Search Results (81)

This study evaluated the effects of late pruning and late apical leaf removal on grapevine phenology, fruit composition, yield parameters, xylem sap carbohydrate content, and grape skin polyphenol levels over two consecutive vintages (2022 and 2023). As expected, delayed pruning shifted the phenological stages, with more pronounced delays observed in 2022 than in 2023. However, by August, all the treatments had reached the berry-softening stage, indicating a convergence in ripening. The grape juice composition showed no significant differences in sugar content in 2022; however, in 2023, the °Brix was notably reduced in control vines subjected to late apical defoliation. The titratable acidity and pH remained stable across treatments and years, while the malic acid concentrations were consistently higher in the late-pruned treatments, particularly LP2 (late pruning 2 was performed when the control vines had reached the eight-leaves-folded development stage). Late pruning significantly reduced the yield and bunch size, especially for the 2023 LP2 treatment. In contrast, late apical defoliation had minimal impact on the yield components. Vegetative growth, as assessed by cane diameter and weight, also declined under late pruning. Despite this, the xylem sap analysis revealed no significant changes in the glucose, fructose, or myo-inositol levels, suggesting that the carbohydrate reserves remained unaffected. Notably, LP2 consistently resulted in the highest total polyphenol content in the grape skins across both years, indicating enhanced phenolic maturity. Although the polyphenol concentrations were generally higher in 2023, the treatment effects varied more widely, likely due to the differing environmental conditions. These findings suggest that late pruning—particularly LP2—can be a valuable tool for improving grape phenolic quality, albeit at the cost of reduced yield and vine vigor. This study highlights the importance of site- and season-specific canopy management strategies in balancing fruit quality with productivity under variable climatic conditions. Full article

This study synthesizes zinc oxide (ZnO) and graphene oxide (GO) nanomaterials using a green and sustainable method. ZnO nanoparticles were synthesized from lime peel extract, while GO was obtained utilizing oil palm empty fruit bunch (OPEFB) fibre. The resulting ZnO/GO nanocomposites were characterized using Fourier transform infrared (FTIR), photoluminescence (PL), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), ultraviolet–visible diffuse reflectance spectroscopy (UVDRS), and Raman spectroscopy (RS), confirming their successful synthesis, reduced particle size, altered band gap, and enhanced charge separation properties. The photocatalytic activities of the ZnO/GO nanocomposites were evaluated for MB degradation under visible light. Notably, the ZnO/GO (7%) composite exhibited better degradation efficiency (87% in 90 min) compared to commercial and synthesized ZnO. The study also optimized key parameters including catalyst loading (1 g L⁻¹), initial dye concentration (0.03 mM), and pH (pH 12 showed highest efficiency). The kinetic studies confirmed a pseudo-first-order reaction, with ZnO/GO (7%) showing the highest rate constant (0.0208 min⁻¹). The scavenger tests identified hydroxyl radicals (^•OH) as the dominant reactive species. This research presents a sustainable and efficient approach for wastewater treatment, utilizing waste materials to produce high-performance photocatalysts for environmental remediation. Full article

For fruit harvesting robots, rapid and accurate detection of fruits and picking points is one of the main challenges for their practical deployment. Several fruits typically grow in clusters or bunches, such as grapes, cherry tomatoes, and blueberries. For such clustered fruits, it is desired for them to be picked by bunches instead of individually. This study proposes utilizing a low-cost off-the-shelf RGB-D sensor mounted on the end effector and a lightweight improved YOLOv8-Pose neural network to detect cherry tomato bunches and picking points for robotic harvesting. The problem of occlusion and overlap is alleviated by merging RGB and depth images from the RGB-D sensor. To enhance detection robustness in complex backgrounds and reduce the complexity of the model, the Starblock module from StarNet and the coordinate attention mechanism are incorporated into the YOLOv8-Pose network, termed StarBL-YOLO, to improve the efficiency of feature extraction and reinforce spatial information. Additionally, we replaced the original OKS loss function with the L1 loss function for keypoint loss calculation, which improves the accuracy in picking points localization. The proposed method has been evaluated on a dataset with 843 cherry tomato RGB-D image pairs acquired by a harvesting robot at a commercial greenhouse farm. Experimental results demonstrate that the proposed StarBL-YOLO model achieves a 12% reduction in model parameters compared to the original YOLOv8-Pose while improving detection accuracy for cherry tomato bunches and picking points. Specifically, the model shows significant improvements across all metrics: for computational efficiency, model size (−11.60%) and GFLOPs (−7.23%); for pickable bunch detection, mAP50 (+4.4%) and mAP50-95 (+4.7%); for non-pickable bunch detection, mAP50 (+8.0%) and mAP50-95 (+6.2%); and for picking point detection, mAP50 (+4.3%), mAP50-95 (+4.6%), and RMSE (−23.98%). These results validate that StarBL-YOLO substantially enhances detection accuracy for cherry tomato bunches and picking points while improving computational efficiency, which is valuable for resource-constrained edge-computing deployment for harvesting robots. Full article

Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R² = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R² = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions. Full article

The generation of high-quality mid-infrared free-electron laser (MIR-FEL) radiation depends critically on precise control of electron beam parameters, including energy, energy spread, transverse emittance, bunch charge, and bunch length. At the PBP-CMU Electron Linac Laboratory (PCELL), effective beam diagnostics are essential for optimizing FEL performance. However, dedicated systems for direct measurement of transverse emittance and bunch length at the undulator entrance have been lacking. This paper addresses this gap by presenting the design, simulation, and analysis of diagnostic stations for accurate characterization of these parameters. A two-quadrupole emittance measurement system was developed, enabling independent control of beam-focusing in both transverse planes. An analytical model was formulated specifically for this configuration to enhance emittance reconstruction accuracy. Systematic error analysis was conducted using ASTRA beam dynamics simulations, incorporating 3D field maps from CST Studio Suite and fully including space-charge effects. Results show that transverse emittance values as low as 0.15 mm·mrad can be measured with less than 20% error when the initial RMS beam size is under 2 mm. Additionally, quadrupole misalignment effects were quantified, showing that alignment within ±0.95 mm limits systematic errors to below 33.3%. For bunch length measurements, a transition radiation (TR) station coupled with a Michelson interferometer was designed. Spectral and interferometric simulations reveal that transverse beam size and beam splitter properties significantly affect measurement accuracy. A 6% error due to transverse size was identified, while Kapton beam splitters introduced additional systematic distortions. In contrast, a 6 mm-thick silicon beam splitter enabled accurate, correction-free measurements. The finite size of the radiator was also found to suppress low-frequency components, resulting in up to 10.6% underestimation of bunch length. This work provides a practical and comprehensive diagnostic framework that accounts for multiple error sources in both transverse emittance and bunch length measurements. These findings contribute valuable insight for the beam diagnostics community and support improved control of beam quality in MIR FEL systems. Full article

The ‘BRS Vitoria’ grape has sensory characteristics that favor its consumption. However, different rootstocks and harvest periods can directly influence its phenolic composition, physicochemical and morphological characteristics, and mineral content. This study evaluates the mineral and anthocyanin composition of the ‘BRS Vitoria’ grape from a production cycle (PC1: ‘IAC 572’ rootstock, main harvest) and compares its physicochemical, morphological, and mineral characteristics to other cycles (PC2: ‘Paulsen 1103’ rootstock, second harvest; PC3: ‘IAC 572’ rootstock, second harvest; and PC4: ‘Paulsen 1103’ rootstock, main harvest), highlighting its potential for use and providing initial insights into the influence of rootstocks and environmental conditions. PC1 grapes contained important amounts of potassium, phosphorus, calcium, magnesium, iron, manganese, and zinc (345.16, 50.50, 20.34, 13.61, 0.54, 0.27, and 0.03 mg⋅100 g⁻¹, respectively), and a complex anthocyanin profile, predominantly derived from malvidin, which supports their use in processing due to the thermal stability. In the second part of the study, PC2 grapes stood out for their skin percentage and acidity. PC3 grapes exhibited higher values in parameters associated with size, mass, and mineral content, which may have been influenced by the use of the ‘IAC 572’ rootstock. PC4 grapes showed the highest maturation index (38.68), total phenolic compounds (1750.88 mg EGA⋅kg⁻¹), and total monomeric anthocyanins (742.86 mg mv-3,5-glc⋅kg⁻¹). These results may have been influenced by the environmental conditions during the main harvest season. Bunches from all cycles were cylindrical, very compact, with dark red-violet berries and featuring thick skin with pruine and firm colorless, seedless flesh. The study of the influence of these factors is complex due to the impact of various other variables and the synergistic effect between them. Despite physicochemical and morphological differences, ‘BRS Vitoria’ grapes from different PCs are suitable for fresh consumption and processing, potentially as a nutraceutical ingredient. Full article

Near-infrared imaging devices are extensively used in medical diagnosis, night vision, and security monitoring. However, existing traditional imaging devices rely on a bunch of refracting lenses, resulting in large, bulky imaging systems that restrict their broader utility. The emergence of flat meta-optics offers a potential solution to these limitations, but existing research on compact integrated devices based on near-infrared meta-optics is insufficient. In this study, we propose an integrated NIR imaging camera that utilizes large-size metalens with a silicon nanostructure with high transmission efficiency. Through the detection of target and animal and plant tissue samples, the ability to capture biological structures and their imaging performance was verified. Through further integration of the NIR imaging device, the device significantly reduces the size and weight of the system and optimizes the aperture to achieve excellent image brightness and contrast. Additionally, venous imaging of human skin shows the potential of the device for biomedical applications. This research has an important role in promoting the miniaturization and lightweight of near-infrared optical imaging devices, which is expected to be applied to medical testing and night vision imaging. Full article

Despite extensive research on Bacillus sp. as lignin degraders, the enzyme mechanisms involved, particularly in Bacillus cereus isolated from termite guts, remain unclear. In this study, the selected Bacillus cereus was fermented to extract the lignin-degrading enzymes to identify the enzymes responsible for lignin degradation using the sample substrate empty fruit bunch (EFB) as their sole carbon source. After 7 days of submerged fermentation (SmF), the crude enzyme was extracted, and SDS-PAGE gel was used to determine the weight of the proteins, and bands with sizes of 20 kDa–97 kDa were extracted for further analysis. The extracted proteins were partially characterized and sequenced using liquid chromatography–mass spectrometry (LC–MS/MS). The results identified 11 enzymes that are responsible for lignin degradation, such as 4-aminobutyrate aminotransferase (GABA), amidohydrolase, chemotaxis protein, serine hydrolase, GMC family protein, glycosyltransferase, phosphate binding protein PstS, ABC transporter ATP-binding protein, heme peroxidase, nitrate reductase, and nitrite reductase. The value of the mutual relationships between all the enzymes in Bacillus cereus indicates the synergistic mechanism under carbon scrutinization. Also, the peptides sequenced in this study identified various uncharacterized proteins and hypothetical proteins that might not be discovered for their protein functions. Further analysis is essential to uncover more lignin degradation enzymes that can work synergically for paper and pulp bioprocessing. Full article

Biomass, though a major energy source, remains underutilized. Biochar from biomass pyrolysis, with its high porosity and surface area, is especially useful as catalyst support, enhancing catalytic activity and reducing electron recombination in photocatalysis. Indonesia, the world’s top palm oil producer, generated around 12 million tons of empty fruit bunches (EFBs) in 2023, making EFBs a promising biochar source. This study synthesizes biochar from leftover EFB fibers at 500, 800, and 1000 °C, analyzing structural changes via infrared and Raman spectroscopy, along with particle size and surface area analysis, laying the groundwork for future biochar research. The smallest particle size and highest surface area gained was 71.1 nm and 10.6 × 10² m²/g. Spectroscopic analysis indicates that biochar produced at 1000 °C has produced nano-crystalline graphite with a crystallite size of approximately 5.47 nm. This provides higher defect density, although with lower conductivity. Other studies indicate that our biochar can be used as catalyst support for various green energy-related applications, i.e., counter electrodes, electrocatalysts, and photocatalysts. Full article

This paper addresses the design of bus bridging services in response to urban rail disruption, which plays a critical role in enhancing the resilience and sustainability of urban transportation systems. Specifically, it focuses on unplanned urban rail disruptions that result in temporary closure of line sections, including transfer stations. Under this “transfer scenario”, a heuristic-rule based method is firstly presented to generate candidate bus bridging routes. Non-parallel bridging routes are introduced to facilitate transfer passengers affected by the disruption. Meanwhile, the bridging stops visited by parallel routes are extended beyond the disrupted section, mitigating passenger congestion and bus bunching at turnover stations. Then, we propose an integrated optimization model that collaboratively addresses bus route selection and vehicle deployment issues. Capturing passenger reneging behavior, the model aims to maximize the number of served passengers with tolerable waiting times and minimize total passenger waiting times. A two-stage genetic algorithm is developed to solve the model, which incorporates a multi-agent simulation method to demonstrate dynamic passenger and bus flow within a time–space network. Finally, a case study is conducted to validate the effectiveness of the proposed methods. Sensitivity analyses are performed to explore the impacts of fleet size and route diversity on the overall bridging performance. The results offer valuable insights for transit agencies in designing bus bridging services under transfer scenarios, supporting sustainable urban mobility by promoting efficient public transit solutions that mitigate the social impacts of sudden service disruptions. Full article

The accurate identification of tomato maturity and picking positions is essential for efficient picking. Current deep-learning models face challenges such as large parameter sizes, single-task limitations, and insufficient precision. This study proposes MTS-YOLO, a lightweight and efficient model for detecting tomato fruit bunch maturity and stem picking positions. We reconstruct the YOLOv8 neck network and propose the high- and low-level interactive screening path aggregation network (HLIS-PAN), which achieves excellent multi-scale feature extraction through the alternating screening and fusion of high- and low-level information while reducing the number of parameters. Furthermore, We utilize DySample for efficient upsampling, bypassing complex kernel computations with point sampling. Moreover, context anchor attention (CAA) is introduced to enhance the model’s ability to recognize elongated targets such as tomato fruit bunches and stems. Experimental results indicate that MTS-YOLO achieves an F1-score of 88.7% and an mAP@0.5 of 92.0%. Compared to mainstream models, MTS-YOLO not only enhances accuracy but also optimizes the model size, effectively reducing computational costs and inference time. The model precisely identifies the foreground targets that need to be harvested while ignoring background objects, contributing to improved picking efficiency. This study provides a lightweight and efficient technical solution for intelligent agricultural picking. Full article

More than 40% of the total oil palm area in Indonesia is owned and managed by smallholders. For large plantations, guidelines are available on so-called ‘best management practices’, which should give superior yields at acceptable costs when followed carefully. We tested a subset of such practices in a sample of smallholder plantations, aiming to increase yields and profitability. We implemented improved practices (weeding, pruning, harvesting, and fertiliser application) in 14 smallholder plantations of 13–15 years after planting in Jambi province (Sumatra) and in West-Kalimantan province (Kalimantan) for a duration of 3 to 3.5 years. During this period, we recorded yields, measured palm leaf parameters and analysed leaf nutrient contents. Yield recording then continued for an additional two years. In the treatment plots, the yields did not increase significantly, but the size of the bunches and the size of the palm leaves increased significantly and substantially. The tissue nutrient concentrations also increased significantly, although after three years, the potassium concentrations in the rachis were still below the critical value. Because of the absence of yield increase and the additional costs for fertiliser inputs, the net profit of implementing better management practices was negative, and ‘business as usual’ was justified financially. Some practices, such as harvesting at 10-day intervals and the weeding of circles and paths, were received positively by those farmers who could implement them autonomously, and were applied beyond the experiment. It is challenging to find and implement intensification options that are both sustainable and profitable, that have a substantial impact on yield, and that fit in the smallholders’ realities. On-farm experimentation and data collection are essential for achieving sustainable intensification in smallholder oil palm plantations. Full article

This paper presents an approach for hydrolyzing cellulose nanocrystals from oil palm empty fruit bunch (OPEFB) presented through hydrochloric acid hydrolysis under sonication–hydrothermal conditions. Differences in concentration, reaction time, and acid-to-cellulose ratio affect toward the yield, crystallinity, microstructure, and thermal stability were obtained. The highest yield of cellulose nanocrystals up to 74.82%, crystallinity up to 78.59%, and a maximum degradation temperature (T_max) of 339.82 °C were achieved through hydrolysis using 3 M HCl at 110 °C during 1 h. X-ray diffraction analysis indicated a higher diffraction peak pattern at 2θ = 22.6° and a low diffraction peak pattern at 2θ = 18°. All cellulose nanocrystals showed a crystalline size of under 1 nm, and it was indicated that the sonication–hydrothermal process could reduce the crystalline size of cellulose. Infrared spectroscopy analysis showed that a deletion of lignin and hemicellulose was demonstrated in the spectrum. Cellulose nanocrystal morphology showed a more compact structure and well-ordered surface arrangement than cellulose. Cellulose nanocrystals also had good thermal stability, as a high maximum degradation temperature was indicated, where CNC-D1 began degrading at temperatures (T₀) of 307.09 °C and decomposed (T_max) at 340.56 °C. Full article

Agro-industrial residue valorization under the umbrella of the circular bioeconomy (CBE) has prompted the search for further forward-thinking alternatives that encourage the mitigation of the industry’s environmental footprint. From this perspective, second-life valorization (viz., thermoplastic composites) has been explored for agro-industrial waste (viz., oil palm empty fruit bunch fibers, OPEFBFs) that has already been used previously in other circular applications (viz., the removal of domestic wastewater contaminants). Particularly, this ongoing study evaluated the performance of raw residues (R-OPEFBFs) within three different size ranges (250–425, 425–600, 600–800 µm) both before and after their utilization in biofiltration processes (as post-adsorbents, P-OPEFBFs) to reinforce a polymer matrix of acrylic resin. The research examined the changes in R-OPEFBF composition and morphology caused by microorganisms in the biofilters and their impact on the mechanical properties of the composites. Smaller R-OPEFBFs (250–425 µm) demonstrated superior mechanical performance. Additionally, the composites with P-OPEFBFs displayed significant enhancements in their mechanical properties (3.9–40.3%) compared to those with R-OPEFBFs. The combination of the three fiber sizes improved the mechanical behavior of the composites, indicating the potential for both R-OPEFBFs and P-OPEFBFs as reinforcement materials in composite applications. Full article

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R² value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots. Full article