Search Results (401)

‘Candidatus Phytoplasma solani’ of the 16SrXII group is an emerging vector-borne pathogen in European crop production. The cixiid planthopper Hyalesthes obsoletus transmits 16SrXII-A stolbur phytoplasmas that are associated with diseases in grapevine, potato, and various weeds. While 16SrXII-P genomes transmitted by Pentastiridius leporinus are available, no genome of an H. obsoletus-transmissible 16SrXII-A phytoplasma has been reported from Germany. Here, we present insights into the phylogenetic position and pathogen–host interactions through the functional reconstruction of the complete 832,614 bp genome of the H. obsoletus transmissible ‘Ca. P. solani’ 16SrXII-A strain POT from a potato field. Phylogenetic analyses highlight the heterogeneity within the stolbur group using whole-genome alignment and a BUSCO-based core gene analysis approach. The POT chromosome shares highest average nucleotide identity with Italian bindweed-associated genomes and displays strong synteny with the c5 strain. Consistent with the typical phytoplasma architecture, the POT genome combines mobile-element-driven instability with a conserved core metabolism. Virulence factors include transposon-linked effectors but lack pathogenicity island organisation. POT further differs from other 16SrXII-group phytoplasmas through unique collagen-like proteins that could contribute to virulence. These findings provide a robust genomic framework that improves diagnostics, enables strain-level resolution and supports the assessment of breeding materials under stolbur phytoplasma pressure, thereby refining our understanding of stolbur phytoplasma diversity and highlighting the evolutionary divergence within the 16SrXII subgroup. Full article

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

Weed competition represents a primary constraint in sesame production, causing substantial yield losses typically ranging from 18 to 68% under inadequate control measures. Precise crop–weed discrimination remains challenging due to morphological similarities, complex field conditions, and vegetation overlapping. To address these issues, we developed Context-Aware Feature-Enhanced Detection Transformer (CAFE-DETR), an enhanced Real-Time Detection Transformer (RT-DETR) architecture optimized for sesame–weed identification. First, the C2f with a Unified Attention-Gating (C2f-UAG) module integrates unified head attention with convolutional gating mechanisms to enhance morphological discrimination capabilities. Second, the Hierarchical Context-Adaptive Fusion Network (HCAF-Net) incorporates hierarchical context extraction and spatial–channel enhancement to achieve multi-scale feature representation. Furthermore, the Polarized Linear Spatial Multi-scale Fusion Network (PLSM-Encoder) reduces computational complexity from O(N²) to O(N) through polarized linear attention while maintaining global semantic modeling. Additionally, the Focaler-MPDIoU loss function improves localization accuracy through point distance constraints and adaptive sample focusing. Experimental results on the sesame–weed dataset demonstrate that CAFE-DETR achieves 90.0% precision, 89.5% mAP₅₀, and 59.5% mAP_50–95, representing improvements of 13.07%, 4.92%, and 2.06% above the baseline RT-DETR, respectively, while reducing computational cost by 23.73% (43.4 GFLOPs) and parameter count by 10.55% (17.8 M). These results suggest that CAFE-DETR is a viable alternative for implementation in intelligent spraying systems and precision agriculture platforms. Notably, this study lacks external validation, cross-dataset testing, and field trials, which limits the generalizability of the model to diverse real-world agricultural scenarios. 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

Weeds pose a ubiquitous challenge to researchers as a source of unintended variation on crop yield and other metrics in designed experiments, creating a need for practical and spatially comprehensive techniques for weed detection. To that end, imagery acquired using unmanned aerial systems (UASs) and classified using pixel-based, object-based, or neural network-based approaches provides researchers a promising avenue. However, in scenarios where spectral differences cannot be used to distinguish between crop and weed foliage, where physical overlap between crop and weed foliage obstructs object-based detection, or where large datasets are not available to train neural networks, alternative methods may be required. For instances where there is a consistent difference in height between crop and weed plants, a mask can be applied to a canopy height model (CHM) such that pixels are determined to be weed or non-weed based on height alone. The CHM Mask (CHMM) approach, which produces a measure of weed area coverage using UAS-acquired, red–green–blue imagery, was used to detect Palmer amaranth in Sweetpotato with an overall accuracy of 86% as well as explain significant variation in sweetpotato yield (p < 0.01). The CHMM approach contributes to the diverse methodologies needed to conduct weed detection in different agricultural settings. Full article

The invasive plant Sphagneticola trilobata (Asteraceae), known for its rapid growth and strong adaptability, has spread widely across tropical and subtropical regions worldwide, posing a serious threat to local plant diversity. Traditional weed control approaches have limited effectiveness, and the overuse of chemical herbicides such as glyphosate not only leads to resistance but also harms the environment. This study elucidated the important roles of jasmonic acid (JA) and arbuscular mycorrhizal fungi (AMF) in jointly promoting the herbicide resistance of S. trilobata. Firstly, the herbicide tolerance of S. trilobata was tested. Then, a field experiment was conducted to test the relation between AMF colonization and herbicide resistance in S. trilobata by high-throughput sequencing, and the metabolomics analysis was conducted to test the secondary metabolite difference by AMF colonization. Lastly, a greenhouse experiment was conducted to assess the synergistic effects of JA and AMF on S. trilobata’s herbicide resistance. Results showed that invasive S. trilobata has stronger glyphosate tolerance than its native congener. The field experiment showed that glyphosate treatment significantly increased the AMF colonization in S. trilobata and altered the composition of the rhizosphere AMF community. Metabolomics analysis revealed that AMF colonization upregulates the expression of stress-related metabolites, especially JA content. The greenhouse experiment further validated that both AMF colonization and JA application could enhance the stem and root length and plant biomass. Under glyphosate application, AMF and JA enhanced plant growth and relative chlorophyll content, while reducing relative flavonol and anthocyanin contents. Furthermore, the interaction of AMF and JA treatments led to a significant synergistic effect in plant growth and resistance to glyphosate. Our findings emphasize the necessity to simultaneously consider eliminating the promoting effects of JA and AMF on the herbicide resistance when implementing chemical control management strategies for the control of S. trilobata. This study provides new theoretical bases and sustainable control strategies for invasive plant management, as well as important references for research on plant-microbe interactions and stress resistance mechanisms. Full article

This research addresses the critical need for efficient image annotation in precision agriculture, using the wild blueberry (Vaccinium angustifolium Ait.) cropping system as a representative application to enable data-driven crop management. Tasks such as automated berry ripeness detection, plant disease identification, plant growth stage monitoring, and weed detection rely on extensive annotated datasets. However, manual annotation is labor-intensive, time-consuming, and impractical for large-scale agricultural systems. To address this challenge, this study evaluates an automated annotation pipeline that integrates zero-shot detection models from two frameworks (Grounding DINO and YOLO-World) with the Segment Anything Model version 2 (SAM2). The models were tested on detecting and segmenting ripe wild blueberries, developmental wild blueberry buds, hair fescue (Festuca filiformis Pourr.), and red leaf disease (Exobasidium vaccinii). Grounding DINO consistently outperformed YOLO-World, with its Swin-T achieving mean Intersection over Union (mIoU) scores of 0.694 ± 0.175 for fescue grass and 0.905 ± 0.114 for red leaf disease when paired with SAM2-Large. For ripe wild blueberry detection, Swin-B with SAM2-Small achieved the highest performance (mIoU of 0.738 ± 0.189). Whereas for wild blueberry buds, Swin-B with SAM2-Large yielded the highest performance (0.751 ± 0.154). Processing times were also evaluated, with SAM2-Tiny, Small, and Base demonstrating the shortest durations when paired with Swin-T (0.30–0.33 s) and Swin-B (0.35–0.38 s). SAM2-Large, despite higher segmentation accuracy, had significantly longer processing times (significance level α = 0.05), making it less practical for real-time applications. This research offers a scalable solution for rapid, accurate annotation of agricultural images, improving targeted crop management. Future research should optimize these models for different cropping systems, such as orchard-based agriculture, row crops, and greenhouse farming, and expand their application to diverse crops to validate their generalizability. Full article

Intercropping medicinal and aromatic plants with other crops has demonstrated substantial potential for improving sustainable agricultural systems. Across a wide range of species, including yarrow, dill, wormwood, pot marigold, ajowan, coriander, saffron, cumin, lemongrass, Moldavian dragonhead, fennel, hyssop, dragons head, lavender, chamomile, lemon balm, mint, black cumin, basil, rose-scented geranium, aniseed, patchouli, rosemary, sage, summer savory, marigold, thyme, fenugreek, and vetiver, integration with cereals, legumes, vegetables, and perennial trees enhanced both land use efficiency and overall crop productivity. These systems often resulted in improved essential oil (EO) yield and composition, optimized plant growth, and increased economic returns, particularly when combined with organic inputs or biofertilizers. In addition to productivity gains, intercropping provides important ecological benefits. It can enhance soil fertility, stimulate microbial activity, and contribute to effective pest and weed management. Incorporating medicinal and aromatic plants into orchards, vineyards, or agroforestry systems further supported biodiversity. It influenced secondary metabolite production in companion crops, demonstrating the multifunctional role of these species in integrated farming systems. Overall, intercropping medicinal and aromatic plants represents a versatile and economically viable approach for sustainable crop production. The selection of compatible species, careful management of planting ratios, and appropriate agronomic practices are critical to maximizing both biological and economic benefits. Such strategies not only increase farm profitability but also promote environmental sustainability and resilience in diverse cropping systems. This review explores the effects of MAP integration on agroecological performance and identifies key mechanisms and practical outcomes. Full article

This study investigated the interactive effects of different light conditions and weed control methods on the medicinal compound composition and rhizosphere bacterial community structure of Epimedium sagittatum. A completely randomized block design was employed, incorporating four treatments: full light with manual weeding (LN), shade with manual weeding (SN), full light with weed-control fabric mulch (LG), and shade with mulch (SG). Active compound levels in two-year-old plants were quantified using HPLC, and rhizobacterial diversity was assessed via high-throughput sequencing. The results indicated that the SG treatment significantly enhanced the photosynthetic efficiency and yielded the highest levels of epimedin C and total active compounds. In contrast, the SN treatment fostered a beneficial rhizosphere environment—characterized by increased pH, ammonium nitrogen (NH₄⁺-N), bacterial diversity, and the abundance of Flavobacterium—which supported the highest production of epimedin B and icariin. Redundancy analysis confirmed that these microbial shifts were primarily driven by soil pH, nitrate nitrogen (NO₃⁻-N), and shading. Furthermore, while stochastic processes governed bacterial community assembly, deterministic selection intensified across the treatments from LN to SG. Collectively, our findings demonstrate that light and mulching can be strategically tailored to manipulate the plant–soil-microbe system, thereby enabling precise modulation of the medicinal quality of E. sagittatum. Full article

Ancient wheats can be understood as dynamic populations of historically cultivated wheat, which, unlike modern varieties, have not been developed through organised genetic improvement programmes, but rather through traditional farmer selection and local adaptation over centuries. Recently, ancient wheats have enjoyed renewed popularity, particularly in Italy, due to their wide genetic diversity and the significant role of wheat and its derivatives (e.g., bread, pasta, and baked goods) in the country’s culinary and cultural heritage. However, information on the characteristics of Italian ancient wheats remains limited and fragmented. Therefore, this review aims to collect, organise and compare the available evidence on the historical, agronomic, economic and sustainability parameters of ancient wheats, in order to provide an overall assessment of these varieties. The results showed that 34 Italian ancient wheats were studied, mainly from Tuscany and Sicily. With plant heights of up to 180 cm and yields of 1.4–4.8 t/ha, ancient wheats are characterised by greater height but lower productivity compared to modern wheats. They demonstrate good adaptability to poor soils and climatic stress, natural competitiveness with weeds and potential resistance to pathogens, rendering them suitable for sustainable, low-input agricultural systems. Furthermore, ancient wheat flours cost more than twice as much as commercial flours, with average prices of €3.00–5.10/kg, mainly due to artisanal production methods and belonging to short or niche supply chains. Finally, considerable variability in test weight (TW) and thousand kernel weight (TKW) could negatively affect flour or semolina yields. In conclusion, despite their low productivity, ancient wheats could offer significant opportunities in terms of environmental sustainability and biodiversity conservation, proving to be a strategic resource for more resilient and sustainable agriculture. Full article

The curation of large-scale, diverse datasets for robust weed detection is extremely time-consuming and resource-intensive in practice. Generative artificial intelligence (AI) opens up opportunities for image generation to supplement real-world image acquisition and annotation efforts. However, it is not a trial task to generate high-quality, multi-class weed images that capture the nuances and variations in visual representations for enhanced weed detection. This study presents a novel investigation of advanced stable diffusion (SD) integrated with a module with image prompt capability, IP-Adapter, for weed image generation. Using the IP-Adapter-based model, two image feature encoders, CLIP (contrastive language image pre-training) and BioCLIP (a vision foundation model for biological images), were utilized to generate weed instances, which were then inserted into existing weed images. Image generation and weed detection experiments are conducted on a 10-class weed dataset captured in vegetable fields. The perceptual quality of generated images is assessed in terms of Fréchet Inception Distance (FID) and Inception Score (IS). YOLOv11 (You Only Look Once version 11) models were trained for weed detection, achieving an improved mAP@50:95 of 1.26% on average when combining inserted weed instances with real ones in training, compared to using original images alone. Both the weed dataset and software programs in this study will be made publicly available. This study offers valuable perspectives into the use of IP-adapter-based SD for generating weed images and weed detection. Full article

Global agricultural production is challenging due to climate change and a number of phyto-pathogenic organisms and pests that pose a significant threat to both crop growth and productivity. The growing resistance of pests and diseases to synthetic chemicals makes crop production even more difficult, which highlights the urgent need for alternative solutions. From this perspective, marine microorganisms have emerged as a significant natural product source for their distinctive bioactive compounds and environmentally sustainable potential pesticidal activity. The unique microbial resources and structurally diverse metabolites of the marine ecosystem have been proven to have strong antagonistic effects against a broad spectrum of agricultural diseases and pests, making them a valuable candidate for the development of novel pesticides. This review highlights 126 marine natural products from marine microorganisms with diverse metabolic pathways and bioactivities against agricultural pests, pathogens, and weeds. The findings underscore the potential of marine-derived compounds in addressing the growing challenges of crop protection and offering an appealing strategy for future agrochemical research and development. Full article

Sesame (Sesamum indicum L.) is one of the most important oilseed crops globally, valued for its high content of unsaturated fatty acids, proteins, and essential nutrients. However, weed infestation represents a major constraint on sesame productivity, competing for resources and releasing allelopathic compounds that can significantly reduce both yield and quality without timely control. To address the challenge of low detection accuracy in complex agricultural environments with dense weed distributions, this study proposes YOLOv12-BDA, a dynamic multi-scale architecture for small weed detection in sesame fields. The proposed architecture incorporates three key dynamic innovations: (1) an Adaptive Feature Selection (AFS) dual-backbone network with a Dynamic Learning Unit (DLU) module that enhances cross-branch feature extraction while reducing computational redundancy; (2) a Dynamic Grouped Convolution and Channel Mixing Transformer (DGCS) module that replaces the C3K2 component to enhance real-time detection of small weeds against complex farmland backgrounds; and (3) a Dynamic Adaptive Scale-aware Interactive (DASI) module integrated into the neck network to strengthen multi-scale feature fusion and detection accuracy. Experimental validation on high-resolution sesame field datasets demonstrates that YOLOv12-BDA significantly outperforms baseline models. The proposed method achieves mAP@50 improvements of 6.43%, 11.72%, 7.15%, 5.33%, and 4.67% over YOLOv5n, YOLOv8n, YOLOv10n, YOLOv11n, and YOLOv12n, respectively. The results confirm that the proposed dynamic architecture effectively improves small-target weed detection accuracy at the cost of increased computational requirements (4.51 M parameters, 10.7 GFLOPs). Despite these increases, the model maintains real-time capability (113 FPS), demonstrating its suitability for precision agriculture applications prioritizing detection quality. Future work will focus on expanding dataset diversity to include multiple crop types and optimizing the architecture for broader agricultural applications. Full article

Conventional agriculture, focusing on productivity rather than sustainability, have long abandoned hulled wheats. With them not only striking genetic diversity but valuable, health-promoting food sources became lost. Although einkorn and emmer—two of the most ancient wheat species—are generally considered good candidates of sustainable agriculture especially for pesticide-free cropping, they have remained largely unrecognized. To assess their agronomic potential in comparison with modern wheats grown under the same conditions, comprehensive research was conducted, combining multi-location participatory on-farm and small-plot trials. Our findings confirmed that most landraces of emmer and einkorn exhibited strong weed suppression ability, making them suitable for organic cultivation, and effective resistance against diseases—including Fusarium spp. and associated deoxynivalenol (DON) mycotoxin accumulation. Both species were entirely avoided by cereal leaf beetles (Oulema spp.) and had, on average, 2.6% more grain protein content than common wheat. Although they command significantly higher market prices, their (hulled) yields were comparable to modern wheat only in extreme years or at sites typically producing 3–5 t/ha of wheat. Nevertheless, the cultivation of emmer and einkorn presents a more sustainable "sow-and-harvest" alternative, free from pesticide and mycotoxin residue risks, while also enhances biodiversity from the field to the table. Full article

Weeds are among the primary constraints reducing soybean productivity, and their effective control is especially important in edamame, a vegetable soybean valued for its nutritional potential. As chemical control remains the dominant strategy, rational herbicide use is essential. This study aimed to evaluate the response of two edamame varieties (Chiba Green and Midori Giant) and the effectiveness of applied herbicides in weed control during the 2022–2024 growing seasons. Treatments included the following: pre-emergence herbicides (S-metolachlor + metribuzin) (H1); pre- (S-metolachlor + metribuzin) and post-emergence herbicides (imazamox + cycloxydim) (H2); and an untreated control (H0). The growing season influenced pod yield and biomass, with the highest yield recorded in 2022 (11.7 t ha⁻¹), while variety affected only pod yield: on average, Midori Giant outperformed Chiba Green (10.6 vs. 6.1 t ha⁻¹). Herbicide treatment affected weed dry biomass (3.3 g m⁻² in H2 compared to 341.8 g m⁻² in H0) and pod yield (4.3 t ha⁻¹ in H0 for Chiba Green compared to 11.9 t ha⁻¹ in H2 for Midori Giant). The results indicate that pre-emergence herbicides could satisfactorily reduce weed infestation under suitable meteorological conditions. The combined application of pre- and post-emergence herbicides increases production security (particularly in seasons with higher weed infestation), likely by extending the weed control period through pre- and post-emergence herbicide combinations, targeting different weed species during the soybean vegetative period. In addition, weed diversity was associated with a yield increase in Midori Giant. This research provides practical information and options for weed management in edamame production in the Western Balkan region. Full article