Search Results (1,276)

Aims: This study aimed to evaluate the agreement of a frequency sampling method (FR) as a tool for species identification while measuring undisturbed sward height. Methods: The botanical composition of both grazing systems was evaluated during the pre-grazing and post-grazing periods using two methods: the Dry Weight Rank (DWR) and FR. A non-parametric Friedman test was applied to compare evaluation methods and grazing moments. Differences in detection frequencies between methods were assessed using McNemar’s test for paired binary data. Results: The evaluation method did not influence the relative abundance of the three most abundant plant species identified: U. decumbens, Paspalum genus, and Commelinaceae weeds. A high positive Lin’s concordance correlation coefficient (CCC) was observed between the two methods in U. decumbens, Paspalum genus, U. brizantha cv. Marandú, U. plantaginea, U. arrecta, and U. humidicola (CCC ≥ 0.70). We observed lower agreement for some functional groups, particularly Commelinaceae weeds (CCC = 0.38), narrow-leaf weeds (CCC = 0.46), and Cyperaceae weeds (CCC = 0.17). Canonical correlation analysis (CCA) between the chemical composition of leaves and the botanical composition estimated by the DWR revealed two significant canonical functions (p < 0.01), with canonical correlations of 0.692 and 0.478 for the first and second functions, respectively. When botanical composition estimated by the FR was used as a regressor for leaf chemical composition, three significant canonical functions (p < 0.01) were identified, with canonical correlations of 0.632, 0.529, and 0.425 for the first, second, and third functions, respectively. Conclusions: FR represents a practical and complementary approach for assessing botanical composition and plant diversity in heterogeneous tropical grasslands, particularly for the rapid monitoring of dominant species. However, lower agreement was observed for some low-abundance functional groups, indicating reduced FR sensitivity for certain plant types. Full article

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy under controlled and semi-controlled conditions. However, most existing approaches still follow a weed-centric paradigm in which models are trained to explicitly recognize diverse weed species or weed classes. Such strategies face persistent limitations caused by extreme weed morphological variability, crop-weed similarity, high annotation cost, and spatial-temporal heterogeneity across fields, seasons, and cropping systems. This review therefore reframes weed detection as a broader transition from feature-based vision and direct weed recognition toward crop-guided, context-aware, and decision-oriented intelligence. Specifically, we synthesize the literature from three perspectives: (i) methodological evolution, including handcrafted features, machine learning, deep learning, segmentation, and multimodal sensing; (ii) paradigm transformation, from weed-centric detection to crop-guided inference based on crop structure, crop rows, and non-crop vegetation; and (iii) deployment-oriented integration, including edge devices, latency-accuracy-energy trade-offs, and robotic actuation. We further summarize representative public datasets, method categories, crop-guided studies, and edge-platform reporting requirements. Finally, we outline a decision-aware hybrid framework in which crop-guided perception provides low-latency weed localization, while species-level recognition is conditionally activated when required by herbicide selection, resistance management, or high-risk weed control. This synthesis clarifies both the value and the limitations of crop-guided weed detection and outlines actionable directions for scalable, robust, and field-deployable intelligent weeding systems. Full article

Plants have developed several dormancy mechanisms essential for resilience in adverse environments. Understanding these mechanisms allows for the development of weed control strategies and enhances seedling production. This study aimed to investigate the anatomical and ecophysiological mechanisms associated with seed coat impermeability and physical dormancy in Mimosa camporum Benth. and other Amazonian Mimosa L. species, emphasizing their effects on water uptake, germination behavior, and ecological adaptation. For M. camporum, the imbibition curve, seed coat anatomy through scanning electron microscopy, and germination tests of seeds subjected to chemical scarification (H₂SO₄) were determined. Data from 25 Amazonian Mimosa species were compiled for ecological and physiological characterization, with subsequent Multiple Correspondence Analysis. Immersion in H₂SO₄ for 5 min is adequate to break dormancy in Mimosa camporum Benth. seeds. In Mimosa camporum Benth., sulfuric acid scarification effectively promoted water uptake and germination, demonstrating the close relationship between seed anatomy, imbibition behavior, and dormancy regulation. Physical dormancy in Amazonian Mimosa L. species is directly associated with seed coat impermeability, especially the presence of macrosclereids in the palisade layer. In the Amazon, the reproductive success and resilience of Mimosa L. species are related to the physical dormancy and desiccation tolerance of their seeds. Full article

Visual injury estimates are criticized for their subjective nature. Thus, a quantitative method might improve glyphosate injury assessment. This study aimed to develop a quantitative method to determine glyphosate injury on two bermudagrass [Cynodon dactylon (L.) Pers.] cultivars, ‘Greenfield’ and ‘Goodwell’, based on a Canopeo-based green canopy cover reduction (GCCR) method. The experimental design was a completely randomized factorial containing the two bermudagrass cultivars and five glyphosate rates (0.39, 0.53, 1.06, 1.54, and 3.08 kg a.i. ha⁻¹) plus a nontreated control. Visual green canopy cover and GCCR ratings were measured at 8, 16, and 24 days after glyphosate application (DAG). The commonly used visual rating and the Canopeo-based GCCR method correlated. Bland–Altman analysis showed that at low glyphosate rates (0.39 and 0.53 kg a.i. ha⁻¹), the GCCR method overestimated injury compared to visual ratings, while at higher rates (1.54 and 3.08 kg a.i. ha⁻¹), GCCR underestimated injury values by over 30% for Greenfield and 40% for Goodwell. Despite these inconsistencies, both methods yielded similar conclusions. Further research is needed to validate the Canopeo-based GCCR method for other weed species in addition to traditional visual ratings. Full article

Understanding how weed species interact over long timescales is essential for predicting community dynamics and developing sustainable weed management strategies. This study analyzes the structure and stability of competitive interactions within a weed community composed of Descurainia sophia, Fumaria spp., Papaver rhoeas and Veronica hederifolia using a 20-year dataset from a long-term cereal–legume rotation experiment conducted under conventional and no-tillage systems in central Spain. Intra- and interspecific interactions were quantified using discrete-time, density-dependent population models based on Lotka–Volterra competition theory, and the extent to which crop phase and tillage system altered competitive relationships was evaluated. Results indicated that competitive structures remained stable as neither tillage nor crop phase modified the estimated coefficients. This suggests that management primarily affected population densities rather than the strength of the interactions. Competitive hierarchies were characterized by asymmetric effects among species, with Fumaria spp. exerting strong suppressive effects on weaker competitors. Overall, the findings support the prominent role of density-dependent regulation, particularly intraspecific competition, in shaping long-term weed community dynamics. These results provide a basis for more targeted weed management by helping to identify species that may require active control versus those that could be tolerated under specific conditions, potentially supporting reduced herbicide inputs and more sustainable agroecosystem management. Full article

The intercropping system is used for weed control in orchards, but the intercrops need to be well-designed to fit into the row spaces of fruit trees. In this study, the citrus (Citrus reticulata cv. Chachiensis) row spaces were intercropped with either soybean (Glycine max (L.) Merr.) or sweet potato (Ipomoea batatas (L.) Lam.), and their effects on weed control, soil physiochemical properties, and soil microbiome were compared to the natural weeds. Both plant species were effective in reducing the orchard weeds, and their different varieties commonly improved soil organic matter, available P and K, and beneficial metal elements compared to the weeds. Even though the soil fungal and bacterial richness and diversity of the intercrops were not significantly altered, their composition, structure, and function were distinctive to those of the weeds. The soils of the intercrops generally enriched with the fungal genera of Talaromyces and Penicillium and the bacterial genera Sphingomonas, Knoellia, and Nocardioides. Accordingly, the altered microbial communities, in taxonomy, correlated to the enriched cellular functional pathways of glycolysis and gluconeogenesis, homologous recombination, nitrogen metabolism, lipoic acid metabolism, mismatch repair, DNA replication, nicotinate and nicotinamide metabolism. Taken together, these results imply that intercrops and weeds exert distinct effects on soil nutrient accumulation, and these effects are associated with their differential impacts on soil microbiomes—which are likely driven by the rhizosphere activities of the intercrops. Full article

Celosia cristata and Alternanthera philoxeroides both belong to the family Amaranthaceae. Of the two species, C. cristata serves as a medicinal herb as well as an ornamental plant, whereas A. philoxeroides is a notorious invasive weed. In 2024, leaf spot symptoms were observed on C. cristata and A. philoxeroides in Jingzhou City, Hubei Province, China. Based on morphological characteristics and multilocus phylogenetic analysis using sequences of ITS, GAPDH, TEF1, RPB2, and Alt a 1, the pathogen isolated from both hosts was identified as the same species, Alternaria alternantherae. However, differences in morphology were observed between the strains from different hosts. Pathogenicity assays confirmed that this species can cross-infect both host plants. In addition, sensitivities of the pathogen to four fungicides (prochloraz, tebuconazole, azoxystrobin, and carbendazim) were tested in vitro and in vivo. The results revealed that the pathogen was highly sensitive to fungicides prochloraz and tebuconazole. These findings provide valuable insights into the management of leaf spot disease on C. cristata and the development of integrated control strategies for A. philoxeroides. Full article

For modern agricultural management, the accuracy of plant identification is crucial. However, the task becomes challenging because crops and weeds at early growth stages often exhibit similar color, leaf morphology, and texture in two-dimensional images captured under field conditions, despite their clear biological differences in terms of botanical species, root systems, and phenological characteristics. Furthermore, computing hardware in the field also has strict limits. Therefore, we developed the RT-BMTR network to handle these physical constraints. Within this architecture, image data is processed through a bilateral hybrid backbone named Bi-HMB. The DSFM captures small local details, and MambaVision understands the broader background information. Then, these features are fused by RepNCSPELAN4. We adopted this structure to reduce redundant calculations. Next, the model determines its bounding boxes using the Inner-ShapeIoU loss function. This geometric constraint improves the detection of small targets. When evaluated on the CropAndWeed dataset, our model achieved an average precision (AP) at IoU threshold 0.5 (AP⁵⁰) of 68.1%, AP⁷⁵ of 54.8%, and a mean AP averaged over IoU thresholds from 0.5 to 0.95 (AP^50–95) of 50.9%. Detection precision recorded 26.5% for small objects, 44.7% for moderate ones, and with 59.3% for large objects. Rates for the first two categories saw enhancements of 16.2% and 4.6%. Overall, our modified model outperforms the original RT-DETR baseline. We also shrank the overall parameter count by 30.1%, alongside a 4.2% decrease in computational demand. Full article

Wild carrot (Daucus carota subsp. carota), the wild relative of cultivated carrot, is globally identified as an invasive weed that threatens hybrid carrot seed production through natural cross-pollination, resulting in compromised genetic purity. Manual identification across the large areas required to ensure genetic purity in carrot seed crops is impractical. Remote sensing offers an alternative; however, morphological similarities among wild carrot, cultivated carrot, and common weeds hinder reliable detection. Early identification, however, remains essential for preventing genetic contamination. This study evaluated leaf-level hyperspectral reflectance spectroscopy (400–2450 nm) with machine learning to discriminate wild carrot from hybrid carrots, parental lines, and 19 co-occurring weed species. Spectral data from 266 wild carrot plants across three New Zealand sites and six weeks (5–10 weeks after emergence) showed negligible spatial effects (R² = 0.034–0.055, pseudo-F = 1.46–2.39, p > 0.05) and moderate temporal variation (R² = 0.136–0.151, pseudo-F = 5.48–6.17, p < 0.001), indicating broadly stable spectral signatures suitable for model generalization. Savitzky–Golay filtering, with min–max normalization outperformed SNV, yielding high full-spectrum accuracies for wild carrot vs. other species (90.35%, κ = 0.80), wild carrot vs. weeds (96.03%, κ = 0.92), and a multi-class model (90.79%, κ = 0.88). After removing atmospheric water-absorption bands to follow airborne sensing, reduced-band models based on airborne-compatible wavelengths maintained strong performance, including 89.40% accuracy (κ = 0.79) for wild carrot vs. weeds using a 20-band Subspace Discriminant model (400–402, 527, 705–720 nm). These findings demonstrate that stable wild carrot spectra and carefully selected visible and red-edge bands can underpin cost-effective UAV/UGV-mounted hyperspectral or multispectral sensors for site-specific wild carrot management. Full article

Strigolactones (SLs) have emerged as important regulators of plant adaptation to abiotic stress, functioning not as isolated hormones but as integrative signaling molecules. Beyond stress responses, SLs regulate key biological processes, including shoot branching, root architecture, leaf senescence, nutrient acquisition, rhizosphere communication, flowering-related development, and growth–developmental plasticity. This review synthesizes current knowledge on how SLs modulate plant responses to drought, salinity, heavy metal toxicity, high temperature, and low temperature through crosstalk with abscisic acid, auxin, cytokinin, ethylene, and gibberellin. We examine SL structural diversity, biosynthesis, transport, and signaling together with their roles in growth–stress coordination, hormonal networking, and stress-specific mitigation, while distinguishing endogenous SL functions from responses inferred from exogenous analogs such as GR24. Across stresses, SL-mediated resilience converges on adaptive modules, including water regulation, root–shoot architectural remodeling, redox protection, ion and osmotic homeostasis, photosynthetic maintenance, and rhizosphere-assisted resource acquisition. The mechanistic basis involves transcriptional reprogramming, ROS/RNS-linked redox regulation, metabolic protection, and root–microbe interactions. Translational prospects include SL analogs, genetic manipulation, and breeding for adaptive plasticity, nutrient efficiency, and stress tolerance. However, species specificity, dosage dependence, limited field validation, unclear structure–function relationships, and parasitic-weed stimulation remain major constraints. Full article

The genus Orthogalumna (Oribatida: Galumnidae) has been recognized for its phytophagous associations with aquatic plants, particularly its potential role in the biocontrol of the invasive weed Water hyacinth (Eichhornia crassipes). Despite its ecological significance, this genus remains poorly studied in terms of its micromorphological architecture, phylogenetics, and genomic resources. In this study, we report Orthogalumna cf. terebrantis from China, providing the first comprehensive characterization of an Orthogalumna species by integrating morphology, phylogeny, and transcriptomics. This record represents the first documented occurrence of O. cf. terebrantis in China, pending confirmation by voucher-based morphological comparison and molecular data. This work provides critical microstructural evidence to complement traditional morphological identification and establishes a foundational molecular dataset for future studies on the systematics, comparative genomics, and environmental adaptation of oribatid mites. Full article

Sugarcane is a high-value crop in Egypt, yet weed communities in the understudied Upper Egypt region have not been systematically characterized. This study provides a comprehensive analysis of weed floristic composition, phytogeographical affinities, and the edaphic and canopy light factors governing vegetation structure across contrasting Nile Valley clay and reclaimed desert lands in Qena Governorate. Fourteen stands were surveyed during the 2024/2025 sugarcane growing season, recording 110 species from 33 families (68 annuals and 42 perennials), which were dominated by Poaceae, Asteraceae, Fabaceae, Euphorbiaceae, and Amaranthaceae (54.6% of the flora recorded). Therophytes were the most abundant life form (60.9%), and 51.8% of species belonged to Neotropical, Palaeotropical, Cosmopolitan, and Pantropical chorotypes. Diversity indices showed high and balanced species diversity, with no dominance by any single species. Seasonal variation showed that species richness peaked in spring, decreased through summer and autumn, and correlated with light intensity under the canopy. TWINSPAN identified four vegetation groups, which were merged into three primary vegetation groups (A, B, and C) via DCA and CCA ordinations and linked to microhabitats shaped by elevation and soil physicochemical properties. CCA revealed that Group C (stands in the Nile Riverbank lands) had the highest diversity, which was associated with organic matter, clay, and field capacity. In contrast, Group A (stands of reclaimed desert land) had low richness linked to high levels of Total Dissolved Solids (TDS), Electrical Conductivity (EC), Na, K, Mg, CaCO₃, and sandy soils. Group B (stands of Nile clay lands) was an intermediate transitional community between groups A and C. These findings establish edaphic factors as the primary determinant of weed community structure, with salinity as the critical constraint in reclaimed lands and seasonal light variation as a secondary diversity filter. Full article

In temperate climates, diversifying rotations with overwintering cover crops provides many benefits, including reducing nutrient losses, restoring soil organic matter, and managing weeds. However, there is limited understanding of where and when cover crops have been planted, especially relative to harvested winter crops, such as wheat and alfalfa. In this study, we use Sentinel-1 and Sentinel-2 satellite data to map winter land cover, including cover crops, across three sites in the Lower Peninsula of Michigan using random forest models. Our results show overall moderate accuracy (60–80%) across all three sites, with individual-level accuracies varying by region and land cover type. Generally, models that combined Sentinel-1 and Sentinel-2 bands, polarizations, and indices performed better than models that relied on one sensor alone. F1 scores for cover crop mapping were moderate, with the highest accuracies achieved for mapping any cover crop (0.77) compared to individual cover crop species—cereal rye (0.72) or ryegrass (0.50). Considering which bands and time periods were the most important for the classification, we found that vegetation indices developed using the red edge bands in the earlier part of the growing season were particularly important for classification accuracy. This work suggests that readily available Sentinel-1 and Sentinel-2 satellite data can be used to accurately map winter land cover, including cover crops, in the US Midwest. Full article

Pesticides play a crucial role in modern agriculture by protecting crops from pests, diseases, and weeds, thereby contributing to increased agricultural productivity and food security. However, their extensive use may lead to the presence of residues in food products, particularly vegetables, which can pose potential risks to human health. Therefore, continuous monitoring of pesticide residues in vegetables is essential to ensure food safety, assess dietary exposure, and protect consumers from possible acute and chronic health effects associated with pesticide intake. In this study, the concentrations of pesticide residues were determined in 1236 samples of 44 vegetable species collected over a four-year period. Vegetables originated from 39 countries, including Serbia (n = 213). Pesticide residues were determined by liquid chromatography–tandem mass spectrometry (LC-MS/MS) and gas chromatography–tandem mass spectrometry (GC-MS/MS) after extraction using a modified QuEChERS protocol. A total of 148 pesticide residues were detected. Of the vegetable samples, 40.13% had pesticide residues at or above 0.01 mg/kg, and 1.78% exceeded the maximum residue limits (MRLs) set by the Serbian regulation. MRL values were most often exceeded in ginger, cucumber, and spinach. The most frequently found pesticide was imidacloprid (detected in 74 samples, 5.99%). Multiple pesticides were detected in 22.01% of the vegetable samples, and one tomato sample contained up to 10 pesticide residues. Based on the available data and further development of a representative dataset, together with appropriate statistical analyses, dietary exposure assessments for pesticides can be conducted. Full article

Deltas are highly susceptible to biological invasions because of strong hydrological connectivity, frequent disturbance, and intense human use. Here, we synthesise coordinated monitoring observations and literature evidence on invasive alien plant species (IAS) recorded in four Black Sea riparian protected areas located across five countries, surveyed under the IASON/IASON+ initiatives (Danube Delta, Nestos Delta and Lake Vistonida, Kızılırmak Delta, Chorokhi Delta and Kolkheti National Park). Across the study sites, 17 IAS were documented, mainly represented by taxa native to North America and characterised by high propagule production and/or strong vegetative regeneration. Woody riparian invaders (e.g., Amorpha fruticosa, Robinia pseudoacacia, Acer negundo, Gleditsia triacanthos and Ailanthus altissima) exploited nutrient-rich floodplain soils and disturbances. In contrast, annual weeds (e.g., Ambrosia artemisiifolia, Sicyos angulatus and Xanthium orientale) remained associated with disturbed habitat edges. Aquatic dominance was confined to the Danube Delta, where Elodea nuttallii and Elodea canadensis formed dense submerged stands. Species were assigned to broad range expansion categories (slowly, moderately and rapidly spreading species) based on project observations and supporting records. We discuss shared invasion syndromes linked to reproductive and dispersal traits and outline management implications for Black Sea deltas, emphasising pathway prevention, early detection and rapid response for localised taxa, and sustained control combined with restoration for dominant invaders. Full article