Search Results (3,906)

The expansion of organic farming in Europe increases the co-occurrence of medicinal and aromatic plant crops and pyrrolizidine alkaloid (PA)-producing weeds, raising serious contamination concerns. This study evaluated the risk of PA contamination in organically grown chamomile (Matricaria recutita L.) under field conditions in the North Vidzeme region of Latvia, with particular emphasis on vertical PA distribution in dominant weeds and on whether PA occurrence could be detected in chamomile plants growing adjacent to PA-producing weeds under field conditions. Three commercial fields were surveyed using systematic quadrat sampling to quantify weed density, biomass, and height. PA-producing weeds were segmented into 5 cm fractions, and pyrrolizidine alkaloids were quantified by LC-HRMS. Myosotis arvensis was the dominant species (up to 48,000 plants ha⁻¹), contributing the highest field-level PA load (up to 669.3 mg ha⁻¹), whereas Anchusa arvensis occurred at lower densities (≤2400 plants ha⁻¹) with a total PA load of 104.8 mg ha⁻¹. In both species, PA concentrations increased toward upper plant segments, while contamination hazard at harvest was determined by the amount of PA-bearing biomass in the harvest-relevant zone. No PAs were detected in chamomile samples collected within 10 cm of PA-producing weeds (<LOQ). Under the investigated conditions, contamination hazard was primarily associated with mechanical admixture during harvest rather than soil-mediated transfer. 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

Complexes of colchicine, colchiceine, and 10-methylthiocolchicine with Li⁺, Na⁺, and K⁺ cations in the form of chlorides were synthesized and then subjected to spectral analysis, DFT theoretical studies, and molecular modeling. The values for water solubility and lipophilicity were also determined using various platforms; both factors are very important for determining the bioavailability of the tested compounds. These compounds were also tested for their fungicidal, herbicidal, insecticidal, and cytotoxic activities. Preliminary in silico studies showed that colchicine, colchiceine, 10-methylthio-colchicine, and their chloride complexes are inactive against selected fungi, weeds, and insects. Colchicine did not show antifungal properties in biological tests and was only active against Aureobasidium pullulans, as were its chloride complexes. The process of complexing colchiceine with metal cations in chloride salts significantly improved the antifungal potency against the selected species A. pullulans and Chaetomium globosum. The highest efficacy of colchiceine complexes was observed only against A. pullulans (MIC = 130 µg/mL) and Ch. globosum (MIC = 65 μg/mL). In contrast to the antifungal activity results, anticancer studies showed that 10-methylthiocolchicine complexes are more active against the SKOV-3 cell line (~IC50 = 2 nM) than colchicine or colchiceine. Molecular-modeling studies confirmed that lithium-coordinated compounds strongly stabilized the active ligand-tubulin complex, which may contribute to the observed cytotoxic activity. 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

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

Intercropping is a pivotal strategy for achieving Sustainable Development Goal (SDG) number 2—End hunger, achieve food security and improved nutrition and promote sustainable agriculture (SDG 2)—by enhancing food security agroecosystem resilience and sustainability. By integrating diverse species within the same plot, this sustainable approach takes advantage of the beneficial interactions between them. The simultaneous cultivation of multiple crop species within the same field increases agricultural diversification and contributes to a more resilient production system, breaking the uniformity of modern intensive agriculture. The objective of this review is to evaluate intercropping practices throughout the Mediterranean, specifically in Southern Europe (Portugal, Spain, Italy, and Greece), North Africa (Morocco, Algeria, and Tunisia), and the Middle East (Turkey, Israel, and Jordan). This review intends to show advantages and disadvantages of intercropping and crops used and also highlight how intercropping systems affect crop production and quality, soil quality and microbiome, and proliferation of weeds, pests and diseases. The literature suggests that diversification in agriculture supports biodiversity and ecosystem services by the cultivation of diverse crop species together and, hence, may reduce independence in external outputs such as nutrient supply, pesticides and soil amendment. Despite the potential benefits of intercropping, the major caveats of this practice are the competition between different crops on resources, potential risks of plant protection, technical challenges of integrating the different requirements of each crop used in the system, and culture-related restrictions or regulations. Full article

Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11–v13) and 15 in the RT-DETR (v1–v4) families at various model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieved the best mAP@0.5 of 95.1% among all the evaluated models, while RT-DETRv2-R101 was the most accurate variant among the RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrated significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. This work lays a foundation for developing AI-based, vision-guided intelligent chestnut harvest systems. Full article

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article

Triazolopyrimidine sulfonamide herbicides, a prominent class of acetohydroxyacid synthase (AHAS) inhibitors, are exceptionally effective in controlling weeds in agricultural settings. To overcome metabolic resistance caused by the 5-demethylation of pyroxsulam, we sought to replace its 5-methoxy group on the triazolopyrimidine ring with alkyl substituents. This led to the synthesis of a series of N-(7-oxo-4,7-dihydro-[1,2,4]triazolo[1,5-a]pyrimidin-2-yl)arylsulfon-amides, which displayed significant structural diversification potential, culminating in the identification of the herbicidal hit compound I-20. However, the suboptimal lipophilicity compromised its herbicidal efficacy. To rectify this limitation, we modified the 7-carbonyl group to a tert-butoxy group, resulting in the highly active compound I-29. This compound demonstrated herbicidal activity comparable to or exceeding that of penoxsulam against various tested weeds, establishing it as a promising new lead compound and a candidate herbicide for further investigation. Subsequent studies revealed that I-29 exhibited a receptor binding mode and herbicidal activity profiles that closely aligned with those of penoxsulam. Moreover, its spatial structure was found to be even more conducive to inhibiting flavin adenine dinucleotide (FAD)-mediated AHAS activity. This research not only sheds light on addressing the challenge of 5-demethylation metabolic resistance in triazolopyrimidine sulfonamide herbicides but also offers new avenues for the development of AHAS-inhibiting triazolopyrimidine sulfonamide herbicides. Full article

Management of legume cover crops to reduce their cost by using no-tillage and reducing seed rate could increase their adoption. Despite the growing interest in cover crops, no information exists simultaneously regarding the potential of different species and how the sowing method and seed rate affect nitrogen (N) contribution and the yield of the subsequent maize crop. During a four-year field trial, under irrigated conditions in the Ebro valley (NE Spain), three leguminous cover crop species (pea, common vetch and hairy vetch), two cover crop seeding methods (conventional tillage and no-tillage) and two seeding rates (normal and 25% reduced) were tested and compared with a control treatment without a cover crop. The aboveground cover crop biomass and the N derived from biological fixation (BNF); aboveground biomass and total N in weeds; soil mineral nitrogen; and the effect on maize grain yield and N content were evaluated. Pea and common vetch produced more biomass (+76%) and had a higher N uptake (+50 to 60%) compared to hairy vetch. The sowing of the cover crops after no-tillage combined with a reduced sowing rate reduced biomass production by 14%. The percentage of nitrogen derived from the atmosphere (Ndfa) was above 60% for all species and the differences in total N derived from biological fixation (BNF) among treatments were related to the aboveground biomass. The introduction of cover crops reduced weed growth compared to the control especially in the no-tillage treatment. Cover crops increased maize grain yield by 12% and N uptake by 17% compared to the control treatment without a cover crop. Full article

The widespread use of synthetic pesticides has ensured crop productivity but has also raised serious environmental and human health concerns, including water contamination, biodiversity loss, and intoxication risks. In this context, global strategies for sustainable agriculture, safer alternatives are urgently needed. This systematic review, conducted in accordance with PRISMA guidelines, examines the potential of agri-food by-products as sources of bioactive compounds for biopesticide development within a circular economy framework. Residues from major agri-food chains, including the olive, potato, banana, citrus, and winery industries, were systematically analyzed with respect to their phytochemical composition, such as phenolics, flavonoids, terpenoids, fatty acids, and essential oils, and their reported bioactivity against insects, weeds, fungi, bacteria, and nematodes. The mechanisms of action, technological recovery strategies, and formulation challenges are critically discussed. Additionally, regulatory challenges and opportunities in the European and U.S. markets are described together with the role of Industry 4.0 technologies in optimizing recovery processes and product development. By promoting biopesticides from agri-food biowaste, this approach contributes to sustainable production (SDG 12), innovation in industrial processes (SDG 9), and the protection of terrestrial and aquatic ecosystems (SDGs 14 and 15), positioning food industry residues as a strategic resource for green crop protection. Full article

Reliable identification of weed species is essential for effective and sustainable weed management. In this study, we explored the use of hyperspectral imaging to distinguish nine weed species based on their spectral signatures. Although the species showed similarities in their spectral curves due to comparable growing conditions, clear differences emerged related to morphological traits and pigment composition. We analysed the spectral data using five classification algorithms: Random Forest, Support Vector Machine, Artificial Neural Network, Maximum Entropy, and SIMCA. Model performance was assessed using per-class and overall accuracy. Random Forest outperformed the other methods, achieving 93.5% accuracy despite limited and imbalanced training data. This work contributes to the development of a spectral library for weed species and demonstrates the value of machine learning for species identification across different crops and environmental conditions. Expanding such spectral databases can enhance the speed and accuracy of weed monitoring, reduce herbicide reliance, and reduce environmental impact. The proposed approach shows strong potential for integration into precision agriculture and agroecological monitoring systems, supporting more efficient and environmentally responsible farmland management. Full article

Managing weeds and improving soil health are priorities for South Dakota vegetable farmers. Clover (Trifolium spp.), used as a living mulch within and along cash crop rows, may aid in weed suppression and prevent soil erosion. However, prior research has shown living mulch often leads to yield decreases in cash crops. Research conducted in eastern South Dakota investigated the effects of four clover and four in-row soil management treatments on small-scale broccoli production. Whole plots of red (Trifolium pratense), white (Trifolium repens), and white × kura (Trifolium repens × ambiguum) clovers were direct-seeded in early spring; each clover plot and a bare ground control included four in-row management treatments: no-till + fabric, tilled + fabric, no-till, and tilled. Clover and weed growth were measured throughout the season. During the establishment year, 12.8 cm of precipitation was received, which effectively established the clover living mulch plots. However, in 2023, 5.6 cm of precipitation was received, which negatively affected the clover living mulch plots and created favorable conditions for weeds to outcompete the clover and broccoli. The results highlight the potential challenges and opportunities for managing clover cover crops as a living mulch during the first year of establishment in organic broccoli production. Full article