Search Results (269)

Mulberry (Morus spp.), as an economically significant crop in sericulture and medicinal applications, faces severe threats to leaf yield and quality from pest and disease infestations. Traditional detection methods relying on chemical pesticides and manual observation prove inefficient and unsustainable. Although computer vision and deep learning technologies offer new solutions, existing models exhibit limitations in natural environments, including low recognition rates for small targets, insufficient computational efficiency, poor adaptability to occlusions, and inability to accurately identify structural features such as leaf veins. We propose Mamba-YOLO-ML, an optimized model addressing three key challenges in vision-based detection: Phase-Modular Design (PMSS) with dual blocks enhancing multi-scale feature representation and SSM selective mechanisms and Mamba Block, Haar wavelet downsampling preserving critical texture details, and Normalized Wasserstein Distance loss improving small-target robustness. Visualization analysis of the detection performance on the test set using GradCAM revealed that the enhanced Mamba-YOLO-ML model demonstrates earlier and more effective focus on characteristic regions of different diseases compared with its predecessor. The improved model achieved superior detection accuracy with 78.2% mAP50 and 59.9% mAP50:95, outperforming YOLO variants and comparable Transformer-based models, establishing new state-of-the-art performance. Its lightweight architecture (5.6 million parameters, 13.4 GFLOPS) maintains compatibility with embedded devices, enabling real-time field deployment. This study provides an extensible technical solution for precision agriculture, facilitating sustainable mulberry cultivation through efficient pest and disease management. Full article

The extensive use of copper in thermal and electrical systems calls for constant performance enhancement by means of innovative material treatments. The effects on the microstructural, mechanical, and electrical characteristics of copper in deep cryogenic treatment (DCT) and deep cryogenic treatment followed by heat treatment (DCT + HT) are investigated in this work. Copper samples were treated for various soaking durations ranging from 6 to 24 h. Mechanical properties such as tensile strength, hardness, and wear rate were analyzed. In the DCT-treated samples, tensile strength increased, reaching a peak of 343 MPa at 18 h, alongside increased hardness (128 HV) and a refined grain size of 9.58 µm, primarily due to elevated dislocation density and microstrain. At 18 h of soaking, DCT + HT resulted in improved structural stability, high hardness (149 HV), a fine grain size (7.42 µm), and the lowest wear rate (7.73 × 10⁻¹⁰ mm³/Nm), consistent with Hall–Petch strengthening. Electrical measurements revealed improved electron mobility (52.08 cm²/V·s) for samples soaked for 24 h in DCT + HT, attributed to increased crystallite size (39.9 nm), reduced lattice strain, and higher (111) texture intensity. SEM–EBSD analysis showed a substantial increase in low-angle grain boundaries (LAGBs) in DCT + HT-treated samples, correlating with enhanced electrical conductivity. Overall, an 18 h soaking duration was found to be optimal for both treatments. However, the strengthening mechanism in DCT + HT is influenced by grain boundary stabilization and thermal recovery and is different to DCT, which is strain-induced enhancement. Full article

This study tackles the challenge of automatically estimating age from pelvis radiographs. Furthermore, we aim to develop a methodology for applying artificial intelligence to classify or regress medical imagery data. Our dataset comprises 684 pelvis X-ray images of patients, each accompanied by annotations and masks for various regions of interest (e.g., the femur shaft). Radiomic features, e.g., the co-occurrence matrix, were computed to characterize the image content. We assessed statistical analysis, machine learning, and deep learning methods for their effectiveness in this task. Correlation analysis indicated that using certain features in specific regions of interest is promising for accurate age estimation. Machine learning models demonstrated that when using uncorrelated features, the optimal mean absolute error (MAE) for age estimation is 5.20, whereas when employing convolutional networks on the texture feature maps yields the best result of 9.56. Automatically selecting radiomic features for machine learning models achieves a MAE of 7.99, whereas utilizing well-known convolutional architectures on the original image results in a system efficacy of 7.96. The use of artificial intelligence in medical data analysis produces comparable outcomes; however, when dealing with a large number of descriptors, selecting the most optimal ones through statistical analysis enables the identification of the best solution quickly. Full article

Precise identification and quantification of fish movement states are of significant importance for conducting fish behavior research and guiding aquaculture production, with object tracking serving as a key technical approach for achieving behavioral quantification. The traditional DeepSORT algorithm has been widely applied to object tracking tasks; however, in practical aquaculture environments, high-density cultured fish exhibit visual characteristics such as similar textural features and frequent occlusions, leading to high misidentification rates and frequent ID switching during the tracking process. This study proposes an underwater fish object tracking method based on the improved DeepSORT algorithm, utilizing ResNet as the backbone network, embedding Deformable Convolutional Networks v2 to enhance adaptive receptive field capabilities, introducing Triplet Loss function to improve discrimination ability among similar fish, and integrating Convolutional Block Attention Module to enhance key feature learning. Finally, by combining the aforementioned improvement modules, the ReID feature extraction network was redesigned and optimized. Experimental results demonstrate that the improved algorithm significantly enhances tracking performance under frequent occlusion conditions, with the MOTA metric improving from 64.26% to 66.93% and the IDF1 metric improving from 53.73% to 63.70% compared to the baseline algorithm, providing more reliable technical support for underwater fish behavior analysis. Full article

Background/Objectives: Renal cell carcinoma (RCC) is a malignant disease that requires rapid and reliable diagnosis to determine the correct treatment protocol and to manage the disease effectively. However, the fact that the textural and morphological features obtained from medical images do not differ even among different tumor types poses a significant diagnostic challenge for radiologists. In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. Methods: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. The proposed model performs a more comprehensive analysis by combining the T2 phase obtained before the administration of contrast material with the arterial (A) and venous (V) phases recorded after the injection of contrast material. Results: The model performs RCC subtype classification at the end of a five-step process. These are regions of interest (ROI), preprocessing, augmentation, feature extraction, and classification. A total of 1275 MRI images from different phases were classified with SVM, and 90% accuracy was achieved. Conclusions: The findings reveal that the integration of multiphase MRI data and deep learning-based models can provide a significant improvement in RCC subtype classification and contribute to clinical decision support processes. Full article

Robust visual perception and geometric alignment are crucial for intelligent automation in various domains, such as industrial processes and infrastructure monitoring. Accurately aligning structured visual elements, such as floor markings or road-marking templates, is essential for tasks like automated guidance, verification, and condition assessment. However, traditional feature-based methods struggle with templates that feature simple geometries and lack rich textures, making reliable feature matching and alignment difficult, even under controlled conditions. To address this, we propose GeoTemplateKPNet, a novel self-supervised deep-learning framework, built upon Convolutional Neural Networks (CNNs), designed to learn robust, geometrically consistent keypoints specifically in synthetic template images. The model is trained exclusively in a synthetic template dataset by enforcing equivariance to geometric transformations and utilizing self-supervised losses, including inside mask loss, peakiness loss, repulsion loss, and keypoint-driven image reprojection loss, thereby eliminating the need for manual keypoint annotations. We evaluate the method in a synthetic template test set, using metrics such as a keypoint-matching comparison, the Inside Mask Rate (IMR), and the Alignment Reconstruction Error (ARE). The results demonstrate that GeoTemplateKPNet successfully learns to predict meaningful keypoints on template structures, enabling accurate alignment between templates and their transformed counterparts. Ablation studies reveal that the number of keypoints (K) impacts the performance, with K = 3 providing the most suitable balance for the overall alignment accuracy, although the performance varies across different template geometries. GeoTemplateKPNet offers a foundational self-supervised solution for the robust geometric analysis of templates, which is crucial for downstream alignment tasks and applications. Full article

The present study aims to characterize complex geological structures and significant mineralization using remote sensing and aeromagnetic studies. Structural lineaments play a crucial role in the localization and concentration of mineral deposits. For the first time over the study district, a combination of aeromagnetic data, Landsat 9, ASTER, and PRISMA hyperspectral data was utilized to enhance the characterization of both lithological units and structural features. Advanced image processing techniques, including false color composites, principal component analysis (PCA), independent component analysis (ICA), and SMACC, were applied to the remote sensing datasets. These methods enabled effective discrimination between Phanerozoic rock formations and the complex basement units, which comprise the island arc assemblage, Dokhan volcanics, and late-orogenic granites. The local and deep magnetic sources were separated using Gaussian filters. The Neoproterozoic basement rocks were estimated using the radial average power spectrum technique and the Euler deconvolution technique (ED). According to the RAPS technique, the average depths to shallow and deep magnetic sources are approximately 0.4 km and 1.6 km, respectively. The obtained ED contacts range in depth from 0.081 to 1.5 km. The research area revealed massive structural lineaments, particularly in the northeast and northwest sides, where a dense concentration of these lineaments was identified. The locations with the highest densities are thought to signify more fracturization in the rocks that are thought to be connected to mineralization. According to the automatic lineament extraction methods and rose diagram, NW-SE, NNE-SSW, and N-S are the major structural directions. These trends were confirmed and visually represented through textural analysis and drainage pattern control. The lithological mapping results were validated through field observations and petrographic analysis. This integrated approach has proven highly effective, showcasing significant potential for both detailed structural analysis and accurate lithological discrimination, which may be related to further mineralization exploration. Full article

Background: Optical coherence tomography (OCT) is limited by inherent speckle noise, degrading retinal microarchitecture visualization and pathological analysis. Existing denoising methods inadequately balance noise suppression and structural preservation, necessitating advanced solutions for clinical OCT reconstruction. Methods: We propose MFFA-GAN, a generative adversarial network integrating multilevel feature fusion and an efficient local attention (ELA) mechanism. It optimizes cross-feature interactions and channel-wise information flow. Evaluations on three public OCT datasets compared traditional methods and deep learning models using PSNR, SSIM, CNR, and ENL metrics. Results: MFFA-GAN achieved good performance (PSNR:30.107 dB, SSIM:0.727, CNR:3.927, ENL:529.161) on smaller datasets, outperforming benchmarks and further enhanced interpretability through pixel error maps. It preserved retinal layers and textures while suppressing noise. Ablation studies confirmed the synergy of multilevel features and ELA, improving PSNR by 1.8 dB and SSIM by 0.12 versus baselines. Conclusions: MFFA-GAN offers a reliable OCT denoising solution by harmonizing noise reduction and structural fidelity. Its hybrid attention mechanism enhances clinical image quality, aiding retinal analysis and diagnosis. Full article

Myeloproliferative neoplasms, particularly the Philadelphia chromosome-negative (Ph-negative) subtypes such as essential thrombocythemia, polycythemia vera, and primary myelofibrosis, present diagnostic challenges due to overlapping morphological features and clinical heterogeneity. Traditional diagnostic approaches, including imaging and histopathological analysis, are often limited by interobserver variability, delayed diagnosis, and subjective interpretations. To address these limitations, we propose a novel framework that integrates handcrafted and automatic feature extraction techniques for improved classification of Ph-negative myeloproliferative neoplasms. Handcrafted features capture interpretable morphological and textural characteristics. In contrast, automatic features utilize deep learning models to identify complex patterns in histopathological images. The extracted features were used to train machine learning models, with hyperparameter optimization performed using Optuna. Our framework achieved high performance across multiple metrics, including precision, recall, F1 score, accuracy, specificity, and weighted average. The concatenated probabilities, which combine both feature types, demonstrated the highest mean weighted average of 0.9969, surpassing the individual performances of handcrafted (0.9765) and embedded features (0.9686). Statistical analysis confirmed the robustness and reliability of the results. However, challenges remain in assuming normal distributions for certain feature types. This study highlights the potential of combining domain-specific knowledge with data-driven approaches to enhance diagnostic accuracy and support clinical decision-making. Full article

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder causing memory, cognitive, and behavioral decline. Early and accurate diagnosis is critical for timely treatment and management. This study proposes a novel hybrid deep learning framework, GLCM + VGG16 + FMO + CNN-LSTM, to improve AD diagnosis using MRI data. Methods: MRI images were preprocessed through normalization and noise reduction. Feature extraction combined texture features from the Gray-Level Co-occurrence Matrix (GLCM) and spatial features extracted from a pretrained VGG-16 network. Fisher Mantis Optimization (FMO) was employed for optimal feature selection. The selected features were classified using a CNN-LSTM model, capturing both spatial and temporal patterns. The MLP-LSTM model was included only for benchmarking purposes. The framework was evaluated on The ADNI and MIRIAD datasets. Results: The proposed method achieved 98.63% accuracy, 98.69% sensitivity, 98.66% precision, and 98.67% F1-score, outperforming CNN + SVM and 3D-CNN + BiLSTM by 2.4–3.5%. Comparative analysis confirmed FMO’s superiority over other metaheuristics, such as PSO, ACO, GWO, and BFO. Sensitivity analysis demonstrated robustness to hyperparameter changes. Conclusions: The results confirm the efficacy and stability of the GLCM + VGG16 + FMO + CNN-LSTM model for accurate and early AD diagnosis, supporting its potential clinical application. Full article

Benefiting from the development of deep learning, the super-resolution technology for remote sensing hyperspectral images (HSIs) has achieved impressive progress. However, due to the high coupling of complex components in remote sensing HSIs, it is challenging to achieve a complete characterization of the internal information, which in turn limits the precise reconstruction of detailed texture and spectral features. Therefore, we propose the multi-attitude hybrid network (MAHN) for extracting and characterizing information from multiple feature spaces. On the one hand, we construct the spectral hypergraph cross-attention module (SHCAM) and the spatial hypergraph self-attention module (SHSAM) based on the high and low-frequency features in the spectral and the spatial domains, respectively, which are used to capture the main structure and detail changes within the image. On the other hand, high-level semantic information in mixed pixels is parsed by spectral mixture analysis, and semantic hypergraph 3D module (SH3M) are constructed based on the abundance of each category to enhance the propagation and reconstruction of semantic information. Furthermore, to mitigate the domain discrepancies among features, we introduce a sensitive bands attention mechanism (SBAM) to enhance the cross-guidance and fusion of multi-domain features. Extensive experiments demonstrate that our method achieves optimal reconstruction results compared to other state-of-the-art algorithms while effectively reducing the computational complexity. Full article

The identification and prediction of effective reservoir rocks are important for evaluating the energy production potential of ultra-deep tight sandstone reservoirs. Taking the Keshen gas field, Tarim Basin, as an example, three distinct petrofacies are divided according to petrology, pores, and diagenesis. Petrofacies, well logs, and factor analysis are combined to predict effective reservoir rocks. We find that petrofacies A has a relatively coarse grain size, moderate mechanical compaction, diverse but low-abundance authigenic minerals, and well-developed primary and secondary pores. It is an effective reservoir rock. Petrofacies B and petrofacies C are tight sandstones with a poorly developed pore system and almost no dissolution. Petrofacies B features abundant compaction-susceptible ductile grains, intense mechanical compaction, and underdeveloped authigenic minerals, while petrofacies C features pervasive carbonate cementation with a poikilotopic texture. We combine well logging with gamma ray, acoustic, bulk density, neutron porosity, resistivity, and factor analyses to facilitate the development of petrofacies prediction models. The models reveal interbedded architecture where effective reservoir rocks are interbedded with tight sandstone, resulting in the restricted connectivity and pronounced reservoir heterogeneity. Classifying and combining well logs with a factor analysis to predict petrofacies provide an effective means for evaluating the energy potential of ultra-deep reservoirs. Full article

Background/Objectives: Breast cancer diagnosis heavily relies on histopathological assessment, which is prone to subjectivity and inefficiency, especially with whole-slide imaging (WSI). This study addressed these limitations by developing an automated breast cancer cell classification algorithm using an information-extreme machine learning approach and universal cytological features, aiming for objective and generalized histopathological diagnosis. Methods: Digitized histological images were processed to identify hyperchromatic cells. A set of 21 cytological features (10 geometric and 11 textural), chosen for their potential universality across cancers, were extracted from individual cells. These features were then used to classify cells as normal or malignant using an information-extreme algorithm. This algorithm optimizes an information criterion within a binary Hamming space to achieve robust recognition with minimal input features. The architectural innovation lies in the application of this information-extreme approach to cytological feature analysis for cancer cell classification. Results: The algorithm’s functional efficiency was evaluated on a dataset of 176 labeled cell images, yielding promising results: an accuracy of 89%, a precision of 85%, a recall of 84%, and an F1-score of 88%. These metrics demonstrate a balanced and effective model for automated breast cancer cell classification. Conclusions: The proposed information-extreme algorithm utilizing universal cytological features offers a potentially objective and computationally efficient alternative to traditional methods and may mitigate some limitations of deep learning in histopathological analysis. Future work will focus on validating the algorithm on larger datasets and exploring its applicability to other cancer types. Full article

In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural characteristics of plants, traditional 2D image analysis methods are difficult to meet the modeling requirements, highlighting the growing importance of 3D reconstruction technology. This paper reviews active vision techniques (such as structured light, time-of-flight, and laser scanning methods), passive vision techniques (such as stereo vision and structure from motion), and deep learning-based 3D reconstruction methods (such as NeRF, CNN, and 3DGS). These technologies enhance crop analysis accuracy from multiple perspectives, provide strong support for agricultural production, and significantly promote the development of the field of plant research. Full article

Irish potato (Solanum tuberosum) is a critical crop for food security and economic growth in Tsangano and Angónia Districts, Central Mozambique. Challenges like inconsistent yields and variable quality are often linked to suboptimal soil conditions, which limit production. This study aimed to classify and evaluate the suitability of soils for potato cultivation in Tete Province, where detailed soil assessments remain limited. Four pedons—TSA-P01 and TSA-P02 in Tsangano and ANGO-P01 and ANGO-P02 in Angónia—were examined for bulk density, texture, pH, organic carbon, and nutrient content using a combination of pedological methods and laboratory soil analysis. To determine each site’s potential for growing Irish potatoes, these factors were compared to predetermined land suitability standards. The pedons were very deep (>150 cm) and had textures ranging from sandy clay loam to sandy loam. TSA-P02 had the lowest bulk density (0.78 Mg m⁻³) and the highest available water capacity (182.0 mm m⁻¹). The soil pH ranged from 5.6 to 7.9, indicating neutral to slightly acidic conditions. Nutrient analysis revealed low total nitrogen (0.12–0.22%), varying soil organic carbon (0.16–2.73%), and cation exchange capacity (10.1–11.33 cmol⁽⁺⁾ kg⁻¹). Pedons TSA-P01, ANGO-P1, and ANGO-P02 were characterized by eluviation and illuviation as dominant pedogenic processes, while in pedon TSA-P02, shrinking and swelling were the dominant pedogenic processes. Weathering indices identified ANGO-P01 as most highly weathered, while TSA-P02 was least weathered and had better fertility indicators. According to USDA Taxonomy, the soils were classified as Ultisols, Vertisols, and Alfisols, corresponding to Acrisols, Alisols, Vertisols, and Luvisols in the WRB for Soil Resources. All studied soils were marginally suitable for potato production (S3f) due to dominant fertility constraints, but with varying minor limitations in climate, topography, and soil physical properties. The findings hence recommended targeted soil fertility management to enhance productivity and sustainability in potato cultivation. Full article