Search Results (544)

Addressing the lack of dynamic monitoring methods for assessing the combined toxicity of mixed pesticides, this study developed a fluorescent probe, CCHL, specifically responsive to leucine aminopeptidase (LAP). The probe utilized Cy7-COOH (CCH) as the fluorophore, with fluorescence recovery triggered by enzymatic hydrolysis. Spectral characterization confirmed a linear response between the probe and LAP activity within a concentration range of 0–0.9 μg/mL (R² = 0.992), along with excellent selectivity in the presence of coexisting biomolecules. Application experiments demonstrated that the combination of chlorfenapyr and beta-cyfluthrin significantly reduced LAP activity, revealing a notable antagonistic effect. The novel sensing strategy developed here provides a real-time, visualized analytical tool for evaluating the combined effects of mixed pollutants, demonstrating significant potential for environmental toxicology monitoring. Full article

The oasis area of the Cherchen River Basin (OACRB) is located in the southeast edge of the Tarim Basin in Xinjiang, China. High boron (B) groundwater is observed in the OACRB according to 40 groundwater samples collected in May 2023. Identification of the chemical characteristics and B sources of groundwater in the OACRB is of great significance for the sustainable development and utilization of groundwater resources and the protection of animals, plants and human health. To explore the chemical characteristics and main B sources of groundwater, Piper three-line diagram, Gibbs diagram, correlation analysis, hydrogeochemical simulation and absolute principal component analysis (PCA-APCS-MLR) were used for analysis. The contribution of different factors to groundwater B was quantitatively evaluated. The results showed that the groundwater is weakly alkaline (with an average pH of 7.94) and mainly brackish water and saline water with Cl⁻ and Na⁺ as the main anions and cations. The groundwater is dominated by SO₄ · Cl-Na type. The average concentration (ρ) of groundwater B in the study area was 1.48 mg·L⁻¹ with the over-standard rate was 45.0%. The APCS-MLR receptor model analysis revealed that groundwater chemical components including B were mainly derived from leaching-enrichment, human activity, primary geological factors, and unknown sources. Groundwater B is obviously greater than the standard limit, which is mainly due to agricultural activities (fertilizers and pesticides) and unknown sources. Full article

Pesticides play a significant role in agriculture, but their leaching into soil and water poses serious environmental risks. This study examines pesticide contamination in surface and groundwater in northern Tunisia, specifically in Kef governorate, involving a survey of 140 farmers to gather data on agricultural practices and pesticide use. Twenty-four pesticides were monitored and utilized within the Pesticide Environmental Risk Indicator (PERI) model to evaluate environmental risk scores for each substance. Soil and water samples were analyzed using a multi-residue method and liquid chromatography–tandem mass spectrometry. Results showed that 50% of the pesticides assessed had an Environmental Risk Score of 5 or higher. Contamination was identified in water and soil, with 18 and 15 pesticide residues, respectively. Notable concentrations included 7.8 µg/L of linuron and flupyradifurone in water and 1718.4 µg/kg of linuron in soil. Commonly detected substances included the insecticide acetamiprid and fungicides like cyflufenamid and penconazole in water, while soil contamination was linked to fungicides metalaxyl and metalaxyl-m, as well as herbicides linuron and s-metolachlor. Factors such as proximity to treated water points and poor packaging management were discussed as risks. The findings emphasize the need for better monitoring and sustainable agricultural practices to mitigate contamination. Full article

Corn is one of the most produced cereals in the world and exerts a significant economic impact on a billion-dollar market. It is utilized globally as a food source for humans and livestock and as a source of carbohydrates, fiber, vitamins, minerals, and antioxidants, and also for fuel production and industrial products. However, their production is adversely affected by chemical contamination, primarily by mycotoxins, pesticides, and trace elements. Sensors and biosensors have become reliable alternatives to traditional spectroscopic and chromatographic methods for detecting these substances to enhance processes from harvesting to consumption. Here, we thoroughly evaluated studies on sensors and biosensors as alternatives to the growing demand for the determination of these contaminants as point-of-care devices in the past five years. This review reports innovative systems, using cutting-edge technology in expanded interdisciplinary research, supported by computational simulations to elucidate the interaction/reaction prior to experimentation, exploring the latest developments in nanostructures to create devices with excellent analytical performance. Many systems meet the demands of multiple and simultaneous determinations with fast results, in loco analyses with portable devices connected to personal smartphones, and simple operations to assist farmers, producers, and consumers in monitoring product quality throughout each stage of corn production. Full article

Organophosphate pesticides, fungicides, and neonicotinoid insecticides are frequently employed in the cultivation and production of leafy vegetables. The conventional detection methods for these pesticides rely on chromatographic techniques, which are characterized by good precision and sensitivity. Nevertheless, these methods suffer from drawbacks such as complex sample pretreatment, prolonged detection times, and high costs, hindering the realization of on-site detection. This paper introduces a detection method based on surface-enhanced Raman spectroscopy (SERS) for the quantitative and qualitative analysis of pesticide residues in leafy vegetables. Gold nanoparticles (AuNPs) were meticulously synthesized to serve as the substrate for enhancing Raman signals. The average particle size was approximately 50 nm, and a significant absorption peak appeared at 536 nm. The density functional theory (DFT) with the B3LYP/6-311G was utilized to calculate the theoretical Raman spectra of the pesticides. The characteristic Raman peaks of the pesticides were selected as calibration peaks to establish calibration equations relating the concentration of pesticide residues to the intensity of these calibration peaks. By substituting the intensity of the calibration peak corresponding to the lowest detectable limit in the SERS spectra into the calibration equation, the quantitative detection limit was calculated. The study revealed that the detection limit for phosmet residues in Chinese cabbage could be was below 0.5 mg/kg, with an R² of 0.93363, a standard deviation ranging from 3.87% to 8.56%, and recovery rates between 94.67% and 112.89%. For thiabendazole residues in water spinach, the detection limit could be below 1 mg/kg, with an R² of 0.98291, a standard deviation of between 1.71% and 9.29%, and recovery rates ranging from 87.67% to 107.83%. In the case of acetamiprid residues in pakchoi, the detection limit could also be below 1 mg/kg, with an R² of 0.95332, a standard deviation of between 4.00% and 9.10%, and recovery rates ranging from 90.67% to 113.75%. These findings demonstrate that the SERS-based detection method for the semi-quantitative and qualitative analysis of pesticide residues in leafy vegetables is an effective approach, enabling rapid and reliable detection of pesticide residues in leafy vegetables. Full article

With the advancement of precision agriculture, variable-rate spraying (VRS) technology has demonstrated significant potential in enhancing pesticide utilization efficiency and promoting environmental sustainability, particularly in orchard applications. As a critical medium for pesticide transport, the dynamic structural characteristics of orchard canopies exert a profound influence on spraying effectiveness. This review systematically summarizes recent progress in the dynamic perception and modeling of orchard canopies, with a particular focus on key sensing technologies such as LiDAR, Vision Sensor, multispectral/hyperspectral sensors, and point cloud processing techniques. Furthermore, it discusses the construction methodologies of static, quasi-dynamic, and fully dynamic canopy modeling frameworks. The integration of canopy sensing technologies into VRS systems is also analyzed, including their roles in spray path planning, nozzle control strategies, and precise droplet transport regulation. Finally, the review identifies key challenges—particularly the trade-offs between real-time performance, seasonal adaptability, and modeling accuracy—and outlines future research directions centered on multimodal perception, hybrid modeling approaches combining physics-based and data-driven methods, and intelligent control strategies. Full article

Cruciferous vegetables are popular in Asian dishes. However, striped flea beetles prefer to feed on leaves, which can damage the appearance of crops and reduce their economic value. Due to the lack of pest monitoring, the occurrence of pests is often irregular and unpredictable. Regular and quantitative spraying of pesticides for pest control is an alternative method. Nevertheless, this requires manual execution and is inefficient. This paper presents a system powered by solar energy, utilizing batteries and supercapacitors for energy storage to support the implementation of edge AI devices in outdoor environments. Raspberry Pi is utilized for artificial intelligence image recognition and the Internet of Things (IoT). YOLOv5 is implemented on the edge device, Raspberry Pi, for detecting striped flea beetles, and StyleGAN3 is also utilized for data augmentation in the proposed system. The recognition accuracy reaches 85.4%, and the results are transmitted to the server through a 4G network. The experimental results indicate that the system can operate effectively for an extended period. This system enhances sustainability and reliability and greatly improves the practicality of deploying smart pest detection technology in remote or resource-limited agricultural areas. In subsequent applications, drones can plan routes for pesticide spraying based on the distribution of pests. Full article

Forensic entomotoxicology is a subdiscipline that utilizes necrophagous insects as bioindicators for detecting drugs and toxicants in decomposed remains, particularly in cases where conventional biological matrices are no longer available. Toxic substances can profoundly alter insect development, physiology, and community succession, potentially impacting the accuracy of postmortem interval (PMI) estimation. This review systematically summarizes the effects of various xenobiotics, including pesticides, illicit drugs, sedatives, heavy metals, and antibiotics on larval growth, physiological traits, and gut microbial composition in forensically relevant flies. However, most studies to date have relied primarily on phenotypic observations, with limited insight into underlying molecular mechanisms. Significant interspecies and dose-dependent variability also exists in the absorption, metabolism, and physiological responses to xenobiotics. We highlight recent advances in multi-omics technologies that facilitate the identification of molecular biomarkers associated with xenobiotic exposure, particularly within the insect detoxification system. Key components such as cytochrome P450 monooxygenases (P450s), glutathione S-transferases (GSTs), and ATP-binding cassette (ABC) transporters play essential roles in xenobiotic metabolism and insecticide resistance. Additionally, the insect fat body serves as a central hub for detoxification, hormonal regulation, and energy metabolism. It integrates signals related to xenobiotic exposure and modulates larval development, making it a promising model for future mechanistic studies in insect toxicology. Altogether, this review offers a comprehensive and reliable framework for understanding the complex interactions between toxic substance exposure, insect ecology, and decomposition in forensic investigations. 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

Downy mildew is a highly destructive fungal disease that significantly reduces both the yield and quality of kimchi cabbage. Conventional detection methods rely on manual scouting, which is labor-intensive and prone to subjectivity. This study proposes an automated detection approach using RGB imagery acquired by an unmanned aerial vehicle (UAV), integrated with lightweight deep learning models for leaf-level identification of downy mildew. To improve disease feature extraction, Simple Linear Iterative Clustering (SLIC) segmentation was applied to the images. Among the evaluated models, Vision Transformer (ViT)-based architectures outperformed Convolutional Neural Network (CNN)-based models in terms of classification accuracy and generalization capability. For late-stage disease detection, DeiT-Tiny recorded the highest test accuracy (0.948) and macro F1-score (0.913), while MobileViT-S achieved the highest diseased recall (0.931). In early-stage detection, TinyViT-5M achieved the highest test accuracy (0.970) and macro F1-score (0.918); however, all models demonstrated reduced diseased recall under early-stage conditions, with DeiT-Tiny achieving the highest recall at 0.774. These findings underscore the challenges of identifying early symptoms using RGB imagery. Based on the classification results, prescription maps were generated to facilitate variable-rate pesticide application. Overall, this study demonstrates the potential of UAV-based RGB imaging for precision agriculture, while highlighting the importance of integrating multispectral data and utilizing domain adaptation techniques to enhance early-stage disease detection. Full article

The plant microbiome plays a crucial role in the health of the tea plant, while Bacillus thuringiensis (Bt) is widely utilized as a biological pesticide in tea gardens, promoting sustainable agricultural practices. However, the effects of Bt spraying on tea quality and the structure and function of the phyllosphere microbiome remain unclear. This study evaluated the effects of Bt spraying on tea quality, microbiome composition, diversity, and potential functions using tea leaf quality measurements and high-throughput sequencing of the 16S/ITS rDNA genes. Results showed that spraying Bt1 significantly increased the contents of free amino acids (by 15.27%), flavonoids (by 18.00%), soluble sugars (by 62.55%), and key compounds such as epicatechin gallate (by 10.50%), gallocatechin gallate (by 122.52%), and epigallocatechin gallate (by 61.29%), leading to improved leaf quality. Co-occurrence network analysis indicated that the community structure of both epiphytic and endophytic microbes became more complex after Bt treatment. The abundance of beneficial bacteria, such as Novosphingobium, Methylobacterium, and Sphingomonas, increased significantly, while pathogenic fungi like Aspergillus and Phyllosticta decreased. Functional prediction indicated enhanced amino acid metabolism, secondary metabolism, and carbohydrate metabolism, particularly the biosynthesis of flavonoids, which supports disease resistance and boosts secondary metabolite levels. Furthermore, Bt application reduced pathogenic fungi, enhancing the tea plant’s resistance to diseases. Overall, foliar spraying of Bt can positively alter the phyllosphere microbiome by enriching beneficial bacteria and improving metabolic functions, ultimately enhancing tea plant resistance and quality, and providing a scientific basis for sustainable pest management in tea cultivation. Full article

Kasugamycin (KSM) is easily affected by photolysis, acid–base destruction, and oxidative decomposition in the natural environment, leading to its poor durability and low effective utilization rate, which affects its control effect on plant bacterial diseases. Nanomaterials modified with environment-responsive agents enable the control of the release of pesticides through intelligently responding to external stimuli, thereby improving efficacy and reducing environmental impact. In this study, a pH-responsive controlled release system was constructed using zeolitic imidazolate frameworks (ZIF-93) for the sustained and targeted delivery of KSM. The synthesized KSM@ZIF-93 exhibited a diameter of 63.93 ± 11.19 nm with a drug loading capacity of 20.0%. Under acidic conditions mimicking bacterial infection sites, the Schiff base bonds and coordination bonds in ZIF-93 dissociated, triggering the simultaneous release of KSM and Zn²⁺, achieving a synergistic antibacterial effect. Light stability experiments revealed a 34.81% reduction in UV-induced degradation of KSM when encapsulated in ZIF-93. In vitro antimicrobial assays demonstrated that KSM@ZIF-93 completely inhibited Erwinia amylovora at 200 mg/L and had better antibacterial activity and persistence than KSM and ZIF-93. The field experiment and safety evaluation showed that the control effect of KSM@ZIF-93 on pear fire blight at the concentration of 200 mg/L was (75.19 ± 3.63)% and had no toxic effect on pollen germination. This pH-responsive system not only enhances the stability and bioavailability of KSM but also provides a targeted and environmentally compatible strategy for managing bacterial infections during the flowering period of pear trees. Full article

The widespread application of pesticides in agriculture has raised increasing concerns regarding their ecological impact, particularly in aquatic environments. Among these, α-cypermethrin, a highly active isomeric form of cypermethrin, has been extensively used due to its potent insecticidal efficacy and low mammalian toxicity. However, its toxicity to non-target aquatic organisms remains insufficiently understood at the metabolic level. In this study, a targeted metabolomics approach was employed to investigate the biochemical effects of α-cypermethrin in adult zebrafish. Acute toxicity was first determined to establish sublethal exposure concentrations (0.15 µg/L and 1.5 µg/L), followed by a 48 h exposure under a controlled flow-through system. GC-MS/MS-based analysis quantified 395 metabolites, and multivariate statistical models (principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA)) revealed clear dose-dependent metabolic alterations at two time points. Pathway analysis identified disruptions in glycolysis, glycerolipid metabolism, amino acid turnover, and glutathione pathways. Notably, glutamate depletion and associated reductions in GABA (4-Aminobutanoate) and TCA (Tricarboxylic acid) cycle intermediates suggest oxidative stress-induced metabolic bottlenecks. These results provide mechanistic insights into α-cypermethrin-induced toxicity and demonstrate the utility of metabolite-level biomarkers for environmental monitoring. This study contributes to a systems-level understanding of how sublethal pesticide exposure affects vertebrate metabolism, offering a basis for improved ecological risk assessment and pesticide regulation. Full article

Chlorpyrifos (CPF) has been extensively utilized in recent decades due to its highly efficient insecticidal properties. However, the widespread use of pesticides has posed new challenges to male reproduction. This study aims to explore the potential molecular mechanisms of male reproductive decline induced by CPF. We employ flow cytometry, qRT-PCR, Western blot, RNA sequencing, and bioinformatics analysis to investigate the potential molecular mechanisms involved in CPF-induced male reproductive damage in GC2_spd cells. Our results revealed that after 24 h of CPF treatment, the cell viability, cell cycle, apoptosis, and reactive oxygen species (ROS) accumulation of GC2_spd cells were significantly affected in vitro. RNA sequencing analysis data indicated that a total of 626 genes were differentially expressed compared to the DMSO group, especially for Efcab6, Nox3, and Cmpk2. These differential genes were mainly enriched in signaling pathways such as PI3K-AKT and glutamine metabolism. In addition, further validation through qRT-PCR, Western blot, and experiments involving the inhibition of intracellular ROS generation with N-acetylcysteine collectively confirmed that CPF induces male reproductive damage through the ROS/AKT/Efcab6 pathway. These studies elucidate potential targets and molecular mechanisms underlying CPF-induced male infertility, providing a theoretical basis for the prevention of male reproductive damage caused by pesticide residues. Full article

Precision spraying technology has attracted increasing attention in orchard production management. Traditional chemical pesticide application relies on subjective judgment, leading to fluctuations in pesticide usage, low application efficiency, and environmental pollution. This study proposes a machine vision-based precision spraying control system for orchards. First, a canopy leaf wall area calculation method was developed based on a multi-iteration GrabCut image segmentation algorithm, and a spray volume calculation model was established. Next, a fuzzy adaptive control algorithm based on an extended state observer (ESO) was proposed, along with the design of flow and pressure controllers. Finally, the precision spraying system’s performance tests were conducted in laboratory and field environments. The indoor experiments consisted of three test sets, each involving six citrus trees, totaling eighteen trees arranged in two staggered rows, with an interrow spacing of 3.4 m and an intra-row spacing of 2.5 m; the nozzle was positioned approximately 1.3 m from the canopy surface. Similarly, the field experiments included three test sets, each selecting eight citrus trees, totaling twenty-four trees, with an average height of approximately 1.5 m and a row spacing of 3 m, representing a typical orchard environment for performance validation. Experimental results demonstrated that the system reduced spray volume by 59.73% compared to continuous spraying, by 30.24% compared to PID control, and by 19.19% compared to traditional fuzzy control; meanwhile, the pesticide utilization efficiency increased by 61.42%, 26.8%, and 19.54%, respectively. The findings of this study provide a novel technical approach to improving agricultural production efficiency, enhancing fruit quality, reducing pesticide use, and promoting environmental protection, demonstrating significant application value. Full article