Search Results (192)

Environmental DNA (eDNA) is a highly sensitive tool widely used in biodiversity studies and for detecting organisms of biosecurity or conservation importance. However, the persistence of eDNA, even after the removal of an organism, can complicate interpretation and lead to false indications of ongoing presence. In contrast, environmental RNA (eRNA) is short-lived and may offer improved spatiotemporal precision for detecting living organisms. In this study, we evaluated the viability of environmental messenger RNA (emRNA) for targeted detection of aquatic weeds, using Amazon frogbit (Hydrocharis laevigata) as a model species. A highly sensitive qPCR assay, targeting the chloroplast rpoB transcript, was used alongside positive and negative controls to ensure workflow reliability. Our results showed that emRNA was undetectable at low density (0.56 plants per litre) and was consistently detectable at very high abundance (over eight plants per litre), although copy numbers were very low. These findings suggest that the effective detection threshold for emRNA may exceed densities typically encountered during early invasion. Consequently, the qPCR-based emRNA approaches evaluated in this study showed limited applicability for biosecurity surveillance, where reliable detection at low abundance is essential. Future research should focus on improving detection sensitivity, including eRNA enrichment. Full article

Wild carrot (Daucus carota subsp. carota), the wild relative of cultivated carrot, is globally identified as an invasive weed that threatens hybrid carrot seed production through natural cross-pollination, resulting in compromised genetic purity. Manual identification across the large areas required to ensure genetic purity in carrot seed crops is impractical. Remote sensing offers an alternative; however, morphological similarities among wild carrot, cultivated carrot, and common weeds hinder reliable detection. Early identification, however, remains essential for preventing genetic contamination. This study evaluated leaf-level hyperspectral reflectance spectroscopy (400–2450 nm) with machine learning to discriminate wild carrot from hybrid carrots, parental lines, and 19 co-occurring weed species. Spectral data from 266 wild carrot plants across three New Zealand sites and six weeks (5–10 weeks after emergence) showed negligible spatial effects (R² = 0.034–0.055, pseudo-F = 1.46–2.39, p > 0.05) and moderate temporal variation (R² = 0.136–0.151, pseudo-F = 5.48–6.17, p < 0.001), indicating broadly stable spectral signatures suitable for model generalization. Savitzky–Golay filtering, with min–max normalization outperformed SNV, yielding high full-spectrum accuracies for wild carrot vs. other species (90.35%, κ = 0.80), wild carrot vs. weeds (96.03%, κ = 0.92), and a multi-class model (90.79%, κ = 0.88). After removing atmospheric water-absorption bands to follow airborne sensing, reduced-band models based on airborne-compatible wavelengths maintained strong performance, including 89.40% accuracy (κ = 0.79) for wild carrot vs. weeds using a 20-band Subspace Discriminant model (400–402, 527, 705–720 nm). These findings demonstrate that stable wild carrot spectra and carefully selected visible and red-edge bands can underpin cost-effective UAV/UGV-mounted hyperspectral or multispectral sensors for site-specific wild carrot management. Full article

The objective of this study was to design, fabricate, and test an automated inter-row robotic system for the precision management of Palmer amaranth (Amaranthus palmeri) in cotton. A Farm-ng robotic platform with custom-designed weed pulling and cutting attachments was used to achieve weed control. The pulling system consisted of two counter-rotating rollers with a frictional cover to uproot weeds, followed by a cutting operation to shred the weeds into smaller pieces, preventing regrowth. A deep learning model, YOLOv11s, was used for weed identification, while point cloud data from a stereo camera was used to estimate weed height in real-time for dynamic adjustment of the puller height. The system was evaluated at three forward speeds (0.06, 0.15, and 0.25 m/s), two roller speeds (107 and 161 RPM), and three attachment configurations (puller-only, cutter-only, and combined). The combined configuration consistently outperformed individual operations, achieving 80% control at 0.15 m/s and a roller speed of 161 RPM. Optimal performance was observed when the angular puller velocity was 15–25 times the forward speed of the rover. This approach demonstrates the potential of integrating mechanical weed removal with real-time computer vision to improve weed management and reduce labor requirements. Full article

Protecting peanut (Arachis hypogaea L.) from weed interference is important for optimizing yield in Ghana and other West African countries. Hand removal is the main approach for controlling weeds in this region of the world. Herbicides are an alternative to hand weeding. Information on the balance between herbicides and hand labor required to control weeds is limited. To address this, research was conducted to determine weed biomass, peanut yield, time required to remove weeds by hand and apply herbicides, and financial return when metolachlor was applied immediately after seeding (preemergence), imazethapyr was applied 4 weeks after seeding (postemergence), and both herbicides were applied either with or without hand weeding. Controls without weed management and hand removal of weeds without herbicides were included. Weed biomass was the lowest and peanut yield the highest when herbicides and hand weeding were included and when hand weeding was performed twice without herbicides. Herbicides were only less effective than combinations of herbicides and hand weeding. For weed management approaches that resulted in the greatest financial return, the time required for weeding was reduced from 44.5 workdays/ha for hand weeding twice to 14.1, 17.5, and 16.0 workdays/ha for preemergence herbicide plus hand weeding, postemergence herbicide plus hand weeding, and both herbicide timings plus hand weeding, respectively. These results indicate that the combination of preemergence and postemergence herbicides and the combination of herbicide and hand weeding protect peanut yield as well as hand weeding twice. Although herbicides resulted in less labor allocated for weed management, proper stewardship of herbicide use is needed to protect farm workers and decrease potential negative impact on the environment and reduce risk from herbicide residues in peanut-based foods to consumers. Full article

Paraquat is an herbicide widely used to control weeds in various crops. Due to its use in large quantities, its dispersal into the environment is frequent, leading to contamination and negative health effects on non-target organisms because of its high toxicity and persistence in soils. Therefore, it is necessary to develop sustainable strategies to remediate sites contaminated by this compound. Bacterial remediation is a promising alternative for removing paraquat from the environment; however, the metabolic pathways used by bacteria for its degradation have not yet been precisely described. In this context, it is essential to characterize bacterial species capable of resisting and degrading paraquat, as well as to elucidate the molecular mechanisms involved in these processes. The objective of this work was to evaluate the paraquat resistance and degradation potential of the bacterial strain Caballeronia zhejiangensis CEIB S4-3, and to identify genes with a possible role in the resistance and degradation of this herbicide by analyzing the strain’s genome. The results of this research showed that, in solid medium, C. zhejiangensis CEIB S4-3 can withstand concentrations of up to 200 mg/L of paraquat supplemented as a commercial formulation (Gramoxone^®) and 400 mg/L of analytical-grade paraquat. In tryptic soy broth, the strain grew in the presence of both the commercial formulation and analytical-grade paraquat at concentrations up to 15 mg/L, whereas in mineral salts medium, supplemented with paraquat or its commercial formulation as the sole nutrient source, the strain survived exposure to paraquat at the same concentrations. Furthermore, the bacterial strain removed 40.8% of the paraquat supplemented in the culture medium at a concentration of 12 mg/L within 48 h. Finally, genomic analysis revealed the presence of genes related to paraquat resistance mechanisms and encoding enzymes involved in the degradation of this herbicide. These results position C. zhejiangensis CEIB S4-3 as a promising candidate for developing remediation alternatives for sites contaminated with this herbicide. Full article

Real-time and accurate crop row extraction is a fundamental requirement for vision-based perception in autonomous agricultural machinery. In maize fields, however, row detection is easily affected by variable illumination, leaf occlusion, weed interference, and uneven soil backgrounds, which can reduce the reliability of both GNSS- and image-based navigation methods. To address these challenges, this study proposes a plant-oriented crop row perception framework that reconstructs row structures from individual maize plant detections. A lightweight detection model, named Maize–YOLO, was developed based on YOLOv11n for maize seedling detection. Three key improvements were introduced to enhance the balance between accuracy and efficiency. First, the C3k2_Faster_CGLU module replaces the original C3k2 block to reduce redundant convolutional computation while improving selective feature representation through convolutional gated linear units, thereby enhancing robustness under complex field backgrounds. Second, a lightweight shared detection head, Detect_LSH, was designed to share convolutional parameters across multi-scale feature maps and adaptively adjust feature amplitudes, reducing detection-head redundancy while maintaining multi-scale prediction capability. Third, a Layer-Adaptive Magnitude-Based Pruning strategy was applied to remove low-contribution channels and further improve computational efficiency for CPU-based deployment. Experimental results on field-collected maize seedling images showed that Maize–YOLO achieved an mAP@0.5 of 97.6%, reduced GFLOPs by 61.9%, and maintained a CPU inference speed of 84.4 FPS. After plant detection, row centerlines were estimated using an IPM–DBSCAN–LSM pipeline, which transformed detected plant centers into a quasi-top-view space, clustered them into crop rows, and fitted continuous centerlines. The extracted crop rows reached a positional accuracy of 98.6%, with a mean angular deviation of 0.44°. These results demonstrate that the proposed method can provide accurate, lightweight, and real-time crop row perception for autonomous agricultural navigation and precision field operations. 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 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

The remediation of large-areas of Cd-contaminated soil, especially agricultural land, remains a major global challenge. Phytoremediation using hyperaccumulators is an effective method for treating Cd-contaminated soils; however, its long-term effectiveness over successive growing seasons has been insufficiently investigated. This study evaluated the sustained phytoremediation capacity of the farmland weed Bidens pilosa L., a known Cd hyperaccumulator, in a three-year pot experiment using contaminated agricultural soil from the Shenyang Zhangshi Irrigation Area (2.08 mg/kg Cd). Two harvest regimes were compared: short-term (harvest at the flowering stage, 70 days) and long-term (harvest at the fruit maturity stage, 108 days). The results showed that although higher total Cd accumulation per harvest was obtained in long-term treatments, short-term experiments resulted in a 14.7% higher net removal rate per day (NR) due to their shorter growth cycle (64.8% of the long-term period). Soil extractable Cd concentrations decreased by an average of 31.2% over three consecutive years of phytoremediation, reducing environmental risk but also limiting subsequent Cd uptake by plants. These findings demonstrate that optimizing harvest timing can substantially improve remediation efficiency per unit of time without the need for soil quality improvement measures. The short growing season characteristic of weeds found in agricultural areas is a practical advantage of phytoremediation. Full article

Sustainable alternatives to synthetic polymer-based sanitary napkins are essential to reduce the environmental impact and health concerns. This study presents a method for using water hyacinth (Eichhornia crassipes), an invasive aquatic weed, as biomass to produce biodegradable absorbent material for sanitary pads. Water hyacinth fibers were treated with an alkaline solution and incorporated into the absorbent core. Morphological, chemical, structural, functional, microbiological, and biodegradability evaluations were then conducted systematically. Scanning electron microscopy showed that non-cellulosic components were successfully removed, producing a rougher surface topology and enhanced fiber interactions. Fourier-transform infrared spectroscopy confirmed structural changes in cellulose after treatment. Additionally, X-ray diffraction showed that the crystallinity index increased from 53.21% in untreated fibers to 62.56% in treated fibers, indicating improved order and stability. The developed absorbent sanitary pad showed rapid fluid uptake, absorbing 10 mL within three seconds while maintaining a skin-compatible neutral pH of 6.87, as specified in Indian Standard IS 5405:1980. Microbial contamination remained low, with a total bacterial count of 360 CFU/g, no yeast or mold at ≤1 CFU/g, and no presence of Staphylococcus aureus. Soil burial tests showed 70% biodegradability at 40 days and approximately 95% at 60 days, indicating high biodegradability. These findings demonstrate the potential of water hyacinth as an inexpensive and environmentally friendly material for manufacturing hygienic sanitary pads, highlighting the sustainability benefits of valorizing invasive biomass and reducing reliance on synthetic polymers. Full article

Weed control is crucial for safeguarding the yield and quality of fresh maize. To achieve comprehensive, low-damage removal of weeds in fresh maize fields, an intelligent mechanical weeding system was developed. Based on the spatial distribution of maize seedling roots and agronomic requirements, a three-dimensional protection zone was established and a dedicated intra-row weeding knife was designed. An EDEM–RecurDyn co-simulation was then performed; single-factor and orthogonal experiments were used to evaluate the effects of operating speed, hydraulic cylinder extension–retraction speed, and knife bending angle on the coverage rate and intrusion rate, and to determine the optimal parameter combination. Seedling detection and field weeding trials were subsequently conducted. The detection accuracies under good and low illumination were 95.82% and 93.32%, respectively. Under the optimal settings (operating speed 1.5 km/h, hydraulic cylinder extension–retraction speed 0.22 m/s, and knife bending angle 20°), the system achieved a mean weeding rate of 90.79% and a mean seedling damage rate of 2.27%. The results demonstrate stable performance and confirm that the proposed system meets the requirements for comprehensive, low-damage weeding in fresh maize fields, providing a reference for the design of intelligent mechanical weeding equipment. Full article

Pursuing the so-defined biorefinery approach, residual biomass, such as agro-industrial wastes, should first be exploited for the extraction and production of high-value-added products and then processed for energy valorisation through anaerobic digestion (AD). However, the treatments applied to achieve the first goal could impact biogas yield. This problem can be solved by co-digesting the treated biomass with others. In this study, Brewery’ Spent Grain (by itself, a good biogas producer) was treated with an ionic liquid (IL) composed of triethylamine and sulfuric acid [TEA][HSO₄] for lignin removal. The residual biomass (pulp, BSGp) was then used for biogas production. The tests revealed a marked reduction in the total quantity of biomethane (per unit of volatile solid—VS). In detail, 6.82 × 10⁻⁴ Nm³CH₄/gVS of biomethane was produced with BSGp, against 1.31 × 10⁻³ Nm³CH₄/gVS with BSG. The lack of organic nitrogen after the IL-based treatment prevented biogas production, resulting in a shorter production period. To compensate for the nitrogen deficiency and restore the optimal C/N ratio, BSGp was mixed with Lemna minor (LM), an aquatic weed with a high nitrogen content. By itself, LM cannot be considered a good biogas producer as proven in this study. However, the co-digestion of LM with BSGp extended the production period and kept the daily production close to that registered in test made with the sole BSGp, thus achieving a total biomethane production equal to 1.83 × 10⁻³ Nm³CH₄/gVS, even higher than the one registered with untreated BSG. Full article

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

Accurate weed detection in crop fields remains a challenging task due to the diversity of weed species and their visual similarity to crops, especially under natural field conditions where lighting and occlusion vary. Traditional methods typically attempt to directly identify various weed species, which demand large-scale, finely annotated datasets and often suffer from low generalization. To address these challenges, this study proposes a novel dual-model framework that simplifies the task by dividing it into two tractable stages. First, a crop segmentation network is used to identify and remove cabbage (Brassica oleracea L. ssp. pekinensis) regions from field images. Since crop categories are visually consistent and singular, this stage achieves high precision with relatively low complexity. The remaining non-crop areas, which contain only weeds and background, are then subdivided into grid cells. Each cell is classified by a second lightweight classification network as either background, broadleaf weeds, or grass weeds. The classification model achieved F₁ scores of 95.1%, 91.1%, and 92.2% for background, broadleaf weeds, and grass weeds, respectively. This two-stage approach transforms a complex multi-class detection task into simpler, more manageable subtasks, improving detection accuracy while reducing annotation burden and enhancing robustness under the tested field conditions. Full article

Market gardening plays a central role in food security and improving household income in Ziguinchor, Senegal. Faced with growing environmental and socio-economic challenges, agroecology emerges as a sustainable pathway for strengthening this agro-economic activity. This study evaluates the adoption of agroecological practices by urban and peri-urban market gardeners, identifying influencing factors and constraints. A survey of 300 farmers was conducted in Ziguinchor, and data were analyzed using Excel. Relative Importance Index (RII), Weighted Average Index (WAI), and Problem Confrontation Index (PCI) ranked the most used practices, influencing factors, and adoption barriers. Results show that 79.67% of respondents were women, mostly over 45, with secondary education. Most of market gardeners consider this activity main source of income, and have been doing so for more than 10 years. Common agroecological practices include: removing weeds and diseased plants, organic fertilization, watering, crop rotation, and recommended fertilizer application, with relative importance indices of 0.75, 0.75, 0.72, 0.73, and 0.62, respectively. Key constraints include the lack of labor (PCI = 789), lack of information and training (PCI = 597), high cost of improved seeds (PCI = 549), and limited access to organic fertilizer (PCI = 538). Reinforcing extension services, capacity building, and both technical and financial support is essential to promote agroecological practices. Full article