Search Results (1,749)

Chickpea is a highly weed-prone crop with limited herbicide options and high labor demands, raising the following question: Can fall-planted legume–cereal cover crops (CCs) improve soil properties while reducing herbicide use and manual weeding pressure? To explore this, we evaluated the effect of fall-planted winter rye (WR) alone in 2021 and mixed with hairy vetch (HV) in 2022 and 2023 at Randolph farm in Petersburg, Virginia. The objectives were two-fold: (a) to examine the effect of CCs on soil properties using monthly growth dynamics and biomass harvested from fifteen 0.25 m²-quadrants and (b) to evaluate the efficiency of five termination methods: (1) green manure (GM); (2) GM plus pre-emergence herbicide (GMH); (3) burn (BOH); (4) crimp mulch (CRM); and (5) mow-mulch (MW) in suppressing weeds in chickpea fields. Weed distribution, particularly nutsedge, was patchy and dominant on the eastern side. Growth dynamics followed an exponential growth rate in fall 2022 (R² ≥ 0.994, p < 0.0002) and a three-parameter sigmoidal curve in 2023 (R² ≥ 0.972, p < 0.0047). Biomass averaged 55.8 and 96.9 t/ha for 2022 and 2023, respectively. GMH consistently outperformed GM in weed suppression, though GM was not significantly different from no-till systems by the season’s end. Kabuli-type chickpeas under GMH had significantly higher yields than desi types. Pooled data fitted well to a three-parametric logistic curve, predicting half-time to 50% weed coverage at 35 (MM), 38 (CRM), 40 (BOH), 46 (GM), and 53 (GMH) days. Relapses of CCs were consistent in no-till systems, especially BOH and MW. Although soil properties improved, CCs alone did not significantly suppress weed. 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

The accuracy of classification and localization of plants on images obtained from the board of an unmanned aerial vehicle (UAV) is of great importance when implementing precision farming technologies. It allows for the effective application of variable rate technologies, which not only saves chemicals but also reduces the environmental load on cultivated fields. Machine learning algorithms are widely used for plant classification. Research on the application of the YOLO algorithm is conducted for simultaneous identification, localization, and classification of plants. However, the quality of the algorithm significantly depends on the training set. The aim of this study is not only the detection of a cultivated plant (soybean) but also weeds growing in the field. The dataset developed in the course of the research allows for solving this issue by detecting not only soybean but also seven weed species common in the fields of Kazakhstan. The article describes an approach to the preparation of a training set of images for soybean fields using preliminary thresholding and bound box (Bbox) segmentation of marked images, which allows for improving the quality of plant classification and localization. The conducted research and computational experiments determined that Bbox segmentation shows the best results. The quality of classification and localization with the application of Bbox segmentation significantly increased (f1 score increased from 0.64 to 0.959, mAP50 from 0.72 to 0.979); for a cultivated plant (soybean), the best classification results known to date were achieved with the application of YOLOv8x on images obtained from the UAV, with an f1 score = 0.984. At the same time, the plant detection rate increased by 13 times compared to the model proposed earlier in the literature. 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

Weeds pose a significant challenge to agricultural productivity, requiring control strategies that are both effective and environmentally sustainable. Therefore, this study evaluated the inhibitory potential of aqueous extracts from Ricinus communis L. leaves to manage the weeds Oryza sativa L. (weedy rice) and Cyperus ferax. Extracts were obtained through pressurized liquid extraction using water as the solvent. Bioassays were conducted during pre- and post-emergence stages by foliar spraying 15 and 30 days after sowing (DAS). The effect of extraction time (1–30 min) on inhibitory efficacy was also assessed. Chemical profiles of the extracts were characterized using high-performance liquid chromatography. The extracts significantly inhibited seed germination, with suppression rates reaching 92.7%. Plant growth was also diminished, particularly with earlier treatments (at 15 DAS), resulting in reductions of up to 32% and 53% in shoot length, and 69% and 73% in total dry mass for O. sativa L. and C. ferax, respectively. Mortality rates of O. sativa L. and C. ferax reached 64% and 58%, respectively. Phenolic compounds were identified in the extracts, and higher concentrations were observed at shorter extraction times. These findings underscore the potential of R. communis L. leaf extracts as an ecologically sustainable alternative for weed management, providing an effective and natural approach that may reduce reliance on synthetic herbicides and mitigate their environmental impact. Full article

The role of rhizosphere bacteria in facilitating plant invasion is increasingly acknowledged, yet the influence of specific microbial functional traits remains insufficiently understood. This study addresses this gap by isolating two bacterial strains, Bacillus sp. ScRB44 and Pseudomonas sp. ScRB22, from the rhizosphere of the invasive weed Solidago canadensis. We assessed their nitrogen utilization capacity and indoleacetic acid (IAA) production capabilities to evaluate their ecological functions. Our three-stage experimental design encompassed strain promotion, nutrient stress, and competition phases. Bacillus sp. ScRB44 demonstrated robust IAA production and significantly improved the nitrogen utilization efficiency, significantly enhancing S. canadensis growth, especially under nutrient-poor conditions, and promoting a shift in biomass allocation toward the roots, thereby conferring a competitive advantage over native species. Conversely, Pseudomonas sp. ScRB22 exhibited limited functional activity and a negligible impact on plant performance. These findings underscore that the ecological impact of rhizosphere bacteria on invasive weeds is closely linked to their specific growth-promoting functions. By enhancing stress adaptation and optimizing resource allocation, certain microorganisms may facilitate the establishment of invasive weeds in adverse environments. This study highlights the significance of microbial functional traits in invasion ecology and suggests novel approaches for microbiome-based invasive weed management, with potential applications in agricultural soil health improvement and ecological restoration. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. 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

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

Weeds represent increasingly troublesome threats to modern wheat production. Jiangsu Province, China, is one of the largest wheat-planting areas, characterized by a high level of agricultural mechanization, in the country. In 2024, we surveyed weed communities of 924 wheat lands from 308 sites in Jiangsu and compared them with historical data surveyed in 1999–2000 in this province. A total of 156 weed species belonging to 103 genera and 39 families were recorded. Compositae and Poaceae showed the highest species richness, with 26 and 23 species, respectively. Poaceae weeds accounted for 54.3% of the overall weeds in dominance values. Beckmannia syzigachne showed the highest proportion of the overall weeds in dominance values (19.2%). Compared with the historical data, weed diversity increased by 33.3% in species, and by 62.5% in families; grassy weeds such as Beckmannia syzigachne, Alopecurus japonicus, and Alopecurus myosuroides became more dominant, while several low-growing grassy weeds became less dominant. The dominance of broadleaf weeds, such as Galium spp., Vicia spp., and Veronica spp., decreased greatly, and a clear trend of weed homogenization among different areas and types of wheat lands was observed. This is the first investigation of weed community succession against the background of agricultural modernization in China. Full article

This study conducted a comprehensive evaluation of the effects of biodegradable (BD) mulching film in onion cultivation, with a focus on plant growth, yield, soil environment, weed suppression, and film degradation, in comparison to conventional polyethylene (PE) film and non-mulching (NM) treatment across multiple regions and years (2023–2024). The BD and PE films demonstrated similar impacts on onion growth, bulb size, yield, and weed suppression, significantly outperforming NM, with yield increases of over 13%. There were no consistent differences in soil pH, electrical conductivity, and physical properties in crops that used either BD or PE film. Soil temperature and moisture were also comparable regardless of which film type was used, confirming BD’s viability as an alternative to PE. However, areas that used BD film had soils which exhibited reduced microbial populations, particularly Bacillus and actinomycetes which was likely caused by degradation by-products. BD film degradation was evident from 150 days post-transplantation, with near-complete decomposition at 60 days post-burial, whereas PE remained largely intact (≈98%) during the same period. These results confirm that BD film can match the agronomic performance of PE while offering the advantage of environmentally friendly degradation. Further research should optimize BD film durability and assess its cost-effectiveness for large-scale sustainable agriculture. Full article

Common ragweed, Ambrosia artemisiifolia (Asteraceae), is an invasive weed that causes problems in cropping systems and to human health both in its native range in North and Central America and the introduced range in Europe, Asia, Africa, and Australia. Ophraella communa, an herbivorous chrysomelid beetle from North America, was accidentally introduced into East Asia and Europe, where it significantly reduces weed populations and pollen production. Despite extensive research on its host specificity and risk assessment, the potential environmental risk of this biological control agent in southeastern Central Europe, one of the most heavily invaded areas by A. artemisiifolia, remains to be determined. This literature review attempts to summarize the results of host-range testing conducted so far and identifies plant taxa native to southeastern Central Europe that have not been tested yet. The results suggest that the host range of O. communa is not yet entirely clear, but may include some plant species from the tribes Heliantheae, Inuleae, Anthemideae, Cardueae, Astereae, and/or Coreopsideae. So far, only some of the 21 genera from those tribes with species in southeastern Central Europe have been tested. We therefore suggest further host specificity studies with representatives of these plant genera to fully assess the potential non-target risks by O. communa in agricultural and natural habitats. Full article

Moringa (Moringa oleifera Lam.) is widely recognised as a multipurpose crop suitable for human and animal consumption, medicinal, and industrial purposes, making it attractive for introduction into new ranges. Its extracts have been found to have beneficial impacts on various crop species and biological activity against multiple weeds, making their use in agriculture promising. However, concerns have also been raised about moringa’s potential to negatively impact the growth and development of other cultivated and non-cultivated plant species, especially in areas where it has been introduced outside its native range. To understand the positive and negative interactions between moringa and other plants, it is essential to investigate its allelopathic potential. Allelopathy is a biological activity by which one plant species produces and releases chemical compounds that influence the reproduction, growth, survival, or behaviour of other plants with either beneficial or detrimental effects on the receiver. Plants produce and release allelochemicals by leaching, volatilisation, or through root exudation. These biochemical compounds can affect critical biological processes such as seed germination, root and shoot elongation, photosynthesis, enzymatic activities, and hormonal balance in neighboring plants. Therefore, allelopathy is an important driver of plant composition and ecological interactions in an ecosystem. This review explores the positive and negative allelopathic effects of moringa extracts on other plant species, which may help to inform decisions regarding its introduction into new biogeographical regions and incorporation into existing farming systems, as well as the use of moringa plant extracts in agriculture. Full article

The practice in agriculture of spreading polyethylene (PE) film over the soil surface as mulch is a common, global practice that aids in conserving water, increasing crop yields, suppressing weed growth, and decreasing growing time. However, these films are typically only used for a single growing season, and thus, their use and non-biodegradability come with some serious environmental consequences due to their persistence in the soil and potential for microplastic pollution, particularly when retrieval and disposal options are poor. On the microscale, particles < 5 mm from degraded films have been observed to disrupt soil structure, impede water and nutrient cycling, and affect soil organisms and plant health. On the macroscale, there are obvious and serious environmental consequences associated with the burning of plastic film and its leakage from poorly managed landfills. To maintain the crop productivity afforded by mulching with PE film while avoiding the environmental downsides, the development and use of biodegradable polymer technologies is being explored. Here, the efficacy of a newly developed, water-dispersible, sprayable, and biodegradable polyester–urethane–urea (PEUU)-based polymer was compared with two commercial PE mulches, non-degradable polyethylene (NPE) and OPE (ox-degradable polyethylene), in a greenhouse tomato growth trial. Water savings and the effects on plant growth and soil characteristics were studied. It was found that PEUU provided similar water savings to the commercial PE-based mulches, up to 30–35%, while showing no deleterious effects on plant growth. The results should be taken as preliminary indications that the sprayable, biodegradable PEUU shows promise as a replacement for PE mulch, with further studies under outside field conditions warranted to assess its cost effectiveness in improving crop yields and, importantly, its longer-term impacts on soil and terrestrial fauna. 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