Search Results (47)

Stomata play a crucial role in plant immune responses, with their morphological characteristics closely linked to disease resistance. Accurate detection and analysis of stomatal phenotypic parameters are essential for soybean disease resistance research and variety breeding. However, traditional stoma detection methods are challenged by complex backgrounds and leaf vein structures in soybean images. To address these issues, we proposed a Soybean Stoma-YOLO (You Only Look Once) model (SS-YOLO) by incorporating large separable kernel attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8 and Deformable Large Kernel Attention (DLKA) in the Neck part. These architectural modifications enhanced YOLOV8′s ability to extract multi-scale and irregular stomatal features, thus improving detection accuracy. Experimental results showed that SS-YOLO achieved a detection accuracy of 98.7%. SS-YOLO can effectively extract the stomatal features (e.g., length, width, area, and orientation) and calculate related indices (e.g., density, area ratio, variance, and distribution). Across different soybean rust disease stages, the variety Dandou21 (DD21) exhibited less variation in length, width, area, and orientation compared with Fudou9 (FD9) and Huaixian5 (HX5). Furthermore, DD21 demonstrated greater uniformity in stomatal distribution (SEve: 1.02–1.08) and a stable stomatal area ratio (0.06–0.09). The analysis results indicate that DD21 maintained stable stomatal morphology with rust disease resistance. This study demonstrates that SS-YOLO significantly improved stoma detection and provided valuable insights into the relationship between stomatal characteristics and soybean disease resistance, offering a novel approach for breeding and plant disease resistance research. Full article

Background/Objectives: Cinnamon leaves, an important source of the functional compound cinnamaldehyde (CA), have been shown to be effective in improving type II diabetes and Parkinson’s disease (PD) in rats following the incorporation of cinnamon leaf extract into a nanoemulsion. However, the effect of a cinnamon leaf extract nanoemulsion (CLEN) on improving Alzheimer’s disease (AD), the most prevalent type of dementia, remains unexplored. The objectives of this study were to determine functional compounds in cinnamon leaves by UPLC-MS/MS, followed by the preparation of a nanoemulsion and its byproducts to study their effects on AD and PD in rats. Methods: Oven-dried (60 °C for 2 h) cinnamon leaf powder and hydrosol, obtained by steam distillation of cinnamon leaf powder, were stored at 4 °C. After determination of basic composition (crude protein, crude fat, carbohydrate, moisture and ash) of cinnamon leaf powder, it was extracted with 80% ethanol with sonication at 60 °C for 2 h and analyzed for bioactive compounds by UPLC-MS/MS. Then, the CLEN was prepared by mixing cinnamon leaf extract rich in CA with lecithin, soybean oil, tween 80 and ethanol in an optimal ratio, followed by evaporation to form thin-film and redissolving in deionized water. For characterization, mean particle size, polydispersity index (PDI), zeta potential, encapsulation efficiency, and surface morphology were determined. Animal experiments were done by dividing 90 male rats into 10 groups (n = 9), with groups 2–8 being subjected to mini-osmotic pump implantation surgery in brain to infuse Amyloid-beta 40 (Aβ40) solution in groups 2–8 for induction of AD, while groups 9 and 10 were pre-fed respectively with cinnamon powder in water (0.5 g/10 mL) and in hydrosol for 4 weeks, followed by induction of AD as shown above. Different treatments for a period of 4 weeks included groups 1–9, with group 1 (control) and group 2 feeding with sterilized water, while groups 3, 4 and 5 were fed respectively with high (90 mg/kg), medium (60 mg/kg) and low (30 mg/kg) doses of cinnamon leaf extracts, groups 6, 7 and 8 fed respectively with high (90 mg/kg), medium (60 mg/kg) and low (30 mg/kg) doses of nanoemulsions, groups 9 and 10 fed respectively with 10 mL/kg of cinnamon powder in water and hydrosol (0.5 g/10 mL). Morris water maze test was conducted to determine short-term memory, long-term memory and space probing of rats. After sacrificing of rats, brain and liver tissues were collected for determination of Aβ40, BACE1 and 8-oxodG in hippocampi, and AchE and malondialdehyde (MDA) in cortices, antioxidant enzymes (SOD, CAT, GSH-Px) and MDA in both cortices and livers, and dopamine in brain striata by using commercial kits. Results: The results showed that the highest level of CA (18,250.7 μg/g) was in the cinnamon leaf powder. The CLEN was prepared successfully, with an average particle size of 17.1 nm, a polydispersity index of 0.236, a zeta potential of −42.68 mV, and high stability over a 90-day storage period at 4 °C. The Morris water maze test revealed that the CLEN treatment was the most effective in improving short-term memory, long-term memory, and spatial probe test results in AD rats, followed by the cinnamon leaf extract (CLE), powder in hydrosol (PH), and powder in water (PW). Additionally, both CLEN and CLE treatments indicated a dose-dependent improvement in AD rats, while PH and PW were effective in preventing AD occurrence. Furthermore, AD occurrence accompanied by PD development was demonstrated in this study. With the exception of the induction group, declines in Aβ40, BACE1, and 8-oxodG in the hippocampi and AchE and MDA in the cortices of rats were observed for all the treatments, with the high-dose CLEN (90 mg/kg bw) exhibiting the highest efficiency. The antioxidant enzyme activity, including that of SOD, CAT, and GSH-Px, in the cortices of rats increased. In addition, dopamine content, a vital index of PD, was increased in the striata of rats, accompanied by elevations in SOD, CAT, and GSH-Px and decreased MDA in rat livers. Conclusions: These outcomes suggest that the CLEN possesses significant potential for formulation into a functional food or botanical drug for the prevention and treatment of AD and/or PD in the future. Full article

Ginkgo biloba leaf flavonoids, while demonstrating antioxidant potential, remain underexplored as natural stabilizers for frying oils under thermo-oxidative stress. This study assessed Ginkgo biloba leaf flavonoids efficacy against synthetic tertiary-butylhydroquinone (0.02%). Ginkgo biloba leaf flavonoids were extracted via optimized ultrasonic-assisted methods (15 mL/g solvent, 80% ethanol, 45 °C, 120 s). Frying stability in flaxseed and soybean oils over 6 days (24 cycles/day) was evaluated using multi-indicator kinetic analysis. Ginkgo biloba leaf flavonoids significantly outperformed tertiary-butylhydroquinone in reducing oxidation markers after 6 days. In flaxseed oil, Ginkgo biloba leaf flavonoids reduced acid value (18.4%), peroxide value (33.79%), and polar compounds (52.03%); reductions in soybean oil reached 61.34% for polar compounds. Ginkgo biloba leaf flavonoids better preserved γ-tocopherol in flaxseed oil (increased 2.09% retention) and key tocopherols in soybean oil. Critically, it mitigated unsaturated fatty acid losses (flaxseed C18:3: 2.72% higher; soybean C18:2: 4.4% higher) and limited saturated fatty acid increases. Optimized Ginkgo biloba leaf flavonoid extraction facilitates its application as a promising natural candidate for high-temperature frying, where its matrix-specific stabilization effect shows potential in commercial functional oil formulations. Full article

Natural enemies commonly probe larval bodies and frass with their antennae for prey hunting. However, the attractants to natural enemies emitted directly from hosts and host-associated tissues remained largely unknown. Here, we used two generalist noctuid species, Helicoverpa armigera (Hübner) and Spodoptera frugiperda (JE Smith), along with the larval endoparasitoid Microplitis mediator (Haliday) to address the question. Extracts of larval frass of both the noctuid species were strongly attractive to M. mediator females when hosts were fed either maize, cotton, soybean leaves, or an artificial diet without leaf tissues. By using a combination of electrophysiological measurements and behavioral tests, we found that the attractiveness of frass mainly relied on a volatile compound ethyl palmitate. The compound was likely to be a by-product of host digestion involving gut bacteria because an antibiotic supplement in diets reduced the production of the compound in frass and led to the decreased attractiveness of frass to the parasitoids. In contrast, extracts of the larval bodies of both the noctuid species appeared to be less attractive to the parasitoids than their respective fecal extracts, independently of types of food supplied to the larvae. Altogether, larval frass of the two noctuid species was likely to be more important than their bodies in attracting the endoparasitoid species, and the main attractant of frass was probably one of the common metabolites of digestion involving gut microbes, and its emission is likely to be independent of host plant species. Full article

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article

Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate disease identification is crucial for improving crop yield and quality. While most existing deep learning-based methods focus primarily on image datasets for disease recognition, they often overlook the complementary role of textual features in enhancing visual understanding. To address this problem, we proposed a cross-modal data fusion via a vision-language model for crop disease recognition. Our approach leverages the Zhipu.ai multi-model to generate comprehensive textual descriptions of crop leaf diseases, including global description, local lesion description, and color-texture description. These descriptions are encoded into feature vectors, while an image encoder extracts image features. A cross-attention mechanism then iteratively fuses multimodal features across multiple layers, and a classification prediction module generates classification probabilities. Extensive experiments on the Soybean Disease, AI Challenge 2018, and PlantVillage datasets demonstrate that our method outperforms state-of-the-art image-only approaches with higher accuracy and fewer parameters. Specifically, with only 1.14M model parameters, our model achieves a 98.74%, 87.64% and 99.08% recognition accuracy on the three datasets, respectively. The results highlight the effectiveness of cross-modal learning in leveraging both visual and textual cues for precise and efficient disease recognition, offering a scalable solution for crop disease recognition. Full article

Soybean frogeye leaf spot (FLS), a serious soybean disease, causes severe yield losses in the largest production regions of China. However, both conventional field monitoring and machine learning algorithms remain challenged in achieving rapid and accurate detection. In this study, an HSDT-TabNet model was proposed for the grading of soybean FLS under field conditions by analyzing unmanned aerial vehicle (UAV)-based hyperspectral data. This model employs a dual-path parallel feature extraction strategy: the TabNet path performs sparse feature selection to capture fine-grained local discriminative information, while the hierarchical soft decision tree (HSDT) path models global nonlinear relationships across hyperspectral bands. The features from both paths are then dynamically fused via a multi-head attention mechanism to integrate complementary information. Furthermore, the overall generalization ability of the model is improved through hyperparameter optimization based on the tree-structured Parzen estimator (TPE). Experimental results show that HSDT-TabNet achieved a macro-accuracy of 96.37% under five-fold cross-validation. It outperformed the TabTransformer and SVM baselines by 2.08% and 2.23%, respectively. For high-severity cases (Level 4–5), the classification accuracy exceeded 97%. This study provides an effective method for precise field-scale crop disease monitoring. Full article

Soil-borne fungal pathogens such as Sclerotium spp., Rhizoctonia spp., and Macrophomina spp. pose significant threats to global agriculture, with soybean crops among the most severely affected due to damping-off disease. These pathogens cause substantial yield losses, making their management a critical concern. In this study, we investigated the potential of Bacillus siamensis BCL, a novel Neotropical strain, as an eco-friendly solution for managing Sclerotium, Rhizoctonia, and Macrophomina species. The strain exhibited strong antifungal activity, significantly inhibiting fungal growth in vitro, with the greatest suppression observed against Macrophomina spp., reaching up to 81%. In vivo assays further confirmed the biocontrol potential of B. siamensis. When applied at 10⁶ colony-forming units (CFU)/mL, the strain reduced disease symptoms and improved plant growth parameters—including root length, shoot biomass, and leaf number—compared to untreated, infected controls. The protective effect varied by pathogen, with the most significant recovery in root length observed against Macrophomina spp. (85%) and Sclerotium spp. (78%). In preventive treatments, fermentation extracts of the B. siamensis strain suppressed disease progression, although they did not promote seedling growth. A genomic analysis of B. siamensis BCL revealed genes encoding antimicrobial secondary metabolites, including terpenes, fengycins, and surfactins. These findings highlight B. siamensis BCL as a promising candidate for sustainable crop protection and a valuable resource for developing novel antimicrobial strategies in agriculture. Full article

Although tea (Camellia sinensis)/soybean (Glycine max) intercropping is widely applied in tea gardens, the underlying mechanisms driving tea quality promotion remain largely unclear. This study explores the effects of intercropping on tea quality, soil nutrient availability, and soybean growth and analyzes their mutual relationship. Field experiments revealed that intercropping increased tea leaf water extracts, polyphenols, and amino acids by 4.36–8.99%, 14.76–15.23%, and 14.73–16.36%, respectively, across two growth stages. Furthermore, intercropping boosted organic matter, available nitrogen (N), phosphorus (P), and potassium (K) in the tea rhizosphere. Enzyme activities, including acid phosphatase, alkaline phosphatase, urease, and β-glucosidase, were also elevated in tea/soybean intercropping. In soybean, shoot and root biomass, weight and number of nodules, and N, P, and K content increased over cultivation time. Correlation analysis showed that tea water extracts and polyphenols were positively linked to soil available P and alkaline phosphatase activities. Soybean root and nodule growth were correlated with soil N and P activation and tea water extracts, indicating that soybean-mediated underground interactions drive mineral nutrient mobilization in rhizosphere, further improving tea quality. This study provides mechanistic insights into tea/soybean intercropping, offering practical implications for sustainable tea cultivation practices. Full article

Soybean cultivation plays a crucial role in the global food system, providing raw materials for both the food and feed industries. To enhance cultivation efficiency, plant biostimulants are used to improve metabolism and stimulate growth. A key aspect of modern cultivation is the ability to rapidly and non-invasively assess crop status. One such method involves the use of drones equipped with multispectral cameras. This paper presents the results of an experimental study on soybean cultivation involving a natural biostimulant in the form of Epilobium angustifolium extract (commonly known as fireweed) and a commercial seaweed-based biostimulant, Kelpak. The research was conducted at an experimental farm in eastern Poland. The effectiveness of the preparations was evaluated using a drone-mounted multispectral camera. Changes in the values of selected spectral indices were analyzed: the Normalized Difference Red Edge Index (NDRE), the Leaf Chlorophyll Index (LCI), and the Optimized Soil-Adjusted Vegetation Index (OSAVI). The study included a control group treated with pure water. Mathematical and statistical analyses of the mean values and standard deviations of the indices were conducted. The results demonstrated that multispectral scanning allows for the detection of significant differences between the effects of the E. angustifolium extract, the seaweed-based biostimulant, and the water control. These findings confirm the utility of this method for assessing the effectiveness of biostimulant applications in soybean cultivation. Full article

Accurate early-season crop classification is critical for food security, agricultural applications and policymaking. However, when classification is performed earlier, the available time-series data gradually become scarce. Existing methods mainly focus on enhancing the model’s ability to extract features from limited data to address this challenge, but the extracted critical phenological information remains insufficient. This study proposes a Cascade Learning Early Classification (CLEC) framework, which consists of two components: data preprocessing and a cascade learning model. Data preprocessing generates high-quality time-series data from the optical, radar and thermodynamic data in the early stages of crop growth. The cascade learning model integrates a prediction task and a classification task, which are interconnected through the cascade learning mechanism. First, the prediction task is performed to supplement more time-series data of the growing stage. Then, crop classification is carried out. Meanwhile, the cascade learning mechanism is used to iteratively optimize the prediction and classification results. To validate the effectiveness of CLEC, we conducted early-season classification experiments on soybean, corn and rice in Northeast China. The experimental results show that CLEC significantly improves crop classification accuracy compared to the five state-of-the-art models in the early stages of crop growth. Furthermore, under the premise of obtaining reliable results, CLEC advances the earliest identifiable timing, moving from the flowing to the third true leaf stage for soybean and from the flooding to the sowing stage for rice. Although the earliest identifiable timing for corn remains unchanged, its classification accuracy improved. Overall, CLEC offers new ideas for solving early-season classification challenges. Full article

Wild soybean (Glycine soja, GS) is a traditional medicine used to treat inflammation. In this study, the anti-atopic properties of GS leaf and stem extract on skin inflammation were evaluated in the Dermatophagoides farinae-extract-induced mouse model and keratinocytes. Oral administration of the GS extract reduced scratching, dermatitis score, transepidermal water loss, thickness of epidermis, inflammatory cell accumulation, and serum concentrations of thymic stromal lymphopoietin and immunoglobulin E. GS downregulated the expression of inflammatory gene markers of atopic dermatitis (AD), including interleukin (IL)-6; regulated on activation, normal T cell expressed and secreted (RANTES); thymus- and activation-regulated chemokine (TARC); and macrophage-derived chemokine (MDC) and upregulated the expression of filaggrin, a keratinocyte differentiation marker, in skin tissue. GS downregulated Janus kinase 1, signal transducer and activation of transcription (STAT) 1, and STAT3 pathways. Using ultra-performance liquid chromatography, we identified seven flavonoids in GS extract, including apigenin, epicatechin, genistein, genistin, daidzin, daidzein, and soyasaponin Bb. GS, apigenin, and genistein reduced the expression of IL-6, MDC, TARC, and RANTES and increased filaggrin via the downregulation of STAT3 phosphorylation in interferon-γ/tumor necrosis factor-α-stimulated keratinocytes. Our results suggest that GS leaf and stem extract ameliorates AD-like skin inflammation by regulating the immune response and restoring skin barrier function. Full article

With daily oil consumption approaching 100 million barrels, the global demand continues to generate significant quantities of oily wastewater during oil extraction, refining, and transportation, and the development of effective oil–water separation technologies has become crucial. However, membrane corrosion is a challenge under the harsh conditions involved. Here, we are inspired by the lotus leaf to create a corrosion-resistant and robust superhydrophobic membrane using a general spraying method. By using this spraying process to apply the Graphene@PDMS heptane dispersion onto the mesh substrate, we create a biomimetic corrosion-resistant and robust superhydrophobic stainless steel mesh (SSM). The modified SSM can still maintain superhydrophobic properties after soaking in a strong acidity solution (pH = 1), robust alkalinity solution (pH = 14), or NaCl solution (15 days), which demonstrates excellent chemical stability. Moreover, the modified SSM shows strong mechanical stability during ultrasonic treatment for 2 h. The superhydrophobic SSM can be used to separate various kinds of oils from water with high flux and separation efficiency. It shows a high flux of 27,400 L·m⁻²·h⁻¹ and high separation efficiency of 99.42% for soybean oil–water separation using 400-mesh SSM. The biomimetic modified SSM demonstrates great potential for oil–water separation under harsh conditions, which gives it promise as a candidate in practical applications of oil–water separation. Full article

A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 to 839 sites by February 2025. However, agricultural applications present unique challenges because of rapid crop development and the need for precise phenological monitoring. Despite the increasing number of PhenoCam sites, clear guidelines are missing on (i) the phenological analysis of images, (ii) the selection of a suitable color vegetation index (CVI), and (iii) the extraction of growth stages. This knowledge gap limits the full potential of PhenoCams in agricultural applications. Therefore, a study was conducted in two soybean (Glycine max L.) fields to formulate image analysis guidelines for PhenoCam images. Weekly visual assessments of soybean phenological stages were compared with PhenoCam images. A total of 15 CVIs were tested for their ability to reproduce the seasonal variation from RGB, HSB, and Lab color spaces. The effects of image acquisition time groups (10:00 $\mathrm{h}$–14:00 $\mathrm{h}$) and object position (ROI locations: far, middle, and near) on selected CVIs were statistically analyzed. Excess green minus excess red (EXGR), color index of vegetation (CIVE), green leaf index (GLI), and normalized green red difference index (NGRDI) were selected based on the least deviation from their loess-smoothed phenological curve at each image acquisition time. For the selected four CVIs, the time groups did not have a significant effect on CVI values, while the object position had significant effects at the reproductive phase. Among the selected CVIs, GLI and EXGR exhibited the least deviation within the image acquisition time and object position groups. Overall, we recommend employing a consistent image acquisition time to ensure sufficient light, capture the largest possible image ROI in the middle region of the field, and apply any of the selected CVIs in order of GLI, EXGR, NGRDI, and CIVE. These results provide a standardized methodology and serve as guidelines for PhenoCam image analysis in agricultural cropping environments. These guidelines can be incorporated into the standard protocol of the PhenoCam network. Full article

In agricultural production, the underside of crop leaves and the middle-lower canopy are key areas where pests and diseases typically develop at early stages. Increasing droplet deposition in these critical regions is essential for improving pesticide efficacy and crop yield. This study aims to optimize airflow-assisted parameters to enhance spray operation quality. By extracting the physical characteristics of soybean leaves at the V7 growth stage and conducting theoretical analysis, the study explored the factors influencing leaf orientation and droplet deposition, as well as the coupling relationship between these two aspects. A one-way fluid–structure coupling model was established using COMSOL software 6.1 to simulate the interaction between airflow and soybean leaves. The simulation results showed that airflow caused 71.1% of upper leaves, 66.7% of middle leaves, and 43.3% of lower leaves to have a flipping angle greater than 10°, with most flipped leaves (61.9%) concentrated on the windward side. Using droplet deposition on the middle-lower canopy and the underside of leaves as evaluation indices, a numerical simulation orthogonal experiment was conducted. The results indicated that the optimal operational parameters were an initial airflow speed of 20 m/s, an outlet-to-canopy distance of 0.45 m, and a forward airflow deflection angle of 32°. This optimal parameter combination improved droplet deposition. Field experiments confirmed these results, showing that compared to the spraying without optimization, droplet deposition on the lower and middle canopy and the underside of the leaves increased by 2.1 times and 2.3 times, respectively. Full article