Search Results (164)

The genus Rhynchosia includes more than 550 species, some exhibiting invasive behavior. Rynchosia senna var. senna (RS) is a challenging weed to control in its native range; however, its invasive potential remains unknown. The aim of this study was to evaluate RS demographic parameters to determine its invasive potential, including (i) plant fecundity during the first year of young adult and in adult plants, (ii) seed dispersal, (iii) pre- and post-dispersal predation, (iv) soil seedbank persistence, and (v) field emergence patterns. RS fecundity declined in autumn and mainly in early established cohorts. Fecundity was influenced by pre-dispersal predation (Bruchus spp. 12 ± 2%), and post-dispersal removal by birds (66 ± 4%) and arthropods (37 ± 5%). Seed dispersal decreased with distance. Seedling emergence occurred mainly during early summer (75%), and to a lesser extent during late summer (20%) and autumn (5%). Seed physical dormancy loss (~80% in the first year) defines a short persistent seedbank. Under the evaluated conditions (native environment), RS shows a limited invasive potential. However, in non-native environments, in the absence of natural predators, its prolific fecundity and the occurrence of staggered emergence patterns could easily enhance invasiveness, enabling rapid colonization, as observed in Medicago polymorpha L. Full article

Passiflora edulis, introduced to the Republic of Korea in 1989 and commercially cultivated since 2012, has faced recent challenges due to viral infections impacting growth, yield, and quality. This study aimed to investigate the viral infections in P. edulis and surrounding weeds at cultivation sites in the Republic of Korea, examining possible correlations between the infections for sustainable agriculture. Over five years, P. edulis and weed samples were collected for virus diagnosis using PCR and RT-PCR assays, analyzing the infection status in both P. edulis and weeds and across weed species/families. The findings revealed infections with EuLCV, PaLCuGdV, CMV, and EAPV in both P. edulis and weeds, with PaLCuGdV showing the highest infection rate. Although no direct correlation was found between the presence of the same viruses in P. edulis and weeds, suggesting that there may be interactions among different viruses, the study highlighted that EuLCV infection could exacerbate symptoms when coinfected by other viruses. The study underscores the importance of implementing preventive measures within greenhouses to control virus transmission, offering insights for strategic management of viral diseases in P. edulis cultivation. These findings support the sustainable production of agricultural products by providing actionable strategies, such as the removal of weeds to eliminate habitats for vectors like whiteflies and aphids and the targeted management of high-incidence weeds from the Asteraceae, Solanaceae, and Oxalidaceae families to prevent and control the spread of EuLCV. Full article

Weeds are a significant adverse factor affecting rice growth, and their efficient removal necessitates an accurate, efficient, and well-generalizing weed detection method. However, weed detection faces challenges such as a complex vegetation environment, the similar morphology and color of weeds, and crops and varying lighting conditions. The current research has yet to address these issues adequately. Therefore, we propose GE-YOLO to identify three common types of weeds in rice fields in the Hunan province of China and to validate its generalization performance. GE-YOLO is an improvement based on the YOLOv8 baseline model. It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network’s ability to fuse multi-scale features and detect weeds of different sizes. Additionally, an EMA attention mechanism is used to better learn weed feature representations, while a GIOU loss function provides smoother gradients and reduces computational complexity. Multiple experiments demonstrate that GE-YOLO achieves 93.1% mAP, 90.3% F1 Score, and 85.9 FPS, surpassing almost all mainstream object detection algorithms such as YOLOv8, YOLOv10, and YOLOv11 in terms of detection accuracy and overall performance. Furthermore, the detection results under different lighting conditions consistently maintained a high level above 90% mAP, and under conditions of heavy occlusion, the average mAP for all weed types reached 88.7%. These results indicate that GE-YOLO has excellent detection accuracy and generalization performance, highlighting the potential of GE-YOLO as a valuable tool for enhancing weed management practices in rice cultivation. Full article

Fimbristylis littoralis, also known as globe fringerush, is one of the most troublesome annual Cyperaceae weeds in dryland fields and nurseries in the Yangtze Plain, Middle and Lower in China. The chloroplast (cp) genome of F. littoralis, and even this genus, has not been studied yet. In this study, the feature of the cp genome of F. littoralis and its phylogenetic relationships has been reported for the first time. It exhibited a typical circular tetramerous structure, with 86 protein-encoding genes. There were 149 simple sequence repeats (SSRs) and 1932 long repeats (LRs) detected. The IR expansion and contraction revealed the uniqueness of F. littoralis because there is a special cross-boundary gene, rps3, located at the LSC/IRb junction. Phylogenetic and divergence time dating analysis showed the close relationship between F. littoralis and the genus Cyperus, as well as many evolutionary directions of Cyperaceae family plants. The most recommended chemical method for removing this weed from nurseries is to spray 13 g ai ha⁻¹ (the amount of active ingredient applied per hectare) of saflufenacil before emergence or 7.5 g ai ha⁻¹ of halosulfuron-methyl after emergence. In conclusion, this study was the first to report the complete cp genome of a plant in the genus Fimbristylis. Our findings also provided valuable biological information for studying the phylogenetic relationships and evolution among the family Cyperaceae. Full article

Water pollution from herbicide contamination poses a significant environmental challenge, necessitating effective regenerative materials for their removal. 2,4-dichlorophenoxyacetic acid and glyphosate are among the most widely used herbicides for weed control. This study aimed to synthesize polymeric materials for the removal of these compounds from aqueous media. The study evaluated adsorption capacity, isotherms, kinetics, regeneration capacity, and the influence of pH on adsorption, alongside disinfection tests. Biodegradable polymers including chitosan, sodium alginate, and guar gum were cross-linked and characterized using infrared and Raman spectroscopy. Two samples (experiment C and M) exhibited adsorption capacities of 49.75 ± 1.474 mg g⁻¹ and 26.53 ± 1.326 mg g⁻¹ for glyphosate and 2,4-dichlorophenoxyacetic acid, respectively. Optimal adsorption was observed at pH 3.00 and 6.00 for glyphosate and 3.00 for 2,4-dichlorophenoxyacetic acid. The Langmuir and Dubinin–Radushkevich isotherms best described the adsorption behavior of glyphosate and 2,4-dichlorophenoxyacetic acid, respectively. Kinetic studies indicated that the adsorption process followed a pseudo-second-order model. Infrared and Raman absorption spectra confirmed cross-linking in the polymer samples. Regeneration tests showed that 2,4-dichlorophenoxyacetic acid adsorption remained consistent over three reuse cycles, while glyphosate adsorption increased. Disinfection tests using Escherichia coli and total coliforms demonstrated a significant reduction in colony-forming units, supporting the suitability of the material for this application. Full article

The crop yield can be affected by many factors, including various levels of weed presence. Therefore, we conducted a study to evaluate the effect of time of weed removal in combination with planting pattern and pre-emergence-applied herbicides on maize yield and yield components in 2015, 2016 and 2017. The experiments were designed in a split–split plot arrangements with three replications, which consisted of the two main plots (standard/conventional and twin-row planting pattern), two subplots (with and without pre-emergence herbicide application) and seven sub-subplots (seven weed removal timings). In the dry season of 2015, maize yield was much lower (413–9045 kg ha⁻¹) than in the wet 2016 seasons with yields of 5759–14,067 kg ha⁻¹ across both planting patterns. Yield and yield components were inversely correlated with the time of weed removal. The application of pre-emergence herbicides delayed the critical time for weed removal (CTWR), which ranged from V4 to V10 and from V3 to V11 for standard and twin-row planting patterns, respectively. Herbicides also protected various yield components, including 1000 seeds weight and number of seeds per cob. Full article

In order to replace chemical herbicides, which harm the environment and health, we seek sustainable methods to control weeds. We remove a seed-beetle species, Acanthoscelides atrocephalus, from synonymy with Acanthoscelides modestus and recognize it as a potential bioagent for Aeschynomene denticulata and A. indica. Belonging the megacornis group of the genus Acanthoscelides, its fine morphology was analyzed using high-resolution photography and scanning electron microscopy. The species differs from others of the A. megacornis group based on integument coloration, distinctive patterns of vestiture on the pronotum and pygidium, large and sexually dimorphic eyes, a strong frontal carina extending from the vertex of the head to the clypeus, and distinctive armature in the internal sac of the male genitalia. It stands out as a biological control agent due to the larvae’s habit of feeding on seeds, which hinders the development of the embryo. Through tetrazolium and germination tests, it was discovered that 100% of the infested seeds had no viable seed integument and did not germinate. A. atrocephalus is no longer a synonym of Acanthoscelides modestus. This species is a predator of A. denticulata and A. indica and prevents seed germination, becoming promising as a bioagent for the control of these weeds. Full article

Pesticides, essential for controlling pests and weeds, significantly boost agricultural productivity. However, their excessive use leads to substantial contamination of environmental matrices, including soil and water. Organophosphorus compounds, which constitute more than 30% of the global use of insecticides and herbicides, are particularly concerning, and their widespread application raises alarms among environmentalists and regulatory agencies due to their high toxicity to aquatic organisms. Therefore, to avoid the spread of these compounds within the environment, the contaminated sites may be treated with various methods. This study explored a soil detoxification procedure utilizing gaseous ozone. As a representative of organophosphorus pesticides, chlorfenvinphos was utilized as soil contaminant. This compound is still reported to occur in a number of environmental matrixes. The method used in this study involved the exposure of the soil matrix in a fluidized state to an ozone-enriched atmosphere. The ozonation procedure enabled the removal of the pesticide from the soil matrix. During its oxidation, some degradation products were detected; in particular, they included 2,4-dichlorobenzoic acid and 2-chloro-1-(2,4-dichloro-phenyl)-ethanone, whose presence was confirmed by a GC-MS system and the NIST database. However, they also underwent degradation. Moreover, on the basis of stereoselective reaction of Z and E isomers, the pesticide degradation pathway was proposed. Additionally, the efficacy of this detoxication method was evaluated using a combination of chronic and acute toxicity tests, employing Eisenia foetida earthworms as bioindicators. On the basis of the obtained results, it can be concluded that organophosphorus herbicides containing unsaturated bonds in their structure, including glyphosate, can be removed using this method. Full article

Automated agricultural robots are becoming more common with the decreased cost of sensor devices and increased computational capabilities of single-board computers. Weeding is one of the mundane and repetitive tasks that robots could be used to perform. The detection of weeds in crops is now common, and commercial solutions are entering the market rapidly. However, less work is carried out on combatting weeds in pastures. Weeds decrease the grazing yield of pastures and spread over time. Mowing the remaining weeds after grazing is not guaranteed to remove entrenched weeds. Periodic but selective cutting of weeds can be a solution to this problem. However, many weeds share similar textures and structures with grazing plants, making their detection difficult using the classic RGB (Red, Green, Blue) approach. Pixel depth estimation is considered a viable source of data for weed detection. However, systems utilizing RGBD (RGB plus Depth) are computationally expensive, making them nonviable for small, lightweight robots. Substituting one of the RGB bands with depth data could be a solution to this problem. In this study, we examined the effect of band substitution on the performance of lightweight YOLOv8 models using precision, recall and mAP50 metrics. Overall, the RDB band combination proved to be the best option for YOLOv8 small and medium detection models, with 0.621 and 0.634 mAP50 (for a mean average precision at 50% intersection over union) scores, respectively. In both instances, the classic RGB approach yielded lower accuracies of 0.574 and 0.613. Full article

Thousands of species of parasitic weeds, such as dodder, pose significant threats to agricultural crops due to their ability to spread rapidly through seeds. When cultivated lands become infested with dodder, the quality of production declines, leading to substantial damage. The most effective way to limit the infestation of agricultural lands by parasitic weeds, particularly dodder, is to control the quality of seeds intended for sowing. To obtain seed material free of dodder seeds, special separation machines equipped with magnetic drums are used. These machines operate on the principle of magnetic fields acting on ferromagnetic particles, which helps differentiate the physical states of the seeds intended for separation. This paper presents experimental research on magnetic drum separation techniques for removing dodder seeds from alfalfa seed mixtures. The study examines variables such as magnetic drum speed, feed rate, amounts of iron powder, water, solution (water and glycerin), and the initial content of dodder seeds. The experimental results indicated that using a water–glycerin solution at optimal concentrations for moistening the seeds enhances the separation efficiency of dodder seeds from alfalfa seed mixtures, compared to using only water. Additionally, the numerical content of dodder seeds in the A and C sorts, which primarily contain the seeds of the main crop, decreases with each pass through the machine, resulting in higher quality seed material. The research found that using appropriate parameters—drum rotation speed (20 rpm), iron powder quantity (19 g/min), and seed feed rate (25.39 g/s)—achieved a “free” classification for dodder in alfalfa seeds. These findings are valuable for evaluating the performance of separation equipment with magnetic drums to obtain high-quality seed material. They are also beneficial for designers, machine-building units, and economic agents specializing in this field. Full article

Weed management plays a crucial role in the growth and yield of lettuce, with timely and effective weed control significantly enhancing production. However, the increasing labor costs and the detrimental environmental impact of chemical herbicides have posed serious challenges to the development of lettuce farming. Mechanical weeding has emerged as an effective solution to address these issues. In precision agriculture, the prerequisite for autonomous weeding is the accurate identification, classification, and localization of lettuce and weeds. This study used an intelligent mechanical intra-row lettuce-weeding system based on a vision system, integrating the newly proposed LettWd-YOLOv8l model for lettuce–weed recognition and lettuce localization. The proposed LettWd-YOLOv8l model was compared with other YOLOv8 series and YOLOv10 series models in terms of performance, and the experimental results demonstrated its superior performance in precision, recall, F1-score, mAP50, and mAP95, achieving 99.732%, 99.907%, 99.500%, 99.500%, and 98.995%, respectively. Additionally, the mechanical component of the autonomous intra-row lettuce-weeding system, consisting of an oscillating pneumatic mechanism, effectively performs intra-row weeding. The system successfully completed lettuce localization tasks with an accuracy of 89.273% at a speed of 3.28 km/h and achieved a weeding rate of 83.729% for intra-row weed removal. This integration of LettWd-YOLOv8l and a robust mechanical system ensures efficient and precise weed control in lettuce cultivation. Full article

Seagrass meadows are among the most threatened ecosystems on Earth, with losses attributed to increasing coastal populations, degraded water quality and climate change. As coastal communities work to improve water quality, there is increased concern regarding the use of herbicides within the watersheds of these sensitive ecosystems. Glyphosate is the most widely used herbicide on Earth because it is non-selective and lethal to most plants. Also, the targeted amino acid synthesis pathway of glyphosate is not carried out by vertebrates, and it is generally considered one of the safer but effective herbicides on the market. At least partially due to its cost-effectiveness compared to other techniques, including mechanical harvesting, glyphosate use in the aquatic environment has increased in coastal areas to manage aquatic weeds, maintain navigable waterways and mitigate upland flooding. This has prompted concerns regarding potential ecosystem-level impacts. To test the acute toxicity of glyphosate to seagrasses, mesocosm experiments exposed Ruppia maritima and Halodule wrightii to 1 ppm, 100 ppm and 1000 ppm of glyphosate (as glyphosate acid). No significant decrease in leaf chlorophyll a (Chl a) was identified for either species at 1 ppm versus a control; however, significant decreases were observed at higher concentrations. In all except 1000 ppm mesocosms, water column Chl a increased, with a 7-fold increase at 100 ppm. These data demonstrate that at very high glyphosate concentrations, both acute toxicity and light limitation from enhanced algal biomass may have adverse impacts on seagrasses. Despite these observations, no significant adverse impacts attributed to acute toxicity were observed at 1 ppm, which is >1000 times higher than concentrations measured in the Indian River Lagoon system. Overall, herbicide use and associated decaying biomass contribute nutrients to these systems, in contrast to the removal of nutrients when mechanical harvesting is used. Based on our data and calculations, when used at recommended application rates, contributions to eutrophication, degraded water quality and harmful algal blooms were more likely to impact seagrasses than acute toxicity of glyphosate. Full article

Monitoring row centerlines during early growth stages is essential for effective production management. However, detection becomes more challenging due to weed interference and crop row intersection in images. This study proposed an enhanced Region of Interest (ROI)-based approach for detecting early-stage maize rows. It integrated a modified green vegetation index with a dual-threshold algorithm for background segmentation. The median filtering algorithm was also selected to effectively remove most noise points. Next, an improved ROI-based feature point extraction method was used to eliminate residual noises and extract feature points. Finally, the least square method was employed to fit the row centerlines. The detection accuracy of the proposed method was evaluated using the unmanned aerial vehicle (UAV) image data set containing both regular and intersecting crop rows. The average detection accuracy of the proposed approach was between 0.456° and 0.789° (the angle between the fitted centerline and the expert line), depending on whether crop rows were regular/intersecting. Compared to the Hough Transform (HT) algorithm, the results demonstrated that the proposed method achieved higher accuracy and robustness in detecting regular and intersecting crop rows. The proposed method in this study is helpful for refined agricultural management such as fertilization and irrigation. Additionally, it can detect the missing-seedling regions and replenish seedings in time to increase crop yields. Full article

Traditional Chinese medicinal herbs have strict environmental requirements and are highly susceptible to weed damage, while conventional herbicides can adversely affect their quality. Laser weeding has emerged as an effective method for managing weeds in precious medicinal herbs. This technique allows for precise weed removal without chemical residue and protects the surrounding ecosystem. To maximize the effectiveness of this technology, accurate detection and localization of weeds in the medicinal herb fields are crucial. This paper studied seven species of weeds in the field of Dictamnus dasycarpus, a traditional Chinese medicinal herb. We propose a lightweight YOLO-Riny weed-detection algorithm and develop a YOLO-Riny-ByteTrack Multiple Object Tracking method by combining it with the ByteTrack algorithm. This approach enables accurate detection and localization of weeds in medicinal fields. The YOLO-Riny weed-detection algorithm is based on the YOLOv7-tiny network, which utilizes the FasterNet lightweight structure as the backbone, incorporates a lightweight upsampling operator, and adds structure reparameterization to the detection network for precise and rapid weed detection. The YOLO-Riny-ByteTrack Multiple Object Tracking method provides quick and accurate feedback on weed identification and location, reducing redundant weeding and saving on laser weeding costs. The experimental results indicate that (1) YOLO-Riny improves detection accuracy for Digitaria sanguinalis and Acalypha australis, ultimately amounting to 5.4% and 10%, respectively, compared to the original network. It also diminishes the model size by 2 MB and inference time by 10 ms, making it more suitable for resource-constrained edge devices. (2) YOLO-Riny-ByteTrack enhances Multiple Object Tracking accuracy by 3%, reduces ID switching by 14 times, and improves overall tracking accuracy by 3.4%. The proposed weed-detection and localization method for Dictamnus dasycarpus offers fast detection speed, high localization accuracy, and stable tracking, supporting the implementation of laser weeding during the seedling stage of Dictamnus dasycarpus. Full article

This study presents an improved weed detection model, ADL-YOLOv8, designed to enhance detection accuracy for small targets while achieving model lightweighting. It addresses the challenge of attaining both high accuracy and low memory usage in current intelligent weeding equipment. By overcoming this issue, the research not only reduces the hardware costs of automated impurity removal equipment but also enhances software recognition accuracy, contributing to reduced pesticide use and the promotion of sustainable agriculture. The ADL-YOLOv8 model incorporates a lighter AKConv network for better processing of specific features, an ultra-lightweight DySample upsampling module to improve accuracy and efficiency, and the LSKA-Attention mechanism for enhanced detection, particularly of small targets. On the same dataset, ADL-YOLOv8 demonstrated a 2.2% increase in precision, a 2.45% rise in recall, a 3.07% boost in mAP@0.5, and a 1.9% enhancement in mAP@0.95. The model’s size was cut by 15.77%, and its computational complexity was reduced by 10.98%. These findings indicate that ADL-YOLOv8 not only exceeds the original YOLOv8n model but also surpasses the newer YOLOv9t and YOLOv10n in overall performance. The improved algorithm model makes the hardware cost required for embedded terminals lower. Full article