Search Results (360)

In this work, a lightweight framework enabled by the modified YOLOv11s-seg model for tea shoot detection and plucking point localization is proposed. Detecting tea shoots and localizing plucking points with higher accuracy generally require larger model size and more model parameters, making it difficult to balance accuracy and lightweighting. To overcome this limitation, a modified lightweight YOLOv11s-seg model is developed. First, the multi-scale edge information enhancement is introduced into the conventional YOLOv11s-seg to extract edge feature better and improve the detection accuracy of tea shoots. Meanwhile, context anchor attention is utilized to modify the cross stage partial spatial attention module in a backbone network to improve the detection capability for small objects. Moreover, the detail calibration reconstruction feature pyramid network is proposed. It utilizes spatial and contextual semantic information to reconstruct and calibrate features in key regions, enhancing the capability for object fusion and recognition at various scales. Furthermore, with the modified model performing instance segmentation to acquire the contour of each tea shoot, the coordinates of the three lowest pixel points in the contour are captured to localize the plucking point based on the average coordinates. In addition, the layer-adaptive magnitude-based pruning (LAMP) method is used to lighten the model. The experimental results show that the LAMP-pruned modified YOLOv11s-seg model with a speedup ratio of 1.5 achieves a mAP@0.5 of 86.5% for tea shoot detection, exhibiting a 4.7 percentage point improvement over the conventional YOLOv11s-seg model. Moreover, it exhibits an accuracy of 81.9% for plucking point localization on the validation and test subsets with 232 images in total, and its number of parameters, model size and floating point operations (FLOPs) separately achieve reductions of 67.3%, 66.2%, and 24.9% over the conventional model as well. Therefore, the proposed LAMP-pruned modified model shows good balance between lightweighting and detection accuracy. Finally, the modified LAMP-pruned YOLOv11s-seg model is deployed on a Jetson Orin NX edge module and measured in a tea plantation, with the measured results exhibiting a detection speed of 34.1 FPS and verifying its availability in practical applications. Full article

Plant phenotyping based on unmanned aerial vehicles still faces challenges regarding the direct correlation between spectral information with field-collected variables, due to the influence of environmental factors and the considerable variation among maize phenological stages. Therefore, the objectives of this research were: I) to evaluate the interaction of nitrogen doses and evaluation environments (phenological stages and growing seasons) and variance components for field variables and vegetation indices; II) to identify the most suitable indices according to the evaluation environments; and III) to predict field variables based on relevant vegetation indices identified through the proposed methodology. The study was conducted using a randomized complete block design with four repetitions, in which treatments consisted of six nitrogen (N) topdressing doses (0, 50, 100, 200, 300, and 400 kg ha⁻¹) during the 2022/2023 and 2023/2024 growing seasons. Evaluations of agronomic variables and image acquisition were performed in five distinct phenological stages throughout the maize crop cycle. The data were analyzed using deviance analysis and variance components, principal component analysis (PCA), and multivariate linear modeling for the prediction of field variables. Our results demonstrated that all indices were affected by the interaction between N doses and evaluation environments (phenological stages and growing seasons). Additionally, the most reliable were EXGRaw, TGI, GNDVI, NDRE, CIRE, GVI, CVI, BNDVI, PanNDVI, SRNIRRe, SFDVI, RGBindex, NDVI, SAVI, MSAVI, and OSAVI, which showed clustering patterns according to growing season condition and phenological stage. Finally, the variables predicted using the proposed methodology achieved coefficients of determination above 0.80, except for shoot biomass and 100-grain weight. Therefore, it can be concluded that vegetation indices are influenced by the evaluated environment; however, the proposed framework based on the deduction of fixed and random effects enables the prediction of field variables with high accuracy using relatively simple models. Full article

Background: Shooting velocity is a critical determinant of competitive success in ice hockey, yet evidence for the use of weighted-implement training in high-level junior players is limited and the long-term retention of such adaptations has not been documented. The aim of this study was to compare the effects of off-ice shooting training performed with a heavy (260 g) versus a regular (170 g) puck on on-ice shooting velocity, accuracy and handgrip strength in junior players, and to examine the retention of these changes. Methods: Twenty male junior ice hockey players (18–19 years) were randomly allocated to a Heavy-puck group (n = 10) or a Regular-puck group (n = 10) and completed an identical six-week off-ice shooting programme (18 sessions, 100 shots per session) with their respective pucks. On-ice wrist-shot and snap-shot speed (radar; standard 170 g puck for both groups), on-ice shooting accuracy and bilateral handgrip strength were assessed before the intervention (pre-test), immediately after six weeks (post 6 weeks) and after a six-week retention period of normal on-ice training (post 12 weeks). Data were analysed with 2 × 3 mixed-model ANOVA with Bonferroni-corrected post hoc comparisons. Results: A significant Group × Time interaction was found for wrist-shot speed (ηp² = 0.61), snap-shot speed (ηp² = 0.78), left-hand handgrip strength (ηp² = 0.30) and shooting accuracy (ηp² = 0.24). The Heavy-puck group displayed substantially larger velocity gains at both post 6 weeks (wrist shot d = 2.97; snap shot d = 4.73) and post 12 weeks (d = 2.56 and d = 3.21, respectively). Left-hand handgrip strength gain was also greater in the Heavy-puck group at post 12 weeks (d = 1.40). A short-term cost on accuracy was observed in the Heavy-puck group at post 6 weeks (d = −1.21), which was fully recovered at post 12 weeks. Conclusions: Heavy-puck off-ice training produced large and durable improvements in on-ice puck velocity, with a transient and recoverable cost on accuracy, supporting its inclusion in the off-ice preparation of junior ice hockey players. Full article

In ice hockey, automatic activity detection using wearable sensors and machine learning could provide objective feedback to support coaches and players during performance evaluation. The primary objective was to assess the predictive ability of a deep learning model to recognize common ice hockey stick striking actions (passing, shooting) from inertial measurement unit sensors. This study implemented a fully connected convolutional neural network model to classify seven ice hockey-related technical actions (wrist shot, slap shot, backhand shot, one-timers, pass, other, and rest) using acceleration data via two setups: an all-sensor configuration (17 sensors) and a hands-only sensor configuration (2 sensors) in 43 elite players. Data were split into 80/20 train/test sets, with a five-fold cross-validation applied to the training data. The train/test split was repeated 10 times with different random splits to assess stability of results. The model achieved high classification accuracy, with the all-sensor model reaching an average F1 score of 95.0 ± 3.0% and the hands-only model achieving 93.5 ± 1.6%. These findings support the use of convolutional neural networks for automatic shooting action classification in ice hockey and highlight the feasibility of using minimal sensor configurations, such as sensor-integrated gloves, for real-world applications. This approach could further enhance training practices by providing objective performance metrics and allowing coaches to deliver data-driven feedback to players. Full article

This study examined whether acute exercise performed within individualized physiological intensity zones affects multidimensional soccer shooting performance. Twenty male collegiate soccer players completed a Yo-Yo Intermittent Recovery Test Level 1 with portable gas analysis to determine the ventilatory threshold (VT) and respiratory compensation point (RCP). Three individualized zones were defined: Low (<VT), Moderate (VT–RCP), and High (>RCP). In a randomized design, players completed three 3 min shuttle-running bouts, each followed immediately by the 356 Soccer Shooting Test. Ball velocity (BV), shooting accuracy (SA), and shooting quality (SQ) were analyzed using repeated-measures ANOVA. Exercise condition significantly affected SA (p = 0.013) and SQ (p = 0.007), but not BV (p = 0.216). Bonferroni-adjusted comparisons showed that SA and SQ were lower in High than in Low, whereas no pairwise BV comparison reached significance. A sensitivity analysis using all ten recorded attempts rather than the original best-seven scoring approach showed an overall condition effect for BV without a significant pairwise comparison, retained overall effects for SA and SQ, and showed that the Low–High contrast remained robust only for SQ. Baseline comparisons were not significant. These findings indicate condition-specific shooting responses, with the clearest evidence for lower SQ after High compared with Low, supportive evidence for lower SA, and no significant primary condition effect for BV. Full article

The picking of tea leaves in tea gardens requires multiple batches in the short and valuable tea harvest period. To realize timely and efficient tea plucking, it is feasible to use unmanned aerial vehicles (UAV) for tea shoot detection in large tea gardens. For the typical small targets of tea buds in unmanned aerial vehicle (UAV) aerial images, it is necessary to design an efficient tea buds detection model. In order to improve the accuracy and the speed of the tea buds detection in the UAV images, we designed the SH-CoordMapping hash space mapping algorithm to accelerate the remerging of the detection results into the original image. The C2PSA-BI module and the CARAFE upsampling module are applied to improve detail preservation during feature fusion. A lightweight detection head is further used to reduce redundant computation in the detection stage. By comparing with the traditional detection methods, it can be proved that the SWO sections are necessary for UAV-scale tea shoots detection. Based on the accuracy and the number of model parameters, the YOLO11n model with slice size as 640 and overlap rate as 0.1 performs the best. The TSDet-UAV was deployed on the NVIDIA Jetson Orin NX chip to construct an inspection system capable of real-time acquisition and detection. The experimental results demonstrate that the proposed TSDet-UAV achieves excellent performance, recording an mAP₅₀ of 52.9% on the constructed UAV-TS dataset while maintaining high efficiency. With a parameter size of 2.4 M and a total processing time of 1.32 s per high-resolution image under TensorRT FP16, the processing speed is highly suitable for real-time edge deployment on agricultural UAV platforms. The UAV image-based tea garden shoot inspection platform proposed in this paper can effectively confirm the growth status of tea shoots, assisting farm management in formulating precise picking plans. Full article

Seagrasses are vital ecosystem engineers and habitat architects in coastal environments, with Posidonia oceanica in the Mediterranean playing a crucial role as an indicator of ecological health. As an endemic and vulnerable species, P. oceanica meadows are highly susceptible to environmental degradation, underscoring the importance of non-destructive monitoring techniques. Traditional SCUBA-based surveys are accurate but resource-intensive and difficult to scale, especially for estimating shoot density and leaf length. This study applies a conservative acoustic-based approach to assess Posidonia oceanica biometrics, habitat characteristics, and ecological status along the Turkish Levant coast. The method offers a non-destructive alternative to SCUBA surveys and addresses a regional knowledge gap in Mediterranean seagrass monitoring. Acoustic data collected during winter and summer 2019 along the Turkish Levant coast were analyzed to estimate seagrass biometrics and derive ecological indicators, with validation via SCUBA observations. Results show that acoustic methods can reliably estimate shoot density, leaf area index, and canopy height. They provide broad-scale coverage and efficiency, though further refinement is required to improve calibration across depths and substrates. While acoustic methods provide broad, non-invasive coverage, they are affected by spatial and temporal variability that SCUBA surveys capture more reliably. Calibration of the POSIBIOM (vers 1.1) algorithm was based on specimens collected at 15 m depth on rocky substrates. While this provided consistent regression relationships, it may limit accuracy when extrapolated to habitats such as sand, mud, or matte. This study represents the first high-resolution, spatiotemporal mapping of P. oceanica meadows and benthic habitats along a significant portion of the Turkish Levant coast using acoustics alone. Overall, the study highlights the potential of acoustics as a scalable, non-invasive tool for seagrass monitoring. This approach contributes to ecosystem-based management and conservation strategies in the Mediterranean. Future work will focus on refining models to address bottom type- and depth-dependent acoustic responses and improve biometric accuracy. Full article

The increasing pressure on water resources has stimulated the use of treated wastewater in agricultural irrigation, although its effects on plant development remain uncertain. This study evaluated the effects of wastewater treatments and irrigation depths on the morphophysiological development of lettuce (Lactuca sativa L.). A split-plot experiment was conducted with crop cycles in the main plots and a factorial arrangement in the subplots, consisting of five water sources and five irrigation depths (50% to 150% ETc), with three replications. Seven variables were evaluated, including growth traits and water productivity. Irrigation depth significantly affected all variables (p ≤ 0.01) and was the main driver of vegetative growth, increasing shoot fresh mass, stem diameter, and plant height. In contrast, water sources showed smaller effects. Water productivity decreased with increasing irrigation depth and showed weak correlations with other variables (r ≤ 0.468). Machine learning models achieved moderate accuracy for irrigation depth prediction (≈55%), with confusion among adjacent classes, indicating detection of a gradient rather than precise classification. Prediction of water sources was low (<30%), confirming limited morphological differentiation. Plant height and stem diameter were the most informative variables. These results indicate that irrigation management has a stronger influence on lettuce growth than water source. Full article

Globba bicolor Gagnep., an ornamental ginger of cultural importance in Thailand’s “Tak Bat Dok Mai” festival, faces conservation challenges due to climate change and slow natural propagation. Limited understanding of its cultivation and chemical composition further constrains sustainable utilization. This study provides the first integrated investigation of micropropagation using rhizome-derived explants under various combinations of exogenous hormones, acclimatization strategies, and comparative phytochemical profiling between wild and in vitro-propagated plants. An optimized clonal regeneration system was established from plantlets, with Murashige and Skoog (MS) medium containing 2.0 mg/L 6-benzylaminopurine (BA) and 0.5 mg/L 1-naphthaleneacetic acid (NAA), yielding the highest multiplication (9.10 shoots/explant and 12.40 roots/explant) after eight weeks of cultivation. During acclimatization, sand substrate proved superior, facilitating a 90% survival rate and enhanced physiological vigor. Comparative analysis revealed that while wild plants possessed significantly higher total phenolic (TPC) and total flavonoid (TFC) contents and antioxidant activities (DPPH, ABTS, and FRAP) than their in vitro counterparts, both sources maintained a rich diversity of chemical constituents. HPLC analysis identified cinnamic acid, rutin, and quercetin as major metabolites, while GC–MS detected 90 volatile compounds, with β-caryophyllene and β-pinene as predominant constituents. Notably, rhizomes of wild plants exhibited particularly high-value detections. To provide a rapid and non-destructive approach for linking chemical composition with antioxidant activity, FTIR-based chemometric models were applied, demonstrating high predictive accuracy (R²–cv = 0.9712–0.9862). These results provide a scientific foundation for the conservation and sustainable commercial utilization of G. bicolor as a potential source of bioactive natural products. Full article

Efficient phenotyping is essential for accelerating genetic improvement in turfgrass breeding, where manual measurements are labor-intensive. This study evaluated hyperspectral imaging (HSI) as a high-throughput tool for assessing Zoysia spp. breeding populations consisting of 464 genotypes. HSI data (400–1000 nm) were processed through a user-in-the-loop hybrid segmentation pipeline integrating UMAP dimensionality reduction, DBSCAN clustering, Random Forest classification, and pseudo-RGB refinement. To independently assess vegetation classification performance, 10,000 manually annotated reference points from 50 pseudo-RGB images were compared with the automated module, yielding an overall accuracy of 0.9697, a precision of 0.8830, a recall of 0.9240, a specificity of 0.9779, an F1-score of 0.9030, and Cohen’s kappa of 0.8851. A Combined Ranking Score (CRS) integrating five vegetation indices and vegetation pixel count was significantly associated with aerial shoot count (r = −0.445, p < 0.001) and runner count (r = −0.207, p < 0.001). The highest-ranked genotype showed a 9370.3-pixel increase in vegetation area between 6 and 16 weeks after transplanting, compared with 1417.7 pixels for the lowest-ranked genotype. Classification performance declined under high-coverage conditions, indicating increased mixed-pixel ambiguity in dense canopies. These results suggest that HSI-based CRS can support rapid, objective, and non-destructive relative ranking of density-related vegetative growth in turfgrass breeding. Because the study was conducted at a single location and season and correlations with manual traits were moderate, the framework is best interpreted as a screening and ranking tool rather than a direct predictive model. Full article

Background: Operational performance in security-force settings depends on maintaining accurate motor–cognitive and shooting performance under acute physical strain. This descriptive pilot study examined pre–post performance patterns during a fixed-order acute-load protocol and explored whether trial-level and spatial analyses identified changes beyond aggregate scores. Methods: Nineteen applicants (10 men, 9 women; 21.6 ± 1.0 years) completed two testing sequences separated by one week. All participants completed Sequence 1 first and Sequence 2 second; therefore, sequence-related observations were interpreted descriptively rather than causally. In both sequences, participants performed Hawk Eye testing, IPSC-based shooting, and the Jaciak Motor Coordination Test, with the order of Hawk Eye and shooting reversed between sequences. Primary outcomes were first-shot hit rate and Hawk Eye error count. Secondary and exploratory outcomes included shooting miss rate and time, Hawk Eye stimulus time, minimum and maximum response times, trial-level timing, spatial distributions, and cross-task coupling. Results: Heart rate increased markedly after the Jaciak test in both sequences, with end-of-test values corresponding to approximately 86–88% of age-predicted HRmax. Model-based analysis indicated lower post-load odds of a first-shot hit compared with pre-load performance. In contrast, no detectable pre–post change was observed for Hawk Eye error probability. Descriptively, first-shot hit rate decreased in Sequence 1 (62.1 ± 19.9% vs. 42.1 ± 28.2%; p = 0.029), while the decrease in Sequence 2 was smaller and not statistically significant (61.1 ± 24.5% vs. 52.6 ± 28.4%; p = 0.267). Hawk Eye error count showed no statistically detectable pre–post change in either sequence, although maximum response time decreased in Sequence 1 (p = 0.008). Trial-level and spatial analyses indicated additional temporal and location-specific patterns, but exploratory cross-task spatial associations were inconsistent. Conclusions: In this fixed-order descriptive pilot study, post-load testing was associated with lower first-shot shooting performance in this sample, whereas no statistically detectable deterioration was observed for Hawk Eye error probability. However, because the design lacked a no-load control condition and all participants completed the same sequence order, the observed pre-to-post differences cannot be attributed specifically to acute physical load. They should be interpreted as descriptive performance patterns within the implemented protocol. Full article

Background/Objective: Mental fatigue (MF) significantly impairs psychomotor performance in dynamic sports; however, its specific impact on closed-skill precision-demanding tasks remains underexplored. This study investigated the acute effects of experimentally induced MF exposure on psychophysiological responses, mood states, and archery shooting performance. Methods: Fifteen well-trained male compound-bow archers participated in a randomized crossover study. Participants completed an MF condition (30 min modified Stroop task) and a control condition (CON; passive viewing of a neutral documentary), separated by a 72 h washout period. Continuous heart rate (HR), archery shooting accuracy, ratings of perceived exertion (RPE), rating scale of mental effort (RSME), state anxiety (VAS-A), mood states, and exercise enjoyment scale (EES) were assessed. Results: The Stroop task successfully induced subjective MF. Consequently, shooting accuracy significantly deteriorated in the MF condition compared to that in the CON condition (p < 0.001; g = 0.731). While HR and VAS-A remained consistent across conditions, the MF condition elicited a significant increase in RPE (p = 0.007; g = 0.836) and RSME (p = 0.010; g = 0.794). Furthermore, MF significantly increased feelings of anger and fatigue while drastically reducing PACES (p < 0.001; g = 1.530). Conclusions: Acute MF significantly degrades fine motor accuracy in precision sports. The pronounced dissociation between elevated RPE and stable peripheral physiological strain suggests that performance decline is driven by top-down cognitive burden rather than physiological limitations. Therefore, systematic monitoring of cognitive load is crucial for optimizing performance in precision sports. Full article

This systematic review synthesized the existing evidence on neurofeedback interventions applied to football players, aiming to clarify their effects on cognitive, technical–tactical, physical and psychological performance. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, four databases (PubMed, Web of Science, SCOPUS and SportsDiscus) were searched up to November 2025. Seven studies met the inclusion criteria, involving 133 players across youth, amateur, national and elite levels. Neurofeedback protocols primarily targeted alpha or sensorimotor rhythm (SMR) activity, and some were combined with heart rate variability (HRV) biofeedback. Across studies, neurofeedback may be associated with improvements in several cognitive outcomes, including improvements in working memory, visuospatial memory, task switching, mental rotation and decision-making. Limited evidence suggests potential improvements in technical skills (particularly shooting accuracy) and tactical decision-making. Some studies reported changes in physiological markers and stress-recovery capacity, although their interpretation remains uncertain. However, the evidence base remains constrained by small samples, heterogeneous protocols and limited use of randomized controlled designs. Overall, neurofeedback appears to be a potentially promising but still experimental tool to support cognitive and psychophysiological readiness in football, warranting more rigorous and standardized research to establish efficacy and optimal training parameters. Full article

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

Accurate tea-shoot detection in real tea gardens is essential for intelligent harvesting, yet dynamic illumination (low light, strong light, and shadows) can cause brightness/contrast fluctuations and feature distribution shifts, degrading detection stability and localization accuracy. This paper proposes LA-YOLO, a dynamic-light tea-shoot detector based on YOLOv11. First, we construct a dynamic-light benchmark dataset and a difficulty-stratified evaluation protocol with four single-light subsets (A–D) and a mixed-light subset (E). Second, we design LA-CSNorm, an input-side brightness-adaptive preprocessing module that applies gated enhancement to dark samples followed by channel-selective normalization to reduce illumination-induced drift. Third, we propose RECA, a residual efficient channel-attention module to enhance discriminative channels and improve localization stability. Ablation studies show that LA-CSNorm and RECA provide complementary gains, and their combination improves the YOLOv11 baseline to 0.831 mAP@0.5 and 0.621 mAP@0.5:0.95, with only 0.01 M additional parameters. On the mixed-light subset E, LA-YOLO achieves 0.816 mAP@0.5 and 0.613 mAP@0.5:0.95, and consistently outperforms mainstream YOLO variants (e.g., YOLOv11m) under dynamic lighting conditions. These results demonstrate that LA-YOLO offers a robust and deployment-friendly solution for tea-shoot detection in complex natural illumination. Full article