Search Results (186)

Soil degradation, nutrient depletion, and persistent weed pressure represent critical challenges in the adoption of sustainable agriculture practices in subtropical organic farming systems. Reliance on conventional inputs threatens long-term soil health and ecosystem resilience, highlighting the need for regenerative alternatives. Cover crops are widely recognized as multifunctional agroecological tools with the capacity to enhance nutrient cycling, perform weed suppression, and improve soil organic matter. To evaluate their effectiveness in South Florida's subtropical climate and organic raised bed systems, a field experiment was conducted as a Randomized Block Design (RBD) at the Florida International University Organic Garden during the 2024 summer season. The six cover crops species that were tested include green gram (Vigna radiata), hibiscus (Hibiscus sabdariffa), sorghum (Sorghum bicolor), soybean (Glycine max), sunn hemp (Crotalaria juncea), and pearl millet (Pennisetum glaucum). Data collected includes plant establishment, biomass accumulation, weed suppression, soil physiochemical properties, and plant nutrient composition. Sorghum and sunn hemp produced the highest fresh and dry biomass, with sorghum achieving the most effective weed suppression with the lowest weed biomass and weed population. Sunn hemp contributed to enhanced nitrogen content in plant tissues, while hibiscus promoted the highest soil P and N concentrations. Pearl millet exhibited the highest total carbon and organic matter content, indicating potential for enhancing soil carbon content and soil fertility. Results show that each cover crop species can provide a specialized or generalized ecosystem service depending on management goals. Full article

Waterhemp (Amaranthus tuberculatus (Moq.) J. D. Sauer) is one of the most competitive and herbicide-resistant weed species in soybean cropping systems across North America. While its competitive and adaptive traits are well-documented, its role as an alternative host for plant pathogens remains underexplored. This review synthesizes current knowledge on fungal, bacterial, viral, and nematode pathogens that infect waterhemp and examines the ecological and management implications of these interactions. We discuss how waterhemp may serve as a reservoir for inoculum, potentially influencing disease dynamics in soybean under changing climate conditions. Furthermore, we assess the potential of host-specific pathogens as biological control agents within the integrated weed management (IWM) approach. Despite promising experimental results, several barriers limit large-scale adoption of bioherbicides, including environmental sensitivity, narrow host specificity, challenges in mass production, and regulatory constraints. Understanding weed–pathogen interactions could inform dual-purpose strategies that reduce both weed pressure and disease risk in soybean systems. Further research is needed to optimize biocontrol scalability, assess climate-driven epidemiological shifts, and develop robust integrated weed and disease management approaches. Full article

Under maize–soybean rotation systems, weeds and crops at the seedling stage in dryland fields exhibit high similarity in morphological structure, scale distribution, and spatial arrangement. In addition, complex illumination conditions, occlusion, and background interference further complicate accurate weed discrimination. To address these challenges, this study proposes an improved YOLOv11n-based weed detection method for seedling-stage crops under dryland rotation conditions, aiming to enhance detection accuracy and robustness in UAV-acquired field images. Three key improvements were introduced to enhance model performance: (1) the incorporation of Dynamic Convolution (DynamicConv) to adaptively strengthen feature representation for weeds with varying morphologies and scales in low-altitude remote sensing imagery; (2) the design of a SlimNeck lightweight feature fusion architecture to improve multi-scale feature propagation efficiency while reducing computational cost; (3) the cascaded group attention mechanism (CGA) is integrated into the C2PSA module, thereby improving discrimination capability under complex background conditions. These results represent consistent improvements over baseline models, including YOLOv5, YOLOv6, YOLOv8, YOLOv11, and YOLOv12. Specifically, detection performance for broadleaf weeds and Poaceae weeds reached mAP@0.5 values of 87.2% and 73.9%, respectively. Overall, the proposed method demonstrates superior detection accuracy and stability for seedling-stage weed identification under rotation conditions, providing reliable technical support for variable-rate herbicide application and precision field management. Full article

The soybean–maize strip intercropping system enhances soybean yield while maintaining maize production, improving nitrogen use efficiency, and fostering intercropping mutualism. However, vigorous weed growth in warm and humid regions competes for nitrogen, while elevated soil temperatures accelerate nitrification, promoting nitrogen loss, especially during the peak nitrogen demand period of maize. Plastic film mulching, which conserves moisture, regulates temperature, and suppresses weeds, can improve the soil environment. A two-year field experiment was conducted with polyethylene (PE) films of various thicknesses (0.01, 0.014, 0.02 millimeters) and colors (black, white, silver-black) with an un-mulched control plot. Soil nitrogen content, microbial diversity, soil properties, and crop productivity were analyzed. The results indicated that plastic film mulching significantly altered soil nutrient availability and rhizosphere microbial community structures, while simultaneously enhancing crop productivity. The 0.014 mm black and white films performed best, showing a positive association with enhanced nitrogen transformation indices, which coincided with increased available nitrogen, biomass, and crop yield. However, long-term soil nutrient depletion remains a risk, suggesting the need for strategies like organic fertilizers or crop rotation to maintain soil fertility and ecological sustainability. Full article

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present. Full article

Phytoplasmas are obligate bacterial pathogens transmitted by phloem-feeding insects and responsible for severe diseases in numerous crops worldwide. In Iran, insect-associated phytoplasma transmission pathways remain poorly resolved, particularly at fine phylogenetic and vector-specific scales. Here, we investigated phytoplasma strains detected in four plant species, grapevine (Vitis vinifera), soybean (Glycine max), barberry (Berberis vulgaris), and the weed Conyza canadensis, and in three potential insect vectors (Tropidocephala prasina, Eysarcoris ventralis, and Nysius graminicola) collected from distinct agroecosystems across Iran. Phytoplasmas were characterized by using nearly full-length 16S rRNA gene sequences and a multilocus dataset of protein-coding genes obtained through a targeted next-generation sequencing approach. Five phytoplasma strains belonging to ribosomal groups 16SrI, 16SrVI, 16SrIX, and 16SrXII were identified, including two novel ribosomal subgroups, 16SrI-AS and 16SrIX-K. Several previously unreported plant–phytoplasma and insect–phytoplasma associations were documented. Comparative phylogenetic analyses revealed that ribosomal and multilocus markers capture complementary evolutionary signals, with protein-coding genes providing additional resolution beyond 16S-based classification. These findings highlight the potential role of diverse hosts and polyphagous insects, not yet confirmed as vectors, in phytoplasma circulation and underscore how high-throughput next-generation sequencing and multilocus approaches advance our understanding of phytoplasma diversity and evolution. Full article

Weed management during the seedling stage is a critical component of soybean production. Efficient weed control can significantly improve crop yield and crop quality. However, conventional spraying techniques exhibit low pesticide utilization and contribute to environmental pollution. To address these challenges, this study proposes a deep learning-based precision target spraying method. A lightweight YOLOv5-MobileNetv3-SE model was developed by replacing the backbone feature extraction network and incorporating an attention mechanism. Field images of weeds were collected to construct a dedicated dataset, and the detection performance of the model was evaluated. Furthermore, a grid-based matching spraying algorithm was developed to synchronize target detection with spray actuation. The system time delay, including image processing delay, communication and control delay, and spray deposition delay, was analyzed and measured, and a time-delay compensation strategy was implemented to ensure accurate spraying. Experimental results demonstrated that the improved model achieved an mAP@0.5 of 86.9%, a model size of 7.5 MB, and a frame rate of 38.17 frames per second. The weed detection accuracy exceeded 92.94%, and spraying accuracy exceeded 85.88% at forward speeds of 1–4 km·h⁻¹. Compared with conventional continuous spraying, the proposed method achieved pesticide reduction rates of 79.0%, 72.5%, 55.8%, and 48.6% at weed coverage rates of 5%, 10%, 15%, and 20%, respectively. The proposed method provides a practical approach for precise herbicide application, effectively reducing chemical usage and minimizing environmental impact. Full article

Knowledge of weed community structure in agricultural systems is important for sustainable management, especially for evaluating the effects of different herbicides on soybean–corn succession crops. This study evaluated, over two crop seasons, weed community structure in response to chemical weed management strategies for soybean–corn succession in Brazil’s Triângulo Mineiro region. Phytosociological surveys of the weed community were conducted during harvest periods throughout the experimental phase, with referenced data for generating spatial distribution maps of biomass and density of the main present species. The survey identified 33 weed species, predominantly from the Poaceae and Asteraceae families. Regardless of the management system, the total weed biomass was lower in corn crops compared to soybean crops. In management systems using six different herbicides, the IVI of Commelina benghalensis was the lowest due to greater diversification of herbicide mechanisms of action. The results demonstrate that chemical weed management strategies strongly influence weed community structure, with significant effects on weed community structure and evenness in intensive agricultural regions. These changes also have implications for resistance management. Full article

Weeds will compete with soybeans for resources such as light, water and nutrients, inhibit the growth of soybeans, and reduce their yield and quality. Aiming at the problems of low efficiency, high environmental risk and insufficient weed identification accuracy in complex farmland scenarios of traditional weed management methods, this study proposes a weed segmentation method for soybean fields based on unmanned aerial vehicle remote sensing. This method enhances the channel feature selection capability by introducing a lightweight ECA module, improves the target boundary recognition by combining Canny edge detection, and designs directional consistency filtering and morphological post-processing to optimize the spatial structure of the segmentation results. The experimental results show that the EDM-UNet method achieves the best performance effect on the self-built dataset, and the MIoU, Recall and Precision on the test set reach 89.45%, 93.53% and 94.78% respectively. In terms of model inference speed, EDM-UNet also performs well, with an FPS of 40.36, which can meet the requirements of real-time detection models. Compared with the baseline network model, the MIoU, Recall and Precision of EDM-UNet increased by 6.71%, 5.67% and 3.03% respectively, and the FPS decreased by 11.25. In addition, performance evaluation experiments were conducted under different degrees of weed interference conditions. The models all showed good detection effects, verifying that the model proposed in this study can accurately segment weeds in soybean fields. This research provides an efficient solution for weed segmentation in complex farmland environments that takes into account both computational efficiency and segmentation accuracy, and has significant practical value for promoting the development of smart agricultural technology. Full article

Intercropping of maize (Zea mays) and soybean (Glycine max) is a sustainable practice, but herbicide safety is critical for weed control without crop injury. This study evaluated the safety of pre-emergence (acetochlor and flufenacet) and post-emergence (2,4-D iso-octyl ester, sulfentrazone, and thifensulfuron-methyl) herbicides on seven maize and eight soybean varieties under greenhouse conditions. Greenhouse results showed that flufenacet had lower growth inhibition rates (~32% maize and ~4% soybean) compared to acetochlor (~35% maize and ~24% soybean). Among the post-emergence herbicides, thifensulfuron-methyl caused minimal inhibition (~4% maize and ~25% soybean), while 2,4-D and sulfentrazone showed higher phytotoxicity (up to 74% soybean). For thifensulfuron-methyl, soybean exhibited increased antioxidant enzyme activity (SOD, CAT, APX, and POD) at the highest concentration, reaching 35–40% above control levels. In contrast, maize had higher enzyme activity (SOD, CAT, APX, and POD) at the highest herbicide dose for flufenacet. This suggests that maize’s antioxidant induction was insufficient to fully counteract flufenacet’s phytotoxicity at elevated doses. In conclusion, flufenacet demonstrated superior crop safety and weed control compared to post-emergence herbicides, making it more suitable for maize–soybean intercropping systems. Full article

Extensive research on path planning and automated navigation has been carried out for weeding robots in fields such as corn, soybean, wheat, and sugar beet, but until now, no literature reports relative studies in turfs that are not cultivated using row-crop methods. This paper proposes a practical solution that comprises path planning and path tracking to minimize the weeding robot’s travel distance in turfs for the first time. An inter-sub-region scheduling algorithm is developed using the Traveling Salesman Problem (TSP) model, followed by a boundary-shifting-based coverage path planning algorithm to achieve full coverage within each weed subregion. For path tracking, a Real-Time Kinematic Global Positioning System (RTK-GPS) fusion positioning method is developed and combined with a dynamic pure pursuit algorithm featuring a variable preview distance to enable precise path following. After path planning based on real-world site data, the weeding robot traverses all weed subregions via the shortest possible path. Field experiments showed that the robot traveled along the shortest path at speeds of 0.6, 0.8, and 1.0 m/s; the root mean square errors of autonomous navigation deviation were 0.35, 0.81, and 1.41 cm, respectively. The proposed autonomous navigation solution significantly reduces the robot’s travel distance while maintaining acceptable tracking accuracy. Full article

Seedling-stage weeds are one of the key factors affecting the crop growth and yield formation of soybean. Accurate detection and density mapping of these weeds are crucial for achieving precise weed management in agricultural fields. To overcome the limitations of traditional large-scale uniform herbicide application, this study proposes an improved YOLOv11n-based method for weed detection and spatial distribution mapping by integrating low-altitude UAV imagery with field elevation data. The second convolution in the C3K2 module was replaced with Wavelet Convolution (WTConv) to reduce complexity. A SENetv2-based C2PSA module was introduced to enhance feature representation and context fusion with minimal parameter increase. Soft-NMS-SIoU replaced traditional NMS, improving detection accuracy and robustness for dense overlaps. The improved YOLOv11n algorithm achieved a 3.4% increase in mAP@50% on the test set, outperforming the original YOLOv11n in FPS, while FLOPs and parameter count increased by only 1.2% and 0.2%, respectively. More importantly, the model reliably detected small grass weeds with morphology highly similar to soybean seedlings, which were undetectable by the original model, thus meeting agricultural production monitoring requirements. In addition, the pixel-level weed detection results from the model were converted into coordinates and interpolated using Kriging in ArcGIS (10.8.1) Pro to generate continuous weed density maps, resulting in high-resolution spatial distribution maps directly applicable to variable-rate spraying equipment. The proposed approach greatly improves both the precision and operational efficiency of weed detection and management across large agricultural fields, providing scientific support for intelligent variable-rate spraying using plant protection UAVs and ground-based sprayers, thereby promoting sustainable agriculture. 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

Soybean (Glycine max (L.) Merr.) is an essential legume crop in Thailand, valued for its high protein content and economic significance. However, weed competition can reduce yields by up to 82% if not managed effectively. This study evaluates the efficacy of post-emergence herbicides for weed control and their impact on soybean yield. A field experiment was conducted during the 2023 rainy and 2024/2025 dry seasons at Khon Kaen University using a split-plot design with four replications. Weed management treatments included hand weeding, an untreated control, and three herbicides, fluazifop-P-butyl + fomesafen, clethodim + fomesafen, and quizalofop-P-tefuryl + fomesafen, applied to two soybean varieties (Morkhor60 and CM60). Quizalofop-P-tefuryl + fomesafen was found to be the most effective herbicide, achieving 87.66% weed control efficiency (WCE) in the dry season and 72.43% in the rainy season. Hand weeding produced the highest yield (1324.00 kg ha⁻¹), followed by quizalofop-P-tefuryl + fomesafen (1148.90 kg ha⁻¹). Morkhor60 outperformed CM60 in yield and growth performance. These findings highlight the importance of selecting suitable herbicide treatments to optimize weed control and enhance soybean productivity under different seasonal conditions. Full article

This study, which began in the 2013/2014 agricultural year, aimed to assess the suitability of two soil tillage systems for wheat cultivation: conventional soil tillage (CS), which involved moldboard plowing to a depth of 28 cm followed by a single pass with a rotary harrow to prepare the seedbed, and no-tillage (NT). It also sought to analyze the impacts of these systems on weed infestation levels and, consequently, on yield. A moderate level of fertilization was applied. The experimental field was established with a three-year crop rotation system: soybean–winter wheat–maize. The total number of weed species was 30 in CS, the representative species being Xanthium strumarium, and in NT there were 29 species, with Xanthium strumarium, Cirsium arvense, Bromus tectorum, and Agropyron repens predominating. There was an increase in the number of perennials (dicots and monocots). The total dry matter of weeds was 35.4 t ha⁻¹ in CS and 38.8 t ha⁻¹ in NT. After 11 agricultural years, it was found that there were no significant differences between the two soil tillage systems in terms of wheat yield (6.55 t ha⁻¹ in CS and 6.46 t ha⁻¹ in NT). The uneven rainfall negatively affected wheat growth and favored the spread of weeds, especially dicotyledonous ones. Full article