Search Results (1,881)

As the key enzyme catalyzing the final step in heme and chlorophyll biosynthesis, protoporphyrinogen oxidase (PPO) is a crucial target for herbicide development. To date, over 40 PPO inhibitors have been commercialized. They offer high efficacy, environmental safety, low application rates, and broad-spectrum weed control. Recently, significant progress has been made in PPO structural biology, with several crystal structures resolved. Despite decades of use, the emergence of resistant weeds necessitates the continuous innovation of novel PPO inhibitors. This review systematically summarizes PPO three-dimensional structures, enzyme-inhibitor interaction mechanisms, and quantitative structure–activity relationships (QSARs). Finally, we outline rational molecular design strategies for the next generation of PPO inhibitors. Full article

Lettuce (Lactuca sativa L.) is an important leafy vegetable crop, yet the efficiency and reliability of genome editing platforms in lettuce remain limited, particularly for precision base editing applications. In this study, we established an optimized PEG-mediated protoplast transformation system for lettuce through systematic evaluation of key parameters, including protoplast density, incubation time, plasmid size, and transformation method. Under optimized conditions, a maximum transient transformation efficiency of up to 81% was achieved. Using this optimized protoplast platform, we comparatively evaluated the performance of three single-base editing systems—adenosine base editor (ABE), glycosylase-based guanine base editor (gGBE), and rice alkylpurine DNA glycosylase-mediated A-to-K base editor (rAKBE)—targeting the LsALS gene, encoding acetolactate synthetase as a herbicide target with great value in weed control. Among the tested editors, ABE exhibited the highest A-to-G editing efficiency, reaching 9.3%. In contrast, gGBE and rAKBE showed lower editing efficiencies. Together, this study established a robust and reproducible protoplast-based platform for transient genome editing in lettuce and provides a practical framework for the rapid evaluation of base editing tools and target sites, firstly for gGBE and rAKBE evaluation in lettuce. The optimized system facilitates functional genomics studies and supports the development of precision breeding strategies in lettuce. Full article

Cotton represents one of the world’s most significant agricultural commodities. However, severe weed proliferation in cotton fields seriously hampers the development of the cotton industry, making precise weed control essential for ensuring healthy cotton growth. Traditional object detection methods often suffer from computational complexity, rendering them difficult to deploy on resource-constrained edge devices. To address this challenge, this paper proposes AVGS-YOLO, a lightweight and enhanced model employing a Quadruple Synergistic Lightweight Perception Mechanism (QSLPM) for precise weed detection in complex cotton field environments. The QSLPM emphasizes synergistic interactions between modules. It integrates lightweight neck architecture (Slimneck) to optimize feature extraction pathways for cotton weeds; the ADown module (Adaptive Downsampling) replaces Conv modules to address model parameter redundancy; the small object attention modulation module (SEAM) enhances the recognition of small-scale cotton weed features; and angle-sensitive geometric regression (SIoU) improves bounding box localization accuracy. Experimental results demonstrate that the AVGS-YOLO model achieves 95.9% precision, 94.2% recall, 98.2% mAP50, and 93.3% mAP50-95. While maintaining high detection accuracy, the model achieves a lightweight design with reductions of 17.4% in parameters, 27% in GFLOPs, and 14.5% in model size. Demonstrating strong performance in identifying cotton weeds within complex cotton field environments, this model provides technical support for deployment on resource-constrained edge devices, thereby advancing intelligent agricultural development and safeguarding the healthy growth of cotton crops. Full article

The impacts of climate change on Mediterranean weed flora were investigated to inform future weed management strategies. Projections indicate that rising temperatures and increased atmospheric CO₂ concentrations are likely to favor ruderal species characterized by rapid phenological development and high dispersal capacity. Enhanced abiotic stressors—such as elevated temperatures, water scarcity, and increased UV-B radiation—are expected to affect crops more severely than weeds, given the latter’s greater evolutionary potential to develop stress-tolerant biotypes. Moreover, the increased frequency and intensity of extreme events (e.g., drought, flooding, and soil salinization) may reduce weed community diversity, potentially leading to dominance by a limited number of highly competitive species and consequently intensifying reliance on chemical weed control. Simplification of weed communities may also increase vulnerability to the introduction and establishment of alien species, particularly those originating from hot and arid regions, some of which may be parasitic, toxic, or allergenic. Climate change-induced phenological mismatches between flowering plants and pollinators are likely to favor wind-pollinated weed species, further compromising the aesthetic and ecological quality of agricultural landscapes. Additionally, increased production of wind-dispersed allergenic pollen, together with the anticipated rise in herbicide applications, may pose significant risks to human health. An effective agronomic strategy to address future weed scenarios should include the genetic improvement in crops to enhance adaptive plasticity, exploiting germplasm from ancestral lines and related wild species. 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

Weed control in rice remains a critical challenge in direct-seeded rice cultivation. This study combined field and laboratory experiments to compare the efficacy of nine herbicide combinations against weeds in dryland rice fields and to evaluate their impact on rice economic traits. A model was constructed using principal component analysis for comprehensive evaluation, aiming to identify optimal herbicide combinations for direct-seeded rice under shallow drip irrigation in Hinggan League. The results indicate that pendimethalin provides better pre-emergence control compared to oxadiargyl and pretilachlor. The combination of florpyrauxifen-benzyl + benzobicyclon provided optimal weed control efficacy and rice economic performance when applied as a foliar treatment. Forty-five days after application, weed control efficacy against Echinochloa crus-galli (L.) P. Beauv. and Amaranthus retroflexus L. was 72% and 85%, respectively, with fresh weight reduction of 63%. Theoretical yield reached 4285.48 kg·ha⁻¹. At rice harvest, no herbicide residues were detected in rice straw or grains across all treatments, confirming the safety of the applied treatment for rice. Principal component analysis (PCA) was used to evaluate the comprehensive scores of each treatment, with pendimethalin + florpyrauxifen-benzyl + benzobicyclon achieving the highest score of 0.65. The study indicates that the combination of pendimethalin as a pre-emergence and florpyrauxifen-benzyl + benzobicyclon offers significant advantages in weed control efficacy and rice growth, achieving the highest comprehensive evaluation score. This combination holds important application value for weed control and grain yield assurance in direct-seeded rice fields. Full article

Precision agriculture is a key technology for addressing challenges such as increasing food demand, labour shortages, and the environmental impact of intensive agrochemical use. In this context, selective weed management remains a critical issue due to its direct effect on crop productivity and sustainability. This article presents a simulation-based framework for the design and evaluation of an agricultural robotic module for the detection, classification, and selective intervention of weeds. The proposed system integrates convolutional neural networks and the kinematic model of a 2DOF robot manipulator with 5 links for weed classification and treatment. The system is evaluated in a virtual environment, where camera calibration, perception accuracy, and the performance of the kinematic model are analysed. Quantitative results include detection accuracy, localization error, and intervention success rate under simulated field conditions. The results demonstrate selective weed management and the feasibility of simulation for developing weed control systems, while also identifying the main challenges for real-world deployment. Full article

The need to investigate ecological and sustainable approaches to weed management, as well as to reduce the negative environmental impact of chemical herbicides, is becoming increasingly important in modern agriculture and land management. Among alternative strategies, allelopathy is a natural mechanism by which particular plant species release bioactive compounds that can influence the germination, growth, and development of neighboring plants. Harnessing allelopathic interactions offers an opportunity to develop environmentally friendly alternatives to synthetic herbicides and helps preserve ecological balance within agroecosystems. This review examines the potential of allelopathic plant-derived substances for weed control in agricultural systems, with particular emphasis on managing weed populations in vegetable crops and gardens in urban and peri-urban areas. This study introduces the concept of allelopathy with definitions and general information. Subsequently, the paper analyzes the phenomenon’s presence at the plant level, its interactions, and the extracts obtained from allelopathic plants. The paper focuses on essential oils and fatty acid-derived compounds, such as pelargonic acid, which have demonstrated significant inhibitory effects on weed germination and biomass accumulation. Overall, the presented results establish a scientific basis for developing bioherbicides and support implementing sustainable, environmentally responsible horticultural practices. Full article

Triazine herbicides are widely used for weed control in agricultural systems, and their occurrence in water bodies has been frequently reported worldwide. This study assessed the efficiency of a hydrochar derived from the epicarp and mesocarp of passion fruit residues for the removal of three triazine herbicides (atrazine, ametryn, and metribuzin), with the aim of developing a material suitable for application in water remediation programs. The adsorption capacity of biomass and hydrochar derived from passion fruit residues was evaluated with and without activation using 0.5 mol L⁻¹ phosphoric acid. The adsorption of herbicides was not significantly affected by pH within the range of 4 to 8. The acid hydrochar, which exhibited the highest removal capacity among the evaluated adsorbents, presented adsorption capacities of 18.05, 10.83, and 5.05 µg g⁻¹ for atrazine, ametryn, and metribuzin, respectively. These values correspond to removal efficiencies of approximately 62%, 72%, and 52% at initial concentrations of 0.33, 0.25, and 0.15 mg L⁻¹. The adsorption equilibrium time varied among the herbicides, reaching 4 h for atrazine and ametryn and 5 h for metribuzin. The adsorption dynamics between the adsorbents and adsorbates were best described by the pseudo-second-order kinetic model for ametryn and metribuzin, while atrazine had a higher correlation with the Elovich equation. The Weber–Morris model did not adequately describe the adsorption process. Among the isotherms tested, the Freundlich model provided the best fit for all three herbicides. The desorption rates of the acid hydrochar were 51%, 13%, and 83% for atrazine, ametryn, and metribuzin, respectively. Therefore, hydrochar derived from passion fruit residues represents a promising alternative for the remediation of triazine herbicides. Full article

Weed management, particularly in organic farming, poses a significant challenge due to high manual labor costs and the crop’s low competitive ability. Precision laser technology offers a promising non-chemical alternative. This study evaluates the field performance of a novel robotic system based on a Thulium fiber laser. The validation was conducted on commercial fields of the Westhof Bio GmbH in Friedrichsgabekoog, Germany. The Weeding Success rate of the laser weeding robot was 95% and the Detection Rate 85% for carrots for one weeding cycle. For beetroot, these values are 98% and 88%, respectively, after two weeding cycles. The field trials validate the Thulium fiber laser system as an agronomically effective and economically viable alternative for sustainable weed management. The technology demonstrates the potential to significantly reduce manual labor and reliance on herbicides in challenging crops. Full article

Modern agriculture must combine profitability with environmental protection and food safety by using advanced knowledge and continuously introducing new technologies. The study aimed to evaluate the diversification of precision farming in Poland and identify limitations to its development. The study used literature reviews and two secondary data sources: the Local Database of the Central Statistical Office (GUS) regarding the share of farms using precision farming solutions by voivodeship and the nationwide precision farming survey conducted by the Polish Space Industry Foundation. The survey included 432 agricultural producers from across Poland. Data analysis utilized descriptive statistics, comparative analysis, cluster analysis, and a chi-squared (χ²) test. Existing research shows that advanced precision farming technologies in Poland have been implemented only on a limited number of farms. This is due to limited knowledge among agricultural producers, the small scale of production on most farms, and high investment costs. These technologies include equipping farms with sprayers for strip application of plant protection products during sowing or planting, precision irrigation or weed control, variable-dose fertilizers or plant protection products, and soil sampling for analysis. The use of precision farming technologies varies regionally. They are primarily used on large farms located in western and northern Poland. The study’s results may be helpful to decision-makers in agricultural policy and to agricultural producers. Full article

Transitioning from traditional to organic production is gaining popularity worldwide with significant challenges including weed management. We evaluated how legumes sown as cover crops in a synchronous intercropping (SI) system with organic oat (Avena sativa) as the main crop impacted weed communities. A split-plot design was set up on a farm in Poularies (Quebec, Canada) to compare Melilotus officinalis, Trifolium incarnatum, Trifolium repens and a control without legumes for two years (2019–2020). We determined the botanical composition, calculated diversity indices, and measured plant functional traits. Species richness was similar (S = 5.5 ± 0.4) across treatments in 2019, but higher in the control (S = 12.2 ± 2.6) and lower (S = 6.0 ± 1.2) under T. incarnatum in 2020. Shannon diversity was lower in 2019 (H′ = 1.49 ± 0.07) than in 2020 (H′ = 1.99 ± 0.04), and higher under the control (H′ = 1.87 ± 0.05) than under T. incarnatum (H′ = 1.46 ± 0.04). Weeds under T. incarnatum had a high specific leaf area and a resource-acquisition strategy, while those in the control had a higher leaf dry matter content and a resource-conservation strategy. Our study brings novel results on the use of legumes in SI systems to control weeds. Using T. incarnatum in a SI system with oat had the greatest capacity to cover the ground, control weeds and reduce their diversity, but this species and the acquisitive weeds in this treatment could compete with the main crop. Future research should evaluate the quantity and quality of yields to complete this ecological study and give appropriate agronomic recommendations. Our results could provide agronomists and farmers with indications on the level of competition weeds exert on the cropping system depending on the SI treatment. Full article

The invasive fall armyworm (Spodoptera frugiperda) threatens Philippine crops, highlighting the need for sustainable pest management. This study therefore optimizes the green synthesis of silver nanoparticles (AgNPs) from water hyacinth (Eichhornia crassipes), an abundant and problematic aquatic weed, as a potential pest control tool. Methanolic leaf extracts were prepared under varying methanol concentrations, temperatures, and extraction times, and total phenolic content was quantified using the Folin–Ciocalteu method. SEM–EDX confirmed the formation of silver nanoparticles synthesized from Eichhornia crassipes (Ec-AgNPs), with particles observed at ≤100 nm. Optimal extraction occurred at 47 °C, 90% methanol, and 76 min, maximizing phenolic yield. Overall, results suggest phenolic content and extract volume influence nanoparticle size and stability, with larger extract volumes increasing agglomeration risk. Pesticidal efficacy was not evaluated; further work is needed to assess pest control performance. Full article

Weed identification and quantification are processes that are usually manual, subjective, and error-prone. Weeds compete with crops for nutrients, minerals, physical space, sunlight, and water. Thus, weed identification is a crucial component of precision agriculture for autonomous removal and site-specific treatments, efficient weed control, and sustainability. Convolutional Neural Networks (CNNs) are very common in weed identification. This work implemented CNN models for semantic segmentation based on the U-Net architecture for automatically segmenting and quantifying weeds in potato crops using RGB images acquired by a drone at 9–10 m height, flying at 1 m/s. Remote sensing images are affected by factors that degrade image quality and the model’s accuracy. Five U-Net variants were evaluated: the original U-Net, Residual U-Net, Double U-Net, Modified U-Net, and AU-Net. The models were trained using the TensorFlow/Keras frameworks on Google Colab Pro+, following the Knowledge Discovery in Databases (KDD) methodology for image analysis. Each model was trained using a diverse custom dataset in uncontrolled environments, considering six classes: background, Broadleaf dock (Rumex obtusifolius), Dandelion (Taraxacum officinale), Kikuyu grass (Cenchrus clandestinum), other weed species, and the crop potato (Solanum tuberosum L.). The models’ segmentation was widely assessed using Mean Dice Coefficient, Mean IoU, and Dice Loss metrics. The results showed that the Residual U-Net model performed the best in multi-class segmentation, achieving a Mean IoU of 0.8021, a performance comparable to or superior to that reported by other authors. Additionally, a Student’s t-test was applied to complement the data analysis, suggesting that the model is reliable for weed quantification. Full article

Aminopyralid (2-pyridine carboxylic acid, 4-amino-3, and 6-dichloro-2-pyridine carboxylic acid) is an auxin herbicide widely used to control broad leaf weeds in pasture and hay fields. Aminopyralid compound in forage material can pass through livestock into manure. Composts derived from aminopyralid-contaminated manure can cause phytotoxic effects in sensitive crop plants. Biochar has shown synergetic effects in composting and can immobilize organic pollutants that present in compost. This experiment examined the effects of incorporating 0%, 2%, 4%, and 10% (w/w) biochar for composting dairy manure containing 50 µg kg⁻¹ aminopyralid (wet base) in 140 L plastic rotary drum reactors. Residual aminopyralid concentration after 2, 6, and 12 m composting periods, phytotoxicity effects of compost on tomato (Lycopersicon esculentum L.) plants, and the key chemical characteristics of composts after 6 and 12 m curing were assessed in two runs. After 12 months of curing, the aminopyralid concentration in the 10% biochar treatment decreased by more than 90% and eliminated the phytotoxicity of the compost. Improved adsorption and immobilization by biochar accounted for over 57% of the reduction in the 10% BC treatment. Biochar addition slightly increased the C/N ratio and total N content significantly but did not markedly impact the N transformation. The results indicate that biochar incorporation can be used as an effective practical tool to enhance the agronomic biosafety of bovine compost originated from persistent auxin herbicide aminopyralid-contaminated dairy manure. Full article