Search Results (107)

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

Parboiled rice hull mulch is becoming a widely used non-chemical weed control method in container nurseries, but research is lacking on the performance of rice hulls in outdoor production nursery environments. Two separate experiments were conducted to assess the effect of rice hull mulch on the emergence and growth of several common nursery weed species in an outdoor container nursery environment and to determine how well rice hulls performed over time. In a 12-week container experiment, rice hulls provided significantly better control of common container nursery weed species when seeds were placed on top of rice hull mulch applied at depths of 1.3 or 2.5 cm. In contrast, there was little difference in control when seeds were placed below the mulch layer regardless of mulch depth. In a long-term evaluation over 12 months, rice hulls applied at depths of 1.3, 2.5 or 5.0 cm provided control similar to pre-emergence herbicides through 6 months, but herbicides provided better control thereafter. Overall, rice hulls proved to be a suitable alternative to commercial herbicides and would be recommended in cases where pre-emergence herbicides cannot be used due to crop safety or grower preferences. Full article

Weeds represent a major constraint in the cultivation of medicinal and aromatic plants (MAPs), causing significant reductions in yield, biomass, and essential oil quality while increasing labor and production costs. Effective weed management is particularly critical during early crop growth, when young plants are most vulnerable to competition. Non-chemical strategies, including cultural practices, mechanical and thermal weeding, mulching, and crop diversification, have proven effective in suppressing weeds, enhancing crop competitiveness, and maintaining yield and quality, especially in organic or low-input systems. Mulching and optimized cultivation strategies consistently provide reliable weed control, improve soil moisture and nutrient use efficiency, and can influence secondary metabolite accumulation. Chemical weed control, including selective pre- and post-emergence herbicides, remains important in slow-growing MAPs but is increasingly constrained by regulatory restrictions and concerns over residues in raw plant material and essential oils. Integrated weed management combining cultural, physical, and reduced chemical approaches offers the most effective solution, balancing efficacy, crop safety, and product quality. Emerging strategies such as bioherbicides, precision agriculture, and robotic systems hold promise but require further research. Advancing weed management in MAPs will depend on interdisciplinary studies, field-scale validation, and technology-driven innovations to support sustainable, high-quality production. Full article

There is a growing demand for alternative low cost and sustainable weed management technology suitable for aerobic and organic farming. This study evaluates 915 MHz microwave heating as a potential non-chemical approach for managing weedy rice while assessing its impact on soil physicochemical properties and selected microbial groups. Microwave power levels of 10, 20, and 30 kW were applied to soil at depths of 2.5, 8.9, and 15.2 cm under controlled laboratory conditions. Weed emergence was quantified using the total germinability index (TGI), and soil physicochemical and microbial responses were analyzed in separate experiments. TGI decreased significantly with increasing microwave power and decreasing soil depth, ranging from 0.84 (10 kW at 15.2 cm) to 0 (20 kW at 2.5 cm and 30 kW at 8.9 cm). For 8.9 cm soil depth, energy levels between 176 and 265 kJ/kg resulted in 80–100% emergence suppression, while treatment of 15.2 cm soil at 30 kW for 30 s (188 kJ/kg) reduced TGI by approximately 80% and germination by 64% relative to control. Soil physicochemical properties showed minimal changes, with values remaining within agronomically acceptable ranges. Total bacterial abundance was not significantly affected, whereas ammonia-oxidizing archaea and bacteria were reduced following treatment. These results indicate that microwave heating can effectively suppress weedy rice emergence under controlled conditions, primarily through thermal effects. However, TGI reflects emergence suppression and does not distinguish underlying mechanisms such as lethality, injury, or dormancy. Additionally, limitations including low replication, lack of depth-matched controls, and limited spatial temperature measurements should be considered. Further field-scale studies are needed to validate performance, optimize energy requirements, and assess long-term soil impacts. Full article

The potential withdrawal of glyphosate necessitates a comprehensive evaluation of alternative weed control strategies that balances human health safety with environmental concerns. This study applied a decision-support grid to compare the impacts of glyphosate-based reference strategies against chemical and non-chemical alternatives across four Belgian case studies: pome fruit orchards, grassland renewal, arable weed patches, and railways. The assessment integrated twelve risk indicators including human, environmental and biodiversity risk, and life cycle assessment for global warming potential (GWP) into a Final Scenario Score (FSS). The results indicated that only one alternative strategy, the chemical alternative in local weed patch control, achieved the FSS threshold (<0.75) required to justify substitution (FSS = 0.70). Chemical alternatives in other case studies frequently shifted burdens; for instance, bio-herbicides in railways increased risks to residents and aquatic organisms compared to the reference. Conversely, mechanical and thermal alternatives eliminated chemical toxicity but resulted in GWP increases up to 32 times higher than glyphosate-based practices. These findings demonstrate that chemical substitutes often maintain toxicity risks while non-chemical strategies trade them for increased climate impacts. Consequently, a ban on glyphosate is currently unsupported by the environmental performance of available alternatives in these temperate high-intensity systems. Sustainable progress requires a transition period where optimized conventional strategies remain available within integrated weed management, while innovations in electrification and precision technology are accelerated to resolve current trade-offs. Full article

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future. Full article

Weeds pose a significant threat to crop yields, both in quantitative and qualitative terms. Modern agriculture relies heavily on herbicides; however, their excessive use can lead to negative environmental impacts. As a result, recent research has increasingly focused on Integrated Weed Management (IWM), which employs multiple complementary strategies to control weeds in a holistic manner. Nevertheless, large-scale adoption of this approach requires a solid understanding of the underlying tactics. This systematic review analyses recent studies (2013–2022) on herbicide alternatives for weed control across major cropping systems in the EU-27 and the UK, providing an overview of current knowledge, the extent to which IWM tactics have been investigated, and the main gaps that help define future research priorities. The review relied on the IWMPRAISE framework, which classifies weed control tactics into five pillars (direct control, field and soil management, cultivar choice and crop establishment, diverse cropping systems, and monitoring and evaluation) and used Scopus as a scientific database. The search yielded a total of 666 entries, and the most represented pillars were Direct Control (193), Diverse Cropping System (183), and Field and Soil Management (172). The type of crop most frequently studied was arable crops (450), and the macro-area where the studies were mostly conducted was Southern Europe (268). The tactics with the highest number of entries were Tillage Type and Cultivation Depth (110), Cover Crops (82), and Biological Control (72), while those with the lowest numbers were Seed Vigor (2) and Sowing Depth (2). Overall, this review identifies research gaps and sets priorities to boost IWM adoption, leading policy and funding to expand sustainable weed management across Europe. Full article

Aeschynomene indica L. has become a problematic weed in the upland direct-seeding rice fields of the lower Yangtze River region, China, leading to substantial yield reductions. A comprehensive understanding of its seed germination ecology and response to herbicides is crucial for developing effective control strategies. This study examined the effects of major environmental factors including temperature, light, pH, salt stress, osmotic potential, and burial depth on seed germination of A. indica and assessed the efficacy of 20 commonly used herbicides in rice under controlled conditions. Results revealed that germination was highly sensitive to temperature, with optimum constant and alternating temperatures of 35 °C and 40/30 °C (day/night), respectively, both achieving germination rates above 90%. The seeds were non-photoblastic, maintaining a high germination rate of 83.33% under complete darkness. Germination remained consistently high across a broad pH range from 4 to 9, with rates ranging from 83.33% to 96.67%. Salt and osmotic stresses markedly suppressed germination, with EC₅₀ values of 195.08 mmol·L⁻¹ NaCl and −0.43 MPa, respectively. Seedling emergence decreased significantly with increasing burial depth, with no emergence occurring at depths greater than 7 cm. The EC₅₀ for emergence was 4.21 cm. Among the herbicides screened, saflufenacil and mesotrione were the most effective pre-emergence treatments, with GR₅₀ values of 5.38 and 12.02 g ai ha⁻¹, respectively. Florpyrauxifen-benzyl and fluroxypyr-meptyl exhibited the highest post-emergence activity, with GR₅₀ values of 0.20 and 19.69 g ai ha⁻¹, respectively. These results underscore the high ecological adaptability of A. indica to paddy fields conditions and provide a scientific foundation for integrating chemical control with cultural practices such as deep tillage into sustainable weed management systems for paddy fields. Full article

Glyphosate is the most widely used herbicide in the world for weed control; however, due to lixiviation, wind and runoff effects, an important fraction can reach the soil, aquifers and surface waters, affecting environmental and human health. The behavior of glyphosate in two agricultural soils (C1: silty clay texture, and C2: silty loam texture) was analyzed in this study using a laboratory-scale model. Water transfer was modeled with the Richards equation, while glyphosate transport was modeled using the advection–dispersion equation, with both solved using finite difference methods. The glyphosate dispersion coefficient was obtained from laboratory concentration data derived from the soil profile via inverse modeling using a non-linear optimization algorithm. The goals of this study were to (i) quantify glyphosate retention in soils with different physical and chemical properties, (ii) calibrate a numerical model for the estimation of dispersivity and simulation of short- and long-term scenarios, and (iii) assess vulnerability to groundwater contamination. The results showed that C1 retained a greater amount of glyphosate in the soil profile, while C2 was considered more vulnerable as it liberated the contaminant more easily. The model accurately reproduced the measured concentrations, as evidenced by the RMSE and R² statistics, thus supporting further scenario simulations allowing for prediction of the fate of the herbicide in soils. The approach utilized in this study may be useful as a tool for authorities in environmental fields, enabling better control and monitoring of soil contamination. These findings highlight potential risks of contamination and reinforce the importance of agricultural management strategies. Full article

The implementation of vegetative cover crops in vineyards is a sustainable alternative to chemical weed control, potentially influencing both soil fertility and grape-associated microbiota. This study evaluated the impact of six groundcover management strategies under vines—white clover (Trifolium repens), red clover (Trifolium pratense), burr medic (Medicago polymorpha), lupine (Lupinus albus), spontaneous weeds, and an herbicide-treated control—on the microbial dynamics and physicochemical properties of Cabernet Sauvignon must and wine from the Maipo Valley, Chile. Amplicon sequencing of bacterial (16S rRNA) and fungal (ITS) communities was combined with spontaneous fermentation trials and chemical analyses of must and wine. Fungal and bacterial communities on grape surfaces were dominated by Ascomycota and Proteobacteria, respectively, with no significant compositional differences among treatments. During fermentation, Metschnikowia and Tatumella were the most abundant non-Saccharomyces and bacterial genera, respectively, showing dynamic shifts across fermentation stages. Legume-based covers, particularly red clover, increased wine total acidity and polyphenol index while reducing pH. Correlation analyses revealed associations between specific microbial taxa (Metschnikowia, Cohnella, Saliterribacillus) and key enological parameters. Overall, these findings demonstrate that leguminous cover crops subtly modulate vineyard microbial ecology and fermentation outcomes, offering an environmentally sustainable pathway to enhance enological differentiation in semi-arid viticultural regions. Full article

Glyphosate, a widely used non-selective herbicide, has been a subject of intense scientific debate due to its environmental persistence and potential health risks. This review examines glyphosate’s mechanisms of action, its effects on crop production, and its broader environmental impact, including soil degradation, water contamination, and biodiversity loss. Furthermore, it examines the expanding body of research linking glyphosate exposure to various human health concerns, including metabolic, neurological, reproductive, and oncological disorders. The review also assesses glyphosate’s role in hindering the achievement of the Sustainable Development Goals (SDGs), particularly those related to food security, health, access to clean water, and the protection of marine ecosystems. Finally, potential alternatives to glyphosate-based weed control, including organic and non-chemical methods, are discussed to promote sustainable agricultural practices that balance productivity with ecological and public health considerations. The evidence reviewed highlights glyphosate’s pervasive presence across ecosystems and its potential to disrupt both environmental and human health. The findings underscore the urgent need to regulate glyphosate use, prioritize soil and water protection, and accelerate the transition toward sustainable, low-toxicity weed management strategies that align with global sustainability objectives. Full article

Agricultural management in Europe faces increasing pressure to reduce its environmental footprint. Implementing precision agriculture for weed management could offer a solution and minimize the use of chemical products. High spatial resolution imagery from real time kinematic (RTK) unmanned aerial vehicles (UAV) in combination with supervised convolutional neural network (CNNs) models have proven successful in making location specific treatments. This site-specific advice limits the amount of herbicide applied to the field to areas that require action, thereby reducing the environmental impact and inputs for the farmer. To develop performant CNN models, there is a need for sufficient high-quality labelled data. To reduce the labelling effort and time, a new labelling method is proposed whereby image subsection pairs are labelled based on their relative differences in weed pressure to train a CNN ordinal regression model. The model is evaluated on detecting weed pressure in potato (Solanum tuberosum L.). Model performance was evaluated on different levels: pairwise accuracy, linearity (Pearson correlation coefficient), rank consistency (Spearman’s (r_s) and Kendal (τ) rank correlations coefficients) and binary accuracy. After hyperparameter tuning, a pairwise accuracy of 85.2%, significant linearity (r_s = 0.81) and significant rank consistency (r_s = 0.87 and τ = 0.69) were found. This suggests that the model is capable of correctly detecting the gradient in weed pressure for the dataset. A maximum binary accuracy and F1-score of 92% and 88% were found for the dataset after thresholding the predicted weed scores into weed versus non-weed images. The model architecture allows us to visualize the intermediate features of the last convolutional block. This allows data analysts to better evaluate if the model “sees” the features of interest (in this case weeds). The results indicate the potential of ordinal regression with relative labels as a fast, lightweight model that predicts weed pressure gradients. Experts have the freedom to decide which threshold value(s) can be used on predicted weed scores depending on the weed, crop and treatment that they want to use for flexible weed control management. Full article

Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage. Full article

Weeds are one of the main problems in cultivation of medicinal and aromatic plants (MAPs); they negatively affect yield (herba and essential oil), and the overall quantity and quality of biomass, flowers, roots, seeds, and secondary metabolites. This review evaluates mulching as a sustainable, non-chemical method for weed management in the cultivation of MAPs and examines how effectively organic, synthetic, and living mulches reduce weeds and increase yields. Regarding different mulch materials such as straw, sawdust, bark, needles, compost, polyethylene, and biodegradable films, the basic processes of mulch activity, including light interception, physical suppression, and microclimate adjustment, are examined. The review further analyzes the impact of mulching on soil parameters (moisture, temperature, pH, chlorophyll content) and the biosynthesis of secondary metabolites. The findings consistently indicate that mulching substantially reduces weed biomass, improves crop performance, and supports organic farming practices. However, there are still issues with cost, material availability, and possible soil changes, and the efficacy is affected by variables including cultivated plant species, mulch type, and application thickness. The review highlights the importance of further research to optimize the selection of mulch and MAPs and their application across various agroecological conditions, and indicates that mulching is a potential, environmentally friendly technique for weed control in MAP cultivations. 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