollution caused by pesticide residues is an emerging concern in urban environments influenced by nearby agricultural activities. In this study, weekly air samples were collected between May 2018 and March 2020 in Strasbourg, France, to quantify 104 pesticides in both gas and particle phases using GC-MS/MS and LC-MS/MS. Herbicides and fungicides were the most frequently detected classes, appearing in 98% of both phases followed by insecticides. Key compounds such as metalaxyl-M, diphenylamine, and bifenthrin were present in over 90% of samples. Concentrations ranged from 2.5 to 63 ng m⁻³ weekly, with cumulative annual loads exceeding 1200 ng m⁻³. Gas–particle partitioning revealed that highly volatile compounds like azinphos-ethyl favored the gas phase, while less volatile ones like bifenthrin and tebuconazole partitioned >95% into particles. A third-degree polynomial regression (R² of 0.74) revealed a nonlinear relationship between Kₚ and particle-phase concentrations, highlighting a threshold above Kₚ of 0.025 beyond which compounds accumulate disproportionately in the particulate phase. Seasonal variability showed that 36% of the annual pesticide load occurred in autumn, with total airborne levels peaking near 400 ng m⁻³, while the lowest load occurred during summer. Principal component analysis identified rainfall and total suspended particles as major drivers of pesticide phase distribution. The inhalation health risk assessed yielded hazard index values < 1 × 10⁻⁷ for all population groups, suggesting negligible non-cancer risk. This study highlights the prevalence, seasonal dynamics, and partition behavior of airborne pesticides in urban air and underscores the need for regulatory attention to this overlooked exposure route. 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

Pesticide residues pose risks to the environment and human health. Little is known about how tillage and straw management affect herbicide behavior in soil. This study investigated the effects of different tillage practices under varying straw incorporation scenarios on the degradation of five commonly used herbicides in a long-term experimental field located in the maize belt of Siping, Jilin Province. Post-harvest soil samples were analyzed for residual herbicide concentrations and basic soil physicochemical properties. A human health risk assessment was conducted, and a controlled incubation experiment was carried out to evaluate herbicide degradation dynamics under three management systems: straw incorporation with traditional rotary tillage (ST), straw incorporation with strip tillage (SS), and no-till without straw (CK). Residual concentrations of atrazine ranged from not detected (ND) to 21.10 μg/kg (mean: 5.28 μg/kg), while acetochlor showed the highest variability (2.29–120.61 μg/kg, mean: 25.26 μg/kg). Alachlor levels were much lower (ND–5.71 μg/kg, mean: 0.34 μg/kg), and neither nicosulfuron nor mesotrione was detected. Soil organic matter (17.6–20.89 g/kg) positively correlated with available potassium and acetochlor residues. Health risk assessments indicated negligible non-cancer risks for both adults and children via ingestion, dermal contact, and inhalation. The results demonstrate that tillage methods significantly influence herbicide degradation kinetics, thereby affecting environmental persistence and ecological risks. Integrating straw with ST or SS enhanced the dissipation of atrazine and mesotrione, suggesting their potential as effective residue mitigation strategies. This study highlights the importance of tailoring tillage and straw management practices to pesticide type for optimizing herbicide fate and promoting sustainable agroecosystem management. 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

This study explores the use of carbon materials derived from Nocino walnut liqueur pomace residue for the removal of chlorpyrifos, a widely used organophosphate pesticide, from water. Carbon adsorbents were synthesized from young walnut biomass under different thermal and chemical treatment conditions, and their structural and surface properties were characterized using BET analysis, FTIR, SEM-EDX, Boehm titration, and zeta potential measurements. The materials exhibited distinct textural and chemical features, including high surface areas and varied surface functionalizations. Batch adsorption studies revealed that the chlorpyrifos removal followed pseudo-second-order kinetics and was best described by the Freundlich and Langmuir isotherms, indicating a combination of pore filling and physisorption via π-π and van der Waals interactions. The highest adsorption capacity of 45.2 ± 0.2 mg g⁻¹ was achieved at 30 °C. Thermodynamic analysis confirmed the process to be endothermic, spontaneous, and entropy-driven, with desolvation effects enhancing the performance at elevated temperatures. Dynamic filtration experiments validated the practical applicability of the materials, while moderate reusability was achieved through ethanol-based regeneration. These findings demonstrate the potential of walnut pomace-derived carbons as low-cost, renewable, and effective adsorbents for sustainable water decontamination. Full article

The use of unmanned aerial application systems (UAASs) for precision pesticide applications has increased alongside the demand for sustainable agricultural practices. However, limited studies have standardized the necessary flight parameters ensuring the optimal use of UAASs in specialty crops (e.g., fruits and vegetables). Thus, the objective of this study was to evaluate the effects of flight speed, droplet size, and application volume on the spray deposition of UAASs, creating guidelines to facilitate their use in specialty crops. Field experiments were conducted in a three-factorial experimental design of three flight speeds (i.e., 4, 7, and 10 m/s), three droplet sizes (i.e., 150, 250, and 350 µm), and two application volumes (i.e., 18.75 and 28.10 L/ha). Spraying droplet parameters (i.e., coverage, droplet density, and droplet spectra, and application uniformity), measured through the effective swath width, were recorded to assess spray deposition efficiency. Flight speed, droplet size, and application volume significantly influenced spray deposition. Treatments with slower flight speeds (4 m/s) and higher application volumes (28.10 L/ha) increased spray coverage, while droplet density was maximized at 4 m/s with the finest droplet size (150 µm), which are desirable characteristics for pesticide applications in specialty crops. Ultimately, the effective swath width and spray uniformity were maximized at a flight speed of 7.93 m/s with a droplet size of 350 µm. These results help optimize UAAS-based pesticide application, increasing efficiency and reducing environmental impact; however, understanding pesticide translocation dynamics (i.e., systemic or contact) on plants is key for growers to determine flight parameters. Full article

In the field of agriculture, various types of pesticides are used to control crop pests. These chemical agents are applied using nozzles with different geometries. Regardless of their configuration and operational liquid parameters, the applied liquid jet encounters issues with wind drift and penetration efficiency. Therefore, it is necessary to optimize the spraying process. In this study, 3D numerical calculations were performed using computational fluid dynamics (CFD). A two-phase flow model based on the volume of fluid (VOF) method was used to simulate the mixing of water and air. The k-ω SST turbulence model was adopted to capture vortex phenomena. A hollow cone nozzle geometry, commonly used in orchards, was selected. Simulations allowed the analysis of pressure, velocity, and turbulence kinetic energy (TKE) in selected cross-sections. Results show that the maximum velocity of the liquid jet at the nozzle outlet exceeded 23 m/s, with the highest TKE reaching 35 m²/s² in the vortex chamber. The computed spray cone angle was approximately 88°, while the experimental value was 80°, and the simulated mass flow rate differed by 16.7% from the measured reference. The critical flow region was identified between the vortex insert and the ceramic stem, where the highest gradients of pressure and velocity were observed. Full article

Ensuring the security of agricultural systems is essential for achieving national food security and sustainable development. Given that agricultural systems are inherently complex and composed of coupled subsystems—such as water, land, and energy—a comprehensive and multidimensional assessment of system security is necessary. This study focuses on Northeast China, a major food-producing region, and introduces the concept of agricultural system coupling security, defined as the integrated performance of an agricultural system in terms of resource adequacy, internal coordination, and adaptive resilience under external stress. To operationalize this concept, a coupling security evaluation framework is constructed based on three key dimensions: reliability (Rel), coordination (Cor), and resilience (Res). An Agricultural System Coupling Security Index (AS-CSI) is developed using the entropy weight method, the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, while obstacle factor diagnosis is employed to identify key constraints. Furthermore, bivariate and trivariate Copula models are used to estimate joint risk probabilities. The results show that from 2001 to 2022, the AS-CSI in Northeast China increased from 0.38 to 0.62, indicating a transition from insecurity to relative security. Among the provinces, Jilin exhibited the highest CSI due to balanced performance across all Rel-Cor-Res dimensions, while Liaoning experienced lower Rel, hindering its overall security level. Five indicators, including area under soil erosion control, reservoir storage capacity per capita, pesticide application amount, rural electricity consumption per capita, and proportion of agricultural water use, were identified as critical threats to regional agricultural system security. Copula-based risk analysis revealed that the probability of Rel–Cor reaching the relatively secure threshold (0.8) was the highest at 0.7643, and the probabilities for Rel–Res and Cor–Res to reach the same threshold were lower, at 0.7164 and 0.7318, respectively. The probability of Rel–Cor-Res reaching the relatively secure threshold (0.8) exceeds 0.54, with Jilin exhibiting the highest probability at 0.5538. This study provides valuable insights for transitioning from static assessments to dynamic risk identification and offers a scientific basis for enhancing regional sustainability and economic resilience in agricultural systems. Full article

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production. Full article

Globally, plant-derived natural products such as essential oils serve as primary sources of functional substances for spices, pharmaceuticals, and other applications. With the increasing focus on health and well-being, alongside ongoing public health challenges, there is a critical need to enhance the deep utilization of natural plant products. Lauraceae family essential oils, characterized by their aromatic, volatile properties and notable biological activities (e.g., antibacterial, antioxidant, insect-repellent), hold significant application value across fragrance, cosmetics, chemical industries, biological pesticides, and medicine. Integrating multi-disciplinary data from biology, genomics, metabolomics, and related fields can accelerate comprehensive insights into the biosynthesis mechanisms and functional roles of these essential oils, thereby promoting the development and application of Lauraceae natural products. This review systematically summarizes the accumulation patterns and compositional characteristics of essential oils across diverse genera of Lauraceae. It further explores the evolutionary dynamics of terpene synthase (TPS) gene families and key genes involved in terpenoid biosynthesis pathways, leveraging genomic datasets from Lauraceae species. Finally, the review highlights future research trends for optimizing Lauraceae essential oil resource utilization and advancing molecular breeding of high-oil-content species within the family. Full article