Search Results (2,622)

Accurate segmentation of pear leaf diseases is paramount for enhancing diagnostic precision and optimizing agricultural disease management. However, variations in disease color, texture, and morphology, coupled with changes in lighting conditions and gradual disease progression, pose significant challenges. To address these issues, we propose EBMA-Net, an edge-aware multi-scale network. EBMA-Net introduces a Multi-Dimensional Joint Attention Module (MDJA) that leverages atrous convolutions to capture lesion information at different scales, enhancing the model’s receptive field and multi-scale processing capabilities. An Edge Feature Extraction Branch (EFFB) is also designed to extract and integrate edge features, guiding the network’s focus toward edge information and reducing information redundancy. Experiments on a self-constructed pear leaf disease dataset demonstrate that EBMA-Net achieves a Mean Intersection over Union (MIoU) of 86.25%, Mean Pixel Accuracy (MPA) of 91.68%, and Dice coefficient of 92.43%, significantly outperforming comparison models. These results highlight EBMA-Net’s effectiveness in precise pear leaf disease segmentation under complex conditions. Full article

Mammals harbor diverse microbial communities across different body sites, which are crucial to physiological functions and host homeostasis. This study aimed to understand the structure and function of gut and lung microbiota of pregnant Pomona leaf-nosed bats using V3-V4 16S rRNA gene sequencing. Of the 350 bats captured using mist nets in Yunnan, nine pregnant Pomona leaf-nosed bats with similar body sizes were chosen. Gut and lung samples were aseptically collected from each bat following cervical dislocation and placed in sterile cryotubes before microbiota investigation. Microbial taxonomic annotation revealed that the phyla Firmicutes and Actinobacteriota were most abundant in the guts of pregnant bats, whereas Proteobacteria and Bacteroidota were abundant in the lungs. Family-level classification revealed that Bacillaceae, Enterobacteriaceae, and Streptococcaceae were more abundant in the guts, whereas Rhizobiaceae and Burkholderiaceae dominated the lungs. Several opportunistic and potentially pathogenic bacterial genera were present at the two body sites. Bacillus, Cronobacter, and Corynebacterium were abundant in the gut, whereas Bartonella, Burkholderia, and Mycoplasma dominated the lungs. Alpha diversity analysis (using Chao1 and Shannon indices) within sample groups examined read depth and species richness, whereas beta diversity using unweighted and weighted UniFrac distance metrics revealed distinct clustering patterns between the two groups. LEfSe analysis revealed significantly enriched bacterial taxa, indicating distinct microbial clusters within the two body sites. The two Random Forest classifiers (MDA and MDG) evaluated the importance of microbial features in the two groups. Comprehensive functional annotation provided insights into the microbiota roles in metabolic activities, human diseases, signal transduction, etc. This study contributes to our understanding of the microbiota structure and functional potential in pregnant wild bats, which may have implications for host physiology, immunity, and the emergence of diseases. Full article

Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, has become an intermittent yet economically significant disease of wheat in the Upper Midwest during the last decade. Because chemical and cultural controls remain ineffective, breeders rely on developing resistant varieties, yet visual ratings in inoculated nurseries are labor-intensive, subjective, and time-consuming. To accelerate this process, we combined unmanned-aerial-vehicle hyperspectral imaging (UAV-HSI) with a carefully tuned chemometric workflow that delivers rapid, objective estimates of disease severity. Principal component analysis cleanly separated BLS, leaf rust, and Fusarium head blight, with the first component explaining 97.76% of the spectral variance, demonstrating in-field pathogen discrimination. Pre-processing of the hyperspectral cubes, followed by robust Partial Least Squares (RPLS) regression, improved model reliability by managing outliers and heteroscedastic noise. Four variable-selection strategies—Variable Importance in Projection (VIP), Interval PLS (iPLS), Recursive Weighted PLS (rPLS), and Genetic Algorithm (GA)—were evaluated; rPLS provided the best balance between parsimony and accuracy, trimming the predictor set from 244 to 29 bands. Informative wavelengths clustered in the near-infrared and red-edge regions, which are linked to chlorophyll loss and canopy water stress. The best model, RPLS with optimal preprocessing and variable selection based on the rPLS method, showed high predictive accuracy, achieving a cross-validated R² of 0.823 and cross-validated RMSE of 7.452, demonstrating its effectiveness for detecting and quantifying BLS. We also explored the spectral overlap with Sentinel-2 bands, showing how UAV-derived maps can nest within satellite mosaics to link plot-level scouting to landscape-scale surveillance. Together, these results lay a practical foundation for breeders to speed the selection of resistant lines and for agronomists to monitor BLS dynamics across multiple spatial scales. Full article

Seed-level disease detection in soybeans presents significant challenges, including small-sample limitations, spectral interference, and dense occlusions, which are less pronounced in leaf-level analysis. To overcome these obstacles, we propose YOLOv8-ECCI, an enhanced algorithm based on YOLOv8 for high-precision identification of purple spot disease directly on soybean seeds. Experimental results demonstrate that YOLOv8-ECCI substantially outperforms the baseline YOLOv8n model, achieving significant gains of +5.7% precision, +6.5% recall, +8.0% mAP@0.5, and +7.1% mAP@0.5:0.95. Crucially, the model exhibits superior generalization capability, validated through rigorous cross-dataset testing on the African Wildlife dataset, where it surpasses conventional methods by +6.0% precision and +2.9% mAP@0.5. These results confirm that YOLOv8-ECCI effectively addresses the critical challenges in seed-level pathology, providing a robust and accurate solution for practical in-field agricultural disease detection and quality control. Full article

The Yangtze River Basin serves as the primary rapeseed-producing region in China, accounting for over 80% of the national output, yet it is severely impacted by waterlogging, resulting in yield reductions of 17–42.4%. This study investigated the effects of pelleting treatments on growth and waterlogging resistance in Brassica napus varieties Xiangzayou 787 and Fanmingyoutai. Conventional pelleting agents were augmented with waterlogging resistance agents, surfactants, and amino acids as growth-promoting reagents. The results demonstrated that melatonin at $5.0\times {10}^{-5}$ mol/L significantly enhanced rapeseed growth and stress resistance. Specifically, for Xiangzayou 787, root fresh weight increased by 16.9% and stem diameter by 30.6%; for Fanmingyoutai, stem diameter increased by 16.9% and leaf length by 12.3%. The freezing injury index decreased by 90.9% for Xiangzayou 787 and 50% for Fanmingyoutai. The waterlogging injury index was reduced by 43.5% for Xiangzayou 787 and 30.4% for Fanmingyoutai, with stem diameter increasing by 30.6% and 16.5% in the respective varieties. The disease index decreased by 63.2% for Xiangzayou 787 (incidence reduced to 20.5%) and up to 57.1% for Fanmingyoutai (incidence reduced to 23.3%). Under this treatment, soluble protein content in Fanmingyoutai reached 20.37%, representing a 20.37% increase relative to the control. Peroxidase (POD) and superoxide dismutase (SOD) activities exceeded control levels, exhibiting an initial rise followed by a decline; malondialdehyde (MDA) content gradually increased; catalase (CAT) activity and soluble protein content showed an initial increase then decrease. The increase in relative electrical conductivity was reduced by 20.8% for Xiangzayou 787 and 17.3% for Fanmingyoutai. Yield per plant increased by 10.2% for Xiangzayou 787 and 35.6% for Fanmingyoutai. The newly developed pelleting formulation integrates waterlogging resistance agents, surfactants, and amino acids, unlike traditional agents, and proves effective for both hybrid and conventional rapeseed varieties. It enhances waterlogging resistance, promotes growth, improves disease resistance, and elevates seed quality while being cost-effective and simple for production and field application. This approach significantly boosts yield and supports productivity enhancement in southern rice fields, thereby improving rapeseed output and oil supply. Full article

Early and accurate identification of maize diseases is crucial for ensuring sustainable agricultural development. However, existing maize disease identification models face challenges including high inter-class similarity, intra-class variability, and limited capability in identifying early-stage symptoms. To address these limitations, we proposed DSTANet (decomposed spatial token aggregation network), a lightweight and high-performance model for maize leaf disease identification. In this study, we constructed a comprehensive maize leaf image dataset comprising six common disease types and healthy samples, with early and late stages of northern leaf blight and eyespot specifically differentiated. DSTANet employed MobileViT as the backbone architecture, combining the advantages of CNNs for local feature extraction with transformers for global feature modeling. To enhance lesion localization and mitigate interference from complex field backgrounds, DSFM (decomposed spatial fusion module) was introduced. Additionally, the MSTA (multi-scale token aggregator) was designed to leverage hidden-layer feature channels more effectively, improving information flow and preventing gradient vanishing. Experimental results showed that DSTANet achieved an accuracy of 96.11%, precision of 96.17%, recall of 96.11%, and F1-score of 96.14%. With only 1.9M parameters, 0.6 GFLOPs (floating point operations), and an inference speed of 170 images per second, the model meets real-time deployment requirements on edge devices. This study provided a novel and practical approach for fine-grained and early-stage maize disease identification, offering technical support for smart agriculture and precision crop management. Full article

Background/Objectives: High concentration of plasma low-density lipoprotein cholesterol (LDL-C) is the predominant cause of atherosclerotic cardiovascular disease progression and coronary heart disease. Nutraceutical combination together with a cholesterol-lowering action provides an alternative to pharmacotherapy in patients reporting intolerance to statins and in subjects with low cardiovascular risk. The effects on lipid parameters were evaluated over 6 months for a food supplement containing aqueous extract of Berberis aristata and Olea europea, fenugreek seed extract, water/ethanol extract of artichoke leaf and phytosterols from sunflower seeds (Ritmon Colesystem^®). Methods: Laboratory data were obtained at baseline from 44 otherwise healthy subjects (33 males, mean 50 ± 11 years) without cardiovascular disease having LDL-C in the range 115 to 190 mg/dL pharmacologically untreated for hypercholesterolemia. Subjects were re-evaluated at 1, 3 and 6 months during which they took one tablet of Ritmon Colesystem^® after dinner. Results: At baseline, the mean values were 151 ± 21 mg/dL for LDL-C, 223 ± 24 mg/dL for total cholesterol (T-C), 52 ± 14 mg/dL for high-density lipoprotein cholesterol (HDL-C), and 124 ± 58 mg/dL for triglycerides. A significant reduction in LDL-C was observed; 9 mg/dL (95% confidence interval 3–14), 10 (4–17) and 7 (1–14) at 1, 3 and 6 months. A similar significant trend was detected for T-C while triglycerides did not show significant changes and HDL-C had lower values only at 3 months. Conclusions: These nutraceuticals in individuals with sub-optimal blood cholesterol levels at intermediate–low cardiovascular risk reduced LDL-C and T-C over 6 months contributing to the improvement of cholesterol control by dietary supplements. Full article

High-quality disease segmentation plays a crucial role in the precise identification of rice diseases. Although the existing deep learning methods can identify the disease on rice leaves to a certain extent, these methods often face challenges in dealing with multi-scale disease spots and irregularly growing disease spots. In order to solve the challenges of rice leaf disease segmentation, we propose KBNet, a novel multi-modal framework integrating language and visual features for rice disease segmentation, leveraging the complementary strengths of CNN and Transformer architectures. Firstly, we propose the Kalman Filter Enhanced Kolmogorov–Arnold Networks (KF-KAN) module, which combines the modeling ability of KANs for nonlinear features and the dynamic update mechanism of the Kalman filter to achieve accurate extraction and fusion of multi-scale lesion information. Secondly, we introduce the Boundary-Constrained Physical-Information Neural Network (BC-PINN) module, which embeds the physical priors, such as the growth law of the lesion, into the loss function to strengthen the modeling of irregular lesions. At the same time, through the boundary punishment mechanism, the accuracy of edge segmentation is further improved and the overall segmentation effect is optimized. The experimental results show that the KBNet framework demonstrates solid performance in handling complex and diverse rice disease segmentation tasks and provides key technical support for disease identification, prevention, and control in intelligent agriculture. This method has good popularization value and broad application potential in agricultural intelligent monitoring and management. Full article

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article

Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) begins with hepatic lipid accumulation and triggers insulin resistance. Hibiscus leaf extract exhibits antioxidant and anti-atherosclerotic activities, and is rich in (−)-epicatechin gallate (ECG). Despite ECG’s well-known pharmacological activities and its total antioxidant capacity being stronger than that of other catechins, its regulatory effects on MASLD have not been fully described previously. Therefore, this study attempted to evaluate the anti-MASLD potential of ECG isolated from Hibiscus leaves on abnormal lipid and glucose metabolism in hepatocytes. First, oleic acid (OA) was used as an experimental model to induce lipid dysmetabolism in human primary hepatocytes. Treatment with ECG can significantly (p < 0.05) reduce the OA-induced cellular lipid accumulation. Nile red staining revealed, compared to the OA group, the inhibition percentages of 29, 61, and 82% at the tested doses of ECG, respectively. The beneficial effects of ECG were associated with the downregulation of SREBPs/HMGCR and upregulation of PPARα/CPT1 through targeting AMPK. Also, ECG at 0.4 µM produced a significant (p < 0.01) decrease in oxidative stress by 83%, and a marked (p < 0.05) increase in glycogen synthesis by 145% on the OA-exposed hepatocytes with insulin signaling blockade. Mechanistic assays indicated lipid and glucose metabolic homeostasis of ECG might be mediated via regulation of lipogenesis, fatty acid β-oxidation, and insulin resistance, as confirmed by an AMPK inhibitor. These results suggest ECG is a dual modulator of lipid and carbohydrate dysmetabolism in hepatocytes. Full article

The health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available. Full article

Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

In the agricultural domain, the availability of labelled data for disease recognition tasks is often limited due to the cost and expertise required for annotation. In this paper, a novel semi-supervised learning framework named TOM-SSL is proposed for automatic tomato leaf disease recognition using pseudo-labelling. TOM-SSL effectively addresses the challenge of limited labelled data by leveraging a small labelled subset and confidently pseudo-labelled samples from a large pool of unlabelled data to improve classification performance. Utilising only 10% of the labelled data, the proposed framework with a MobileNetV3-Small backbone achieves the best accuracy at 72.51% on the tomato subset of the PlantVillage dataset and 70.87% on the Taiwan tomato leaf disease dataset across 10 disease categories in PlantVillage and 6 in the Taiwan dataset. While achieving recognition performance on par with current state-of-the-art supervised methods, notably, the proposed approach offers a tenfold enhancement in label efficiency. Full article