Search Results (462)

Leaf area (LA) and SPAD value (a proxy for chlorophyll content) are two key determinants of seedling quality. This study aimed to develop and validate approaches for the efficient retrieval of these features in order to facilitate both management and screening practices. In cucumber, different models were developed and tested for the accurate estimation of LA at the scale of the individual organ (cotyledon, leaf) by using its linear dimensions (length (L) and width (W)), and of the whole seedling by using the 2D image-extracted projected area (from three different angles: 0°, 45°, and 90°). At either scale, the SPAD value was computed by using image (90°)-based colorimetric features. The estimation of individual organ area was more accurate when using L alone, compared with W alone. By using the two dimensions and specific colorimetric features, the individual organ area (R² ≥ 0.92) and SPAD value (R² of 0.77) were accurately predicted. When considering a single view, the top one (90°) was associated with the highest accuracy in whole-seedling LA estimation, and the side view (0°) with the lowest (R² of 0.88 and 0.73, respectively). Using any combination of two angles, the whole-seedling LA was accurately retrieved (R² ≥ 0.88). When using colorimetric features, a poor estimation of the whole-seedling SPAD value was noted (R² ≤ 0.43). The deployment of artificial neural networks (ANNs) further allowed the estimation of specific organ shape traits, and improved the accuracy of all the aforementioned predictions, including the whole-seedling SPAD value (R² of 0.597). In conclusion, the findings of this study highlight that features readily retrieved from 2D images hold promising potential for improving screening routines within the nursery industry. Full article

Waxy maize (Zea mays L. ceratina) is extensively cultivated and exhibits substantial market demand in China; however, its yield and quality improvement remain constrained by relatively underdeveloped cultivation techniques. Optimizing plant density and row spacing is critical to improving the yield and nutritional quality of waxy maize, yet their combined effects remain insufficiently explored. A split-plot design evaluated two plant densities, i.e., 5.25 × 10⁴ plants ha⁻¹ (PD_5.25) and 6.75 × 10⁴ plants ha⁻¹ (PD_6.75), and three row configurations, i.e., 80 + 40 cm wide–narrow rows (RS_8-4), 100 + 20 cm wide–narrow rows (RS_10-2) and conventional 60 + 60 cm equal rows (RS_6-6). This study aims to identify the optimal cultivation configuration for waxy maize in the Loess Plateau region. Results showed that the RS_8-4 configuration maximized agronomic traits, dry matter accumulation, and yield relative to RS_6-6 and RS_10-2 treatments. Specifically, RS_8-4 reduced the insertion angle of the lower ear leaf by 12.4% (p < 0.05) and ear height by 8.3% while increasing yield by 19.86–20.00% compared to RS_6-6 and RS_10-2 treatments. At fresh-market maturity, dry matter accumulation under RS_8-4 treatment increased significantly by 34.0% with higher plant density. Under PD_6.75, RS_8-4 boosted dry matter by 29.8% and 39.4% versus RS_6-6 and RS_10-2, respectively. Under the RS_8-4 and PD_6.5 configurations, dry matter accumulation reached 13.56 t ha⁻¹ and a yield of 9.94 t ha⁻¹ was achieved in 2022. In summary, the combination of the PD_6.75 density and the RS_8-4 row spacing configuration achieved the optimal yield for the ‘Jinnuo 20’ cultivar in the Loess Plateau region. This approach provides a scalable planting framework for high-yield waxy maize production in the area, while demonstrating that optimized plant density and row spacing represent not only a key technical measure for enhancing productivity but also a core agronomic strategy for improving resource-use efficiency. Full article

Efficient and non-destructive extraction of organ-level phenotypic parameters of sesame (Sesamum indicum L.) plants is a key bottleneck in current sesame phenotyping research. To address this issue, this study proposes a method for organ segmentation and phenotypic parameter extraction based on CAVF-PointNet++ and geometric clustering. First, this method constructs a high-precision 3D point cloud using multi-view RGB image sequences. Based on the PointNet++ model, a CAVF-PointNet++ model is designed to perform feature learning on point cloud data and realize the automatic segmentation of stems, petioles, and leaves. Meanwhile, different leaves are segmented using curvature-density clustering technology. Based on the results of segmentation, this study extracted a total of six organ-level phenotypic parameters, including plant height, stem diameter, leaf length, leaf width, leaf angle, and leaf area. The experimental results show that in the segmentation tasks of stems, petioles, and leaves, the overall accuracy of CAVF-PointNet++ reaches 96.93%, and the mean intersection over union is 82.56%, which are 1.72% and 3.64% higher than those of PointNet++, demonstrating excellent segmentation performance. Compared with the results of manual segmentation of different leaves, the proposed clustering method achieves high levels in terms of precision, recall, and F1-score, and the segmentation results are highly consistent. In terms of phenotypic parameter measurement, the coefficients of determination between manual measurement values and algorithmic measurement values are 0.984, 0.926, 0.962, 0.942, 0.914, and 0.984 in sequence, with root-mean-square errors of 5.9 cm, 1.24 mm, 1.9 cm, 1.2 cm, 3.5°, and 6.22 cm², respectively. The measurement results of the proposed method show a strong correlation with the actual values, providing strong technical support for sesame phenotyping research and precision agriculture. It is expected to provide reference and support for the automated 3D phenotypic analysis of other crops in the future. Full article

Due to the exceptional corrosion resistance, chemical stability, and dense microstructure, carbon-based thin films are extensively employed in hydrogen energy systems. This study employed magnetron sputtering to fabricate amorphous carbon (a-C) films on SS316L substrates, aiming to improve the corrosion resistance of bipolar plates (BPs) in proton exchange membrane fuel cells (PEMFCs). Using a Taguchi design, the effects of working pressure, sputtering power, substrate bias, and deposition time on film properties were systematically examined and optimized. Films were examined via field emission scanning electron microscopy (FE-SEM), contact angle measurements, and electrochemical tests. Comprehensive evaluation by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method identified optimal conditions of 1.5 Pa pressure, 150 W radio frequency (RF) power, −250 V bias voltage, and 60 min deposition, yielding dense, uniform films with a corrosion current density of 1.61 × 10⁻⁶ A·cm⁻² and a contact angle of 106.36°, indicative of lotus leaf-like hydrophobicity. This work enriches the theoretical understanding of a-C film process optimization, offering a practical approach for modifying fuel cell bipolar plates to support hydrogen energy applications. Full article

Conventional spraying often results in poor deposition on the abaxial (lower) leaf surface and within the middle-to-lower canopy, where pest and disease pressures are typically highest. In this study, we evaluated the performance of electrostatic spraying using basil (Ocimum basilicum), cucumber (Cucumis sativus), and chili pepper (Capsicum annuum) leaves as target surfaces. A high-speed imaging system was employed to map droplet distributions on the abaxial surface, while a neighborhood-matching algorithm combined with droplet tracking was used to quantify the motion of individual droplets near the leaf. At the steady-state stage (frame 4500, 2.25 s), the number of charged droplets detected beneath the abaxial surface increased by 112% (basil), 132% (cucumber), and 213% (chili pepper) compared with non-electrostatic spraying. Smaller charged droplets exhibited higher horizontal velocities and smaller deflection angles in their trajectories near the leaf, indicating a stronger tendency to migrate toward the target surface and into the canopy interior. These findings demonstrate that electrostatic forces substantially enhance abaxial deposition and provide practical guidance for optimizing parameters for electrostatic spraying, such as droplet size, to improve spray efficiency in agricultural applications. Full article

Adhesion of different microorganisms to the surface of dental materials has generated significant interest since one of the most important requirements of biomaterials to be considered successful is their ability to withstand the damage caused by microorganisms that may lead to failure and the onset of different pathologies, such as caries. In vitro testing has demonstrated that surface modification is an alternative approach to reduce the adhesion of microorganisms to surfaces. The objective of this work was to assess the adhesion of Streptococcus mitis, Streptococcus mutans, and Candida albicans to a dental composite surface modified following a biomimetic approach and coated with salivary proteins. Soft lithography was used to copy the topography from the Crocosmia aurea leaf and then transfer it to the surface of dental composite discs that were coated with saliva proteins. Surfaces were characterized by contact angle and atomic force microscopy. S. mitis, S. mutans, and C. albicans were used to assess bacterial and fungal adhesion in monoculture and co-culture. The topographic modification of the surface of a dental composite reduced the adhesion of assessed microorganisms and the adhesion of these species in monoculture and co-culture on saliva-coated surfaces was higher than on topographically modified surfaces. Full article

The sugarcane crop is of great economic relevance to Brazil, and the precise productivity estimation is a major challenge in production. Therefore, the aim of this study was to estimate the productivity of sugarcane cultivars in different regions, using multispectral sensors embedded in RPAs and biometric variables sampled in the field. The study was conducted in two experimental areas, located in the municipalities of Itirapina-SP and Iracemápolis-SP, with 16 cultivars in a randomized block design. The images were acquired using the multispectral sensor MicaSense Altum, allowing the extraction of spectral bands and vegetation indices. In parallel, biometric variables were collected at 149 and 295 days after planting (DAP). The machine learning models Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were calibrated using different sets of variables, and, despite the similar performance, it was decided to use the model derived from XGBoost in the analyses, since it deals more effectively with overfitting. The results indicated a good performance of the model (R² = 0.83 and 0.66; RMSE = 18.7 t ha⁻¹ and 25.3 t ha⁻¹; MAE = 15.7 and 20.2; RPIQ = 3.22 and 2.61) for the validations K-fold and Leave-one-out cross-validation (LOOCV). The correlations between biometric variables, spectral bands, and vegetation indices varied according to crop development stage. The leaf insertion angle presented a strong correlation with near-infrared (NIR) (r = 0.76) and the indices ExG and VARI (r = 0.70 and r = 0.69, respectively). The present work demonstrated that the integration between multispectral and biometric data represents a promising approach for estimating sugarcane productivity. Full article

Accurate light simulations using virtual plant models are essential for analyzing how plant structures influence the micro-light climate within canopies. Such simulations are increasingly important in applications including remote sensing, greenhouse optimization, and synthetic data generation for agricultural systems. However, many current models simplify leaf optical behavior by assuming purely diffuse reflectance, thereby neglecting the spectral and angular variability described by the bidirectional reflectance distribution function (BRDF). To address this limitation, the spectral BRDF of cucumber leaves was experimentally measured and corresponding Phong reflectance model parameters were determined for use in the GroIMP simulation environment. These parameters were optimized to replicate the angular and spectral reflectance distribution patterns and evaluated against a diffuse reflectance model. The Phong model successfully reproduced key features of the BRDF, particularly the increased diffuseness in the green and far-red spectral regions, although deviations in hemispherical reflectance emerged at high incidence angles. The resulting Phong parameters offer a practical method for incorporating wavelength- and direction-dependent reflectance into virtual plant simulations. These parameters can be adapted to other reflectance values of leaves with similar optical properties using hemispherical reflectance measurements, enabling more realistic light modeling in virtual canopies. Within a 30–60° incidence, the Phong BRDF reduced per-wavelength error relative to a diffuse baseline across all spectral regions. Full article

Accurate target recognition and localization remain significant challenges for robotic fruit harvesting in unstructured orchard environments characterized by branch occlusion and leaf clutter. To address the difficulty in identifying and locating apples under such visually complex conditions, this paper proposes an improved YOLOv5-based visual recognition algorithm incorporating an efficient channel attention (ECA) module. The ECA module is strategically integrated into specific C3 layers (C3-3, C3-6, C3-9) of the YOLOv5 network architecture to enhance feature representation for occluded targets. During operation, the system simultaneously acquires apple pose information and achieves precise spatial localization through coordinate transformation matrices. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed system. The custom-designed six-degree-of-freedom (6-DOF) robotic arm exhibits a wide operational range with a maximum working angle of 120°. The ECA-enhanced YOLOv5 model achieves a confidence level of 90% and an impressive in-range apple recognition rate of 98%, representing a 2.5% improvement in the mean Average Precision (mAP) compared to the baseline YOLOv5s algorithm. The end-effector positioning error is consistently controlled within 1.5 mm. The motion planning success rate reaches 92%, with the picking completed within 23 s per apple. This work provides a novel and effective vision recognition solution for future development of harvesting robots. Full article

Inspired by the self-organizing optimization mechanisms in nature, the leaf venation of the royal water lily exhibits a hierarchically branched fractal network that combines excellent mechanical performance with lightweight characteristics. In this study, a structural bionic approach was adopted to systematically investigate the venation architecture through macroscopic morphological observation, experimental testing, 3D scanning-based reverse reconstruction, and finite element simulation. The influence of key fractal geometric parameters under vertical loading on the mechanical behavior and energy absorption capacity was analyzed. The results demonstrate that the leaf venation of the royal water lily exhibits a core-to-margin gradient fractal pattern, with vein thickness linearly decreasing along the radial direction. At each hierarchical bifurcation, the vein width is reduced to 65–75% of the preceding level, while the bifurcation angle progressively increases with branching order. During leaf development, the fractal dimension initially decreases and then increases, indicating a coordinated functional adaptation between the stiff central trunk and the compliant peripheral branches. The veins primarily follow curved trajectories and form a multidirectional interwoven network, effectively extending the energy dissipation path. Finite element simulations reveal that the fractal venation structure of the royal water lily exhibits pronounced nonlinear stiffness behavior. A smaller bifurcation angle and higher fractal branching level contribute to enhanced specific energy absorption and average load-bearing capacity. Moreover, a moderate branching length ratio enables a favorable balance between yield stiffness, ultimate strength, and energy dissipation. These findings highlight the synergistic optimization between energy absorption characteristics and fractal geometry, offering both theoretical insights and bioinspired strategies for the design of impact-resistant structures. Full article

The photochemical efficiency of photosystem II (PSII) was monitored in two-year-old seedlings from six Chilean woody sclerophyllous species differing in foliage habits (evergreen, deciduous, semi-deciduous) and leaf orientation. A common garden experiment was established in July 2020 in a Mediterranean-type climate site under two watering regimes (2 L⁻¹ seedling⁻¹ week⁻¹ for 5 months versus no irrigation). Chlorophyll a fluorescence rise kinetics (OJIP) and JIP test analysis were monitored from December 2021 to January 2022. The semi-deciduous Colliguaja odorifera (leaf angle of 65°) exhibited the highest performance in processes such as absorption and trapping photons, heat dissipation, electron transport, and level of photosynthetic performance (i.e., parameters PI_ABS F_V/F_M, F_V/F₀, and ΔV_IP). In contrast, the evergreen Peumus boldus (leaf rolling) exhibited the opposite behavior for the same parameters. On the other hand, the deciduous Vachelia caven (small compound leaves and leaf angle of 15°) showed the lowest values for minimal and maximal fluorescence (F₀ and F_M) and the highest area above the OJIP transient (S_m) during the study period. Irrigation decreased S_m and the relative contribution of electron transport (parameter ΔV_IP) by 22% and 17%, respectively, but no clear effects of the irrigation treatments were observed among species and dates of measurement. Overall, V. caven and C. odorifera exhibited the highest photosynthetic performance, whereas P. boldus seemed to be more prone to photoinhibition. We conclude that different foliar adaptations among species influence light protection mechanisms more than irrigation treatments. Full article

Vehicle suspension significantly influences the safety of cargo transportation. This study presents a 14-degree-of-freedom vehicle–cargo coupling model that explicitly incorporates the frequency-dependent stiffness of air springs. Systematic parametric investigations of air spring orifice resistance, loading mass, and cargo stiffness reveal the following: (a) Compared with leaf spring suspension, air suspension vehicles attenuated the first peak of acceleration power spectral density by over 50%, while slightly amplifying the second peak; (b) When replacing leaf spring suspension with air suspension, the upper-layer cargo exhibited significantly larger vibration reductions (14% vertical, 28% pitch) than the lower-layer cargo under identical cargo parameters. The roll angle should be controlled to prevent the cargo overturning when equipping air suspensions; (c) Under light loading conditions, the vertical vibration response in upper-layer cargo is amplified. This amplification can be effectively suppressed through two complementary approaches, i.e., employing low-stiffness cushion materials and reducing orifice resistance through tunable orifices, which collectively attenuate characteristic peaks in the frequency-domain response and comprehensively mitigate the vertical vibration of cargo. These findings provide guidance for designing transportation schemes for cargo in air suspension vehicles to enhance cargo safety. Full article

In the design of operating procedures, structures, and control systems for agricultural machinery and equipment, it is necessary to fully consider data on the properties of relevant agricultural materials as the basis for research and design. Therefore, studying the physical properties of agricultural materials is of great significance. The basic physical parameters of agricultural materials include their shape, size, density, porosity, and moisture content. This study focuses on the triaxial dimensions, 1000-grain weight, moisture content, and tribological properties (sliding friction angle, natural repose angle) of the seeds of 16 varieties of small-seeded vegetables commonly grown in Hainan, including flowering Chinese cabbage, Chinese cabbage, lettuce, and leaf lettuce. Measurements were conducted using instruments such as a digital vernier caliper (Deli, Ningbo, China; accuracy 0.01 mm), an electronic balance (LICHEN, Shanghai, China; accuracy 0.001 g), a constant-temperature oven (Shangyi, Shanghai, China), and self-developed sliding friction angle and natural repose angle testers. Discrete element simulations were performed via EDEM 2021 software to validate the tribological properties by establishing particle models (spherical for flowering Chinese cabbage and Chinese cabbage; long–flat for lettuce and leaf lettuce) and instrument geometric models. Additionally, seed germinability (germination potential, germination rate, and germination speed) was tested using a constant-temperature incubation method. The results showed distinct differences between near-spherical and long–flat seeds in geometric characteristics, 1000-grain weight (2.27–3.06 g vs. 1.00–1.29 g), and tribological behavior (e.g., smaller natural repose angles for near-spherical seeds indicating better flowability). Plastic plates were identified as optimal for seed box guides due to lower sliding friction coefficients. EDEM 2021 simulations effectively verified the experimental data. High-germination-rate seeds (e.g., Hong Kong flowering Chinese cabbage, and Lifeng No.3 Chinese cabbage) were recommended for subsequent trials. These findings provide data support for the selection, design, and optimization of seed rope braiding machine components and sustainable seeding process. Full article

In a previous study, GmMYB14 overexpressing (GmMYB14-OX) transgenic soybean plants displayed a semi-dwarfism and compact phenotype, which was regulated by the brassinosteroid (BR) pathway. However, the phenotype of GmMYB14-OX plants could be partly rescued after spraying them with exogenous BR. This indicates that other hormones, in addition to BR, also play a role in regulating the architecture of GmMYB14-OX plants. We observed a significant decrease in the content of endogenous indole-3-acetic acid (IAA) in transgenic soybean lines (OX9 and OX12) compared to wild type (WT) plants. The plant height, leaf area, leaf petiole length, and leaf petiole angle of GmMYB14-OX plants could also be partly rescued after applying exogenous IAA for two weeks. Transcriptome sequencing analysis revealed that the expression of many genes within the Aux/IAA gene family underwent alterations in the GmMYB14-OX transgenic soybean plants. Among them, Glyma.02G000500 (GmIAA1) showed the highest expression in GmMYB14-OX plants. Furthermore, the results of electrophoretic mobility shift assay and dual-luciferase reporter indicate that GmMYB14 protein could bind to the promoter of GmIAA1. In summary, a decrease in endogenous IAA content may be one of the factors contributing to the compact and dwarfed architecture of GmMYB14-OX plants. GmMYB14 also acts as a transcriptional activator of GmIAA1 to potentially block IAA effects. Our findings provide a theoretical basis for further investigation of the regulatory mechanism of GmMYB14 on soybean plant architecture. Full article

The Mount Wudang architectural complex, recognized as a UNESCO World Cultural Heritage site, extensively utilizes green schist as the building material in its rock temple structures. Due to prolonged exposure to weathering and moisture, effective surface protection of these stones is crucial for their preservation. Inspired by the lotus leaf, femtosecond laser fabrication of bioinspired micro/nanostructures offers a promising approach for imparting hydrophobicity to stone surfaces. However, green schist is a typical heterogeneous material primarily composed of quartz, chlorite, and muscovite, and it contains metal elements, such as Fe and Ni. These pronounced compositional differences complicate laser–material interactions, posing considerable challenges to the formation of stable and uniform micro/nanostructures. To address this issue, we performed systematic femtosecond laser scanning experiments on green schist surfaces using a 100 kHz, 40 μJ laser with a 30 μm spot diameter, fabricating microgrooves under various process conditions. Surface morphology and EDS mapping analyses were conducted to elucidate the ablation responses of quartz, chlorite, and muscovite under different groove spacings (100 μm, 80 μm, 60 μm, and 40 μm) and scan repetitions (1, 2, 4, 6, 8, 10). The results revealed distinct differences in energy absorption, material ejection, and surface reorganization among these minerals, significantly influencing the formation mechanisms of laser-induced structures. Based on optimized parameters (60 μm spacing, 2–6 passes), robust and repeatable micro/nanostructures were successfully produced, yielding superhydrophobic performance with contact angles exceeding 155°. This work offers a novel strategy for interface control in heterogeneous natural stone materials and provides a theoretical and technical foundation for the protection and functional modification of green schist in heritage conservation. Full article