Search Results (33)

Millimeter-wave signals experience substantial path loss when penetrating common building materials, hindering seamless indoor coverage from outdoor networks. To address this limitation, we present the 28-GHz “Meta-Window”, a mass-producible, visible transparent device designed to enhance millimeter-wave signal focusing. Fabricated via metal sputtering and etching on a standard soda-lime glass substrate, the meta-window incorporates subwavelength metallic structures arranged in a rotating pattern based on the Pancharatnam–Berry phase principle, enabling 0–360$°$ phase control within the 25–32 GHz frequency band. A 210 mm × 210 mm prototype operating at 28 GHz was constructed using a 69 × 69 array of metasurface unit cells, leveraging planar electromagnetic lens principles. Experimental results demonstrate that the meta-window achieves greater than 20 dB signal focusing gain between 26 and 30 GHz, consistent with full-wave electromagnetic simulations, while maintaining up to 74.93% visible transmittance. This dual transparency—for both visible light and millimeter-wave frequencies—was further validated by a communication prototype system exhibiting a greater than 20 dB signal-to-noise ratio improvement and successful demodulation of a 64-QAM single-carrier signal (1 GHz bandwidth, 28 GHz) with an error vector magnitude of 4.11%. Moreover, cascading the meta-window with a reconfigurable reflecting metasurface antenna array facilitates large-angle beam steering; stable demodulation (error vector magnitude within 6.32%) was achieved within a ±40$°$ range using the same signal parameters. Compared to conventional transmissive metasurfaces, this approach leverages established glass manufacturing techniques and offers potential for direct building integration, providing a promising solution for improving millimeter-wave indoor penetration and coverage. Full article

Object segmentation algorithms have heavily relied on deep learning techniques to estimate the count of grapes which is a strong indicator for the yield success of grapes. The issue with using object segmentation algorithms for grape analytics is that they are limited to counting only the visible grapes, thus omitting hidden grapes, which affect the true estimate of grape yield. Many grapes are occluded because of either the compactness of the grape bunch cluster or due to canopy interference. This introduces the need for models to be able to estimate the unseen berries to give a more accurate estimate of the grape yield by improving grape cluster segmentation. We propose the Grape Counting Network (GCNet), a novel framework for grape cluster segmentation, integrating deep learning techniques with correction factors to address challenges in indoor yield estimation. GCNet incorporates occlusion adjustments, enhancing segmentation accuracy even under conditions of foliage and cluster compactness, and setting new standards in agricultural indoor imaging analysis. This approach improves yield estimation accuracy, achieving a R² of 0.96 and reducing mean absolute error (MAE) by 10% compared to previous methods. We also propose a new dataset called GrapeSet which contains visible imagery of grape clusters imaged indoors, along with their ground truth mask, total grape count, and weight in grams. The proposed framework aims to encourage future research in determining which features of grapes can be leveraged to estimate the correct grape yield count, equip grape harvesters with the knowledge of early yield estimation, and produce accurate results in object segmentation algorithms for grape analytics. Full article

Canopy architecture is fundamental to productivity in grapevines. This research focused on evaluating the impact of opening canopies on the capture of photosynthetically active radiation, photosynthetic activity, and berries’ physicochemical properties in Cabernet Sauvignon grapevines. A completely randomised design was used to compare open and closed canopies, with ten replicates per treatment (20 vines in total), during the vegetative growth period and after harvest. The key measurements included photon flux density (PFD), daily light integral (DLI), photosynthetic rate (PR), stomatal conductance (SC), intercellular CO₂ concentration (IC), leaf area (LA), transpiration, ambient CO₂ concentration, and temperature. Additionally, we assessed berry quality variables, such as total soluble solids (TSS), glucose/fructose ratio, total titratable acidity (TTA), pH, TSS/TTA, and total phenols (TP). During vegetative growth, PFD, DLI, PR, IC, and LA increased significantly (p ≤ 0.05), whereas after harvest, only PR and IC showed variation Closed canopies increased water use efficiency (CO₂/H₂O) by 62.5%, while the temperature was higher in open canopies. Opening canopy increased contacts, gaps and visible sky and reduced leaf area index. Berries from open canopies showed higher TSS, glucose-fructose, pH, TSS/TTA and TP contents. Opening canopy is essential for improving light interception, photosynthetic efficiency, and fruit quality in Cabernet Sauvignon grapevine cultivated in northern Mexico. Full article

Yield estimation is a key point theme for precision agriculture, especially for small fruits and in-field scenarios. This paper focuses on the metrological validation of a novel deep-learning model that robustly estimates both the number and the radii of grape berries in vineyards using color images, allowing the computation of the visible (and total) volume of grape clusters, which is necessary to reach the ultimate goal of estimating yield production. The proposed algorithm is validated by analyzing its performance on a custom dataset. The number of berries, their mean radius, and the grape cluster volume are converted to millimeters and compared to reference values obtained through manual measurements. The validation experiment also analyzes the uncertainties of the parameters. Results show that the algorithm can reliably estimate the number ($\mathrm{MPE}=-5\%$, $\sigma =6\%$) and the radius of the visible portion of the grape clusters ($\mathrm{MPE}=0.8\%$, $\sigma =7\%$). Instead, the volume estimated in ${\mathrm{px}}^3$ results in a $\mathrm{MPE}=0.4\%$ with $\sigma =21\%$, thus the corresponding volume in ${\mathrm{mm}}^3$ is affected by high uncertainty. This analysis highlighted that half of the total uncertainty on the volume is due to the camera–object distance d and parameter R used to take into account the proportion of visible grapes with respect to the total grapes in the grape cluster. This issue is mostly due to the absence of a reliable depth measure between the camera and the grapes, which could be overcome by using depth sensors in combination with color images. Despite being preliminary, the results prove that the model and the metrological analysis are a remarkable advancement toward a reliable approach for directly estimating yield from 2D pictures in the field. Full article

Goji berry is acknowledged for its notable medicinal attributes and elevated free radical scavenger properties. Nevertheless, its susceptibility to mechanical injuries and biological disorders reduces the commercial diffusion of the fruit. A hyperspectral imaging system (HSI) was employed to identify common defects in the Vis-NIR range (400–1000 nm). The sensorial evaluation of visual appearance was used to obtain the reference measurement of defects. A supervised classification model employing PLS-DA was developed using raw and pre-processed spectra, followed by applying a covariance selection algorithm (CovSel). The classification model demonstrated superior performance in two classifications distinguishing between sound and defective fruit, achieving an accuracy and sensitivity of 94.9% and 96.9%, respectively. However, when extended to a more complex task of classifying fruit into four categories, the model exhibited reliable results with an accuracy and sensitivity of 74.5% and 77.9%, respectively. These results indicate that a method based on hyperspectral visible-NIR can be implemented for rapid and reliable methods of online quality inspection securing high-quality goji berries. Full article

Berry production is increasing worldwide each year; however, high production leads to labor shortages and an increase in wasted fruit during harvest seasons. This problem opened new research opportunities in computer vision as one main challenge to address is the uncontrolled light conditions in greenhouses and open fields. The high light variations between zones can lead to underexposure of the regions of interest, making it difficult to classify between vegetation, ripe, and unripe blackberries due to their black color. Therefore, the aim of this work is to automate the process of classifying the ripeness stages of blackberries in normal and low-light conditions by exploring the use of image fusion methods to improve the quality of the input image before the inference process. The proposed algorithm adds information from three sources: visible, an improved version of the visible, and a sensor that captures images in the near-infrared spectra, obtaining a mean F1 score of $0.909\pm 0.074$ and $0.962\pm 0.028$ in underexposed images, without and with model fine-tuning, respectively, which in some cases is an increase of up to 12% in the classification rates. Furthermore, the analysis of the fusion metrics showed that the method could be used in outdoor images to enhance their quality; the weighted fusion helps to improve only underexposed vegetation, improving the contrast of objects in the image without significant changes in saturation and colorfulness. Full article

The present report focuses on a rapid and convenient method applicable in the green synthesis of gold nanoparticles (AuNPs) using goji berry (Lycium barbarum—LB) extracts rich in antioxidant compounds, as well as on the structural analysis and evaluation of the induced antioxidant protection and anti-inflammatory effects of the synthesized gold nanoparticles upon endothelial cells (HUVECs) exposed to hyperglycemia. The synthesized AuNPs were characterized using ultraviolet–visible (UV-Vis) spectroscopy and transmission electron microscopy (TEM), whereas the presence of bioactive compounds from the L. barbarum fruit extract on the surface of the nanoparticles was confirmed using Fourier transform infrared spectroscopy (FTIR). The antioxidant activity of the biosynthesized gold nanoparticles was evaluated on the HUVEC cell line. The results reveal that AuNPs with a predominantly spherical shape and an average size of 30 nm were obtained. The UV-Vis spectrum showed a characteristic absorption band at λ_max = 536 nm of AuNPs. FTIR analysis revealed the presence of phenolic acids, flavonoids and carotenoids acting as capping and stabilizing agents of AuNPs. Both the L. barbarum extract and AuNPs were well tolerated by HUVECs, increased the antioxidant defense and decreased the production of inflammatory cytokines induced via hyperglycemia-mediated oxidative damage. Full article

Primary bud necrosis of grape buds is a physiological disorder that leads to decreased berry yield and has a catastrophic impact on the double cropping system in sub-tropical areas. The pathogenic mechanisms and potential solutions remain unknown. In this study, the progression and irreversibility patterns of primary bud necrosis in ‘Summer Black’ were examined via staining and transmission electron microscopy observation. Primary bud necrosis was initiated at 60 days after bud break and was characterized by plasmolysis, mitochondrial swelling, and severe damage to other organelles. To reveal the underlying regulatory networks, winter buds were collected during primary bud necrosis progression for integrated transcriptome and metabolome analysis. The accumulation of reactive oxygen species and subsequent signaling cascades disrupted the regulation systems for cellular protein quality. ROS cascade reactions were related to mitochondrial stress that can lead to mitochondrial dysfunction, lipid peroxidation causing damage to membrane structure, and endoplasmic reticulum stress leading to misfolded protein aggregates. All these factors ultimately resulted in primary bud necrosis. Visible tissue browning was associated with the oxidation and decreased levels of flavonoids during primary bud necrosis, while the products of polyunsaturated fatty acids and stilbenes exhibited an increasing trend, leading to a shift in carbon flow from flavonoids to stilbene. Increased ethylene may be closely related to primary bud necrosis, while auxin accelerated cell growth and alleviated necrosis by co-chaperone VvP23-regulated redistribution of auxin in meristem cells. Altogether, this study provides important clues for further study on primary bud necrosis. Full article

The RaspberrySet dataset is a valuable resource for those working in the field of agriculture, particularly in the selection and breeding of ecologically adaptable berry cultivars. This is because long-term changes in temperature and weather patterns have made it increasingly important for crops to be able to adapt to their environment. To assess the suitability of different cultivars or to make yield predictions, it is necessary to describe and evaluate berries’ characteristics at various growth stages. This process is typically carried out visually, but it can be time-consuming and labor-intensive, requiring significant expert knowledge. The RaspberrySet dataset was created to assist with this process, and it includes images of raspberry berries at five different stages of development. These stages are flower buds, flowers, unripe berries, and ripe berries. All these stages of raspberry images classified buds, damaged buds, flowers, unripe berries, and ripe berries and were annotated using ground truth ROI and presented in YOLO format. The dataset includes 2039 high-resolution RGB images, with a total of 46,659 annotations provided by experts using Label Studio software (1.7.1). The images were taken in various weather conditions, at different times of the day, and from different angles, and they include fully visible buds, flowers, berries, and partially obscured buds. This dataset is intended to improve the efficiency of berry breeding and yield estimation and to identify the raspberry phenotype more accurately. It may also be useful for breeding other fruit crops, as it allows for the reliable detection and phenotyping of yield components at different stages of development. By providing a homogenized dataset of images taken on-site at the Institute of Horticulture in Dobele, Latvia, the RaspberrySet dataset offers a valuable resource for those working in horticulture. Full article

Lingonberry (Vaccinium vitis-idaea L.) fruits are important Ericaceous berries to include in a healthy diet of the Northern Hemisphere as a source of bioactive phenolics. The waste generated by the V. vitis-idaea processing industry is hard-skinned press cake that can be a potential source of dietary fiber and has not been studied thus far. In this study, water-soluble polysaccharides of V. vitis-idaea press cake were isolated, separated, and purified by ion-exchange and size-exclusion chromatography. The results of elemental composition, monosaccharide analysis, ultraviolet–visible and Fourier-transform infrared spectroscopy, molecular weight determination, linkage analysis, and alkaline destruction allowed us to characterize two polyphenol–polysaccharide conjugates (PPC) as neutral arabinogalactans cross-linked with monomeric and dimeric hydroxycinnamate residues with molecular weights of 108 and 157 kDa and two non-esterified galacturonans with molecular weights of 258 and 318 kDa. A combination of in vitro and in vivo assays confirmed that expressed antioxidant activity of PPC was due to phenolic-scavenged free radicals, nitrogen oxide, hydrogen peroxide, and chelate ferrous ions. Additionally, marked hypolipidemic potential of both PPC and acidic polymers bind bile acids, cholesterol, and fat, inhibit pancreatic lipase in the in vitro study, reduce body weight, serum level of cholesterol, triglycerides, low/high-density lipoprotein–cholesterol, and malondialdehyde, and increase the enzymatic activity of superoxide dismutase, glutathione peroxidase, and catalase in the livers of hamsters with a 1% cholesterol diet. Polysaccharides and PPC of V. vitis-idaea fruit press cake can be regarded as new antioxidants and hypolipidemic agents that can be potentially used to cure hyperlipidemic metabolic disorders. Full article

Manual vineyard yield estimation approaches are easy to use and can provide relevant information at early stages of plant development. However, such methods are subject to spatial and temporal variability as they are sample-based and dependent on historical data. The present work aims at comparing the accuracy of a new non-invasive and multicultivar, image-based yield estimation approach with a manual method. Non-disturbed grapevine images were collected from six cultivars, at three vineyard plots in Portugal, at the very beginning of veraison, in a total of 213 images. A stepwise regression model was used to select the most appropriate set of variables to predict the yield. A combination of derived variables was obtained that included visible bunch area, estimated total bunch area, perimeter, visible berry number and bunch compactness. The model achieved an R² = 0.86 on the validation set. The image-based yield estimates outperformed manual ones on five out of six cultivar data sets, with most estimates achieving absolute errors below 10%. Higher errors were observed on vines with denser canopies. The studied approach has the potential to be fully automated and used across whole vineyards while being able to surpass most bunch occlusions by leaves. Full article

Plants are a treasure trove of several important phytochemicals that are endowed with therapeutic and medicinal properties. Ribes rubrum L. (red currants) are seasonal berries that are widely consumed for their nutritional value and are known for their health benefits. Red currants are a rich source of secondary metabolites such as polyphenols, tocopherols, phenolic acids, ascorbic acid, and flavonoids. In this study, sunlight-mediated synthesis of silver nanoparticles (AgNPs) was successfully accomplished within 9 min after adding the silver nitrate solution to the aqueous extract of red currant. The synthesised AgNPs were characterised with UV–Vis, transmission electron microscopy (TEM), dynamic light scattering (DLS), Fourier transform infrared spectrum (FTIR), and energy-dispersive X-ray spectrum (EDX). The efficacy of aqueous extracts of red currants and AgNPs in controlling the growth of some pathogenic fungi and bacteria was also investigated. The UV–visible (UV–Vis) spectrum displayed an absorption peak at 435 nm, which corresponded to the surface plasmon band. The strong silver signal on the EDX spectrum at 3 keV, authenticated the formation of AgNPs. The several peaks on the FTIR spectrum of the aqueous extract of red currant and the nanoparticles indicated the presence of some important functional groups such as amines, carbonyl compounds, and phenols that are vital in facilitating the process of capping and bioreduction, besides conferring stability to nanoparticles. The TEM microphotographs showed that the nanoparticles were well dispersed, roughly spherical, and the size of the nanoparticles ranged from 8 to 59 nm. The red currant silver nanoparticles were highly potent in inhibiting the growth and proliferation of some fungal and bacterial test isolates, especially Alternaria alternata, Colletotrichum musae, and Trichoderma harzianum. Based on the robust antifungal and antibacterial activity demonstrated in this study, red currant nanoparticles can be investigated as potential replacements for synthetic fungicides and antibiotics. Full article

The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach. A set of 192 bunches from four cultivars, collected at sites located in Portugal and South Africa, were imaged using a conventional digital RGB camera, followed by image analysis, where several bunch features were extracted, along with physical measurements performed in laboratory conditions. Image data features were explored as predictors of bunch weight, individually and in a multiple stepwise regression analysis, which were then tested on 37% of the data. The results show that the variables bunch area and visible berries are good predictors of bunch weight (R² ranging from 0.72 to 0.90); however, the simple regression lines fitted between these predictors and the response variable presented significantly different slopes among cultivars, indicating cultivar dependency. The elected multiple regression model used a combination of four variables: bunch area, bunch perimeter, visible berry number, and average berry area. The regression analysis between the actual and estimated bunch weight yielded a R² = 0.91 on the test set. Our results are an important step towards automatic yield estimation in the vineyard, as they increase the possibility of applying image-based approaches using a generalized model, independent of the cultivar. Full article

Magnesium (Mg) is the most essential element constituent in chlorophyll molecules that regulates photosynthesis processes. The physiological response of ‘Superior Seedless’ grapes was evaluated under different foliar magnesium fertilization such as sulfate magnesium (MgSO₄·7 H₂O), magnesium disodium EDTA (Mg-EDTA), and magnesium nanoparticles (Mg-NPs) during the berry development stages (flowering, fruit set, veraison, and harvest). In general, the ‘Superior Seedless’ vine had a higher performance in photosynthesis with Mg-NPs application than other forms. The Fy/Fm ratio declined rapidly after the fruit set stage; then, it decreased gradually up until the harvesting stage. However, both MgSO₄ and Mg-EDTA forms showed slight differences in Fv/Fm ratio during the berry development stages. The outcomes of this research suggest that the Fv/Fm ratio during the growth season of the ‘Superior Seedless’ vine may be a good tool to assess magnesium fertilization effects before visible deficiency symptoms appear. Mg-NPs are more effective at improving ‘Superior Seedless’ berry development than the other magnesium forms. These findings suggest that applying foliar Mg-NPs to vines grown on salinity-sandy soil alleviates the potential Mg deficiency in ‘Superior Seedless’ vines and improves bunches quality. Full article

Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87; b = 0.82; M2: R = 0.98; b = 0.93; M3: R = 0.99; b = 0.99; M4: R = 0.99; b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV). Full article