Search Results (318)

Dodders (Cuscuta spp.; Convolvulaceae) are parasitic weeds that pose major challenges to agriculture due to their ability to infect a wide range of host plants, extract nutrients, and transmit pathogens. Their control is especially challenging because of the seed longevity, resistance to herbicides, and the capacity for vegetative regeneration. Mechanical methods such as hand-pulling or mowing are labour-intensive and often ineffective for large infestations. Chemical control is limited, as systemic herbicides often affect the host species equally, or even worse than the parasite. Current research is exploring biological control methods, including allelopathic compounds, host-specific fungal pathogens, and epiparasitic insects, though these methods remain largely experimental. An integrated approach that combines prevention, targeted mechanical removal, and biological methods offers the most promising path for long-term management. Continued research is essential to develop effective, sustainable control strategies while exploring possible beneficial uses of these complex parasitic plants. The present review aims to thoroughly summarise the existing literature, emphasising the most recent advances and discussing future perspectives. Full article

False cleavers (Galium spurium L.) is a broadleaf weed species that affects wheat productivity because of its strong competition for resources. It has developed resistance to acetolactate synthase (ALS) inhibitors, such as sulfonylureas and triazolopyrimidines, which are herbicides widely used in durum wheat. Integrated weed management programs can contribute to the control of this species and delay the evolution of herbicide resistance. Thus, a two-year field experiment was conducted to evaluate the effects of sowing time, variety, and herbicides on crop yield, density, and dry weight of a false cleavers population with resistance to ALS inhibitors. In both growing seasons, a split-split-plot design was used with three replicates. The sowing date was chosen as the main plot factor, durum wheat varieties as the subplot factor, and herbicides as the sub-subplot factor. The herbicide treatments were: (1) metsulfuron-methyl/bensulfuron-methyl (4/50 g a.i. ha⁻¹), (2) aminopyralid/florasulam (9.9/4.95 g a.i. ha⁻¹), (3) pyroxsulam and florasulam/2,4-D (18.75 + 4.725/225 g a.i. ha⁻¹), (4) 2,4-D/bromoxynil (633.15/601.2 g a.i. ha⁻¹), non-treated control, and hand-weeded control for the first season, while in the second season one more herbicide treatment (halauxifen-methyl/florasulam, 5.6/5.15 g a.i. ha⁻¹) was added. Herbicide application was performed on 10 March 2021 and 28 March 2022, when the crop was at the end of tillering and the beginning of stem elongation. The results showed that the density of false cleavers was not affected by the variety or sowing time. However, its dry weight was 17.3–23.4% higher in early sowing (16 November in 2020 and 8 November 2021) than in late sowing (24 December 2020 and 2 December 2021). Among the herbicides tested, 2,4-D/bromoxynil and halauxifen-methyl/florasulam effectively controlled false cleavers, showing greater efficacy in late sowing (>88%), which ultimately led to a higher yield. In conclusion, our two-year findings demonstrate that delayed sowing as part of an integrated weed management strategy can contribute to controlling resistant populations of false cleavers to ALS-inhibiting herbicides without affecting the quantity and quality of durum wheat yield in areas with a Mediterranean climate. Full article

Common ragweed (Ambrosia artemisiifolia L.) has been identified as one of the most harmful invasive weed species in Europe due to its allergenic pollen and competitive growth in diverse habitats. In the first part of this review [Common Ragweed—Ambrosia artemisiifolia L.: A Review with Special Regards to the Latest Results in Biology and Ecology], its biological characteristics and ecological behavior were described in detail. In the current paper, control strategies are summarized, focusing on integrated weed management adapted to the specific habitat where the species causes damage—arable land, semi-natural vegetation, urban areas, or along linear infrastructures. A range of management methods is reviewed, including agrotechnical, mechanical, physical, thermal, biological, and chemical approaches. Particular attention is given to the spread of herbicide resistance and the need for diversified, habitat-specific interventions. Among biological control options, the potential of Ophraella communa LeSage, a leaf beetle native to North America, is highlighted. Furthermore, innovative technologies such as UAV-assisted weed mapping, site-specific herbicide application, and autonomous weeding robots are discussed as environmentally sustainable tools. The role of legal regulations and pollen monitoring networks—particularly those implemented in Hungary—is also emphasized. By combining traditional and advanced methods within a coordinated framework, effective and ecologically sound ragweed control can be achieved. Full article

Goosegrass (Eleusine indica) is a major weed in Brazilian soybean, corn, and cotton systems, infesting over 60% of grain-producing areas and potentially reducing yields by more than 50%. Its competitiveness is due to its rapid emergence, fast tillering, C4 metabolism, and adaptability to various environmental conditions. A critical challenge relates to its widespread resistance to multiple herbicide modes of action, notably glyphosate and acetyl-CoA carboxylate (ACCase) inhibitors. Resistance mechanisms include 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) target-site mutations, gene amplification, reduced translocation, glyphosate detoxification, and mainly ACCase target-site mutations. This literature review summarizes the current knowledge on herbicide resistance in goosegrass and its management in Brazil, with an emphasis on integrating chemical and non-chemical strategies. Mechanical and physical controls are effective in early or local infestations but must be combined with chemical methods for lasting control. Herbicides applied post-emergence of weeds, especially systemic ACCase inhibitors and glyphosate, remain important tools, although widespread resistance limits their effectiveness. Sequential applications and mixtures with contact herbicides such as glufosinate and protoporphyrinogen oxidase (PPO) inhibitors can improve control. Pre-emergence herbicides are effective when used before or immediately after planting, with adequate soil moisture being essential for their activation and effectiveness. Given the complexity of resistance mechanisms, chemical control alone is not enough. Integrated weed management programs, combining diverse herbicides, sequential treatments, and local resistance monitoring, are essential for sustainable goosegrass management. Full article

Soybean production in Thailand faces significant challenges from malignant weed competition, potentially reducing yields by up to 37% and incurring annual economic losses of approximately USD 3.8 billion. Pre-emergence herbicides are critical for integrated weed management, but their efficacy varies depending on local conditions and soybean varieties. This study evaluates the performance of three pre-emergence herbicides, pendimethalin (1875 g a.i. ha⁻¹), s-metolachlor (900 g a.i. ha⁻¹), and flumioxazin (125 g a.i. ha⁻¹), on weed control efficiency (WCE), soybean growth, phytotoxicity, and yield in Northeast Thailand using a randomised complete block design with two varieties (CM60 and Morkhor60) across rainy (2023) and dry (2024/2025) seasons. Herbicide performance varied seasonally: s-metolachlor showed optimal rainy season results (61.54% weed control efficiency at 63 days after herbicide application (DAA), with a yield of 1036 kg ha⁻¹), while flumioxazin excelled in dry conditions (64.32% WCE, <4% phytotoxicity, and 1243 kg ha⁻¹ yield). Pendimethalin performed poorly under wet conditions but improved in drier weather. Among five dominant weed species, Cyperus rotundus proved the most resilient. CM60 demonstrated superior herbicide tolerance and yield stability, particularly under rainy conditions. These results emphasise that season-specific herbicide selection and variety matching are crucial for herbicide resistance management and effective weed control in Thailand’s rainfed soybean systems. Full article

Efficient weed detection in dryland spring wheat fields is crucial for sustainable agriculture, as it enables targeted interventions that reduce herbicide use, minimize environmental impact, and optimize resource allocation in water-limited farming systems. This paper presents HSG-Net, a novel lightweight object detection model based on YOLOv8 for weed identification in dryland spring wheat fields. The proposed architecture integrates three key innovations: an HGNetv2 backbone for efficient feature extraction, C2f-S modules with star-shaped attention mechanisms for enhanced feature representation, and Group Head detection heads for parameter-efficient prediction. Experiments on a dataset of eight common weed species in dryland spring wheat fields show that HSG-Net improves detection accuracy while cutting computational costs, outperforming modern deep learning approaches. The model effectively addresses the unique challenges of weed detection in dryland agriculture, including visual similarity between crops and weeds, variable illumination conditions, and complex backgrounds. Ablation studies confirm the complementary contributions of each architectural component, with the full HSG-Net model achieving an optimal balance between accuracy and resource efficiency. The lightweight nature of HSG-Net makes it particularly suitable for deployment on resource-constrained devices used in precision agriculture, enabling real-time weed detection and targeted intervention in field conditions. This work represents an important advancement in developing practical deep learning solutions for sustainable weed management in dryland farming systems. Full article

Weeds present a pervasive challenge in agricultural fields. The integration of herbicide-resistant crops with chemical weed management offers an effective solution for sustainable weed control while reducing labor inputs, particularly in large-scale intensive farming systems. Consequently, the development of herbicide-resistant cultivars has emerged as an urgent priority. In this study, we found that the G311E mutant gene of Arabidopsis MATE (multidrug and toxic compound extrusion) family transporter DTX6, designated DTX6m, confers robust resistance to bipyridyl herbicides paraquat and diquat in rice. DTX6m-overexpression lines exhibited marked resistance to these two herbicides, tolerating diquat concentrations up to 5 g/L, which is five-fold higher than the recommended field application dosage. Agronomic assessments demonstrated that grain yields of DTX6m-overexpressing plants were statistically equivalent to those of wild-type plants. Moreover, the plants displayed beneficial phenotypic changes, such as accelerated flowering and a slight reduction in height. Seed morphometric analysis indicated that in comparison with the wild-type control, DTX6m-transgenic lines exhibited altered grain dimensions while maintaining consistent 1000-grain weight. Nutritional assays further demonstrated that DTX6m increased the levels of free amino acids in seeds, while normal protein and starch contents were retained. Collectively, these results establish that DTX6m effectively boosts rice resistance to paraquat and diquat, validating DTX6m as a candidate gene for engineering plant herbicide resistance and also implying a potential role for DTX6m in amino acid homeostasis in plants. Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

The increasing global demand for resilient, sustainable agricultural systems has intensified the need for advanced monitoring strategies, particularly for climate-adaptive crops such as Moringa oleifera Lam. This study presents an integrated approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral and thermal cameras to monitor the vegetative performance and determine the optimal harvest period of four M. oleifera genotypes in a Mediterranean environment. High-resolution data were collected and processed to generate the NDVI, canopy temperature, and height maps, enabling the assessment of plant vigor, stress conditions, and spatial canopy structure. NDVI analysis revealed robust vegetative growth (0.7–0.9), with optimal harvest timing identified on 30 October 2024, when the mean NDVI exceeded 0.85. Thermal imaging effectively discriminated plant crowns from surrounding weeds by capturing cooler canopy zones due to active transpiration. A clear inverse correlation between NDVI and Land Surface Temperature (LST) was observed, reinforcing its relevance for stress diagnostics and environmental monitoring. The results underscore the value of UAV-based multi-sensor systems for precision agriculture, offering scalable tools for phenotyping, harvest optimization, and sustainable management of medicinal and aromatic crops in semiarid regions. Moreover, in this study, to produce M. oleifera leaf powder intended for use as a food ingredient, the leaves of four M. oleifera genotypes were dried, milled, and evaluated for their hygiene and safety characteristics. Plate count analyses confirmed the absence of pathogenic bacterial colonies in the M. oleifera leaf powders, highlighting their potential application as natural and functional additives in food production. Full article

Object detection is revolutionizing precision agriculture by enabling advanced crop monitoring, weed management, pest detection, and autonomous field operations. This comprehensive review synthesizes object detection methodologies, tracing their evolution from traditional feature-based approaches to cutting-edge deep learning architectures. We analyze key agricultural applications, leveraging datasets like PlantVillage, DeepWeeds, and AgriNet, and introduce a novel framework for evaluating algorithm performance based on mean Average Precision (mAP), inference speed, and computational efficiency. Through a comparative analysis of leading algorithms, including Faster R-CNN, YOLO, and SSD, we identify critical trade-offs and highlight advancements in real-time detection for resource-constrained environments. Persistent challenges, such as environmental variability, limited labeled data, and model generalization, are critically examined, with proposed solutions including multi-modal data fusion and lightweight models for edge deployment. By integrating technical evaluations, meaningful insights, and actionable recommendations, this work bridges technical innovation with practical deployment, paving the way for sustainable, resilient, and productive agricultural systems. Full article

Agricultural water resource management is increasingly challenged by climate variability, land degradation, and socio-economic pressures, particularly in the Mediterranean region. This study, conducted in 2023–2024 within the REACT4MED project (PRIMA initiative), addresses sustainable water use through a comparative analysis of organic and conventional farms in the Stornara and Tara area (Puglia, Italy). The research aimed to identify critical indicators for sustainable water management and develop ecosystem restoration strategies that can be replicated across similar Mediterranean agro-ecosystems. An interdisciplinary, participatory approach was adopted, combining technical analyses and stakeholder engagement through three workshops involving 30 participants from diverse sectors. Fieldwork and laboratory assessments included soil sampling and analysis of parameters such as pH, electrical conductivity, soil organic carbon, nutrients, and salinity. Cartographic studies of vegetation, land use, and pedological characterization supplemented the dataset. The key challenges identified were water loss in distribution systems, seawater intrusion, water pumping from unauthorized wells, and inadequate public policies. Soil quality was significantly influenced by salt stress, hence affecting crop productivity, while socio-economic factors affected farm income. Restoration strategies emphasized the need for water-efficient irrigation, less water-intensive crops, and green vegetation in infrastructure channels while incorporating also the native flora. Enhancing plant biodiversity through weed management in drainage channels proved beneficial for pathogen control. Proposed socio-economic measures include increased inclusion of women and youth in agricultural management activities. Integrated technical and participatory approaches are essential for effective water resource governance in Mediterranean agriculture. This study offers scalable, context-specific indicators and solutions for sustainable land and water management in the face of ongoing desertification and climate stress. Full article

Sugarcane bagasse-derived biochars, produced at 350 °C (B350) and 600 °C (B600), were evaluated for their capacity to modify the sorption behavior of the herbicide sulfentrazone (SFZ) in Red–Yellow Latosol (RYL) and to serve as carriers for its controlled release. Batch sorption experiments indicated that SFZ exhibits low affinity for soil and undergoes sorption–desorption hysteresis. Adding B350 biochar (up to 0.30%) did not significantly affect the herbicide sorption, whereas B600 enhanced its retention. Sequential desorption assays were conducted by incorporating SFZ either directly into the soil or into the biochars, which were subsequently blended into the soil (at 0.15% w/w). The SFZ desorbed more rapidly from the soil than from the biochars, suggesting that the pyrogenic material has potential for modulating herbicide release. Phytotoxicity assessments using Sorghum bicolor confirmed that only SFZ incorporated into B350 (at 0.15% w/w) retained herbicidal efficacy comparable to its direct application in soil. These findings underscore the potential of B350 biochar as a controlled-release carrier for SFZ without compromising its weed control effectiveness. Full article

Barnyard grass is the most problematic weed in paddy fields in Ningxia. Its substantial morphological variation complicates both identification and control, yet the genetic diversity of barnyard grass infesting paddy fields in Ningxia has not been thoroughly studied. In this research, we analyzed the genetic diversity of 46 barnyard grass populations from Ningxia’s paddy fields based on the assessment of morphological traits, DNA barcoding, and SCoT-targeted gene markers. Nine morphological traits were quantitatively analyzed, among which three phenological traits, i.e., leaf length, stem diameter, and plant height, exhibited notable variations. Correlational analysis revealed a positive relationship between morphological traits and multi-herbicide resistance profiles. To assess genetic diversity, four DNA barcodes (ITS, psbA, matK, and trnL-F) were used, among which ITS demonstrated the strongest potential in single-gene barcoding for barnyard grass species identification. Cluster analysis based on ITS barcode sequences was performed to group the populations into five main categories. Additionally, SCoT marker analysis using six primers was performed to classify the 46 barnyard grass samples into five groups. The results showed that the predominant barnyard grass species in Ningxia were E. colona, E. crus-galli var. Formosensis, E. crusgalli, E. oryzoides, and E. crusgalli var. Zelayensis, with E. colona being the most prevalent. The differences observed between the morphological and molecular marker-based classifications were method-dependent. However, both SCoT molecular marker technology and DNA barcoding contributed to identifying the genetic diversity of barnyard grass. Taken together, our study revealed significant morphological and genetic variations among barnyard grass populations, which correlated with herbicide sensitivity in Ningxia’s paddy fields, underscoring the necessity for an integrated weed management approach to combat this troublesome weed species. Full article

Long-term no-till systems have led to shifts in weed communities and reduced the effectiveness of herbicide-based control. Occasional tillage is proposed as an alternative strategy to disrupt weed emergence patterns by redistributing seeds within the soil profile. This study aimed to evaluate the impact of occasional tillage on weed seedbank composition and vertical distribution of viable weed seeds and propagules within the soil profile, after more than 20 years of continuous no-till. A paired-plot experiment was conducted in Carlos Casares, Buenos Aires, Argentina, with three replications. Treatments included continuous no-till and occasional tillage (two disk harrow passes in August 2022 and April 2023) combined with three soil depths (0–5, 5–10, and 10–15 cm). Soil samples were collected in spring 2022 and fall 2023, and weed emergence was recorded under semi-controlled conditions. Overall species richness did not differ significantly between tillage treatments but was consistently greater in the upper 0–5 cm soil layer. Weed abundance also declined with depth. Five species, Chenopodium album, Stellaria media, Eleusine indica, Oxybasis macrosperma, and Heliotropium curassavicum, were frequent across treatments. Some species were exclusive to either no-till or tilled plots, for example, Datura ferox, Poa annua, and Veronica peregrina were found only in tilled plots, while Portulaca oleracea, Medicago lupulina, and Trifolium repens were exclusive to no-till plots. These results indicate that occasional tillage alters species composition and vertical seed distribution in the seedbank without significantly reducing total richness or abundance, offering an additional, but not always effective, tool to influence weed community structure in no-till systems. Full article

With the advancement of Agriculture 4.0 and the ongoing transition toward sustainable and intelligent agricultural systems, deep learning-based multimodal fusion technologies have emerged as a driving force for crop monitoring, plant management, and resource conservation. This article systematically reviews research progress from three perspectives: technical frameworks, application scenarios, and sustainability-driven challenges. At the technical framework level, it outlines an integrated system encompassing data acquisition, feature fusion, and decision optimization, thereby covering the full pipeline of perception, analysis, and decision making essential for sustainable practices. Regarding application scenarios, it focuses on three major tasks—disease diagnosis, maturity and yield prediction, and weed identification—evaluating how deep learning-driven multisource data integration enhances precision and efficiency in sustainable farming operations. It further discusses the efficient translation of detection outcomes into eco-friendly field practices through agricultural navigation systems, harvesting and plant protection robots, and intelligent resource management strategies based on feedback-driven monitoring. In addressing challenges and future directions, the article highlights key bottlenecks such as data heterogeneity, real-time processing limitations, and insufficient model generalization, and proposes potential solutions including cross-modal generative models and federated learning to support more resilient, sustainable agricultural systems. This work offers a comprehensive three-dimensional analysis across technology, application, and sustainability challenges, providing theoretical insights and practical guidance for the intelligent and sustainable transformation of modern agriculture through multimodal fusion. Full article