Search Results (189)

Weeds are a significant factor affecting crop yield and quality. Herbicides have made crucial contributions to ensuring stable and high grain production, but the low effective utilization rate and short duration of traditional formulations have led to excessive application and a range of ecological and environmental issues. Nanoherbicides, particularly carrier-coated systems, can simultaneously leverage the small size, large specific surface area, and high permeability of nanoparticles, as well as the multifunctionality of carriers, to synergistically enhance the efficacy and safety of the formulations. This provides a scientific and promising strategy for overcoming the functional deficiencies of traditional formulations. Nevertheless, there are currently relatively few articles that systematically review the research progress and performance advantages of nanoherbicides. This review provides a concise overview of the preparation methods and structural characteristics of nanoherbicides. It primarily highlights the classification of carrier-coated nanoherbicides, along with representative studies and their distinctive properties across various categories. Based on this foundation, the performance advantages of nanoherbicides are systematically summarized. Finally, the major challenges and future prospects in this research field are proposed. This review offers valuable insights and methodological guidance for the design and rational application of efficient, environmentally friendly nanoherbicides. Full article

This study conducted a comprehensive evaluation of the effects of biodegradable (BD) mulching film in onion cultivation, with a focus on plant growth, yield, soil environment, weed suppression, and film degradation, in comparison to conventional polyethylene (PE) film and non-mulching (NM) treatment across multiple regions and years (2023–2024). The BD and PE films demonstrated similar impacts on onion growth, bulb size, yield, and weed suppression, significantly outperforming NM, with yield increases of over 13%. There were no consistent differences in soil pH, electrical conductivity, and physical properties in crops that used either BD or PE film. Soil temperature and moisture were also comparable regardless of which film type was used, confirming BD’s viability as an alternative to PE. However, areas that used BD film had soils which exhibited reduced microbial populations, particularly Bacillus and actinomycetes which was likely caused by degradation by-products. BD film degradation was evident from 150 days post-transplantation, with near-complete decomposition at 60 days post-burial, whereas PE remained largely intact (≈98%) during the same period. These results confirm that BD film can match the agronomic performance of PE while offering the advantage of environmentally friendly degradation. Further research should optimize BD film durability and assess its cost-effectiveness for large-scale sustainable agriculture. Full article

The diversification of plant protein sources is a strategic priority for European food systems, particularly under the EU Green Deal and Farm to Fork strategies. In this study, dual production of full-fat soy (FFS) and expanded soybean cake (ESC) was evaluated using non-GMO soybeans cultivated under semi-organic conditions in Central Poland. Two agronomic systems—post-emergence mechanical weeding with rotary harrow weed control (P1) and conventional herbicide-based control (P2)—were compared over a four-year period. The P1 system produced consistently higher yields (e.g., 35.6 dt/ha in 2024 vs. 33.4 dt/ha in P2) and larger seed size (TSW: up to 223 g). Barothermal and press-assisted processing yielded FFS with protein content of 32.4–34.5% and oil content of 20.8–22.4%, while ESC exhibited enhanced characteristics: higher protein (37.4–39.0%), lower oil (11.6–13.3%), and elevated dietary fiber (15.8–16.3%). ESC also showed reduced anti-nutritional factors (e.g., trypsin inhibitors and phytic acid) and remained microbiologically and oxidatively stable over six months. The semi-organic P1 system offers a scalable, low-input approach to local soy production, while the dual-product model supports circular, zero-waste protein systems aligned with EU sustainability targets. Full article

The wild tomato (Solanum sisymbriifolium) is a globally distributed shrubby weed with both negative and positive impacts, including its invasive properties and the potential for pharmaceutical and traditional medicinal uses. Despite its ecological significance, the plant’s reproductive biology and pollination ecology remain poorly understood. This study aimed to investigate the floral biology, pollination ecology, and plant reproduction of the weed species. Some flower traits, such as flowering intensity, flower display size, and pollen and ovule production, peaked during spring, summer, and the monsoon, while flower longevity and stigmatic receptivity were the longest in winter. The plant species was self-compatible (ISI = 0.02), heavily depended on pollinators (IDP = 0.72), and experienced minimal pollination limitation (D = 0.10) under open-pollination conditions. Flower visitors’ traits (e.g., abundance, diversity, and richness) were higher in the spring, summer, and the monsoon, and these were lower in winter. The vital pollination service was provided by Amegilla zonata, Ceratina binghami, Lasioglossum cavernifrons, Nomia (Curvinomia) strigata, Tetragonula pagdeni, Xylocopa aestuans, Xylocopa amethystina, Xylocopa fenestrata, and Xylocopa latipes. Reproductive success, as indicated by fruit and seed set, varied seasonally, being higher during the spring–monsoon period and lower in winter. These findings support effective management of this weed species and help conserve the associated bee populations. Full article

Barnyard grass is a major noxious weed in paddy fields. Accurate and efficient identification of barnyard grass is crucial for precision field management. However, existing deep learning models generally suffer from high parameter counts and computational complexity, limiting their practical application in field scenarios. Moreover, the morphological similarity, overlapping, and occlusion between barnyard grass and rice pose challenges for reliable detection in complex environments. To address these issues, this study constructed a barnyard grass detection dataset using high-resolution images captured by a drone equipped with a high-definition camera in rice experimental fields in Haicheng City, Liaoning Province. A lightweight field barnyard grass detection model, YOLOv8n-SSDW, was proposed to enhance detection precision and speed. Based on the baseline YOLOv8n model, a novel Separable Residual Coord Conv (SRCConv) was designed to replace the original convolution module, significantly reducing parameters while maintaining detection accuracy. The Spatio-Channel Enhanced Attention Module (SEAM) was introduced and optimized to improve sensitivity to barnyard grass edge features. Additionally, the lightweight and efficient Dysample upsampling module was incorporated to enhance feature map resolution. A new WIoU loss function was developed to improve bounding box classification and regression accuracy. Comprehensive performance analysis demonstrated that YOLOv8n-SSDW outperformed state-of-the-art models. Ablation studies confirmed the effectiveness of each improvement module. The final fused model achieved lightweight performance while improving detection accuracy, with a 2.2% increase in mAP_50, 3.8% higher precision, 0.6% higher recall, 10.6% fewer parameters, 9.8% lower FLOPs, and an 11.1% reduction in model size compared to the baseline. Field tests using drones combined with ground-based computers further validated the model’s robustness in real-world complex paddy environments. The results indicate that YOLOv8n-SSDW exhibits excellent accuracy and efficiency. This study provides valuable insights for barnyard grass detection in rice fields. Full article

Threshing and cleaning are crucial for efficient harvest procedures that are carried out to separate the grains from the biomass and eliminate any potential contaminants or foreign debris. This study examines the cleaning capabilities of the grain cleaning equipment Kimseed Cleaner MK3, a vibratory sieve and air-screen device, for tiny oilseed crops, particularly kalonji (Nigella sativa) and sesame (Sesamum indicum L.), which are valued for their industrial, medicinal, and nutritional properties. These crops frequently provide post-harvest cleaning issues because of their tiny size and vulnerability to contamination from weed seeds, plant residues, and immature or damaged conditions. In order to determine the ideal operating parameters, 0.5 kg of threshed seed samples with 10% moisture content were utilised in the experiment. A variety of shaker frequencies (0.1–10 Hz) and airflow speeds (0.1–10 m/s) were assessed. A two-stage cleaning method was applied for sesame: the first stage targeted larger contaminants (6.5–7.0 Hz and 1.25–1.5 m/s), while the second stage targeted finer impurities (5.25–5.5 Hz and 1.75–2.0 m/s). With a single-stage procedure (5.5–6.0 Hz and 1.0–1.5 m/s), kalonji was successfully cleaned. The findings demonstrated that sesame attained 98.5% purity at the output rate of 200.6 g/min (12.03 kg/h) while kalonji reached 97.6% seed purity at an output rate of 370.2 g/min (22.2 kg/h). These results demonstrate how important carefully regulated shaker frequency and airflow speed are for improving output quality and cleaning effectiveness. The study shows that the Kimseed MK3 is a suitable low-cost, scalable option for research operations and smallholder farmers, providing better seed quality and processing efficiency for underutilised yet economically valuable oilseed crops. Full article

The control of A. tauschii is critical to ensuring food security. This study investigated a range of different aspects of the biology of A. tauschii, including its emergence characteristics, population development dynamics, and its impact on wheat yield. Moreover, the efficacy of different herbicides and cultural control measures for managing A. tauschii was explored. Through laboratory cultivation and statistical analysis of the emergence rate of A. tauschii, it was found that its emergence rate significantly increased when temperatures ranged from 10 °C to 20 °C and the environmental osmotic potential fell between −0.1 MPa and −0.5 MPa—conditions similar to those found in wheat fields. Additionally, by recording the emergence rates at different depths, A. tauschii emergence was found to occur optimally at a sowing depth of 1–5 cm, which aligns with the shallow rotary tillage currently employed in wheat production. The weed was also found to be tolerant to weakly acidic and alkaline environments, while also presenting with moderate salt tolerance. Through field experiments, it was found that, upon spreading to new areas, A. tauschii rapidly expanded its population size. While its impact on wheat yield was relatively mild during the early stages of growth, it escalated to severe outbreaks with the passage of time. Field experiments were conducted to test the efficacy of five herbicides on weed control. The analysis indicated that Mesosulfuron-methyl was the only effective herbicide in controlling A. tauschii. Adopting three two-year-three-crop rotation patterns reduced the density of A. tauschii from 186 stems/m² to 11–15 stems/m². Watering-induced emergence also proved effective. The most effective watering was performed 15 days before sowing. Deep plowing was another effective measure. The deeper the plowing, the lower the emergence of A. tauschii. Delayed sowing time resulted in the additional suppression of the emergence of A. tauschii. Full article

Laser weeding is an innovative, environmentally friendly method for intra-row weed control. However, its effectiveness depends on accurate weed identification and an efficient control system. This study developed an intra-row laser weeding system for lettuce, combining deep learning and laser technology. The system consisted of three modules: perception, decision, and execution. It used an MV-UB130GM industrial camera to capture images, which are transmitted to a computer for processing. A target detection algorithm located weeds by calculating the central coordinates of anchor frames. The multi-task learning (MTL) decision system then planned the weeding path, generated instructions, and controlled the laser for weeding tasks. The YOLOv8 model, enhanced with an attention mechanism, formed the foundation of target detection. To compress the model, a class knowledge distillation method based on transfer learning was applied, resulting in a lightweight YOLOv8s-CBAM model with a mAP@0.5 of 98.9% and a size of just 6.2 MB. A simulation prototype of the laser weeding system was built, and initial experiments demonstrated that a 450 nm blue semiconductor laser effectively kills weeds in 1 s with 30 W output. Experimental results showed that the system detected and eliminated 100% of weeds in low-density scenes and achieved an 88.9% detection rate in high-density areas. The real-time detection speed reached 21.27 FPS, and the overall weeding success rate was 76.9%. This study provides valuable insights for the development of intra-row weed control systems based on laser technology, contributing to the advancement of precision agriculture. Full article

Accurate crop row detection is an important foundation for agricultural machinery to realize autonomous operation. Existing methods often compromise between real-time performance and detection accuracy, limiting their practical field applicability. This study develops a high-precision, efficient crop row detection algorithm specifically optimized for soybean–corn compound planting conditions, addressing both computational efficiency and recognition accuracy. In this paper, a real-time soybean–corn crop row detection method based on GD-YOLOv10n-seg with principal component analysis (PCA) fitting was proposed. Firstly, the dataset of soybean–corn seedling crop rows was established, and the images were labeled with line labels. Then, an improved model GD-YOLOv10n-seg model was constructed by integrating GhostModule and DynamicConv into the YOLOv10n-segmentation model. The experimental results showed that the improved model performed better in MPA and MIoU, and the model size was reduced by 18.3%. The crop row center lines of the segmentation results were fitted by PCA, where the fitting accuracy reached 95.08%, the angle deviation was 1.75°, and the overall processing speed was 61.47 FPS. This study can provide an efficient and reliable solution for agricultural autonomous navigation operations such as weeding and pesticide application under a soybean–corn compound planting mode. Full article

Accurate weed segmentation in Unmanned Aerial Vehicle (UAV) imagery remains a significant challenge in precision agriculture due to environmental variability, weak contextual representation, and inaccurate boundary detection. To address these limitations, we propose the Multi-Scale and Edge-Aware Network (MSEA-Net), a lightweight and efficient deep learning framework designed to enhance segmentation accuracy while maintaining computational efficiency. Specifically, we introduce the Multi-Scale Spatial-Channel Attention (MSCA) module to recalibrate spatial and channel dependencies, improving local–global feature fusion while reducing redundant computations. Additionally, the Edge-Enhanced Bottleneck Attention (EEBA) module integrates Sobel-based edge detection to refine boundary delineation, ensuring sharper object separation in dense vegetation environments. Extensive evaluations on publicly available datasets demonstrate the effectiveness of MSEA-Net, achieving a mean Intersection over Union (IoU) of 87.42% on the Motion-Blurred UAV Images of Sorghum Fields dataset and 71.35% on the CoFly-WeedDB dataset, outperforming benchmark models. MSEA-Net also maintains a compact architecture with only 6.74 M parameters and a model size of 25.74 MB, making it suitable for UAV-based real-time weed segmentation. These results highlight the potential of MSEA-Net for improving automated weed detection in precision agriculture while ensuring computational efficiency for edge deployment. Full article

In a natural environment, due to the small size of caterpillar fungus, its indistinct features, similar color to surrounding weeds and background, and overlapping instances of caterpillar fungus, identifying caterpillar fungus poses significant challenges. To address these issues, this paper proposes a new MRAA network, which consists of a feature fusion pyramid network (MRFPN) and the backbone network N-CSPDarknet53. MRFPN is used to solve the problem of weak features. In N-CSPDarknet53, the Da-Conv module is proposed to address the background and color interference problems in shallow feature maps. The MRAA network significantly improves accuracy, achieving an accuracy rate of 0.202 AP_S for small-target recognition, which represents a 12% increase compared to the baseline of 0.180 AP_S. Additionally, the model size is small (9.88 M), making it lightweight. It is easy to deploy in embedded devices, which greatly promotes the development and application of caterpillar fungus identification. Full article

The growing demand for sustainable agricultural practices requires the search for innovative solutions to improve crop yield and quality. This study investigated the soil-specific effects of the bio-growth regulator Supporter on seed potato yield and quality in different potato varieties, with the aim of unlocking its sustainable potential under different environmental conditions. Field trials were conducted on several soil types using the bio-growth stimulator Supporter at a rate of 300 mL per hectare. Standardized agronomic practices, including continuous fertilization, weed control, and pest control, were applied at all test sites to ensure comparability. The results showed that the use of the bio-growth stimulator Supporter significantly increased tuber yield and quality, especially in soils with moderate fertility levels. In the treatments, with the Supporter biostimulator, there was better tuber size uniformity and a higher fraction and number of seed potato. A higher average seed potato mass and higher multiplication coefficient were observed. The effectiveness of the Supporter varied across study locations and soil types, with sandy and sandy loam soils showing the most pronounced benefits, while clay soils showed more limited responses. The results underscore the potential of the Supporter as a sustainable tool for increasing potato seed production, while also emphasizing the need for soil-specific recommendations. This study highlights the importance of integrating growth regulators into precision agriculture to optimize crop performance and support global food security goals. Therefore, further research is needed on the use of biostimulants, which will allow us to understand the purpose of their action, which is important in agricultural practice. Full article

This paper aims to determine the preferences of farmers in practicing conservation agriculture (CA) in rural communities in Namibia. The multinomial logit model was used to estimate the main principles of conservation agriculture (CA) to determine the preferences of farmers in practicing conservation agriculture, given their socio-economic characteristics. In each case, farmers were presented with four different exclusive choices to select from. The multinomial logit model reveals that an increase in the education level of the household head $p\le 0.000$, household size $p\le 0.085$, mono-cropping $p\le 0.000$, annual crop rotation $p\le 0.000$, crop rotation after two years $p\le 0.000$, and weeding twice for 5 h per weeding per hectare $p\le 0.028$ significantly affects the preference for using a basin tillage over a direct seeder, with all other model variables held constant. The log odds of preferring mono-cropping over intercropping cereal with cowpeas are higher for farmers practicing crop rotation annually compared to those rotating crops every two years, assuming no change in other predictor variables with $p\le 0.019.$ In addition, the study found that economic status significantly influences the attractiveness of CA with basin tillage being preferred over the direct seeder among the farmers studied. This preference underscores the characteristics of the respondents, who are primarily subsistence farmers reliant on traditional farming tools. This suggests a strategic opportunity to engage younger and more educated farmers to be the lead farmers to mentor others in their communities. Markets for appropriate tools, such as direct seeders and rippers, must be established to make CA tools available to the farmers in the local market. Full article

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

Submerged macrophytes can profoundly influence interactions between aquatic predators and their prey due to changes in foraging efficiencies, pursuit time and swimming behaviors of predator–prey participants. Water hyacinth, Eichhornia crassipes (Mart.) Solms-Laub. (Pontederiaceae), is the most widely distributed of the aquatic invasive weeds in South Africa. This invasive weed contributes to changes in physicochemical (turbidity, temperature and water column stratification) and biological (total chlorophyll-a (Chl-a) concentrations and species composition and distribution of vertebrates and invertebrates) variables within freshwater systems of the region. The current study assessed the influence of varying levels of water hyacinth cover (0, 25, 50 and 100% treatments) on the total Chl-a concentration, size structure of the phytoplankton community and the strength of the interaction between a predatory notonectid, Enithares sobria, and zooplankton using a short-term 10-day long mesocosm study. There were no significant differences in selected physicochemical (temperature, dissolved oxygen, total nitrogen and total phosphate) variables in these different treatments over the duration of this study (ANOVA; p > 0.05 in all cases). Results of this study indicate that treatment had a significant effect on total Chl-a concentrations and total zooplankton abundances. The increased surface cover of water hyacinth contributed to a significant reduction in total Chl-a concentrations and a significant increase in total zooplankton abundances (ANCOVA; p < 0.05 in both cases). The increased habitat complexity conferred by the water hyacinth root system provided refugia for zooplankton. The decline in total Chl-a concentration and the size structure of the phytoplankton community under elevated levels of water hyacinth cover can therefore probably be related to both the unfavorable light environment conferred by the plant cover and the increased grazing activity of zooplankton. The presence of the water hyacinth thus suppressed a predator–prey cascade at the base of the food web. Water hyacinth may, therefore, have important implications for the plankton food web dynamics of freshwater systems by reducing food availability (Chl-a), changing energy flow and alternating the strength of interactions between predators and their prey. Full article