Search Results (314)

Cover crops and mulches are widely used techniques for limiting weeds and pests’ effects on crops. This study compared six practices over two growing seasons in two organic farms in Cuneo province, North-West Italy: two bio-based biodegradable mulch sheets (BM01 and BM02), dead mulch (hazelnut shells), living mulch (Trifolium repens L.), mechanical control, and an untreated control. Spring crops included Lactuca sativa L. var. capitata, Allium cepa L. cv. ‘Tropea’, and Brassica oleracea L. var. italica, while autumn crops were Lactuca sativa L. var. capitata, Allium fistulosum L., and Brassica oleracea L. var. italica. Weed infestation was evaluated through density (n/m²), biomass (g/m²), and diversity (Shannon Index), alongside crop yield and quality. Biodegradable mulch sheets provided the greatest weed suppression, followed by hazelnut shells, while living mulch and untreated control showed the highest weed pressure. Crop yield varied significantly among practices and species: BM01 and BM02 resulted in the highest yields, while living mulch consistently produced the lowest. Lettuce displayed the best quality across both farms, whereas onion quality varied by site. The highest quality scores were observed under biodegradable mulches and mechanical control, while living mulch and untreated control yielded the poorest results. Overall, biodegradable mulches emerged as the most effective balance between weed suppression, crop yield, and quality in organic systems. Full article

Cyperus esculentus is a very destructive perennial weed, rapidly propagating and spreading through large amounts of daughter tubers. Successful control relies on depleting the soil tuber bank. This study investigated the effect of different control measures, applied across several cropping systems, on tuber bank dynamics over time. Therefore, 52 infested fields were monitored over 3 consecutive years, with annual quantification of the C. esculentus tuber bank. In maize monocropping systems, substantial 3-year tuber bank reductions (>90%) are achievable with preplant incorporation of dimethenamid-P or S-metolachlor, followed by a post-emergence application of mesotrione and pyridate at the 4–5 leaf stage, combined with delayed sowing (after 20 May) or mechanical measures (e.g., hoeing, harrowing). On non-maize fields, effective strategies (median tuber bank reductions of 57–70%) include intensive black fallow with at least four control timings or winter cereal cropping followed by intensive control (at least three measures) during the stubble phase. Established, fertilized grasslands also offer moderate reductions (17–67%) via intensive grazing or mowing. These results demonstrate that significant C. esculentus reductions are possible across different crops, but control remains challenging, requiring intensive, repeated strategies over multiple years. Less intensive approaches may undermine previous efforts. Full article

Chinese milk vetch (CMV; Astragalus sinicus L.), serving as winter green manure in rice cropping systems, is widely adopted in the southern China. Field experiments including different incorporation regimes (CMV incorporation, urea substitution incorporation and fertilizer-free incorporation), times (45 days, 30 days and 15 days before rice transplanting) and methods (no flooding, intermittent flooding and continuous flooding) were conducted from 2022 to 2024 to determine the optimal time and method for CMV incorporation that could improve soil nutrients, reduce rice-associated weed infestation, and increase rice yield. Delaying CMV incorporation was beneficial to the accumulation of dry matter and organic matter content in CMV shoots and the increase in the total nitrogen content of the soil before rice transplanting. Broadleaf weed infestation was significantly influenced by flooding method, CMV incorporation and incorporation time. Delaying CMV incorporation combined with flooding significantly reduced the density of broadleaf weeds. Grassy weed infestation was only significantly affected by the flooding method, with significantly lower density under flooding conditions compared to non-flooding conditions when other treatments were consistent. Sedge weed infestation was not affected by any of the experimental treatments. Compared with conventional CMV incorporation (incorporated 30 days before rice transplanting without flooding), incorporating CMV 15 days before rice transplanting with flooding (continuous or intermittent flooding) resulted in a 59.20–66.86% reduction in rice-associated weed infestation. Rice yield was also increased with a delay in CMV incorporation, which mainly manifested in increases in panicle number and seed setting rate. Incorporating CMV 15 days before rice transplanting increased rice yield by 5.34–13.24% compared to conventional CMV incorporation. Therefore, considering the comprehensive effects on soil nutrients, weed infestation and rice yield, incorporating CMV 15 days before rice transplanting combined with intermittent flooding is a recommended green manure management practice in green manure–rice rotation systems. Full article

The cultivation of edible lilies is highly susceptible to weed infestation during its growth period, and the application of herbicides is often impractical, leading to the rampant growth of diverse weed species. Laser weeding, recognized as an efficient and precise method for field weed management, presents a novel solution to the weed challenges in lily fields. The accurate localization of weed regions and the optimal selection of laser targeting points are crucial technologies for successful laser weeding implementation. In this study, we propose a two-stage weed detection and localization method specifically designed for lily fields. In the first stage, we introduce an enhanced detection model named YOLO-Morse, aimed at identifying and removing lily plants. YOLO-Morse is built upon the YOLOv8 architecture and integrates the RCS-MAS backbone, the SPD-Conv spatial enhancement module, and an adaptive focal loss function (ATFL) to enhance detection accuracy in conditions characterized by sample imbalance and complex backgrounds. Experimental results indicate that YOLO-morse achieves a mean Average Precision (mAP) of 86%, reflecting a 3.2% improvement over the original YOLOv8, and facilitates stable identification of lily regions. Subsequently, a ResNet-based segmentation network is employed to conduct semantic segmentation on the detected lily targets. The segmented results are utilized to mask the original lily areas in the image, thereby generating weed-only images for the subsequent stage. In the second stage, the original RGB field images are first converted into weed-only images by removing lily regions; these weed-only images are then analyzed in the HSV color space combined with morphological processing to precisely extract green weed regions. The centroid of the weed coordinate set is automatically determined as the laser targeting point.The proposed system exhibits superior performance in weed detection, achieving a Precision, Recall, and F1-score of 94.97%, 90.00%, and 92.42%, respectively. The proposed two-stage approach significantly enhances multi-weed detection performance in complex environments, improving detection accuracy while maintaining operational efficiency and cost-effectiveness. This method proposes a precise, efficient, and intelligent laser weeding solution for weed management in lily fields. Although certain limitations remain, such as environmental lighting variation, leaf occlusion, and computational resource constraints, the method still exhibits significant potential for broader application in other high-value crops. Full article

Maize, as a critical crop for China’s food security, is constantly challenged by weed infestations and environmental risks associated with herbicide overuse. Improving herbicide utilization efficiency through equipment optimization and intelligent control during spraying has become an essential strategy for weed management in Chinese maize fields. However, most current sprayers fail to achieve coordinated control of spray volume and nozzle parameters, and their performance is typically evaluated using single indices, such as the coefficient of variation (CV) for spray uniformity and deposition density. In this study, a split-split-plot experiment was conducted in 2022–2023 to assess the feasibility of herbicide reduction using intelligent variable-rate boom sprayers in summer maize fields on the North China Plain (NCP). The key variables included spray volume (225 vs. 180 L/ha), nozzle type (AI11003VS/LECHLER11003 in 2022; TTI11004/LECHLER11004 in 2023), and herbicide dose (recommended, −15%, and −30% reduction). Results showed that the coefficients of variation for droplet coverage and density remained below 12% for all treatments (n = 4), indicating stable spray performance. A higher spray volume (225 L/ha) significantly improved deposition uniformity (p < 0.01). In 2022, herbicide input could be reduced by 15–30% while maintaining efficacy above 90% when applied at the 3–4 leaf stage of dominant weeds. However, in 2023, efficacy dropped to 72.67% when the herbicide was applied at a 30% reduced dose with 180 L/ha and when dominant weeds had reached the 5–6 leaf stage or higher, indicating an agronomic risk. Reduced herbicide input decreased maize injury by 47–53%. Only the 30% reduced-dose treatment significantly increased maize yield by 3.05% in 2022 and 2.62% in 2023 compared to the control (both p < 0.05). Spray volume significantly influenced droplet deposition and weed control efficacy; thus, caution is warranted regarding herbicide reduction for later weed growth stages. This study demonstrates that real-time variable-rate boom sprayers, optimized for spray volume and nozzle type, can reduce herbicide use without compromising weed control efficacy or maize yield, providing both theoretical support and practical guidance for sustainable herbicide management in summer maize fields on the NCP. Full article

This study, which began in the 2013/2014 agricultural year, aimed to assess the suitability of two soil tillage systems for wheat cultivation: conventional soil tillage (CS), which involved moldboard plowing to a depth of 28 cm followed by a single pass with a rotary harrow to prepare the seedbed, and no-tillage (NT). It also sought to analyze the impacts of these systems on weed infestation levels and, consequently, on yield. A moderate level of fertilization was applied. The experimental field was established with a three-year crop rotation system: soybean–winter wheat–maize. The total number of weed species was 30 in CS, the representative species being Xanthium strumarium, and in NT there were 29 species, with Xanthium strumarium, Cirsium arvense, Bromus tectorum, and Agropyron repens predominating. There was an increase in the number of perennials (dicots and monocots). The total dry matter of weeds was 35.4 t ha⁻¹ in CS and 38.8 t ha⁻¹ in NT. After 11 agricultural years, it was found that there were no significant differences between the two soil tillage systems in terms of wheat yield (6.55 t ha⁻¹ in CS and 6.46 t ha⁻¹ in NT). The uneven rainfall negatively affected wheat growth and favored the spread of weeds, especially dicotyledonous ones. Full article

Weed management remains a critical challenge in the U.S. container ornamental production industry, where weeds not only compete with crops for limited resources but also harbor pests and pathogens, thereby diminishing plant quality and marketability. The paper explores the economic impact of weed infestations, herbicide resistance development, and the limited availability of selective herbicides for ornamental crops in the United States. This review synthesizes current chemical weed control tactics, focusing not only on both preemergence and postemergence herbicides commonly used in ornamental nurseries, but also organic alternatives and integrated weed management (IWM) approaches as complementary strategies by evaluating their effectiveness, crop safety, and usage. There is a critical need for research in the areas of alternative chemical options such as insecticides, miticides (e.g., Zerotol and Tetra Curb Max), and organic products for liverwort control in greenhouses. Although essential oils and plant-based extracts show some potential, their effectiveness and practical use remain largely unexplored. Full article

Similarly to conventional field crops, weeds often pose significant problems in the cultivation of medicinal plants. To date, no comprehensive documentation exists regarding weed infestation levels in these crops in Serbia. The objective of this study was to provide a valuable foundation for developing effective, site-specific weed management strategies in medicinal crop production. Weeds in five medicinal crops (lemon balm, fennel, peppermint, ribwort plantain, German chamomile), were surveyed based on the agro-phytosociological method between 2019 and 2024, and across 59 plots. A total of 109 weed species were recorded, belonging to 29 families and 88 genera. Among them, 75 were annuals and 34 perennials, including 93 broadleaved species, 10 grasses, and one parasitic species. All surveyed plots were heavily infested with perennial weeds such as Elymus repens, Cirsium arvense, Convolvulus arvensis, Lepidium draba, Rumex crispus, Sorghum halepense, Taraxacum officinale, etc. Also, several annual species were found in high abundance and frequency, including Amaranthus retroflexus, Chenopodium album, Galium aparine, Lactuca serriola, Lamium amplexicaule, L. purpureum, Papaver rhoeas, Stellaria media, Veronica hederifolia, V. persica, etc. The most important ecological factors influencing the composition of weed vegetation in investigated medicinal crops were temperature and light for fennel and peppermint plots, soil reaction for lemon balm and ribwort plantain plots, and nutrient content for German chamomile plots. A perspective for exploitation of these results is the development of effective weed control programs tailored to this specific cropping system. Weed control strategies should consider such information, targeting the control of the most frequent, abundant, and dominant species existing in a crops or locality. Full article

Field bindweed (Convolvulus arvensis L.) is a competitive herbaceous perennial weed that reduces productivity in irrigated pastures. Grazing might reduce competition by field bindweed when it begins growth in the spring, thereby encouraging encroachment by desirable grass species during the summer. To test this hypothesis, a two-year study was conducted in two adjacent, privately owned, irrigated, warm-season perennial grass pastures (replicates) that were heavily infested with field bindweed. Study sites were near Tucumcari, NM, USA. The fields were grazed with exclosures to evaluate ungrazed management. Aboveground biomass of field bindweed, other weeds, and perennial grass were measured, and field bindweed plants were counted in May of 2018 and 2019. There was no difference between years for any variable. Other weed biomass and field bindweed biomass and plant numbers were reduced (p < 0.05) by grazing (61.68 vs. 41.67 g bindweed biomass m⁻² for ungrazed and grazed management, respectively, and 108.5 and 56.8 bindweed plants m⁻² for ungrazed and grazed management, respectively). Otherwise, perennial grass production was unaffected by either year or management. These results indicate that grazing can be an effective tool to reduce field bindweed competition in warm-season perennial grass pastures. Full article

Weed infestation contributes significantly to global agricultural yield loss and increases the reliance on herbicides, raising both economic and environmental concerns. Effective weed detection in agriculture requires high accuracy and architectural efficiency. This is particularly important under challenging field conditions, including densely clustered targets, small weed instances, and low visual contrast between vegetation and soil. In this study, we propose GTDR-YOLOv12, an improved object detection framework based on YOLOv12, tailored for real-time weed identification in complex agricultural environments. The model is evaluated on the publicly available Weeds Detection dataset, which contains a wide range of weed species and challenging visual scenarios. To achieve better accuracy and efficiency, GTDR-YOLOv12 introduces several targeted structural enhancements. The backbone incorporates GDR-Conv, which integrates Ghost convolution and Dynamic ReLU (DyReLU) to improve early-stage feature representation while reducing redundancy. The GTDR-C3 module combines GDR-Conv with Task-Dependent Attention Mechanisms (TDAMs), allowing the network to adaptively refine spatial features critical for accurate weed identification and localization. In addition, the Lookahead optimizer is employed during training to improve convergence efficiency and reduce computational overhead, thereby contributing to the model’s lightweight design. GTDR-YOLOv12 outperforms several representative detectors, including YOLOv7, YOLOv9, YOLOv10, YOLOv11, YOLOv12, ATSS, RTMDet and Double-Head. Compared with YOLOv12, GTDR-YOLOv12 achieves notable improvements across multiple evaluation metrics. Precision increases from 85.0% to 88.0%, recall from 79.7% to 83.9%, and F1-score from 82.3% to 85.9%. In terms of detection accuracy, mAP:0.5 improves from 87.0% to 90.0%, while mAP:0.5:0.95 rises from 58.0% to 63.8%. Furthermore, the model reduces computational complexity. GFLOPs drop from 5.8 to 4.8, and the number of parameters is reduced from 2.51 M to 2.23 M. These reductions reflect a more efficient network design that not only lowers model complexity but also enhances detection performance. With a throughput of 58 FPS on the NVIDIA Jetson AGX Xavier, GTDR-YOLOv12 proves both resource-efficient and deployable for practical, real-time weeding tasks in agricultural settings. Full article

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

The primary aim of this study was to explore the influence of abiotic factors on weed development in maize fields, with the goal of informing more effective weed management practices. We focused on identifying key environmental, edaphic, and agricultural variables that contribute to weed infestations, particularly before the application of spring herbicide treatments. Field investigations were conducted from 2018 to 2021 across selected maize-growing regions in Hungary. Over the four-year period, a total of 51 weed species were recorded, with Echinochloa crus-galli, Chenopodium album, Portulaca oleracea, and Hibiscus trionum emerging as the most prevalent taxa. Collectively, these four species accounted for more than half (52%) of the total weed cover. Altogether, the 20 most dominant species contributed 95% of the overall weed coverage. The analysis revealed that weed cover, species richness, and weed diversity were significantly affected by soil properties, nutrient levels, geographic location, and tillage systems. The results confirm that the composition of weed species was influenced by several environmental and management-related factors, including soil parameters, geographical location, annual precipitation, tillage method, and fertilizer application. Environmental factors collectively explained a slightly higher proportion of the variance (13.37%) than farming factors (12.66%) at a 90% significance level. Seasonal dynamics and crop rotation history also played a notable role in species distribution. Nutrient inputs, particularly nitrogen, phosphorus, and potassium, influenced both species diversity and floristic composition. Deep tillage practices favored the proliferation of perennial species, whereas shallow cultivation tended to promote annual weeds. Overall, the composition of weed vegetation proved to be a valuable indicator of site-specific soil conditions and agricultural practices. These findings underscore the need to tailor weed management strategies to local environmental and soil contexts for sustainable crop production. Full article

Accurate crop row detection is crucial for determining weed pressure (weeds item per square meter). However, this task is complicated by the similarity between crops and weeds, the presence of missing plants within rows, and the varying growth stages of both. Our hypothesis was that in drone imagery captured at altitudes of 20–30 m—where individual plant details are not discernible—weed presence among crops can be statistically detected, allowing for the generation of a weed distribution map. This study proposes a computer vision detection method using images captured by unmanned aerial vehicles (UAVs) consisting of six main phases. The method was tested on 208 images. The algorithm performs well under normal conditions; however, when the weed density is too high, it fails to detect the row direction properly and begins processing misleading data. To investigate these cases, 120 artificial datasets were created with varying parameters, and the scenarios were analyzed. It was found that a rate variable—in-row concentration ratio (IRCR)—can be used to determine whether the result is valid (usable) or invalid (to be discarded). The F1 score is a metric combining precision and recall using a harmonic mean, where “1” indicates that precision and recall are equally weighted, i.e., β = 1 in the general Fβ formula. In the case of moderate weed infestation, where 678 crop plants and 600 weeds were present, the algorithm achieved an F1 score of 86.32% in plant classification, even with a 4% row disturbance level. Furthermore, IRCR also indicates the level of weed pressure in the area. The correlation between the ground truth weed-to-crop ratio and the weed/crop classification rate produced by the algorithm is 98–99%. As a result, the algorithm is capable of filtering out heavily infested areas that require full weed control and capable of generating weed density maps on other cases to support precision weed management. 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

Choosing appropriate tillage methods and nitrogen application are important steps in the management of wheat production for obtaining high-yield and high-quality products, as well as managing the level of weed infestation. The aim of this research was to examine the impacts of three different tillage practices (conventional tillage—CT, mulch tillage—MT, and no tillage—NT), and two top dressing fertilization nitrogen levels (rational—60 kg ha⁻¹ and high—120 kg ha⁻¹) on the grain yield and quality of winter wheat, as well as on weed infestation. The present study was carried out in field experiments on chernozem luvic type soil at the Faculty of Agriculture Belgrade-Zemun Experimental field trial “Radmilovac”, in the growing seasons of 2020/2021–2022/2023. The C/N ratio in the soil was also assessed on all plots. The results showed that the number of weeds and their fresh and air-dry weights were higher on the MT and NT plots, compared to the CT plots. Therefore, the CT system has better effects on the yield (5.91 and 5.36 t ha⁻¹) and the protein content (13.3 and 13.1%). Furthermore, the grain weight per spike and the 1000-grain weight were higher in the wheat from the CT system (41.83 and 42.75 g) than from the MT (40.34 and 41.49 g) and NT (40.26 and 41.08 g) systems. Also, the crops from the CT system had higher values of grain density and grain uniformity compared to the crop from the MT and NT systems. Fertilization with a high nitrogen level (120 kg ha⁻¹) causes higher grain yield and more weediness compared with the rational level (60 kg ha⁻¹). Top dressing fertilization in each tillage system resulted in an increase in the number of weeds, but, at the same time, it also resulted in stronger competitive ability of the wheat crop against weeds. The most favorable C/N ratio occurred on the NT plots, and the least beneficial one on the CT ones. A correlation analysis showed strong negative correlations of number (r = −0.82) and fresh weed mass (r = −0.72) with yield. It is concluded that the conventional tillage practice with a low nitrogen dose manifests its superior performance in minimizing weed infestation and maximizing crop productivity. Full article