Search Results (238)

Background/Objectives: Energy availability (EA), defined as the dietary energy remaining after exercise energy expenditure (EEE), is a central determinant of both health and performance in athletes. Chronic insufficient EA leads to low energy availability (LEA), which is an underlying mechanism of Relative Energy Deficiency in Sport (REDs). This narrative review critically explores the conceptual evolution of EA and LEA, summarizes current physiological evidence, and discusses methodological and practical challenges in their assessment and application in free-living athletes. Methods: Evidence from experimental and observational studies was reviewed to describe the hormonal, metabolic, and performance outcomes associated with LEA. Screening tools, including the Low Energy Availability in Females Questionnaire (LEAF-Q) and the Low Energy Availability in Males Questionnaire (LEAM-Q), were also evaluated for their validity and applicability in different sports contexts. Results: LEA is associated with alterations in thyroid and reproductive hormones, which, in turn, contribute to reduced resting metabolic rate, lower bone mineral density, and delayed recovery. While screening questionnaires can help identify athletes at risk, their accuracy varies by sport and individual characteristics. Incorporating hormonal and metabolic biomarkers provides a more direct and sensitive method for detecting physiological stress. Measuring dietary intake, EEE, endocrine balance and body composition in real-world settings remains a major methodological challenge. Combining hormonal, metabolic, and behavioral indicators may improve the identification of athletes experiencing LEA. Conclusions: EA plays a central role in the interaction between nutrition, exercise, and athlete health, but methodological limitations in its assessment may compromise accurate diagnosis. Improving measurement techniques and adopting integrated monitoring strategies are essential to improve early detection, guide individualized nutrition, and prevent RED-related health and performance impairments. Full article

Erysiphe corylacearum, the causal agent of powdery mildew in hazelnut (Corylus avellana L.), has become an emerging pathogen of concern in Italian hazelnut production requiring rapid and accurate detection to support timely disease management and phytosanitary measures. We developed and validated a field-deployable loop-mediated isothermal amplification (LAMP) assay for the specific detection of E. corylacearum and evaluated three primer sets targeting the Internal Transcribed Spacer (ITS) region, RNA polymerase II second largest subunit (rpb2), and glutamine synthetase (GS) genes; the GS-targeting Ecg set showed the highest sensitivity and specificity. The assay was shown to be sensitive down to 200 fg of fungal DNA, efficiently detected E. corylacearum from diluted crude leaf extracts, and produced results within half an hour, allowing the detection of latent infections before visible symptoms emerged. On-site validation with a portable LAMP instrument showed the assay’s suitability for field-deployable diagnosis and early-warning applications in hazelnut orchards. Full article

A comprehensive two-year investigation (2024–2025) was conducted across Northeast China’s crucial grain production base to assess the status of maize diseases. Field surveys spanning three provinces and Inner Mongolia revealed a significant shift in the regional disease profile, with diagnosis performed by experienced personnel based on characteristic field symptoms. The results demonstrated that maize white spot (MWS) has emerged as a severe new threat, recording remarkably high disease severity indices exceeding 80 at multiple locations (e.g., LDD25-1: 86.83). Concurrently, gray leaf spot (GLS) was confirmed as the most prevalent foliar disease, forming stable areas of high severity in the eastern mountainous regions where its disease indices consistently surpassed 60 (e.g., LFS25-1: 65.26), thereby exceeding the impact of northern corn leaf blight. In contrast, stalk rot (SR) maintained a low field incidence rate below 10%, while other diseases such as Curvularia leaf spot and maize eyespot were only observed locally or were absent during the 2025 survey period. These findings underscore the emergence of MWS as a critical threat and affirm the dominant status of GLS, offering a scientific foundation for prioritizing disease management strategies in the region. Full article

Mobile edge-AI plant monitoring systems enable automated disease control in greenhouses and open fields, reducing dependence on manual inspection and the variability of visual diagnostics. This paper proposes a lightweight two-stage edge-AI system for strawberries, in which a YOLOv10n detector on board a mobile agricultural robot locates leaves affected by seven common diseases (including Leaf Spot) with real-time capability on an embedded platform. Patches are then automatically extracted for leaves classified as Leaf Spot and transmitted to the second module—a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1—mild, S2—moderate, S3—severe) on a specialised set of 373 manually labelled leaf patches. In a comparative experiment with lightweight architectures ResNet-18, EfficientNet-B0, MobileNetV3-Small and Swin-Tiny, the proposed Ordinal MobileViT-S demonstrated the highest accuracy in assessing the severity of Leaf Spot (accuracy ≈ 0.97 with 4.9 million parameters), surpassing both the baseline models and the standard MobileViT-S with a cross-entropy loss function. On the original image set, the YOLOv10n detector achieves an mAP@0.5 of 0.960, an F1 score of 0.93 and a recall of 0.917, ensuring reliable detection of affected leaves for subsequent Leaf Spot severity assessment. The results show that the “YOLOv10n + Ordinal MobileViT-S” cascade provides practical severity-aware Leaf Spot diagnosis on a mobile agricultural robot and can serve as the basis for real-time strawberry crop health monitoring systems. Full article

Background/Objectives: Several studies reported that olive leaf extract (OLE) may exert potent anti-cancer activities against human solid and hematological tumors. Such effects are mostly related to the polyphenol oleuropein and its derivatives, which are highly concentrated in OLE. Here, we investigated the anti-tumor effects of OLE in vitro against human acute leukemia and lymphoma cells. Methods: Cell proliferation and apoptosis have been evaluated by flow cytometry (using CFSE and Annexin-V/7AAD, respectively) in the presence or absence of OLE at different concentrations and in combination with or without chemotherapeutic drugs. Cellular pathways have been analyzed using antibody arrays. Results: OLE inhibited cell proliferation and induced apoptosis in B-acute lymphoblastic leukemia (B-ALL) and, to a lesser extent, in lymphomas and acute myeloid leukemia (AML) cell lines. Notably, OLE-induced apoptosis also occurs in primary leukemic blasts from B-ALL patients, both at diagnosis and at relapse, but only marginally in primary AML blasts. The expression and phosphorylation of proteins involved in the induction of apoptosis were modulated by OLE in B-ALL, whereas modest effects were observed in AML. Interestingly, some proteins were modulated in opposite ways in B-ALL and AML, potentially explaining their different responses to OLE. Finally, a synergistic and additive effect was observed for OLE in combination with cytarabine, but not with cyclophosphamide. Conclusions: We may envisage that OLE may be used as a food supplement in B-ALL patients treated with cytarabine, taking advantage of the potentiated effect of chemotherapy, without additional side effects. Full article

The early detection of plant diseases is critical to improving agricultural productivity and ensuring food security. However, conventional centralized deep learning approaches are often unsuitable for large-scale agricultural deployments, as they rely on continuous data transmission to cloud servers and require high computational resources that are impractical for Internet of Things (IoT)-based field environments. In this article, we present a distributed deep learning framework based on Federated Learning (FL) for the diagnosis of plant diseases in IoT sensor networks. The proposed architecture integrates multiple IoT nodes and an edge computing node that collaboratively train an EfficientNet B0 model using the Federated Averaging (FedAvg) algorithm without transferring local data. Two training pipelines are evaluated: a standard single-model pipeline and a hierarchical pipeline that combines a crop classifier with crop-specific disease models. Experimental results on a multicrop leaf image dataset under realistic augmentation scenarios demonstrate that the hierarchical FL approach improves per-crop classification accuracy and robustness to environmental variations, while the standard pipeline offers lower latency and energy consumption. Full article

Unobserved fruit crop illnesses are a major threat to agricultural productivity worldwide and frequently cause farmers to suffer large financial losses. Manual field inspection-based disease detection techniques are time-consuming, unreliable, and unsuitable for extensive monitoring. Deep learning approaches, in particular convolutional neural networks, have shown promise for automated plant disease identification, although they still face significant obstacles. These include poor generalization across complicated visual backdrops, limited resilience to different illness sizes, and high processing needs that make deployment on resource-constrained edge devices difficult. We suggest a Hybrid Multi-Scale Neural Network (HMCT-AF with GSAF) architecture for precise and effective fruit crop disease identification in order to overcome these drawbacks. In order to extract long-range dependencies, HMCT-AF with GSAF combines a Vision Transformer-based structural branch with multi-scale convolutional branches to capture both high-level contextual patterns and fine-grained local information. These disparate features are adaptively combined using a novel HMCT-AF with a GSAF module, which enhances model interpretability and classification performance. We conduct evaluations on both PlantVillage (controlled environment) and CLD (real-world in-field conditions), observing consistent performance gains that indicate strong resilience to natural lighting variations and background complexity. With an accuracy of up to 93.79%, HMCT-AF with GSAF outperforms vanilla Transformer models, EfficientNet, and traditional CNNs. These findings demonstrate how well the model captures scale-variant disease symptoms and how it may be used in real-time agricultural applications using hardware that is compatible with the edge. According to our research, HMCT-AF with GSAF presents a viable basis for intelligent, scalable plant disease monitoring systems in contemporary precision farming. Full article

Technology-driven agriculture, or precision agriculture (PA), is indispensable in the contemporary world due to its advantages and the availability of technological innovations. Particularly, early disease detection in agricultural crops helps the farming community ensure crop health, reduce expenditure, and increase crop yield. Governments have mainly used current systems for agricultural statistics and strategic decision-making, but there is still a critical need for farmers to have access to cost-effective, user-friendly solutions that can be used by them regardless of their educational level. In this study, we used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases. This framework makes use of deep convolutional neural networks (CNNs) and includes three CNN models created specifically for this application. AACDIS achieves significant performance improvements by combining cascade inception and drawing inspiration from the well-known AlexNet design, making it a potent tool for managing agricultural diseases. AACDIS also has Region of Interest (ROI) awareness, a crucial component that improves the efficiency and precision of illness identification. This feature guarantees that the system can quickly and accurately identify illness-related areas inside images, enabling faster and more accurate disease diagnosis. Experimental findings show a test accuracy of 99.491%, which is better than many state-of-the-art deep learning models. This empirical study reveals the potential benefits of the proposed system for early identification of diseases. This research triggers further investigation to realize full-fledged precision agriculture and smart agriculture. Full article

This study aimed to develop a deep learning-based application for the automatic detection of nutritional deficiencies in coffee plants through the analysis of in-field leaf images. Images were collected from farms in the Shipasbamba district and classified into six deficiency types: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and iron (Fe). A total of 2643 leaves were labeled and preprocessed for model training. Several YOLO architectures were evaluated, with YOLO11x achieving the best performance after 100 epochs, reaching a precision of 88.98%, recall of 88.54%, F1-Score of 88.76%, and mAP50 of 92.68%. An interactive web application was developed to allow real-time image upload and processing, providing both graphical and textual feedback on detected deficiencies. These results demonstrate the model’s effectiveness for automated diagnosis and its potential to support coffee growers in timely, data-driven decision-making, ultimately improving nutrient management and reducing production losses. Full article

Rice diseases pose a critical threat to global food security. While deep learning offers a promising path toward automated diagnosis, clear guidelines for model selection in resource-constrained agricultural environments are still lacking. This study presents a systematic benchmark of five deep convolutional neural networks (CNNs)—Visual Geometry Group (VGG)16, VGG19, Residual Network (ResNet)101V2, Xception, and Densely Connected Convolutional Network (DenseNet)121—for rice disease identification using a public leaf image dataset. The models, initialized with ImageNet pre-trained weights, were rigorously evaluated under a unified framework, including 5-fold cross-validation and a challenging out-of-distribution (OOD) generalization test. Our results demonstrate a clear performance hierarchy, with DenseNet121 emerging as the superior model. It achieved the highest OOD accuracy and F1-score (both 85.08%) while exhibiting the greatest parameter efficiency (8.1 million parameters), making it ideally suited for edge deployment. In contrast, architectures with large fully connected layers (VGG) or less efficient feature learning mechanisms (Xception, ResNet101V2) showed lower performance in this specific task. This study confirms the critical impact of architectural design choices, provides a reproducible performance baseline, and identifies DenseNet121 as a robust, efficient, and highly recommendable CNN for practical rice disease diagnosis in precision agriculture. Full article

Early detection of plant diseases is key to ensuring food production, reducing economic losses, minimizing the use of agrochemicals, and maintaining the sustainability of the agricultural sector. Citrus plants, an important source of vitamin C, fiber, and antioxidants, are among the world’s most significant fruit crops but face threats such as canker and Huanglongbing (HLB), incurable diseases that require management strategies to mitigate their impact. Manual diagnosis, although common, I s imprecise, slow, and costly; therefore, efficient alternatives are emerging to identify diseases from early stages using Artificial Intelligence techniques. This study evaluated four deep learning models, specifically convolutional neural networks. In this study, we evaluated four convolutional neural network models (DenseNet121, ResNet50, EfficientNetB0, and MobileNetV2) to detect canker and HLB in citrus leaf images. We applied preprocessing and data-augmentation techniques; transfer learning via selective fine-tuning; stratified k-fold cross-validation; regularization methods such as dropout and weight decay; and hyperparameter-optimization techniques. The models were evaluated by the loss value and by metrics derived from the confusion matrix, including accuracy, recall, and F1-score. The best-performing model was EfficientNetB0, which achieved an average accuracy of 99.88% and the lowest loss value of 0.0058 using cross-entropy as the loss function. Since EfficientNetB0 is a lightweight model, the results show that lightweight models can achieve favorable performance compared to robust models, models that can be useful for disease detection in the agricultural sector using portable devices or drones for field monitoring. The high accuracy obtained is mainly because only two diseases were considered; consequently, it is possible that these results do not hold in a database that includes a larger number of diseases. Full article

Manganese (Mn) deficiency is a major factor underlying brittle leaf disease in date palm, yet its root-centered mechanisms under field conditions remain poorly understood. Nine mature palms (three per health category: healthy, asymptomatic Mn-deficient, and BLD-affected) were assessed for soil chemistry (pH, salinity), root Mn concentration and hydraulics, canopy pigments and chlorophyll fluorescence (F_v/F_m), as well as metabolic responses. Elevated soil pH and variable salinity significantly constrained root Mn uptake and water conductance, leading to a ~60% decline in root Mn, a 20% reduction in root water content, an 80% loss of chlorophyll, and a 26% decrease in F_v/F_m. These changes induced strong metabolic reprogramming, including a twofold rise in glucose, increased protein content, and a tenfold enhancement in peroxidase activity. Asymptomatic palms already displayed early declines in pigments and fluorescence, highlighting their diagnostic value. This study demonstrates that soil-driven Mn deficiency impairs root function and cascades to canopy physiology and metabolism, offering realistic avenues for rhizosphere management and early field diagnosis in arid oases. Full article

Understory ginseng, with superior quality compared to field-cultivated varieties, is highly susceptible to diseases, which negatively impact both its yield and quality. Therefore, this paper proposes a lightweight, high-precision leaf spot segmentation model, Lightweight DeepLabv3+ with a StarNet Backbone and Attention-guided Gaussian Edge Enhancement (LD-SAGE). This study first introduces StarNet into the DeepLabv3+ framework to replace the Xception backbone, reducing the parameter count and computational complexity. Secondly, the Gaussian-Edge Channel Fusion module uses multi-scale Gaussian convolutions to smooth blurry areas, combining Scharr edge-enhanced features with a lightweight channel attention mechanism for efficient edge and semantic feature integration. Finally, the proposed Multi-scale Attention-guided Context Modulation module replaces the traditional Atrous Spatial Pyramid Pooling. It integrates Multi-scale Grouped Dilated Convolution, Convolutional Multi-Head Self-Attention, and dynamic modulation fusion. This reduces computational costs and improves the model’s ability to capture contextual information and texture details in disease areas. Experimental results show that the LD-SAGE model achieves an mIoU of 92.48%, outperforming other models in terms of precision and recall. The model’s parameter count is only 4.6% of the original, with GFLOPs reduced to 22.1% of the baseline model. Practical deployment experiments on the Jetson Orin Nano device further confirm the advantage of the proposed method in the real-time frame rate, providing support for the diagnosis of leaf diseases in understory ginseng. Full article

Phyllodes tumors, also known as cystosarcoma phyllodes, represent a rare and complex category of fibroepithelial neoplasms that primarily affect the breast. These tumors are characterized by their unique histological architecture, which resembles leaf-like structures, as suggested by the etymology of the term “phyllodes,” derived from the Greek word “phyllodes,” meaning “leaf-like”. The World Health Organization (WHO) has classified these tumors into three distinct categories—benign, borderline, and malignant—based on various histopathological criteria, including cellular atypia, mitotic activity, and stromal overgrowth. With a peak incidence occurring between the ages of 40 and 52, these tumors primarily affect women and constitute 0.3% to 1% of all breast tumors. Imaging modalities currently employed (mammography, ultrasound, and MRI) play a crucial role in the initial assessment of breast masses. Histopathological characteristics, such as stromal cellularity and mitotic activity, and immunohistochemical markers, like Ki-67 and p53, are important in the diagnosis, categorization, treatment plans, and prognosis of breast phyllodes tumors. Surgical intervention, with the goal of achieving complete excision of the tumor along with adequate margins, is the primary treatment option. Adjuvant therapies, such as radiotherapy, may be considered but are still debatable. Understanding the nuances of these tumors is crucial for healthcare professionals, as they present unique challenges in both diagnosis and treatment. Full article

The efficient and non-destructive inspection of the chlorophyll content of grey jujube leaf is of great significance for its growth surveillance and nutritional diagnosis. Near-infrared spectroscopy combined with chemometric methods provides an effective approach to achieve this goal. This study took grey jujube leaves as the research object, systematically collected near-infrared spectral data in the range of 4000–10,000 cm⁻¹, and simultaneously measured their soil and plant analyzer development (SPAD) value as a reference index for chlorophyll content. Through various pretreatment and their combination methods on the original spectrum—smooth, standard normal variable transformation (SNV), first derivative (FD), second derivative (SD), smooth + first derivative (Smooth + FD), smooth + second derivative (Smooth + SD), standard normal variable transformation + first derivative (SNV + FD), standard normal variable transformation + second derivative (SNV + SD)—the effects of different methods on the quality of the spectrum and its correlation with SPAD value were compared. The competitive adaptive reweighted sampling algorithm (CARS) was adopted to extract the characteristic wavelength, aiming to reduce data dimensionality and optimize model input. Both BP neural network and RBF neural network prediction models were established, and the model performance under different training functions was compared. The results indicate that after Smooth + FD pretreatment, followed by CARS screening of the characteristic wavelength, the BP neural network model trained using the LBFGS algorithm demonstrated the best performance, with its coefficient of determination (R²) of 0.87 (training set) and 0.85 (validation set), root mean square error (RMSE) of 1.36 (training set) and 1.35 (validation set), and residual prediction deviation (RPD) of 2.81 (training set) and 2.56 (validation set) showing good prediction accuracy and robustness. Research indicates that by combining near-infrared spectroscopy with feature extraction and machine learning methods, the rapid and non-destructive inspection of the grey jujube leaf SPAD value can be achieved, providing reliable technical support for the real-time monitoring of the nutritional status of jujube trees. Full article