Search Results (317)

Weed infestation remains a major constraint to cotton and wheat productivity in South Asian cropping systems, and the tillage system adopted further modulates weed pressure and control efficacy. The present two-year field study evaluated the interactive effects of tillage (conventional and zero tillage) and weed-management strategies, herbicide, mechanical weeding, crop residue mulch, plastic mulch, and living mulch, on weed dynamics and yield in a cotton–wheat system over two seasons (2024–25 and 2025–26). Weed management, tillage, and their interaction significantly affected density and dry biomass of grassy, broadleaf, and sedge weeds in both crops (p < 0.05). Herbicide and plastic mulch resulted in higher weed suppression, lowering total weed density by 82–92% in cotton and over 89–92% in wheat, with weed control efficiency of 75–93%. Plots under conventional tillage, which also differed in row spacing, recorded up to 18% less weed pressure than zero tillage plots, as this difference may reflect both soil disturbance and planting geometry. Cotton lint yield increased by up to 184–191% over the weedy check under herbicide with conventional tillage, and wheat grain yield showed a comparable response. Yield declined linearly with early-season weed dry biomass in both crops. Cotton was more sensitive per unit weed biomass than wheat, though the slope was unaffected by tillage. These results show that plastic mulch and herbicide, paired with suitable tillage choice, are the most reliable options for limiting weed-related yield loss and offer a basis for herbicide-efficient weed management in cotton–wheat systems. Full article

Growing herbicide resistance and concerns regarding the environmental and health impacts of synthetic herbicides have intensified interest in plant-derived weed-management products. This review assesses the phytotoxic, herbicidal, and allelopathic potential of essential oils, emphasizing efficacy, selectivity, formulation, and delivery. Relevant literature was identified through searches of Scopus, Web of Science, and Google Scholar, using terms related to essential oils, phytotoxicity, allelopathy, herbicidal activity, and weed management, followed by critical comparison of selected studies. Essential oils from diverse aromatic plants inhibited weed germination and growth through mechanisms including membrane disruption, pigment degradation, oxidative imbalance, and altered antioxidant activity. Species- and genus-specific evidence highlights the considerable chemical diversity of essential oils and identifies numerous compounds and formulation strategies with potential for bioherbicide development. Activity varied according to chemical composition, concentration, formulation, application conditions, and target species. Nanoemulsions, nanocapsules, and cyclodextrin-based systems have been investigated to improve stability, controlled release, and delivery, although improved formulation properties do not necessarily translate into greater herbicidal efficacy. Although some formulations achieved substantial weed suppression with limited crop injury, reported cytogenotoxicity, effects on soil microbial activity, and phytotoxicity at high concentrations highlight the need for further evaluation of potential non-target effects. The available evidence is still dominated by laboratory studies, while field-scale validation remains limited. Essential-oil-based bioherbicides are promising components of integrated weed management, but their practical development requires standardized formulations, mechanistic studies, field-scale validation, economic assessment, regulatory support, and comprehensive evaluation of crop and environmental safety. Full article

Mechanical weed control is a crucial sustainable alternative to chemical herbicides, yet optimising implement parameters to balance non-chemical weed suppression with crop safety remains challenging. This study evaluated how forward speed and working depth influence soil fragmentation, weed control efficacy, and immediate plant injury during the mechanical weeding of wide-row wheat. Field experiments tested three tractor speeds (6, 9, and 12 km·h⁻¹) and two working depths of duckfoot sweeps (30 and 50 mm). Key indicators—including the degree of soil crumbling, mean clod diameter, weed control effectiveness, and crop injury rates—were evaluated using analysis of variance. Higher speeds increased soil crumbling and reduced clod size, whereas effective weed destruction depended primarily on depth. Working with duckfoots at 50 mm achieved 95.9% weed removal efficiency, outperforming the 30 mm depth (80.2%). However, increasing speed above 9 km·h⁻¹ caused a sharp rise in the immediate crop injury rate (reaching 26.8%), while direct plant cutting remained low. This indicator represents visible post-treatment injury and should not be interpreted as irreversible crop loss or yield reduction. These findings demonstrate an asymmetric impact of operating parameters: deeper depths maximise weed clearance, but excessively high speeds severely increase crop damage. Optimising these settings is vital for sustainable field operations and underscores the need for improved implement designs to minimise lateral soil discharge. Full article

Herbicides are critical for ensuring food production and improving agricultural yields; however, their residues may alter soil ecology and pose risks to animals, plants, and humans via bioaccumulation. Florpyrauxifen-benzyl is a novel herbicide, and existing studies have mainly focused on its weed control efficacy, crop safety, and general environmental impacts. However, the effects of its application on soil fungal diversity remain poorly understood. To elucidate the degradation pathways of florpyrauxifen-benzyl and its impacts on soil microorganisms, this study employed liquid chromatography and high-throughput sequencing to analyze florpyrauxifen-benzyl residues, shifts in soil physicochemical properties, and alterations in soil fungal communities following herbicide application, thereby revealing the interactions between florpyrauxifen-benzyl and soil fungi. Our results showed that, compared with the untreated control, the 10-fold recommended dose significantly increased the relative abundances of Pseudeurotium bakeri and Phialophora cyclaminis. Furthermore, the fungal diversity indices in the 5-fold and 10-fold recommended dose groups were significantly higher at later sampling stages than those in both the untreated control and the recommended-dose groups. After florpyrauxifen-benzyl application, the core fungal communities in the treatment groups were markedly distinct from those in the untreated control. These findings provide guidance for the safe and rational use of florpyrauxifen-benzyl, and offer a theoretical framework and scientific basis for the ecological monitoring of soil contamination and the elucidation of herbicide degradation mechanisms. Full article

Acetohydroxyacid synthase (AHAS, EC 2.2.1.6) is a core enzymatic target in agrochemical research for herbicide development and has been widely investigated in recent decades. To develop novel AHAS-targeted herbicides, twenty-three acylthiourea derivatives were synthesized via fragment recombination and bioisosteric replacement strategies in this work. All target compounds were fully characterized by elemental analysis, mass spectrometry, FTIR spectroscopy, and ¹H NMR spectroscopy. Dose–response trends from the Petri dish assay at 1, 10, and 100 mg L⁻¹ show that root-growth inhibition increased synchronously with concentration. Preliminary bioassays across gradient concentrations (1, 10, 100 mg L⁻¹) revealed dose-dependent growth-inhibitory effects of several derivatives against the monocot weed Digitaria adscendens and dicot weed Amaranthus retroflexus. At 100 mg/L pre-emergence treatment, compounds 4v (76.48 ± 1.47%), 4m (69.34 ± 1.62%), and 4l (67.77 ± 1.87%) exhibited the strongest inhibitory activity, comparable to or exceeding bensulfuron-methyl (70.41 ± 1.21%). All synthesized acylthiourea derivatives exhibited less than 20% growth inhibition toward wheat and soybean, demonstrating acceptable crop selectivity. In vivo enzymatic assays at 100 mg L⁻¹ showed that 4l, 4m, and 4v achieved AHAS inhibition rates of 38.25 ± 1.81%, 35.74 ± 1.35%, and 38.75 ± 1.93%, comparable to or marginally exceeding that of bensulfuron-methyl (35.64 ± 1.40%). Molecular docking simulations yielded binding energies of −6.67 kcal mol⁻¹ (4l), −6.29 kcal mol⁻¹ (4m), and −6.90 kcal mol⁻¹ (4v), all more favorable than −5.86 kcal mol⁻¹ calculated for bensulfuron-methyl, indicating stronger target-enzyme binding affinity for these three compounds. This research suggests that these acylthiourea derivatives may serve as preliminary lead scaffolds for developing novel AHAS inhibitors via subsequent structural derivatization, pending further dose–response, mechanistic, and field-efficacy validation. Full article

The aim of this work was to synthesize previously unknown thieno[2,3-b]pyridines bearing a β-ketoamide moiety, to study their structure, and to evaluate their agrochemical potential as 2,4-D herbicide safeners. A series of 3-(3-aminothieno[2,3-b]pyridine-2-yl)-3-oxo-N-phenylpropanamides were prepared via S-alkylation of readily available 2-thioxonicotinonitriles or 3-cyanopyridine-2-thiolates with γ-bromoacetoacetanilide, followed by base-promoted Thorpe–Ziegler cyclization. The structure of 4-[(3-cyano-4,6-dimethylpyridin-2-yl)thio]-3-oxo-N-phenylbutanamide was unambiguously established by single-crystal X-ray diffraction. Quantum chemical calculations (B3LYP-D3BJ/6-311+G(2d,p)) revealed that the ketone form is significantly more stable than the enol tautomer with a calculated equilibrium constant in DMSO of 1.52 × 10⁻⁵, which is consistent with the absence of enol form signals in the NMR spectra. The calculated IR frequencies, after scaling, showed excellent agreement with experimental data. Additionally, we found that 4-[(3-cyano-4,6-dimethylpyridin-2-yl)thio]-3-oxo-N-phenylbutanamide undergoes intramolecular cyclization upon heating in AcOH or DMF, to give 4-hydroxy-7,9-dimethylthieno [2,3-b:4,5-b′]dipyridin-2(1H)-one via aniline elimination, thus providing an alternative route to tricyclic thieno [2,3-b;4,5-b]dipyridine ensembles. Agrochemical screening demonstrated that some compounds exhibit pronounced antidote activity against the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) in sunflower seedlings, with root and hypocotyl protection reaching 119–156% in laboratory tests. Field trials for the three most active compounds confirmed their efficacy, providing crop yield increases of 46–57% relative to the herbicide-only control. These results indicate that the newly synthesized thienopyridine β-ketoamides represent a promising class of 2,4-D herbicide safeners. Full article

The scarcity of effective selective herbicides has made weed management a limiting factor for alfalfa yield and large-scale cultivation. This study aimed to preliminarily screen selective herbicides with acceptable safety to alfalfa and establish an effective weed management program based on sequential pre- and post-emergence herbicide applications through greenhouse screening and multi-year field trials. The greenhouse herbicide safety evaluation showed that, among the 22 herbicides tested, the post-application of 2 pre-emergence herbicides (s-metolachlor and prodiamine) and 5 post-emergence herbicides (benazolin, bentazon, fluazifop-p, imazethapyr, and nicosulfuron) showed a relatively higher survival rate (~4× the standard dose) on two alfalfa cultivars through assessing visual efficacy, plant height, and dry biomass per plant. However, even herbicides that result in relatively high plant survival can produce induce significant growth inhibition and biomass reduction when applied at high doses. Those herbicides were further tested under field conditions (2020–2023) for evaluation of weed control efficiency and alfalfa plant biomass yield potential and nutritive values. Among the herbicide application regimes, the pre-application of prodiamine followed by post-application of bentazon or fluazifop-p showed the most effective weed control efficacy (70–85% reduction of weed biomass in the alfalfa plot), which was greater than the reductions achieved with all single-herbicide applications (24–48%) and sequential application of s-metolachlor followed by the five post-emergence herbicides (25–46%). Additionally, the optimized combinations had no significant effect on the 1st and subsequent alfalfa plant height compared to that of alfalfa in weed-free control. No significant reductions in alfalfa yield or nutritive value were observed in the herbicide-treated plots treated with prodiamine followed by bentazon or fluazifop-p compared with the weed-free control. Collectively, sequential application combining pre- (prodiamine) and post-emergence (bentazon or fluazifop-p) herbicides shows effective weed control in alfalfa under the tested conditions, which provides a useful weed management program to enhance alfalfa forage production and nutritive values. Full article

Glyphosate is one of the most widely used herbicides worldwide, and its extensive application has raised increasing concern regarding environmental occurrence, ecological exposure, and potential risks to non-target organisms. Its major degradation product, aminomethylphosphonic acid (AMPA), has also received growing attention because of its persistence, transport behavior, and contribution to long-term contamination profiles. To clarify the development and emerging priorities of this field, we conducted a bibliometric review of glyphosate-related publications indexed in the Web of Science Core Collection from 1974 to 2024. After screening and data cleaning, 7050 articles and reviews were included. Publication output increased markedly over time, with annual publications exceeding 300 after 2019 and reaching a peak of 488 in 2023. The United States ranked first with 1928 publications, accounting for 27.4% of the total output, followed by Brazil and China. Keyword co-occurrence, temporal overlay, and collaboration analyses showed that glyphosate research has shifted from early agronomic topics, including herbicide efficacy, crop selectivity, and resistance management, toward a broader environmental research framework. Three major research fronts were identified: mechanism-oriented ecotoxicology in non-target organisms, environmental fate and transport of glyphosate and AMPA across soil–water–sediment systems, and the development of analytical and sensing technologies for environmental monitoring. The results further indicate that the field is moving from single-compound residue assessment toward integrated contaminant-ecology perspectives linking occurrence, transformation, biological response, exposure assessment, and ecological risk. Future studies should strengthen the integration of long-term environmental monitoring, AMPA-inclusive risk assessment, realistic multi-stressor exposure scenarios, and field-deployable detection technologies. This review provides a quantitative overview of the global research landscape and identifies priority directions for environmental assessment and management of glyphosate and AMPA contamination. Full article

Weed segmentation in soybean fields is essential to improving herbicide use efficiency and supporting precision variable-rate spraying. This study developed an SiLU–CPCA–Gate U-Net (SCG-UNet) using fused UAV RGB and multispectral imagery to improve the delineation of small and partially occluded weeds under complex canopy conditions. SCG-UNet integrates channel–spatial feature enhancement, attention-guided skip-feature fusion, and smooth nonlinear activation within a U-Net framework. A total of 400 spatially aligned RGB–multispectral image groups collected from a soybean field in Henan Province, China, were manually annotated for model development and evaluation. Paired bootstrap comparisons showed that RGB+NIR achieved the highest numerical performance among the tested inputs and significantly outperformed RGB, RGB+R, and RGB+G in mIoU after Holm correction, while remaining statistically comparable to RGB+REdge and RGB+NIR+REdge. With RGB+NIR input, SCG-UNet achieved an mPA of 92.35%, an mIoU of 83.43%, a Dice coefficient of 79.50%, and an F1-score of 80.77%, exceeding the baseline U-Net by 0.71, 1.50, 2.19, and 2.09 percentage points, respectively. Five-fold spatial block cross-validation yielded an mIoU of 82.92 ± 0.29% and an F1-score of 80.06 ± 0.40%, indicating stable performance across different regions of the same field. SCG-UNet also achieved the highest numerical mIoU among the evaluated convolutional, high-resolution, and Transformer-based models, exceeding TransUNet and LeViT-UNet by 0.90 and 0.71 percentage points, respectively, while requiring fewer parameters and lower reported memory consumption. The segmentation results were further converted into a conceptual variable-rate spraying prescription map with five spray volume levels ranging from 220 to 300 L/ha. These results demonstrate the potential of RGB–multispectral fusion for soybean weed mapping, although field validation of prescription execution, weed control efficacy, and economic benefits remains necessary. Full article

Managing invasive alien species (IAS) is a major challenge for conserving ecosystems because of their deleterious impacts. This study evaluated the efficacy of five selective herbicides and wheat straw mulch against six invasive alien plant (IAP) species in Iran. Field observations from 2021 to 2025 showed that these species are concentrated in agricultural regions, particularly in summer crops in northern Iran. Five available herbicides (pendimethalin 3 lit h⁻¹, imazethapyr 0.75 lit h⁻¹, nicosulfuron 2 lit h⁻¹, bentazone 2 lit h⁻¹, and 2,4-D+MCPA 1.5 lit h⁻¹) and wheat straw mulch (2 tons ha⁻¹) were used to reduce the growth of six IAP species during summer 2025. Pendimethalin (as a pre-emergence herbicide) was effective (>90%) against all species apart from Ipomoea hederacea (≈60%). While 2,4-D+MCPA (as a post-emergence herbicide) effectively controlled five species, bentazone was effective only against Sida rhombifolia and I. hederacea. Nicosulfuron showed high efficacy (80%) only against I. hederacea. Straw mulch was more effective against Euphorbia nutans but was not effective properly against Ipomoea species. The efficacy of mulch and some herbicides depended on species identity, even within the same genera (Ipomoea and Euphorbia). Our results can help in the successful management of these invasive plants in northern Iran to minimize their impact on yield and quality of crops. As IAPs in environmental areas, this result will also be advantageous. Full article

Formulation strategies that modify the physicochemical behavior of herbicides may influence their environmental fate and toxicity. In this context, this study investigates the effect of β-cyclodextrin (β-CD) complexation on the solubility and aquatic toxicity of chlorpropham (CLP), monuron (MON), and propanil (PRO), herbicides still in use in different parts of the world and frequently detected in aquatic environments at concentrations ranging from ng/L to µg/L. The solubility enhancement mediated by β-cyclodextrin was explored using UV-Vis and NMR spectroscopy, evaluating the impact of complexation on herbicides’ water solubility. Ecotoxicological evaluations were performed in Raphidocelis subcapitata (R. subcapitata) and Brachionus calyciflorus (B. calyciflorus), representing primary producers and consumers. Acute toxicity in B. calyciflorus significantly increased following complexation, with LC50 values decreasing from 178.09 µM (CLP), 32.32 µM (MON), and 20.77 µM (PRO) to 4.89, 2.55, and 2.29 µM, respectively. Chronic exposure further confirmed heightened sensitivity in rotifers (EC50: 0.04 µM for β-CD: MON; 0.02 µM for β-CD:PRO). R. subcapitata exhibited higher sensitivity to CLP (EC50: 2.57 µM), consistent with its mitosis inhibition mechanism. Risk Quotient (RQ) analysis, based on current environmental concentrations, revealed an ecotoxicological concern for MON and PRO. Overall, our study indicates that although β-cyclodextrin enhances herbicides solubility, it may also increase their bioavailability and toxicity underlining the necessity to evaluate a novel formulation not only from the point of view of the efficacy enhancement. Full article

Herbicide overuse remains a major challenge in sugar beet production because of its environmental and economic impacts. This study addresses three key gaps in UAV-based weed mapping: the lack of leakage-aware benchmarks for North African sugar beet imagery, the limited controlled comparison of one-stage and two-stage detectors under identical experimental conditions, and the limited translation of detection outputs into decision-support layers for site-specific spraying. We develop a reproducible UAV-based deep learning pipeline and present a field case study from Beni Mellal, Morocco. Fast R-CNN, YOLOR, YOLOv7, and YOLOv5 were compared under a unified protocol using identical data partitions, input resolution, augmentation strategies, and evaluation metrics, with locally acquired RGB imagery, COCO-format annotations, and leakage-aware field/flight splits. Under the tested conditions, YOLOv5 achieved the strongest performance, with 97.82% precision, 83.05% recall, 91.61% mAP@0.5, and 72.63% mAP@0.5:0.95. Error analysis indicated that missed detections were mainly associated with small weeds, partial occlusion by sugar beet leaves, and visually similar broadleaf weeds. Detector outputs were further organized into weed-intensity maps and used in a pilot scan-guided spot-treatment workflow on the surveyed parcels. This pilot implementation demonstrates the feasibility of translating UAV detections into prescription layers, but it should not be interpreted as a complete multi-season agronomic or economic validation. The main contribution is therefore a leakage-aware, unified benchmarking protocol and a reproducible end-to-end workflow from UAV detections to field-ready prescription maps. Future work should quantify herbicide savings, treatment efficacy, yield response, economic return, edge-device throughput, and transferability across regions and seasons. Full article

Onions (Allium cepa L.) are widely cultivated and consumed vegetable crops around the world. Weed interference is the main limitation to onion production. Onions grow slowly, are short-statured, and are non-branching, which makes them difficult to compete with weeds. The aim of this study was to summarise the literature published between 2020 and 2025 that evaluated the effectiveness of different weed control methods used in onion cultivation using a systematic review following the PRISMA guidelines. Based on the results, pendimethalin and oxyfluorfen were the most used and most effective herbicides. Combining pre- and post-emergence treatments and spraying herbicide mixtures improved weed control efficiency compared with single treatments. Acetolactate synthase (ALS) inhibitors can adversely affect onions and reduce yield, making them unsuitable for use in onion production. Integrated weed management practises, such as combining herbicides with manual weeding and using plant-based extracts, offer a sustainable approach that can reduce reliance on chemicals. Mechanical weed management is not widely adopted in onion production because its application poses numerous challenges. The future direction of weed management should focus on technological advances in mechanical weed control and the development of bioherbicides to reduce reliance on synthetic herbicides. Full article

Reducing herbicide input in paddy fields is essential for sustainable rice production and long-term soil health. Florpyrauxifen-benzyl effectively controls the dominant paddy weed barnyardgrass (Echinochloa crus-galli), yet excessive application poses environmental risks. Here, we investigated whether the compound adjuvant Sijiling, containing nonionic and anionic surfactants, could enable significant dose reduction in florpyrauxifen-benzyl while maintaining weed control efficacy and improving soil–plant system functions. Greenhouse dose–response assays and two-year field trials conducted in 2021 and 2022 demonstrated that the adjuvant permitted a 50% reduction in herbicide application without compromising control of barnyardgrass or other paddy weeds. Mechanistically, Sijiling disrupted the leaf cuticular wax barrier and amplified ethylene and ABA biosynthesis over two-fold. The reduced herbicide rate lowered residues in rice and soil, increased soil organic carbon and available potassium, and enhanced microbial diversity, particularly enriching beneficial Acidobacteria. Grain yield increased significantly under the reduced-input strategy, with Mantel analysis linking yield gains to improved soil available potassium and organic carbon. Our findings demonstrate that adjuvant-enabled herbicide dose reduction is an effective and sustainable weed management strategy for paddy rice, maintaining robust weed suppression while delivering measurable co-benefits for soil health and crop productivity, thereby supporting the sustainable intensification of rice-based cropping systems. Full article

Palmer amaranth is one of the most problematic weeds in soybean production in the United States and can cause major yield loss if not managed early. This study benchmarked eight object detection models for site-specific Palmer amaranth detection in soybean using high-resolution uncrewed aerial system (UAS) imagery, with the goal of supporting targeted herbicide application and reducing herbicide usage. The models YOLOv8m, YOLOv9m, YOLOv10m, YOLOv11m, Faster R-CNN, RetinaNet, RT-DETR, and a self-supervised Faster R-CNN variant were evaluated using five-fold cross-validation on 2064 annotated aerial RGB image tiles containing 5990 bounding-box instances across multiple growth stages and field conditions, with an additional 7615 unlabeled tiles used for self-supervised pretraining. All detectors followed an identical 150-epoch schedule with early stopping and were compared using Friedman with Iman–Davenport correction and post hoc Nemenyi tests. Detectors were assessed on three axes: detection accuracy (mAP and class-wise AP), operational spraying efficacy summarized by a threshold-independent weed coverage rate area under the curve (WCR-AUC), and computational deployment cost across batch sizes from 1 to 32. The YOLO models achieved the highest detection accuracy along with the lowest inference latency and memory use but showed weaker threshold-independent weed coverage; the two-stage Faster R-CNN models showed the opposite pattern. Weighing all three axes, YOLOv8m provided the most practical balance for real-time deployment. The study also introduced GeoCLR, a self-supervised pretraining framework that constructs positive pairs from UAS flight overlap rather than synthetic augmentation. GeoCLR produced more structured and class-discriminative features than ImageNet pretraining, and a detector fine-tuned on only half of the annotations recovered approximately 95% of full-data accuracy. Together, these results highlight the importance of operational metrics for practical model selection and show that self-supervised pretraining can reduce annotation effort for scalable precision agriculture. Full article