Search Results (311)

Reliable, non-destructive grading of fresh fruit requires simultaneous assessment of external morphology and hidden internal defects. Camera-based grading of fresh fruit using colorimetric (RGB) and near-infrared (NIR) imaging often misses subsurface bruising and cannot capture the fruit’s true shape, leading to inconsistent quality assessment and increased waste. To address this, we developed a 4D-grading pipeline that fuses visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging with structured-light 3D scanning to non-destructively evaluate both internal defects and external form. Our contributions are (1) flagging the defects in fruits based on the reflectance information, (2) accurate shape and defect measurement based on the 3D data of fruits, and (3) an interpretable, decision-tree framework that assigns USDA-style quality (Premium, Grade 1/2, Reject) and size (Small–Extra Large) labels. We demonstrate this approach through preliminary results, suggesting that 4D hyperspectral imaging may offer advantages over single-modality methods by providing clear, interpretable decision rules and the potential for adaptation to other produce types. Full article

Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article

Soluble solids content (SSC) in fruits, as one of the key indicators of fruit quality, plays a critical role in postharvest quality assessment and grading. While traditional destructive methods can provide precise measurements of sugar content, they have limitations such as damaging the fruit’s integrity and the inability to perform rapid detection. In contrast, non-destructive detection technologies offer the advantage of preserving the fruit’s integrity while enabling fast and efficient sugar content measurements, making them highly promising for applications in fruit quality detection. This review summarizes recent advances in non-destructive detection technologies for fruit sugar content measurement. It focuses on elucidating the principles, advantages, and limitations of mainstream technologies, including near-infrared spectroscopy (NIR), X-ray technology, computer vision (CV), electronic nose (EN) technology and so on. Critically, our analysis identifies key challenges hindering the broader implementation of these technologies, namely: the integration and optimization of multi-technology approaches, the development of robust intelligent and automated detection systems, and issues related to high equipment costs and barriers to widespread adoption. Based on this assessment, we conclude by proposing targeted future research directions. These focus on overcoming the identified challenges to advance the development and practical application of non-destructive SSC detection technologies, ultimately contributing to the modernization and intelligentization of the fruit industry. Full article

Background: Cardiovascular disease (CVD) is very widespread in countries with a Western-style diet, representing one of the major causes of morbidity. Genetic factors, obesity, diabetes, dyslipidemia, smoking, and ageing are risk factors for CVD outcomes. From a pathogenic point of view, the condition of low-grade inflammation of the arteries leads to endothelial damage and atherosclerosis development. Nowadays, a broad range of drugs is available to treat CVD, but many of them are associated with side effects. Therefore, alternative therapeutic remedies need to be discovered in combination with conventional drugs. A balanced diet rich in fruits and vegetables, e.g., the Mediterranean diet, has been shown to lower the incidence of CVD. Plant-derived polyphenols are ingested in food, and these compounds can exert beneficial effects on human health, such as antioxidant and anti-inflammatory activities. Objective: In the present review, the cellular and molecular bases of the beneficial effects of polyphenols in the prevention and treatment of CVD will be pointed out. Methods: This review has been conducted on the basis of a literature review spanning mainly the last two decades. Results: We found that an increased dietary intake of polyphenols is associated with a parallel decrease in chronic disease incidence, including CVD. Conclusion: Despite a plethora of preclinical studies, more clinical trials are needed for a more appropriate treatment of CVD with polyphenols. Full article

This study presents a social life cycle assessment (S-LCA) of the F-CUBED Production System (FPS), an innovative process that converts wet biogenic residues—specifically paper biosludge, virgin olive pomace, and fruit and vegetable residues—into intermediate bioenergy carriers via hydrothermal treatment (TORWASH^®), pelletization, and anaerobic digestion. The hydrothermal carbonization of these low-grade, moisture-rich biogenic residues enhances the flexibility and reliability of renewable energy systems while also offering the potential to reduce environmental burdens compared to conventional disposal methods. Through this S-LCA, the study aims to evaluate the cradle-to-gate socioeconomic impacts of the FPS in three European contexts—Sweden, Italy, and Spain—using the 2020 UNEP Guidelines and the Social Hotspots Database (SHDB) and applying quantitative modeling via SimaPro. The functional unit is defined as 1 kWh of electricity produced. The assessment combines SHDB-based modeling with primary data from stakeholder surveys conducted in the three countries. Impact categories are harmonized between SHDB and UNEP typologies, and the results are reported in medium-risk-hour equivalents (mrheq). The results show a heterogeneous social impact profile across case studies. In Sweden, the treatment of paper biosludge delivers substantial benefits with minimal risk. In Spain (orange peel), the introduction of the FPS demonstrated a strong social benefit, particularly in health and safety and labor rights, indicating high institutional performance and good integration with local industry. Conversely, in Italy (olive pomace), the FPS revealed significant social risks, especially in the biopellet production and electricity generation sectors, reflecting regional vulnerabilities in labor conditions and governance. This suggests that targeted mitigation strategies are recommended in contexts like Southern Italy. These findings highlight that the social sustainability of emerging bioenergy technologies is context-dependent and sensitive to sectoral and regional socioeconomic conditions. This S-LCA complements prior environmental assessments and emphasizes the importance of integrating social performance considerations in the deployment and scaling of innovative bioenergy systems. Full article

In a greenhouse environment, the application of artificial intelligence technology for selective tomato harvesting still faces numerous challenges, including varying lighting, background interference, and indistinct fruit surface features. This study proposes an improved instance segmentation model called FGS-YOLOv8s-seg, which achieves accurate detection and maturity grading of tomatoes in greenhouse environments. The model incorporates a novel SegNext_Attention mechanism at the end of the backbone, while simultaneously replacing Bottleneck structures in the neck layer with FasterNet blocks and integrating Gaussian Context Transformer modules to form a lightweight C2f_FasterNet_GCT structure. Experiments show that this model performs significantly better than mainstream segmentation models in core indicators such as precision (86.9%), recall (76.3%), average precision (mAP_@0.5 84.8%), F1-score (81.3%), and GFLOPs (35.6 M). Compared with the YOLOv8s-seg baseline model, these metrics show improvements of 2.6%, 3.8%, 5.1%, 3.3%, and 6.8 M, respectively. Ablation experiments demonstrate that the improved architecture contributes significantly to performance gains, with combined improvements yielding optimal results. The analysis of detection performance videos under different cultivation patterns demonstrates the generalizability of the improved model in complex environments, achieving an optimal balance between detection accuracy (86.9%) and inference speed (53.2 fps). This study provides a reliable technical solution for the selective harvesting of greenhouse tomatoes. Full article

The most common size measurements for agricultural produce, including fruits and vegetables, are length and width. While the length of any agricultural produce can be unique, the width varies continuously along its length. Single-width measurements alone are insufficient for accurately characterizing varying width profiles, resulting in an inaccurate representation of the shape or mean dimension. Consequently, the manual measurement of multiple mean dimensions is laborious or impractical, and no information in this domain is available. Therefore, an efficient alternative computer vision measurement tool was developed utilizing ImageJ (Ver. 1.54p). Twenty sample sets, comprising fruits and vegetables, with each representing different shapes, were selected and measured for length and multiple widths. A statistically significant minimum number of multiple widths was determined for practical measurements based on an object’s shape. The “aspect ratio” (width/length) was identified to serve as an effective indicator of the minimum multiple width measurements. In general, 50 multiple width measurements are recommended; however, even 15 measurements would be satisfactory ($\mathrm{1.0\%}\pm \mathrm{0.6\%}$ deviation from 50 widths). The developed plugin was fast (734 ms ± 365 ms CPU time/image), accurate (>99.6%), and cost-effective, and it incorporated several user-friendly and helpful features. This study’s outcomes have practical applications in the characterization, quality control, grading and sorting, and pricing determination of agricultural produce. Full article

Kiwifruit has attracted much attention in fruit and vegetable processing due to its high nutritional and economic value. However, there is a lack of systematic research on the effects of long-term frozen storage on the pulp quality of kiwifruit. Using kiwifruit pulp stored at −20 °C for 0, 3, 6, 9, and 12 months as the research materials, the dynamic changes in the phenotype, color, antioxidant activity, and flavor compounds were comprehensively evaluated. The results showed that frozen storage caused a significant decline in the quality of the fruit pulp. Specifically, the contents of chlorophyll and carotenoids decreased and the color deteriorated (color difference increased); the turbidity and centrifugal sedimentation rates increased, and pH and viscosity changed in different stages. Additionally, antioxidant compounds, such as vitamin C and total phenols, were significantly reduced with the extension of storage duration, and the 2,2-diphenyl-1-picrylhydrazyl (DPPH)/2,2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radical scavenging ability was decreased. The content of volatile aroma compounds diminished, leading to a notable shift in the flavor profile. Correlation analysis revealed that changes in volatile substances were significantly correlated with physical, chemical, and antioxidant indicators (p < 0.05). These correlations can serve as a key basis for assessing quality deterioration. This study systematically elucidated, for the first time, the mechanism of quality deterioration in kiwifruit pulp during frozen storage, thereby providing theoretical support for enterprises to optimize pulp grading strategies and the timing of by-product development. Hence, it is recommended that the duration of freezing should be limited to less than 9 months for kiwifruit pulp. Moreover, it is essential to consider varietal differences and new pretreatment technologies to further enhance the industrial utilization and economic value of frozen pulp. Full article

Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive approach integrating X-ray imaging and multi-criteria decision (MCD) theory for non-destructive internal defect detection in pears. Internal defects were identified by analyzing grayscale variations in X-ray images. The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. Experimental results demonstrated that the fused model achieved a detection accuracy of 97.1%, significantly outperforming individual classifiers. This approach effectively reduced misclassification caused by structural similarities in X-ray images. The study confirms the efficacy of X-ray imaging coupled with multi-classifier fusion for accurate and non-destructive internal quality evaluation of pears, offering practical value for fruit grading and post-harvest management in the pear industry. Full article

Fruit quality testing plays a crucial role in the advancement of fruit industry, which is related to market competitiveness, consumer satisfaction and production process optimization. In recent years, nondestructive testing technology has become a research hotspot due to its outstanding advantages. In this paper, the principle, application, advantages and disadvantages of optical, acoustic, electromagnetics, dielectric properties research and electronic nose non-destructive testing technology in fruit quality testing are systematically reviewed. These technologies can detect a variety of chemical components of fruit, realize the assessment of maturity, damage degree, disease degree, and are suitable for orchard picking, quality grading, shelf life prediction and other fields. However, there are limitations to these techniques. The optical, acoustic and electronic nose technologies are susceptible to environmental factors, the electromagnetic technology has defects in the detection of complex molecules and fruit internal quality, and the dielectric characteristics are greatly affected by the shape and state of the sample surface. In the future, efforts should be made to enhance the implementation of non-destructive testing technology in the fruit industry through technology integration, optimization algorithm, cost reduction, and expansion of industrial chain application, so as to help the premium growth of the fruit industry. Full article

The external appearance of strawberry fruits serves as a critical criterion for their commercial value and grading standards. However, current research primarily emphasizes ripeness and surface defects, with limited attention given to the quantitative analysis of geometric characteristics such as deformity and symmetry. To address this gap, this study proposes a comprehensive evaluation framework that integrates deep learning-based segmentation with geometric analysis for strawberry appearance quality assessment. First, an enhanced YOLOv11 segmentation model incorporating a Squeeze-and-Excitation attention mechanism was developed to enable high-precision extraction of individual fruits, achieving Precision, Recall, AP₅₀, and F1 scores of 91.11%, 87.46%, 92.90%, and 88.45%, respectively. Second, a deformity quantification method was designed based on the number of deformity points (N_d), deformity rate (R_d), and spatial distance metrics (G_min and G_max). Experimental results demonstrated significant differences in R_d and G_max between deformed and normal strawberries, indicating strong classification capability. Finally, principal component analysis (PCA) was employed to extract the primary axis direction, and morphological symmetry was quantitatively evaluated using Intersection over Union (IoU) and Area Difference Ratio (AreaD_Ratio). The results revealed that most samples fell within an IoU range of 0.6–0.8 and AreaD_Ratio below 0.4, indicating noticeable inter-individual differences in fruit symmetry. This study aims to establish a three-stage analytical framework—segmentation, deformity quantification, and symmetry evaluation—for assessing strawberry appearance quality, with the goal of supporting key applications in automated grading and precision quality inspection. Full article

To calyx persistence in Korla fragrant pear (Pyrus sinkiangensis) significantly impacts fruit marketability, with persistent calyx causing up to 40% reduction in premium-grade fruit yield. Investigating the hormonal mechanisms underlying calyx abscission and persistent in Korla Fragrant Pear, we performed comprehensive phytohormone profiling using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS; EXIONLC system coupled with SCIEX 6500 QTRAP+). Flowers from first-position (persistent-calyx) and fourth-position (deciduous-calyx) inflorescences were collected at six developmental stages (0–10 days after flowering). Fourteen endogenous hormones—ACC, ME-IAA, IPA, TZR, SA, IAA, ICA, IP, tZ, DHJA, ABA, JA-ile, cZ, and JA—were identified in the calyx during the flowering stage. The calyx abscission rate was significantly higher in the fourth position (79%) compared to the first position (32%). ACC and ABA are closely linked to abscission, with increased ACC at 0 DAF signaling early abscission and ABA accumulation accelerating late abscission at 8 DAF. Auxin exhibited spatiotemporal specificity, peaking in first-order flowers at 4–6 DAF, potentially inhibiting abscission by maintaining cell activity. Cytokinins generally decreased, while jasmonates significantly increased during the fourth-position anthesis stage 8–10 DAF, suggesting a role in stress-related senescence. By systematic analysis of the flowers at the first order (persistent calyx) and the fourth order (deciduous calyx) from 0 to 10 days after anthesis, we found three key stages of hormone regulation: early prediction stage (0–2 DAF), ACC accumulation at the fourth order was significantly higher than that at the first order at 0 days after anthesis, ACC accumulation at the early stage predicted abscission; During the middle maintenance stage (4–6 DAF), the accumulation of cytokinin decreased significantly, while the accumulation of IAA increased significantly in the first position (persistent calyx); Execution Phase (8–10 DAF), ABA reached its peak at 8 DAF, coinciding with the final separation time. JA played an important role in the late stage. Gibberellin was undetected, implying a weak association with calyx abscission. Venn diagram identified N6-(delta 2-Isopentenyl)-adenine (IP) in first-position flowers, which may influence calyx persistence or abscission. These findings elucidate hormone interactions in calyx abscission, offering a theoretical basis for optimizing exogenous hormone application to enhance fruit quality. Full article

The present study addresses the inefficiencies of the manual classification of oil palm fresh fruit bunches (FFBs) by introducing a computationally efficient alternative to traditional deep learning approaches that require extensive retraining and large datasets. Using feature-based transfer learning, where pre-trained Convolutional Neural Network architectures, namely EfficientNet_B0, EfficientNet_B4, ResNet152, and VGG16, serve as fixed feature extractors coupled with the Logistic Regression classifier, this research evaluated the performance on a dataset of 466 images categorized as defective or non-defective. The results demonstrate a robust classification performance across all architectures, with the EfficientNet_B4–LR pipeline achieving an exceptional accuracy value of 96.81%, which was further enhanced through hyperparameter optimization. This confirms that feature-based transfer learning offers a reliable, resource-efficient, and practical solution for automated FFB defect detection that can significantly benefit the palm oil industry by providing a scalable alternative to subjective manual-grading methods. Full article

This study develops a value-added processing technique for grade-out cape gooseberry (Physalis peruviana Linn.) by applying mild hydrostatic osmotic pretreatment combined with rotary tray drying. Fruits classified as grade-out, often discarded due to aesthetic flaws, were subjected to osmotic treatment at 0.5 bar for 12 h using a sucrose solution enhanced with citric acid and glycerin. Pretreatment significantly elevated water loss (52.61%) and solid gain (18.12%), reducing moisture content prior to drying. Rotary tray drying was conducted at temperatures of 50, 60, and 70 °C. Drying at 60 °C achieved the ideal balance between efficiency and product quality. Samples pretreated and dried at 60 °C exhibited a 35% reduction in drying time while preserving superior color (ΔE = 13.54 ± 1.81), vitamin C (71.76 ± 2.57 mg/100 g dry matter, DM), total phenolic content (202.9 ± 10.91 mg GAE/100 g DM), and antioxidant activity (ABTS = 95.87 ± 3.41 µmol TE/g DM; DPPH = 89.97 ± 1.27 µmol TE/g DM). A production trial was conducted using 1500 kg of raw material from the Mae Hae Royal Project Development Center in Chiang Mai, Thailand. This process yielded 220 kg of high-quality dried fruit at an overall cost of USD 6.93 per kg. Local farmers successfully applied this technique, demonstrating its potential to enhance livelihoods, avoid postharvest losses, and valorize low-quality produce in line with Sustainable Development Goal 12. This supports the Royal Project Foundation’s vision for sustainable agriculture. Full article

The successful introduction of new cultivars depends on the evaluation of complex parameters essential for the consumers, market, and fruit producers. A new scab-resistant apple cultivar, ‘Wuranda’ (SQ159/Natyra^®/Magic Star^® × Honeycrisp), recently introduced in Norway and managed under the name Fryd^©, is prone to biennial bearing. Therefore, one of the first tasks, investigated in Southwestern Norway by the Norwegian Institute of Bioeconomy Research, NIBIO-Ullensvang in 2021–2024, was the establishment of optimal crop load level based on the combination of productivity, fruit quality, and return bloom. The apple cultivar Fryd (‘Wuranda’) was propagated on ‘M.9’ rootstock and planted in 2019. The trial was performed in the same orchard for four consecutive years, starting three years after planting. Crop load level affected average fruit mass but had no impact on cv. Fryd fruit quality parameters at harvest such as blush, ground color, firmness, soluble solid content, or starch degradation. Fruit size variation was diminished by crop load regulation, and most fruits fell into 2–3 grading classes. Crop load, not the yield per tree, was the determining factor for the return bloom. The optimal crop load level depended on the orchard age. To guarantee a regular bearing mode of cv. Fryd planted on M.9 rootstock at a 3.5 × 1 m distance and trained as slender spindle, crop load of 5.5–6 fruits cm⁻² TCSA (trunk cross-sectional area) in the 3rd year, 7.5–8 fruits cm⁻² TCSA in the 4th year, and 6.5–7 fruits cm⁻² TCSA in the 5th year should be maintained. Full article