Search Results (2,663)

Accurate apple yield estimation is essential for effective orchard management, market planning, and ensuring growers’ income. However, complex orchard conditions, such as dense foliage occlusion and overlapping fruits, present challenges to large-scale yield estimation. This study introduces APYOLO, an enhanced apple detection algorithm based on an improved YOLOv11, integrated with the DeepSORT tracking algorithm to improve both detection accuracy and operational speed. APYOLO incorporates a multi-scale channel attention (MSCA) mechanism and an enhanced multi-scale prior distribution intersection over union (EnMPDIoU) loss function to enhance target localization and recognition under complex environments. Experimental results demonstrate that APYOLO outperforms the original YOLOv11 by improving mAP@0.5, mAP@0.5–0.95, accuracy, and recall by 2.2%, 2.1%, 0.8%, and 2.3%, respectively. Additionally, the combination of a unique ID with the region of line (ROL) strategy in DeepSORT further boosts yield estimation accuracy to 84.45%, surpassing the performance of the unique ID method alone. This study provides a more precise and efficient system for apple yield estimation, offering strong technical support for intelligent and refined orchard management. Full article

Orah mandarin (Citrus reticulata cv. Orah) lacks systematic grading treatment after harvesting, resulting in a high fresh fruit loss rate and affecting the economic value. There are many drawbacks to traditional manual and mechanical sorting. Therefore, intelligent, rapid, non-destructive surface defect detection technology is significant. In addition to the fruit size, surface defects (e.g., canker, sunburn) are another important criterion for grading fruit. To overcome the challenges in detecting surface defects of orah mandarin, like multi-scale features, significant size differences, and slow convergence speed, we propose the YOLOv8-Orah detection model based on YOLOv8n. Path Aggregation Network (PANet) is replaced by a Focusing Diffusion Pyramid Network (FDPN), and the Diffusion and Spatial Interaction (DASI) module is introduced to effectively fuse and enhance features of different scales and improve detection accuracy. The Bottleneck in the C2f module is replaced by the Hybrid Dilated Residual Attention Block (HDRAB) module to reduce missed detections and false detections. We also introduce the NWD-CIoU joint bounding box loss to accelerate the convergence speed and improve the detection accuracy of small defects. The experimental results show that the improved YOLOv8-Orah model performs well in terms of precision, recall, and average precision, reaching 81.9%, 78.8%, and 84.2%, respectively. Compared with the original YOLOv8n, the improved model increased by 4.0%, 1.7%, and 3.0%, respectively. Meanwhile, the parameter count decreased by 7.76%. Compared with other mainstream models, YOLOv8-Orah achieves a good balance between detection accuracy and computational efficiency. The results technically support defect detection in postharvest orah mandarin and real-time grading of their quality. Meanwhile, it can promote the intelligent development of the bergamot industry. Full article

Neoplastic disorders, particularly malignant carcinomas, are complex systemic diseases characterized by unregulated cellular proliferation, the invasion of adjacent tissues, and potential metastasis to distant bodily sites. Among the diverse spectrum of cancer subtypes, malignant melanoma is a highly aggressive form of cutaneous cancer originating in melanocytes, the pigment-producing cells resident in the skin. This malignancy is distinguished by its rapid and uncontrolled growth, as well as its propensity for metastasis to vital organs, thereby posing significant challenges to therapeutic intervention and prognostication. Early detection of melanoma is crucial for optimizing patient outcomes, as diagnosis at an advanced stage often yields a poor prognosis and limited treatment options. Diagnostic modalities for melanoma encompass comprehensive clinical evaluations by dermatologists; radiological imaging techniques such as ultrasonography, magnetic resonance imaging (MRI), computed tomography (CT) scans; and excisional biopsies for accurate histopathological assessment. Malignant melanoma is typically treated with surgery to remove the tumor, followed by immunotherapy to enhance the immune response, targeted therapy for tumors with specific genetic mutations, chemotherapy for advanced stages, radiation therapy to manage metastasis, and other adjunct therapies. This review presents the properties and possible adjunct therapeutic effects against malignant melanoma of quercetin found in the literature and explores, based on the observed physicochemical properties and biological activity, its potential development as a topical formulation for cutaneous application. Quercetin is a naturally occurring flavonoid compound abundant in various plant-based food sources, including apples, onions, berries, and citrus fruits, and has exhibited promising antiproliferative, antioxidant, and anticancer properties. Its distinctive biochemical structure enables quercetin to effectively neutralize reactive oxygen species and modulate key carcinogenic pathways, thereby rendering it a potential candidate for therapeutic intervention in managing malignant tumors, including melanoma. Full article

Accurate detection of fruits and vegetables is a key task in agricultural automation. However, existing detection methods typically focus on identifying a single type of fruit or vegetable and are not equipped to handle complex and diverse environments. To address this, we introduce the first large-scale benchmark dataset for fruit and vegetable detection—FV40. This dataset contains 14,511 images, covering 40 different categories of fruits and vegetables, with over 100,000 annotated bounding boxes. Additionally, we propose a novel framework for fruit and vegetable detection—FVRT-DETR. Based on the Transformer architecture, this framework features an end-to-end real-time detection algorithm. FVRT-DETR enhances feature extraction by integrating the Mamba backbone network and improves detection performance for objects of varying scales through the design of a multi-scale deep feature fusion encoder (MDFF encoder) module. Extensive experiments show that FVRT-DETR performs excellently on the FV40 dataset. In particular, it demonstrates a significant performance advantage in detection of small objects and under complex scenarios. Compared to existing state-of-the-art detection algorithms, such as YOLOv10, FVRT-DETR achieves better results across multiple key metrics. The FVRT-DETR framework and the FV40 dataset provide an efficient and scalable solution for fruit and vegetable detection, offering significant academic value and practical application potential. Full article

The continuous advancement of food safety analytical technologies is ensuring food safety and regulatory compliance. Electrospinning, a versatile fabrication platform, has emerged as a transformative methodology in materials science due to its unique capacity to generate nanoscale fibrous architectures with tunable morphologies. When combined with the inherent biodegradability and biocompatibility of polysaccharides, electrospun polysaccharide nanofibers are positioning themselves as crucial components in innovative applications in the fields of food science. This review systematically elucidates the fundamental principles and operational parameters governing electrospinning processes, with particular emphasis on polysaccharide-specific fiber formation mechanisms. Furthermore, it provides a critical analysis of state-of-the-art applications involving representative polysaccharide nanofibers (e.g., starch, chitosan, cellulose, sodium alginate, and others) in food safety detection, highlighting their innovative application in livestock (chicken, pork, beef), aquatic (yellow croaker, Penaeus vannamei, Plectorhynchus cinctus), fruit and vegetable (olive, peanut, coffee), and dairy (milk) products. The synthesis of current findings not only validates the unique advantages of polysaccharide nanofibers but also establishes new paradigms for advancing rapid, sustainable, and intelligent food safety technologies. This work further proposes a roadmap for translating laboratory innovations into industrial-scale applications while addressing existing technological bottlenecks. Full article

Background: The diet of young people in Spain has changed significantly, with a departure from a balanced dietary pattern and a greater intake of processed foods. Such food generates an acidic environment in the mouth, which promotes the multiplication of bacteria capable of causing inflammation and damage to the gums. Aim: This study aimed to determine the association between the frequency of consuming processed foods and periodontal disease, as well as sex differences, in an adolescent population. Methods: A study was conducted on 233 students aged 15 to examine the frequency of food consumption and its correlation with periodontal disease. Differences were determined via a Student’s t-test to compare the means. A chi-square test was used to compare categorical variables. The 95% confidence interval estimate was used in all cases (p < 0.05). Results: It was observed that girls have a higher mean number of healthy sextants than boys (3.26 $\pm$ 0.20 vs. 2.70 ± 0.21; p = 0.029). A statistically significant difference was noted between healthy and affected subjects in the frequency of consumption of packaged milkshakes (p = 0.003), industrial juices (p = 0.009), industrial pastries (p = 0.018), and fruits in syrup (p = 0.022). When segmented by sex, a statistically significant difference was noted in boys between healthy and affected subjects in the frequency of consumption of packaged milkshakes (p = 0.044), salty snacks (p = 0.032), and cold cuts (p = 0.033); in girls, the difference was detected in industrial juices (0.024). Conclusions: The results of this study suggest that adolescent boys are more affected periodontally than girls. In both sexes, the level of consumption of processed foods affects the presence of periodontal disease. Full article

Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination (UVE) for the rapid non-destructive detection of fruit quality. Near-infrared (NIR) spectra (1000~2500 nm) were acquired and then preprocessed through robust principal component analysis (ROBPCA) for outlier detection combined with z-score normalization for spectral pretreatment. Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels—high-frequency (≥90 times), medium-frequency (30–90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. The experimental findings revealed the following: (1) The model established based on high-frequency wavelengths outperformed both full-spectrum model and full-characteristic wavelength model. (2) The optimized UVE-PLS-Adaboost model achieved the peak performance (R = 0.889, RMSEP = 1.267, MAE = 0.994). This research shows that the UVE-Adaboost fusion method enhances model prediction accuracy and generalization ability through multi-dimensional feature optimization and model weight allocation. The proposed framework enables the rapid, non-destructive detection of apricot TSSs and provides a reference for the quality evaluation of other fruits in agricultural applications. Full article

This study evaluated the efficacy of ice-temperature storage (−1 °C) in preserving volatile compounds (VOCs) in blue honeysuckle (Lonicera caerulea L.) as compared to conventional low-temperature (4 °C) and freezing (−3 °C) storage for 84 d with a 14 d interval. As a flavor-rich berry highly susceptible to postharvest VOC loss, VOC contents and ultrastructural variations were systematically analyzed by coupling gas chromatography–ion mobility spectrometry (GC-IMS), gas chromatography–mass spectrometry (GC-MS), and transmission electron microscopy (TEM). GC-IMS and GC-MS detected 25 and 62 VOCs, respectively, with ice-temperature storage demonstrating well maintaining VOC varieties and relative concentrations. Moreover, TEM analysis further revealed that ice-temperature storage maintained normal cellular ultrastructure integrity, particularly in cell wall organization and organellar morphology. These results conclusively establish ice-temperature storage as the optimal method for preserving both biochemical composition and cytological architecture in blue honeysuckle, thereby providing a scientific foundation for optimizing postharvest protocols and advancing cold-chain technologies for perishable berry fruits. Full article

Grafting is widely used as an effective strategy to enhance tolerance to biotic and abiotic stresses and improve fruit quality in horticultural crops. However, the molecular mechanisms of transcription and the regulatory functions in response to drought stress of mobile mRNAs remain poorly understood. In this study, we developed a grafting system based on the “one grafted plant—three samples” approach using the cultivated tomato/Solanum pennellii (Heinz 1706/LA 0716) heterografting system. A bioinformatics pipeline was developed based on RNA-seq to identify mobile mRNAs in the heterografting systems. A total of 61 upwardly and 990 downwardly mobile mRNAs were identified. Furthermore, we found that the mobility of mRNAs was not correlated with their abundance. The functional annotation and enrichment analysis indicated that mobile mRNAs were mainly involved in RNA binding, photosynthesis, photosystem, response to heat, and translation processes, and ultimately increased the drought tolerance of grafted plants. In addition, we also analyzed the RNA-binding proteins (RBPs) of downwardly mobile mRNAs and found that RBPs were conserved among species. Further, mobile mRNAs may be degraded during transportation. This study provides a pipeline for detecting mobile mRNAs in plant heterografting systems and offers new insights into future studies on long-distance mRNAs transport and regulatory mechanisms involved in drought stress responses. Full article

A disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit (Passiflora edulis [Sims]) disease detection task. Passiflora edulis, as a tropical and subtropical fruit tree, is loved worldwide for its unique flavor and rich nutritional value. The experimental results demonstrate that the proposed model performs excellently across various metrics, achieving a precision of 0.93, a recall of 0.88, an accuracy of 0.91, an mAP@50 (average precision at the IoU threshold of 0.50) of 0.90, an mAP@50–95 (average precision at IoU thresholds from 0.50 to 0.95) of 0.60, and an F1-score of 0.90, significantly outperforming traditional object detection models such as Faster R-CNN, SSD, and YOLO. The experiments show that the sparse parallel attention mechanism offers significant advantages in disease detection with multi-scale and complex backgrounds. This study proposes a lightweight deep learning model incorporating a sparse parallel attention mechanism (SPAM) for passion fruit disease detection. Built upon a Convolutional Neural Network (CNN) backbone, the model integrates a dynamically selective attention mechanism to enhance detection performance in cases with complex backgrounds and multi-scale objects. Experimental results demonstrate that the model has superior precision, recall, and mean average precision (mAP) compared with state-of-the-art detection models while maintaining computational efficiency. Full article

In response to the challenges of low accuracy in traditional pepper blight identification under natural complex conditions, particularly in detecting subtle infections on early-stage leaves, stems, and fruits. This study proposes a multi-site pepper blight disease image recognition algorithm based on YOLOv8, named MSPB-YOLO. This algorithm effectively locates different infection sites on peppers. By incorporating the RVB-EMA module into the model, we can significantly reduce interference from shallow noise in high-resolution depth layers. Additionally, the introduction of the RepGFPN network structure enhances the model’s capability for multi-scale feature fusion, resulting in a marked improvement in multi-target detection accuracy. Furthermore, we optimized CIOU to DIOU by integrating the center distance of bounding boxes into the loss function; as a result, the model achieved an impressive mAP@0.5 score of 96.4%. This represents an enhancement of 2.2% over the original algorithm’s mAP@0.5. Overall, this model provides effective technical support for promoting intelligent management and disease prevention strategies for peppers. Full article

Geoffroea decorticans (Gill. ex Hook. & Arn) Burk. is a native tree of the dry areas of Northwestern and Central Argentina. Its seeds are considered waste material. The flour of seeds was analyzed as a source of nutritional and bioactive compounds. It has a low carbohydrate content, containing about 9% protein and between 10 and 14% fat. Approximately 82–84% of the fatty acids were unsaturated (oleic and linoleic acids). A high polyphenol and dietary fiber content was detected. Flavonoids and condensed tannins were the dominant phenolics. Polyphenol-enriched extracts were obtained from seed flour. The HPLC–ESI-MS/MS analysis of these concentrated extracts allowed for the identification of six compounds including C-glycosyl flavones (vitexin and isovitexin), type A procyanidins (dimer and trimer), and epicatequin gallate. Polyphenolic extracts showed antioxidant capacity and were able to inhibit enzymes (α-glucosidase and α-amylase) related to carbohydrate metabolism and (lipoxygenase) pro-inflammatory enzymes and were not toxic. Flour and polyphenolic extract from chañar seeds could be considered as new alternative ingredients for the formulation of functional foods, nutraceuticals, or food supplements. The use of the seed flour in addition to the pulp of the fruit along with the rest of the plant would encourage the propagation of this species resistant to extreme arid environments for commercial and conservation purposes to boost the regional economies of vulnerable areas of South America. Full article

The Montmorency tart cherry (Prunus cerasus L., MTC) polyphenols may contribute to reduced inflammation and oxidative stress biomarkers in the body. However, a comprehensive polyphenolic profile of MTC products is lacking. This study provides a comparative analysis of the polyphenolic distribution of individual anthocyanins, flavonols, flavanols, hydroxycinnamic acids, and hydroxybenzoic acids in five MTC products (frozen raw fruit, freeze-dried powder, sweet dried fruit, unsweetened dried fruit, juice concentrate). Twenty-three polyphenols were detected, and 21 were positively identified. Results from three replicates indicate that frozen raw MTC has the most total polyphenolics. Juice concentrate, unsweetened dried MTC, freeze-dried MTC powder, and sweet dried MTC contained 26%, 40%, 60%, and 77% fewer total polyphenolics than frozen raw MTC. Hydroxycinnamic acids, flavonols, and anthocyanins predominated, accounting for 87–99% of total polyphenols in MTC products. Chlorogenic acid, rutin, cyanidin-3-sophoroside, feruloquinic acid, ferulic acid, and coumaric acid isomers were noteworthy polyphenolics. Hydroxycinnamic acids predominated in sweet dried (82%), unsweetened dried (74%), juice concentrate (66%), and frozen-raw (54%) MTC. Flavonols predominated in freeze-dried MTC powder (52%). Anthocyanins, particularly cyanidin glycosides, were important polyphenolics in frozen-raw cherries (18%) but less so in other MTC products. These findings highlight the variability in polyphenols in MTC products and emphasize the importance of selecting appropriate MTC products for specific health benefits. Full article

Autonomous navigation is key to improving efficiency and addressing labor shortages in the fruit industry. Semi-structured orchards, with straight tree rows, dense weeds, thick canopies, and varying light conditions, pose challenges for tree identification and navigation line extraction. Traditional 3D lidars suffer from a narrow vertical FoV, sparse point clouds, and high costs. Furthermore, most lidar-based tree-row-detection algorithms struggle to extract high-quality navigation lines in scenarios with thin trunks and dense foliage occlusion. To address these challenges, we developed a 3D perception system using a servo motor to control the rolling motion of a 2D lidar, constructing 3D point clouds with a wide vertical FoV and high resolution. In addition, a method for trunk feature point extraction and tree row line detection for autonomous navigation has been proposed, based on trunk geometric features and RANSAC. Outdoor tests demonstrate the system’s effectiveness. At speeds of 0.2 m/s and 0.5 m/s, the average distance errors are 0.023 m and 0.016 m, respectively, while the average angular errors are 0.272° and 0.146°. This low-cost solution overcomes traditional lidar-based navigation method limitations, making it promising for autonomous navigation in semi-structured orchards. Full article

Deltamethrin (Del), a widely utilized pyrethroid pesticide, exhibits significant risks to human health due to its persistent environmental residues. This study aims to develop an efficient sensing detector for rapid Del detection through aptamer-based recognition. A modified Capture-SELEX strategy successfully identified Del-1, a high-affinity DNA aptamer demonstrating specific binding to Del with a dissociation constant (Kd) of 82.90 ± 6.272 nM. Molecular docking analysis revealed strong intermolecular interactions between Del-1 and Del, exhibiting a favorable binding energy of −7.35 kcal·mol⁻¹. Leveraging these findings, we constructed a colorimetric detector using gold nanoparticles (AuNPs) and poly dimethyl diallyl ammonium chloride (PDDA)-mediated aggregation modulation. The sensing detector employed dual detection parameters: (1) a characteristic color transition from red to blue and (2) a quantitative ∆A650/A520 ratio measurement. This optimized system achieved a detection limit of 54.57 ng·mL⁻¹ with exceptional specificity against other competitive pesticides. Practical validation using spiked fruit samples (apples and pears) yielded satisfactory recoveries of 74–118%, demonstrating the sensor’s reliability in real-sample analysis. The developed methodology presents a promising approach for the on-site monitoring of pyrethroid contaminants in agricultural products. Full article