Search Results (10,173)

Heavy metal and metalloid stress, particularly from toxic elements like cadmium (Cd), poses a growing threat to plant ecosystems, crop productivity, and global food security. Elevated concentrations of these contaminants can trigger cytotoxic and genotoxic effects in plants, severely impairing growth, development, and reproduction. In recent years, epigenetic mechanisms have emerged as crucial regulators of plant responses to heavy metal stress, offering novel insights and strategies for enhancing plant resilience in contaminated environments. This review synthesises current advances in the field of plant epigenetics, focusing on key modifications such as DNA methylation, histone acetylation and remodelling, chromatin dynamics, and small RNA-mediated regulation. These processes not only influence gene expression under metal-induced stress but also hold promise for long-term adaptation through transgenerational epigenetic memory. Recent developments in high-throughput sequencing and functional genomics have accelerated the identification of epigenetic markers associated with stress tolerance, enabling the integration of these markers into breeding programs and targeted epigenome editing strategies. Special attention is given to cadmium stress responses, where specific epigenetic traits have been linked to enhanced tolerance. As plant epigenomic research progresses, its application in sustainable agriculture becomes increasingly evident offering environmentally friendly solutions to mitigate the impact of heavy metal pollution. This review provides a foundation for future research aimed at leveraging epigenetic tools to engineer crops capable of thriving under metal stress, thereby contributing to resilient agricultural systems and sustainable food production. Full article

Background/Objectives: Botrytis cinerea is a necrotrophic fungal plant pathogen responsible for the gray mold disease, affecting several crops of economic importance worldwide. The primary line of control for the disease in the field and post-harvest fruits includes the application of fungicides. However, the emergence of fungal populations resistant to one or more fungicides has increased their application and diminished their effectiveness. Looking at new control strategies, metallic nanoparticles have appeared as a promising alternative for disease treatment. Green-synthesized copper oxide nanoparticles (CuONPs) are considered a feasible alternative, aiming to reduce the generation of environmentally toxic waste through chemical methods. Methods: In this work, CuONPs biosynthesized using the supernatant of Trichoderma asperellum and Trichoderma ghanense were evaluated to determine their antifungal activity against B. cinerea. Results: Four different formulations of CuONPs were obtained: Ta1, Ta2, Tg1, and Tg2. All formulations displayed antifungal properties, with Tg2 being the most effective and having a high potential in controlling the phytopathogen. CuONPs in the Tg2 formulation were quasi-spherical, ranging in size from 1 to 2.7 nm. Conclusions: Furthermore, Tg2 demonstrated greater efficacy than the copper-based commercial fungicide NORDOX^® 75W, which showed no inhibitory effect on B. cinerea mycelial growth. In summary, the CuONPs reported in this work offer a sustainable and effective alternative for managing the gray mold disease. Full article

Driver fatigue is a primary factor in traffic accidents and poses a serious threat to road safety. To address this issue, this paper proposes a multi-feature fusion fatigue detection method based on an improved YOLOv8 model. First, the method uses an enhanced YOLOv8 model to achieve high-precision face detection. Then, it crops the detected face regions. Next, the lightweight PFLD (Practical Facial Landmark Detector) model performs keypoint detection on the cropped images, extracting 68 facial feature points and calculating key indicators related to fatigue status. These indicators include the eye aspect ratio (EAR), eyelid closure percentage (PERCLOS), mouth aspect ratio (MAR), and head posture ratio (HPR). To mitigate the impact of individual differences on detection accuracy, the paper introduces a novel sliding window model that combines a dynamic threshold adjustment strategy with an exponential weighted moving average (EWMA) algorithm. Based on this framework, blink frequency (BF), yawn frequency (YF), and nod frequency (NF) are calculated to extract time-series behavioral features related to fatigue. Finally, the driver’s fatigue state is determined using a comprehensive fatigue assessment algorithm. Experimental results on the WIDER FACE and YAWDD datasets demonstrate this method’s significant advantages in improving detection accuracy and computational efficiency. By striking a better balance between real-time performance and accuracy, the proposed method shows promise for real-world driving applications. Full article

The use of biostimulants offers a sustainable strategy to improve crop quality. This study assessed the effects of an arbuscular mycorrhizal fungi inoculum (consisting of species Claroideoglomus claroideum, Claroideoglomus etunicatum, Funneliformis geosporum, Funneliformis mosseae and Rhizophagus irregularis) and an 0.5% aqueous extract of Landoltia punctata on the growth and biochemical composition of lettuce (Lactuca sativa L. cv. ‘Dubáček’) under indoor conditions. Four variants were tested: control (C), mycorrhiza (M), L. punctata extract (L), and their combination (M + L), with biometric, physiological, and biochemical parameters evaluated. Simultaneously, the amino acid profile of Landoltia extract was determined, and the degree of plant colonization by mycorrhizal fungi was evaluated. While biostimulant treatments did not affect above-ground biomass, L. punctata extract (L and M + L) significantly raised chlorophyll a (by 15.9% and 16.0%) and chlorophyll b (by 55.5% and 42.8%) compared to the control. The combined treatment (M + L) achieved the highest total phenolic content (254.28 mg/kg). All treated variants significantly reduced leaf nitrate content, with M and M + L being most effective (−35.1% and −33.6%). Amino acid metabolomic analysis showed that the extract is rich in γ-aminobutyric acid, valine, phenylalanine, tryptophan, and other proteinogenic amino acids that may drive its biostimulant effects. Microscopy confirmed successful root colonisation in mycorrhizal variants (58% in M, 42% in M + L). Although the biostimulants did not significantly affect growth, their application is recommended to improve lettuce quality by enhancing photosynthetic pigments and phenolic compounds while reducing nitrate content, indicating their potential for producing safe, higher-quality crops. Full article

The partial substitution of chemical fertilizer with organic fertilizer has been regarded as an effective strategy for enhancing crop yield and soil quality. Nevertheless, its effects on soil properties and microbes remain contentious. In this study, we examined the effects of four different fertilization strategies (including without fertilizer (CK), 100% chemical fertilizer (NPK), 30% organic fertilizer + 70% chemical fertilizer (LOM) and 60% organic fertilizer + 40% chemical fertilizer (HOM)) on soil nutrients and microbial communities through metagenomic sequencing in a Camellia oleifera field experiment. Compared to CK and NPK, HOM significantly increased SOC, TN, TP, AK and AN contents. The substitution of organic fertilizer notably increased Camellia oleifera yield, with the highest increase of 93.35% observed in HOM relative to NPK. Soil bacterial and fungal communities responded inconsistently to fertilization patterns. Bacteria predominated as the main soil microorganisms, and higher rates of organic fertilizer substitution facilitated a shift from bacterial to fungal communities. Organic fertilizer substitution significantly increased soil bacteria diversity and fungal richness, particularly in the HOM. Soil bacterial community structure was more sensitive to fertilization regimes than soil fungi. High rates of organic fertilizer substitution substantially suppressed oligotrophic and increased copiotrophic bacterial communities. Mucoromycota emerged as the dominant fungal group, with a considerable increment in HOM soils. SOC and TN were the main factors affecting Camellia oleifera yield and shaping soil bacteria and fungal diversity and composition. This study provided crucial insights into the ecological implications of organic fertilizer application and the potential of managing soil microorganisms for sustainable Camellia oleifera productivity. Full article

Heavy metal (HM) contamination in agricultural soils poses a significant threat to soil health and plant productivity. This study investigates the impact of cadmium (Cd) and zinc (Zn) stress on tomato plants (Solanum lycopersicum) and explores the mitigation potential of microbial biostimulants (MBs), including arbuscular mycorrhizal fungi (AMF) and Pseudomonas fluorescens So_08 (PGPR), over a 52-day period using multi-omics approaches. Root exudate profiling revealed distinct metabolic changes under HM stress, which compromised soil–plant interactions. Cd stress reduced the secretion of phenylpropanoids (sum LogFC: −45.18), lipids (sum LogFC: −27.67), and isoprenoids (sum LogFC: −11−67), key metabolites in antioxidative defense, while also suppressing rhizosphere fungal populations. Conversely, Zn stress enhanced lipid exudation (such as sphingolipids and sterols, as sum LogFC of 8.72 and 9.99, respectively) to maintain membrane integrity and reshaped rhizobacterial communities. The MBs application mitigated HM-induced stress by enhancing specialized metabolite syntheses, including cinnamic acids, terpenoids, and flavonoids, which promoted crop resilience. MBs also reshaped microbial diversity, fostering beneficial species like Portibacter spp., Alkalitalea saponilacus under Cd stress, and stimulating rhizobacteria like Aggregatilinea spp. under Zn stress. Specifically, under Cd stress, bacterial diversity remained relatively stable, suggesting their resilience to Cd. However, fungal communities exhibited greater sensitivity, with a decline in diversity in Cd-treated soils and partial recovery when MBs were applied. Conversely, Zn stress caused decline in bacterial α-diversity, while fungal diversity was maintained, indicating that Zn acts as an ecological filter that suppresses sensitive bacterial taxa and favors Zn-tolerant fungal species. Multi-omics data integration combined with network analysis highlighted key features associated with improved nutrient availability and reduced HM toxicity under MB treatments, including metabolites and microbial taxa linked to sulfur cycling, nitrogen metabolism, and iron reduction pathways. These findings demonstrate that MBs can modulate plant metabolic responses and restore rhizosphere microbial communities under Cd and Zn stress, with PGPR showing broader metabolomic recovery effects and AMF influencing specific metabolite pathways. This study provides new insights into plant–microbe interactions in HM-contaminated environments, supporting the potential application of biostimulants for sustainable soil remediation and plant health improvement. Full article

Plant phenotyping plays a vital role in connecting genotype to environmental adaptability, with important applications in crop breeding and precision agriculture. Traditional leaf measurement methods are laborious and destructive, while modern 3D sensing technologies like LiDAR provide high-resolution point clouds but face challenges in automatic leaf segmentation due to occlusion, geometric similarity, and uneven point density. To address these challenges, we propose MRSliceNet, an end-to-end deep learning framework inspired by human visual cognition. The network integrates three key components: a Multi-scale Recursive Slicing Module (MRSM) for detailed local feature extraction, a Context Fusion Module (CFM) that combines local and global features through attention mechanisms, and an Instance-Aware Clustering Head (IACH) that generates discriminative embeddings for precise instance separation. Extensive experiments on two challenging datasets show that our method establishes new state-of-the-art performance, achieving AP of 55.04%/53.78%, AP₅₀ of 65.37%/64.00%, and AP₂₅ of 74.68%/73.45% on Dataset A and Dataset B, respectively. The proposed framework not only produces clear boundaries and reliable instance identification but also provides an effective solution for automated plant phenotyping, as evidenced by its successful implementation in real-world agricultural research pipelines. Full article

Over the past decade, the rapid development of geospatial tools has significantly expanded the scope of archaeobotanical research, enabling unprecedented insights into ancient plant domestication, agricultural practices, and human-environment interactions. Within the Chinese context, where rich archaeobotanical records intersect with complex socio-ecological histories, GIS-driven approaches have revealed nuanced patterns of crop dispersal, settlement dynamics, and landscape modification. However, despite these advances, current applications remain largely exploratory, constrained by fragmented datasets and underutilized spatial-statistical methods. This paper argues that a more robust integration of large-scale archaeobotanical datasets with advanced ArcGIS functionalities—such as kernel density estimation, least-cost path analysis, and predictive modelling—is essential to address persistent gaps in the field. By synthesizing case studies from key Chinese Neolithic and Bronze Age sites, we demonstrate how spatial analytics can elucidate (1) spatiotemporal trends in plant use, (2) anthropogenic impacts on vegetation, and (3) the feedback loops between subsistence strategies and landscape evolution. Furthermore, we highlight the challenges of data standardization, scale dependency, and interdisciplinary collaboration in archaeobotanical ArcGIS. Ultimately, this study underscores the imperative for methodological harmonization and computational innovation to unravel the intricate relationships between ancient societies, agroecological systems, and long-term environmental change. Full article

Long-term fertilization strategies are crucial for sustainable soil health and crop productivity. However, the synergistic effect of combining straw with lime in long-term fertilization remains underexplored, particularly regarding soil micronutrient availability and tomato yield. This study examined the 10-year effects of chicken manure (M) with straw (S) and/or lime (Ca) on soil properties, micronutrient availability, and tomato yield. The results demonstrated that all of the fertilization treatments significantly altered topsoil (0–20 cm) characteristics, reducing the pH but increasing the EC and nutrient content. The combined MSCa treatment was most effective, achieving the highest levels of total carbon (19 g/kg) and tomato yield (5.6 kg/m²), which was 12–87% higher than that achieved with the other treatments. Fertilization also significantly increased the diethylenetriamine pentaacetic acid (DTPA)-extractable Fe, Mn, Cu, and Zn concentrations in both bulk soil and aggregate fractions, with availability strongly correlated with the soil total carbon and pH. The straw and lime amendments significantly improved the fruit quality by increasing the vitamin C and soluble sugar content while reducing the nitrate content. Furthermore, these treatments altered the distribution of micronutrients within the tomato organs, increasing their proportion in roots and fruits specifically. This study concludes that the integrated application of chicken manure with straw and lime is a highly effective strategy for improving soil fertility, enhancing micronutrient bioavailability, and boosting both the yield and nutritional quality of tomatoes. Full article

Rape is one of the most widely cultivated and highest-yielding fruit crops in the world. However, research on its precise nutrient diagnosis and fertilization theory is severely lacking, significantly restricting the development of the grape industry. In this study, an L₁₆(4⁵) orthogonal experimental design was applied to determine the effects of varying ratios of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) on the fruit quality of ‘87-1’ grape (Vitis vinifera L.) cultivated in a facility, aiming to optimize nutrient application rates and improve fruit quality. Among the treatments T5 (N2P1K2Ca3Mg4), T14 (N4P2K3Ca1Mg4), and T9 (N3P1K3Ca4Mg2), treatment T9 had the most significant effect on single fruit weight, total soluble solids (TSS) content, fruit firmness (FF), and fruit quality index (FQI) and was conducive to the positive accumulation of the above quality indicators. Based on a comprehensive multi-factor analysis of variance, the optimal fertilization combination for achieving a high FQI was N3P1K2Ca1Mg2, corresponding to application rates of 375.0, 0, 168.8, 0, and 70.5 kg·hm⁻² for N, P₂O₅, K₂O, CaO, and MgO, respectively. Furthermore, to establish standards for multivariate compositional nutrient diagnosis (CND) and define the nutrient sufficiency range for ‘87-1’ grape fruit cultivated in a facility, the nutrient concentrations in various plant tissues and the soil and the FQI were measured across 80 treatments over five consecutive years. The nutritional status of the grapes cultivated under these treatments was calculated using the Technique for Order Preference by Similarity to Ideal Solution and the CND method. Based on the optimal nutrient ranges for high FQI sub-populations, a precise fertilization model was developed to facilitate economic fertilizer savings, quality improvement, and standardized grape cultivation in a facility. Full article

Vegetables are crucial to human diet and health. To ensure sustainable vegetable production, regulatory measures are needed to enhance seed germination, plant growth, and resilience to extreme environmental conditions. Nanomaterials (NMs), owing to their high surface area, nanoscale dimensions, and unique photocatalytic properties, exhibit remarkable biological effects, such as promoting germination and growth, as well as improving stress resistance in crops, offering novel solutions to key challenges in vegetable cultivation. This review summarizes the absorption pathways of NMs in plants, specifically through the leaves and roots of vegetables. Their uptake and translocation occur via passive diffusion, active transport, and endocytosis, with key influencing factors including particle size, chemical composition, surface charge, and surface modifications. We further evaluate the advantages of nanofertilizers and nanopesticides, in vegetable production over their traditional counterparts, focusing on improvements in seed germination rates, seedling vigor, biotic and abiotic stress tolerance, and overall yield and quality. Through this review, we aim to offer comprehensive insights into the application of NMs in vegetable crop production. Full article

Efficient water management is essential for optimizing agricultural productivity in water-scarce regions such as the Lis Valley, Portugal. In situ measurements of soil moisture content (SMC) and electrical conductivity (EC), together with Sentinel-2-derived vegetation indices, were used to assess the crop water status and evapotranspiration dynamics of pumpkin (Cucurbita moschata ‘Butternut’) during the 2020 growing season. SMC and EC were measured at depths of 10, 20, 30, 50, and 70 cm using a TDR sensor, with strong correlations observed in the upper layers, indicating that EC can complement direct SMC measurements in characterizing near-surface moisture conditions. Sentinel-2 imagery was acquired to compute NDVI, SAVI, EVI, and GCI. In addition, NDVI values obtained from both a GreenSeeker^® sensor and Sentinel-2 imagery were compared, showing a similar temporal pattern during the season. By replacing the standard FAO-56 K_c values with those derived from each vegetation index, ET_a was recalculated to incorporate actual crop condition variability detected via satellite. ET_a estimates from RS-assisted vegetation indices agreed with those obtained using the FAO-56 method; independent ET_a measurements were not available for validation. Although such agreement is partly expected due to calibration, its confirmation for Cucurbita moschata under Mediterranean conditions—where published references are scarce—reinforces the method’s practical applicability for water management in data-limited settings. Water Productivity (WP) was estimated as 8.32 kg m⁻³, and Water Use Efficiency (WUE FAO-56) was calculated as 0.64 kg m⁻³, indicating high water use efficiency under Mediterranean smallholder irrigation conditions. These findings demonstrate that integrating high-resolution RS with continuous soil moisture monitoring can enhance precision irrigation strategies, increase crop yields, and conserve water resources in the Lis Valley. Full article

Precision agriculture can determine the amount of nitrogen (N) required in each area to optimize yield and nitrogen use efficiency (NUE). The use of variable rate technology (VRT) for planning N fertilization has often relied on techniques that are unfeasible for farmers with limited resources. This study aims to present a variable fertilization plan for wheat and barley, along with a protocol to determine the optimal timing for the second nitrogen (N) application, thereby minimizing the need for in situ crop monitoring. Two approaches are studied: a more straightforward sensor-based method and a map-based method. The sensor-based approach involved modeling the maximum NDVI based on the observed value at the time of application and the required N level, achieving an R² of 0.55 ± 0.06 and 0.72 ± 0.04, an MAE of 0.025 ± 0.002 and 0.039 ± 0.002, and an RMSE of 0.049 ± 0.007 and 0.055 ± 0.004 for wheat and barley, respectively. The map-based approach relied on training models to estimate the nitrogen dose to be applied based on the target yield and reflectance data from Sentinel-2 at the time of application. Using random forest algorithms, an R² of 0.97 ± 0.01 and 0.96 ± 0.02, an MAE of 3.33 ± 0.20 kg N ha⁻¹ and 2.01 ± 0.13 kg N ha⁻¹, and an RMSE of 4.79 ± 0.31 kg N ha⁻¹ and 3.27 ± 0.58 kg N ha⁻¹ for wheat and barley, respectively. Full article

Biochar amendment has been widely recognized for its potential to promote soil carbon sequestration and improve crop productivity; however, the microbial mechanisms underlying carbon sequestration at varying biochar application rates remain insufficiently understood. In this study, a field experiment was conducted in a typical fluvo-aquic soil region of the North China Plain under a maize–wheat rotation, with one-time biochar application at four levels: CK (0 t ha⁻¹), B5 (5 t ha⁻¹), B10 (10 t ha⁻¹), and B20 (20 t ha⁻¹). The effects of these treatments on soil physicochemical properties, organic carbon fractions, microbial community structure, and enzyme activities were systematically examined. The results showed that soil total nitrogen (TN) and pH increased consistently with higher biochar application rates, reaching maximum values under B20 treatment, where TN and pH rose by 35.56% and 7.00% relative to CK, respectively. In contrast, the contents of NH₄⁺-N, available phosphorus (AP), and available potassium were mostly enhanced under B5 during the maize season, while in the wheat season, NH₄⁺-N peaked under B10 and AP peaked under B5. Biochar addition significantly increased soil organic carbon fractions and the carbon pool management index (CMI). In the maize season, soil organic carbon (SOC), microbial biomass carbon (MBC), particulate organic carbon (POC), and CMI under B20 rose by 55.99%, 39.67%, 79.69% and 180.54% over CK, respectively, whereas dissolved organic carbon (DOC) peaked under B5. Throughout the wheat season, SOC, MBC, and POC contents under B20 were 53.70%, 64.31% and 147.81% higher than CK, while DOC peaked under B5 (+56.98%). Soil enzyme activities, including catalase, urease, invertase and alkaline phosphatase, were strongly stimulated by biochar, with B20 increasing their activities by 4.49–18.18%, 3.19–19.77%, 6.14–26.14% and 12.25–33.19%, respectively. Biochar also reshaped microbial community structure: the during maize season, it reduced the relative abundance of Glomeromycetes (65.31%) and Oligohymenophorea (51.64%) while enhancing Deltaproteobacteria (46.15%) and Gammaproteobacteria (29.03%); during wheat season; it enhanced Eurotiomycetes (85.77%) and Dothideomycetes (16.28%) but suppressed Deinococci (74.08%) and Alphaproteobacteria (4.39%). Pathway analysis further indicated that biochar amendments indirectly increased SOC fractions and CMI by simultaneously altering nutrient availability, regulating microbial community structure, and stimulating soil enzyme activities. Collectively, these findings highlight that the effects of biochar are dosage-specific: moderate rates (e.g., 5 t ha ⁻¹) are more suitable for the short-term improvement of soil fertility, while higher rates (e.g., 20 t ha⁻¹) are more effective for long-term carbon sequestration; depending on the objective, biochar application can thus substantially modify soil physicochemical and biological processes to promote agroecosystem sustainability in the North China Plain. Full article

Peas are a popular crop grown in Poland, but their yields are variable and often low; therefore, new cultivation methods are constantly being sought. In this paper, we present the results of a three-year greenhouse study examining the effect of preparations containing rhizobial Nod factors and/or selected microelements (B, Cu, Fe, Mn, Zn, and Mo) on the physiological parameters, growth, and yield of peas. Pea plants were tested at the flowering stage (BBCH 60), at the green ripe stage (BBCH 75), and at the fully ripe stage (BBCH 90). Leaf area, SPAD, gas exchange parameters, and chlorophyll fluorescence were measured, and the number and mass of root nodules, as well as seed yield and yield components, were determined. The treatment was most effective when Nod factors were used in combination with microelements. The increase in pea yield induced by the application of both components can be attributed to the higher number of pods and seeds per plant because no significant variations were noted in the number of seeds per pod and 1000 seed weight. The number and weight of nodules were significantly correlated with the pea yield, and the value of the correlation coefficients was influenced by the application of both components. Full article