Search Results (3,184)

Tomato landraces possess distinct flavors, colors, textures and aromas, making them suitable for traditional cuisine. Tomato landraces contain a wide range of genes, including those involved in fruit quality, that can be isolated and used in local breeding programs. In regions recognized as centers of origin, domestication and diversification, traditional farmers play an important role in the preservation of tomato landraces adapted to local conditions and agricultural practices, on the whole maintaining high genetic diversity. This work aimed to evaluate the effects of the crop cycle (C), genotype (G) and C × G interactions on the contents of soluble solids, reducing sugars, lycopene, total polyphenols, flavonoids, and vitamin C, as well as the pH and antioxidant activity, in fifteen tomato landraces (genotypes) undergoing phenotypic selection and a commercial tomato variety (control). All the varieties were grown in two crop cycles under uniform greenhouse management using a randomized block design with four repetitions. Fruit composition was analyzed with AOAC and spectrophotometric methods. Significant differences (p ≤ 0.01) were detected in the soluble solid content, pH, flavor and maturity indices, polyphenol and flavonoid contents, and antioxidant activity between C, G and C × G interactions. In contrast, titratable acidity, reducing sugars, lycopene and vitamin C did not differ between cycles. Coefficients of phenotypic and genotypic variation and broad-sense heritability (H²) ranged from 4.3 to 33.7, 2.0 to 19.0, and 3.2 to 63.5%, respectively. H² for bioactive compounds ranged from moderate to slightly high (16.3–38.8%). These findings, supported by laboratory analyses, suggest that genotypes under agronomic selection have potential as parents to enhance fruit quality in current and future breeding programs. Full article

In modern intelligent food processing, the unpredictable variability in pomelo orientation on high-speed conveyors poses a significant challenge to automated grading and precision peeling operations. To address this, a deep learning-based method is proposed for the real-time detection of pomelo posture. Firstly, a pomelo posture dataset was constructed to support model training and validation. Secondly, to balance the extraction of posture features from uniform fruits with the low-power constraints of edge deployment, a domain-specific architectural optimization is presented. Building on the YOLOv8n framework, the proposed model synergistically integrates specialized modules. A lightweight GhostHGNetV2 foundation is utilized to significantly reduce computational redundancy while maintaining the resolution required to detect key anatomical landmarks. To overcome spatial confusion and capture multi-scale global appearance information, a multi-path coordinate attention (MPCA) module is introduced. Furthermore, the SlimNeck architecture and VoVGSCSP module streamline multi-scale feature fusion via one-time aggregation, effectively preventing computational bottlenecks. This design optimizes the computational efficiency of the model while maintaining detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv8n model, the proposed method increased the mAP50 accuracy by 3.67% while reducing parameter count and computational load by 17.5% and 23.3%, respectively. Additionally, it achieved a processing speed of 19.3 FPS on the Jetson Orin Nano 6G edge platform. This research provides a critical technical foundation for the recognition of pomelo posture, enabling subsequent orientation rectification and fostering the development of streamlined, automated pomelo processing lines. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic, demyelinating and neurodegenerative disease frequently associated with dysphagia, nutritional imbalances, and alterations in body composition. This study aims to describe the anthropometric profile and body composition in people with MS, estimate the risk and type of dysphagia, analyse dietary intake and habits, and evaluate the evolution of these parameters over six months. Methods: This descriptive analytical longitudinal study included 30 patients with MS (20 women, 10 men), with a median age of 53.3 years at baseline and 54.0 years at final assessment. The prevalence of dysphagia risk was determined, dietary patterns and body composition were characterised, and their interactions were explored through two assessments conducted six months apart. Results: Overall, 90% of the sample had relapsing–remitting MS (RRMS). At both the initial and final assessments, the median BMI was above 25 kg/m² and a high prevalence of dysphagia risk (63.3% and 76.7%), particularly for liquids. Frequent inadequacies were observed in the intake of certain macronutrients and micronutrients, including energy, fibre, potassium and magnesium. Likewise, the analysis by food groups revealed low adherence to recommendations, particularly for fruits, cereals, legumes, fish and lean meats. No significant differences were detected between the two time points. Conclusions: Dysphagia, dietary intake, habits, and body composition are interconnected dimensions in MS; systematically integrating nutritional assessment and dysphagia screening into clinical practice would contribute to a more comprehensive management and to improvements in swallowing disorders and nutritional status in people with MS. Full article

Nicotiana tabacum is a classic model for studying pollination on wet stigma. Reactive oxygen species (ROS) and nitric oxide (NO) production are closely related to stigma fertility and depend on the activity of redox enzymes. This study is devoted to the comparison of two tobacco lines differing in physiological parameters and reproductive success. Samsun is a tobacco variety that is widely used in research due to its low demands; however, the reproductive potential of the variety is quite low. Based on this variety, a new line was obtained, called “Fortune”; the plants are externally similar to the Samsun plants, but are more successful in reproduction. The total production of ROS + NO on the stigmas of the Fortune plants is lower than the Samsun plants, but their ROS production is higher, and the main decrease occurs due to NO. Superoxide dismutase activity differs between the two lines at all stages of stigma development except the fertile stage, while ascorbate peroxidase activity is higher in “Fortune” at all stages. Additional isoforms of ascorbate peroxidase are detected in developing stigmas of the Fortune variety. Presumably due to differences in redox metabolism, Fortune plants produce more seeds, their fruit are larger, and their leaves and flowers are also larger compared to the Samsun plants. In this study, we investigated both redox homeostasis parameters and plant productivity using tobacco as the model plant and suggested that there is a correlation between these groups of parameters, which may be important for breeding highly productive plants. Full article

Dragon fruit (Hylocereus undatus) is a high-value crop in the Philippines that has gained commercial importance due to its nutritional benefits and profitability. However, determining the optimal maturity stage remains challenging for farmers relying on manual classification. We developed an automated system that integrates You Only Look Once Version 7 (YOLOv7) for dragon fruit detection. A dataset of dragon fruit images across three maturity levels, unripe, ripe, and over-ripe, was collected and used to train the model. The system classifies maturity stages based on external features such as color and shape, and its performance will be evaluated using a confusion matrix. By providing accurate classification, the proposed system aims to assist farmers in harvesting dragon fruits at their optimal stage, improving yield quality and market competitiveness while reducing human error. Full article

Durian (Durio zibethinus Murray) is Thailand’s most economically significant fruit export, yet foliar diseases pose a major threat to productivity and crop quality. Early-stage symptoms of several durian leaf diseases are visually similar, making reliable diagnosis difficult for farmers and even trained agronomists. This study aims to develop and evaluate an automated deep learning-based system for durian leaf disease classification under realistic field conditions. A dataset of 6119 leaf images representing six classes—Leaf_Healthy, Leaf_Colletotrichum, Leaf_Algal, Leaf_Phomopsis, Leaf_Blight, and Leaf_Rhizoctonia—was compiled from public datasets and field-collected samples. Six convolutional neural network (CNN) architectures—ConvNeXt, ResNet, DenseNet201, InceptionV3, EfficientNet-B3, and MobileNetV3—were benchmarked using a unified transfer-learning training protocol. Class imbalance was addressed using weighted cross-entropy loss, and performance was evaluated on a stratified held-out test set using accuracy, precision, recall, and F1-score metrics. The results show that ConvNeXt achieved the highest performance with 98.00% accuracy and a weighted F1-score of 0.98, followed by ResNet (96.82%) and DenseNet201 (96.09%), while efficiency-oriented models plateaued near 91%. Confusion matrix analysis revealed consistent misclassification among visually similar disease categories—Leaf_Algal, Leaf_Blight, and Leaf_Phomopsis—indicating biological similarity in lesion appearance rather than model limitations. The best-performing model was deployed as a publicly accessible web application using Gradio, enabling real-time disease diagnosis with an average inference time of approximately 0.54 s per image. Unlike prior studies, this work combines large-scale architecture benchmarking, class imbalance mitigation, and real-world deployment within a single unified framework. These findings demonstrate that modern CNN architectures can provide highly accurate and scalable disease detection tools, supporting precision agriculture by enabling early diagnosis, reducing inappropriate pesticide use, and improving decision-making for durian farmers. Full article

Automated fruit harvesting is crucial for alleviating labor shortages and enhancing agricultural productivity. In this context, it is crucial to obtain information on fruit poses before picking in order to avoid damaging the fruit and/or the plant. However, the complex and unstructured orchard environment poses significant challenges regarding the pose estimation task. In this study, a dragon fruit pose estimation (DFPE) framework using a single RGB image is proposed for dragon fruit automated harvesting, which includes three key components: dataset annotation processing, keypoint detection, and geometric pose estimation. First, a multi-source dataset consisting of 8467 images is constructed to enhance the estimation model’s generalizability. A pseudo four-keypoint annotation strategy is designed to fit the annotation rules of mainstream single-class keypoint detection models and mitigate the inherent limitations of multi-target keypoint detection in agricultural scenarios. This strategy implicitly encodes the fruit’s orientation using bounding box group IDs, while preserving geometric information for pose inference. Then, the fruit body and its two core keypoints (navel and stem) are detected via a real-time keypoint detection model. Notably, the proposed DFPE framework is detector-agnostic: other mainstream keypoint detection models can also be plugged into the subsequent geometric pose inference stage, which guarantees the generality and scalability of the framework. Finally, a dragon fruit pose estimation algorithm based on customized geometric constraints is designed, which takes the detected pose information as the input and outputs the posture of dragon fruit. The results of experiments conducted in natural orchard and laboratory environments demonstrate that the ellipses fitted using the proposed DFPE framework closely aligned with fruit contours, even under foliage occlusion conditions. In the laboratory environment, roll errors reached a maximum of 14.8°, whereas yaw errors peaked at 13.4°. Crucially, all roll and yaw errors remained consistently below 15°, which is well within the tolerance threshold required for non-destructive picking operations using a harvesting robot. In summary, this work presents a low-cost solution for dragon fruit pose estimation from a single RGB image, which can potentially be extended to other ellipsoid crops and is suitable for implementation in harvesting robots operating in orchards. Full article

Consumers believe that expired products are unsafe, and, in most cases, misinterpreting the information on food labels often leads to large amounts of food waste. Yogurt is among the most widely eaten dairy products that can still be consumed after its expiration date, even though most consumers throw it away the very day it expires. The aim of this study was to determine whether commercial yogurts currently available on the market remain safe for consumption after their expiration date, with a view to reducing the amount of food waste generated in households. Therefore, the quality, stability, and edible safety of 10 commercial yogurts (two plain with 2% and 4% fat and the others with fruit, such as apricots, strawberries, bananas, blueberries, berries and strawberries, blackberries and raspberries, and cherries) stored at 4°C before and at the expiration date were investigated. Physicochemical, textural, microbiological, and sensory analyses were performed to evaluate changes in functionality, safety, and acceptability of these yogurts. The results showed that, prior to their expiration date, certain yogurt samples (with apricots, strawberries, and blueberries, as well as plain yogurt with 4% fat) tested positive for total coliform bacteria, with values ranging from 20 to 50 CFU/g, suggesting substandard hygiene practices and insufficient sanitary conditions during and following the production process. No Escherichia coli, Listeria, Salmonella, Enterobacter spp., or Enterococcus spp. were detected in any of the yogurt samples that were within their expiration date. Blueberry, berry, and strawberry yogurts change their physical and chemical properties less than other types of yogurts analyzed after expiration. Yogurts containing berries and strawberries, blackberries, and raspberries remain safe at the expiration date, as they do not show the presence of harmful microorganisms such as coliform bacteria, Escherichia coli, Enterobacter spp., Enterococcus spp., Listeria, or Salmonella. Yogurt with berries and strawberries appears to be the most suitable from a microbiological point of view at expiration, as it has a low total mesophilic bacteria count and lactic acid bacteria exceeding 1 × 10⁶ CFU/g. At the time of expiration, this fruit yogurt type (with berries and strawberries) had a total solids content of 21.29%, 5.22% protein, 2.11% fat, 13.19% carbohydrates, 4.07 pH, 26.79% syneresis, 73.21% water retention capacity, 64.78% total phenolic content, and 10.55% DPPH (inhibition percentage). Nevertheless, at the time of expiration, from a sensory perspective (only appearance and consistency, odor, and color, without taste), the yogurt samples that were most appreciated contained blackberries and raspberries. The obtained results indicate that only certain types of fruit yogurts stored unopened at 4 °C may remain safe and edible after the expiration date, but further studies are needed to help the dairy industry and policymakers promote the reduction in food waste in households. Full article

Achieving precise fruit localization and fine branch segmentation simultaneously in unstructured orchard environments remains challenging due to variable lighting, occlusion, and complex backgrounds. This study proposed a joint detection–segmentation architecture based on an improved YOLOv11 network for collaborative perception of apples and tree branches. First, a dual-task dataset of spindle-type apple orchards was constructed with bounding-box annotations for fruits and pixel-level polygon masks for branches, encompassing diverse illumination and occlusion conditions. Second, Convolutional Block Attention Modules (CBAMs) are strategically embedded into the YOLOv11 backbone to enhance feature discrimination for slender branch structures while preserving high fruit detection accuracy. The enhanced model achieves precision of 0.981, recall of 0.986, and F1-score of 0.983 for apple detection, and precision of 0.803, recall of 0.715, mAP of 0.698, and IoU of 0.6066 for branch segmentation on the validation set. Comparative experiments against YOLOv8 and baseline YOLOv11 confirm improved segmentation continuity and finer branch delineation. The proposed integrated perception framework provides reliable visual guidance for collision-avoidance robotic harvesting and offers a practical reference for multi-task agricultural vision systems. Full article

Since its discovery in higher plants, melatonin has attracted considerable attention for its antioxidant properties and its diverse roles in plant physiology and stress responses. However, its biosynthetic pathway remains only partially elucidated, particularly in horticultural crops of economic and nutritional importance, such as pepper (Capsicum annuum L.) fruits. In our previous work, we identified five genes encoding tryptophan decarboxylase (TDC), the first enzyme in the melatonin biosynthetic pathway in pepper. The present study expands on this by identifying and characterizing additional genes encoding enzymes involved in subsequent steps of the pathway, including four tryptamine 5-hydroxylase (T5H) genes, two serotonin N-acetyltransferase (SNAT) genes, three N-acetylserotonin O-methyltransferase (ASMT) genes, two caffeic acid O-methyltransferase (COMT) genes, and one N-acetylserotonin deacetylase (ASDAC) gene, representing a total of twelve newly identified genes. We further examined their expression in sweet pepper fruits and found that only nine of the identified genes are expressed in the fruit, with generally higher transcript levels during the unripe stages. Melatonin quantification in the California-type ‘Masami’ cultivar using UPLC with fluorescence detection (FD) revealed concentrations of 623 ng melatonin·g⁻¹ dry weight (DW) in green fruits and 431 ng melatonin·g⁻¹ DW in red fruits, consistent with the higher expression of melatonin biosynthetic genes in unripe fruit. Expression analysis of these genes by means of RNA-seq revealed differential modulation in response to exogenous melatonin treatments (20, 50, and 100 µM). To our knowledge, this is the first report demonstrating that exogenous melatonin regulates the expression of genes involved in its own biosynthetic pathway in sweet pepper fruits. Notably, treatment with 100 µM melatonin delayed ripening in these non-climacteric fruits, highlighting its potential biotechnological application for controlling fruit ripening and improving postharvest management. Full article

Accurate fruit localization and efficient harvesting are key challenges for agricultural robots, especially in dynamic orchard environments, where platform vibration, fruit occlusion, and computational resource limitations of embedded devices significantly impact system performance. To address these issues, this paper proposes a lightweight “fruit detection + harvesting” framework. First, by integrating MobileNetV4 and Triplet Attention mechanisms, an improved YOLOv8n network is designed, with the improved YOLOv8n Precision reaching 98.148% and FPS reaching 30 FPS on Jetson Nano, achieving a good balance between detection accuracy and computational efficiency suitable for edge deployment. Second, a strawberry three-dimensional coordinate reconstruction method based on weighted 3D centroid reconstruction is proposed, utilizing depth bias adjustment coefficients to improve spatial accuracy. Third, to address localization errors caused by vibration and platform motion, a dynamic compensation and temporal fusion strategy based on an Inertial Measurement Unit (IMU) is proposed. The rotation matrix estimated from IMU data is first used to correct camera pose variations. Then, an adaptive sliding window is employed to smooth the coordinate sequence. Finally, an Extended Kalman Filter (EKF) is applied to further refine the fused results by incorporating temporal dynamics, ensuring that the reconstructed three-dimensional coordinates in the robotic arm reference frame achieve higher stability and continuity. Experimental results in orchard scenarios show that compared with traditional methods, the system has higher localization accuracy, stronger robustness to dynamic disturbances, and higher harvesting efficiency. This work provides a practical and deployable solution for advancing intelligent fruit-harvesting robots. Full article

5-Hydroxymethylfurfural (5-HMF) and the mycotoxin patulin (PAT) serve as crucial chemical markers for evaluating the quality and safety of fruit-derived beverages, particularly pomegranate juice. This study aimed to quantify the occurrence of 5-HMF and PAT in commercial reconstituted pomegranate juices and assess the associated dietary exposure risks. A total of 154 commercial samples, collected from a Turkish processing facility during the 2024–2025 production seasons, were analysed using high-performance liquid chromatography with diode-array detection. 5-HMF was detected in 152 samples (98.7%) at concentrations ranging from 1.03 to 10.79 mg/kg, with only two samples (1.3%) exceeding the critical threshold of 10 mg/kg. PAT was found in 57 samples (37.0%), with concentrations between 3.61 and 50.69 µg/kg, and only one sample (0.6%) exceeded the European Union maximum level established for fruit juices. Estimated mean daily intakes for adults and children ranged from 0.374 to 2.362 and 1.139 to 8.546 µg/kg bw/day for 5-HMF, and from 0.001 to 0.006 and 0.002 to 0.021 µg/kg bw/day for PAT, respectively. Risk characterisation based on hazard quotient values indicated that PAT exposure did not pose a significant health risk for either population group, highlighting the overall safety of the analysed products. Full article

Conventional spatial frequency domain imaging (SFDI) based optical property inversion is inefficient, while deep learning methods suffer from heavy reliance on large-scale real datasets. To address this contradiction, a simulation-driven approach for subsurface fruit bruise discrimination was proposed. An SFDI simulation environment was built with Blender to generate 800 paired datasets of diffuse reflectance images and optical transport coefficients, overcoming the high cost and long cycle of real dataset acquisition. We designed the CBAM-GAN-U-Net model and adopted surface profile correction in the prediction method to eliminate curved surface-induced non-planar distortion, with the whole method validated on liquid phantoms, green apples and crown pears. This prediction method achieved high accuracy in predicting the reduced scattering coefficient μ_s′, with NMAE of 0.021 ± 0.007 (phantoms), 0.039 ± 0.012 (severely bruised green apples) and 0.044 ± 0.015 (severely bruised crown pears), outperforming U-Net and GANPOP. Based on the predicted μ_s′, a discrimination strategy combining coefficient of variation, mean ratio and receiver operating characteristic (ROC) curve analysis was adopted, attaining 100% accuracy for non-bruised/bruised fruit discrimination, with misclassification rates of 6% (green apples) and 8% (crown pears) for mild/severe bruise differentiation. This method enables accurate subsurface fruit bruise detection, providing a reliable technical solution for the fruit and vegetable industry and helping reduce postharvest supply chain losses. Full article

Feijoa fruits are known for their pronounced post-harvest ripening. Phytopathogen-infected specimens pose a significant risk to storage stability and overall fruit quality. Early detection and removal of defective fruits during the initial storage stages are critical for maintaining market value and preventing the spread of disease. In this study, we analyze how the multispectral reflectance properties of the feijoa surface change in response to various defects. ‘Superba’ cultivar fruits were selected, including healthy controls and samples exhibiting bruises, anthracnose, stink bug damage, tissue suberization, and gray mold. Biochemical analyses were conducted to measure the levels of organic acids, sugars, ascorbic acid, and total polyphenols. Multispectral imaging was performed with a 12-channel camera operating in the 400–1000 nm wavelength range. Results showed that the fruits affected by gray mold had the lowest concentrations of malic and citric acids but the highest levels of succinic acid. Fruits with anthracnose or insect damage exhibited the highest sugar content. Distinct differences in spectral reflectance were observed between healthy and affected areas of fruit. Based on these findings, an image processing algorithm for defective fruit detection was developed. Full article

Identifying pest infestations in fresh fruits is a crucial aspect of international trade. Currently, inspections rely on visual observations and destructive sampling, which are, in most cases, quite demanding. The detection of oviposition signs or early larval development is largely not feasible. Therefore, new methods that are sensitive and non-destructive are urgently needed to detect fruit fly infestation during inspections of fresh produce before their introduction and spread into pest-free areas. Portable electronic olfactory systems, or electronic noses (e-noses), are used in various scientific fields and industries. In this study, we evaluated the potential of a portable PEN3 electronic nose to discriminate between non-infested and infested fruits for three fruit fly species: Ceratitis capitata (Wiedemann), Bactrocera dorsalis (Hendel), and Bactrocera zonata (Saunders) (Diptera: Tephritidae). E-nose datasets were generated from samples of each combination of fruit, fruit fly species, infestation status, and storage condition. These datasets were used to develop classification models. The classification accuracy of the models ranged from 50 to 99% during calibration and cross-validation conditions. However, their performance decreased substantially when applied to independent datasets, highlighting limitations in robustness. These findings indicate that although the PEN3 system shows promise as a non-destructive detection tool, its performance is strongly influenced by seasonal and experimental variability. Further work is needed to incorporate multi-season and multi-variety datasets, improve calibration, and robust validation before practical implementation in field inspection systems. Full article