Seismic energy dispersion compensation by multi-scale morphology

Seismic energy decays while propagating subsurface, which may reduce the resolution of seismic data. This paper studies the method of seismic energy dispersion compensation which provides the basic principles for multi-scale morphology and the spectrum simulation method. These methods are applied in seismic energy compensation. First of all, the seismic data is decomposed into multiple scales and the effective frequency bandwidth is selectively broadened for some scales by using a spectrum simulation method. In this process, according to the amplitude spectrum of each scale, the best simulation range is selected to simulate the middle and low frequency components to ensure the authenticity of the simulation curve which is calculated by the median method, and the high frequency component is broadened. Finally, these scales are reconstructed with reasonable coefficients, and the compensated seismic data can be obtained. Examples are shown to illustrate the feasibility of the energy compensation method.

Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment.In this work,we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum（MPS） through a multi-scale morphology analysis procedure.The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves.Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

A semi-analytical model for coupled flow in stress-sensitive multi-scale shale reservoirs with fractal characteristics

A large number of nanopores and complex fracture structures in shale reservoirs results in multi-scale flow of oil. With the development of shale oil reservoirs, the permeability of multi-scale media undergoes changes due to stress sensitivity, which plays a crucial role in controlling pressure propagation and oil flow. This paper proposes a multi-scale coupled flow mathematical model of matrix nanopores, induced fractures, and hydraulic fractures. In this model, the micro-scale effects of shale oil flow in fractal nanopores, fractal induced fracture network, and stress sensitivity of multi-scale media are considered. We solved the model iteratively using Pedrosa transform, semi-analytic Segmented Bessel function, Laplace transform. The results of this model exhibit good agreement with the numerical solution and field production data, confirming the high accuracy of the model. As well, the influence of stress sensitivity on permeability, pressure and production is analyzed. It is shown that the permeability and production decrease significantly when induced fractures are weakly supported. Closed induced fractures can inhibit interporosity flow in the stimulated reservoir volume (SRV). It has been shown in sensitivity analysis that hydraulic fractures are beneficial to early production, and induced fractures in SRV are beneficial to middle production. The model can characterize multi-scale flow characteristics of shale oil, providing theoretical guidance for rapid productivity evaluation.

Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions

Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.

Morphology and valence state evolution of Cu:Unraveling the impact on nitric oxide electroreduction

Ammonia(NH3)serves as a critical component in the fertilizer industry and fume gas denitrification.However,the conventional NH3production process,namely the Haber-Bosch process,leads to considerable energy consumption and waste gas emissions.To address this,electrocatalytic nitric oxide reduction reaction(NORR)has emerged as a promising strategy to bridge NH3consumption to NH3production,harnessing renewable electricity for a sustainable future.Copper(Cu)stands out as a prominent electrocatalyst for NO reduction,given its exceptional NH3yield and selectivity.However,a crucial aspect that remains insufficiently explored is the effects of morphology and valence states of Cu on the NORR performance.In this investigation,we synthesized CuO nanowires(CuO-NF)and Cu nanocubes(Cu-NF)as cathodes through an in situ growth method.Remarkably,CuO-NF exhibited an impressive NH3yield of 0.50±0.02 mg cm^(-2)h^(-1)at-0.6 V vs.reversible hydrogen electrode(RHE)with faradaic efficiency of29,68%±1,35%,surpassing that of Cu-NF(0.17±0.01 mg cm^(-2)h^(-1),16.18%±1.40%).Throughout the electroreduction process,secondary cubes were generated on the CuO-NF surface,preserving their nanosheet cluster morphology,sustained by an abundant supply of subsurface oxygen(s-O)even after an extended duration of 10 h,until s-O depletion ensued.Conversely,Cu-NF exhibited inadequate s-O content,leading to rapid crystal collapse within the same timeframe.The distinctive current-potential relationship,akin to a volcano-type curve,was attributed to distinct NO hydrogenation mechanisms.Further Tafel analysis revealed the exchange current density(i0)and standard heterogeneous rate constant(k0)for CuO-NF,yielding 3.44×10^(-6)A cm^(-2)and 3.77×10^(-6)cm^(-2)s^(-1)when NORR was driven by overpotentials.These findings revealed the potential of CuO-NF for NO reduction and provided insights into the intricate interplay between crystal morphology,valence states,and electrochemical performance.

Responses of growth performance,antioxidant function,small intestinal morphology and mRNA expression of jejunal tight junction protein to dietary iron in yellow-feathered broilers

This study aimed to investigate the dose-effect of iron on growth performance,antioxidant function.intestinal morphology,and mRNA expression of jejunal tight junction protein in 1-to21-d-old yellow-feathered broilers.A total of 7201-d-old yellow-feathered maleb roilers were allocated to 9 treatments with 8 replicate cages of 10 birds per cage.The dietary treatments were consisted of a basal diet(contained 79.6 mg Fe kg^(-1))supplemented with 0,20,40,60,80,160,320,640,and 1,280 mg Fe kg^(-1)in the form of FeSO_(4)·7H_(2)O.Compared with the birds in the control group,birds supplemented with 20mg Fe kg^(-1)had higher average daily gain(ADG)(P<0.0001).Adding 640 and 1,280 mg Fe kg^(-1)significantly decreased ADG(P<0.0001)and average daily feed intake(ADFI)(P<0.0001)compared with supplementation of 20mg Fe kg^(-1).Malondialdehyde(MDA)concentration in plasma and duodenum increased linearly(P<0.0001),but MDA concentration in liver and jejunum increased linearly(P<0.05)or quadratically(P<0.05)with increased dietary Fe concentration.The villus height(VH)in duodenum and jejunum,and the ratio of villus height to crypt depth(V/C)in duodenum decreased linearly(P?0.05)as dietary Feincreased.As dietary Fe increased,the jejunal relative mRNA abundance of claudin-1 decreased linearly(P=0.001),but the jejunal relative mRNA abundance of zona occludens-1(ZO-1)and occludin decreased linearly(P?0.05)or quadratically(P?0.05).Compared with the supplementation of 20 mg Fe kg^(-1),the supplementation of640 mg Fe kg^(-1)or higher increased(P?0.05)MDA concentrations in plasma,duodenum,and jejunum,decreased VH in the duodenum and jejunum,and the addition of 1,280 mg Fe kg^(-1)reduced(P?0.05)the jejunal tight junction protein(claudin-1,ZO-1,occludin)mRNA abundance.In summary,640 mg of supplemental Fe kg^(-1)or greater was associated with decreased growth performance,increased oxidative stress,disrupted intestinal morphology,and reduced mRNA expression of jejunal tight junction protein.

Droplet morphology analysis of drop-on-demand inkjet printing

As an accurate 2D/3D fabrication tool,inkjet printing technology has great potential in preparation of micro electronic devices.The morphology of droplets produced by the inkjet printer has a great impact on the accuracy of deposition.In this study,the drop-on-demand(DoD)inkjet simulation model was established,and the accuracy of the simulation model was verified by corresponding experiments.The simulation result shows that the velocity of the droplet front and tail,as well as the time to disconnect from the nozzle is mainly affected by density(ρ),viscosity(μ)and surface tension(σ)of droplets.When the liquid filament is about to disconnect from the nozzle,the filament length and filament front velocity are found to have a linear correlation withσ/ρμand ln(ρ/(μσ1/2)).

Fabrication and characterization of multi-scale coated boron powders with improved combustion performance:A brief review

Boron has high mass and volume calorific values,but it is difficult to ignite and has low combustion efficiency.This literature review summarizes the strategies that are used to solve the above-mentioned problems,which include coatings of boron by using fluoride compounds,energetic composites,metal fuels,and metal oxides.Coating techniques include recrystallization,dual-solvent,phase transfer,electrospinning,etc.As one of the effective coating agents,the fluorine compounds can react with the oxide shell of boron powder.In comparison,the energetic composites can effectively improve the flame temperature of boron powder and enhance the evaporation efficiency of oxide film as a condensed product.Metals and metal oxides would react with boron powder to form metal borides with a lower ignition point,which could reduce its ignition temperature.

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

Multi-scale context-aware network for continuous sign language recognition

The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

Engineering fibrillar morphology for highly efficient organic solar cells

The power conversion efficiency(PCE)for single-junction organic solar cells(OSCs),wherein the photoactive layer is a typical bulk-heterojunction containing donor and acceptor materials,has surpassed 19%[1−4].The advance is ascribed to the development of Y-series non-fullerene acceptors(NFAs)[5,6]and polymer donors[7−13],and the refined control of the blend film morphology.

Lineaperpetua gen.nov.:a new diatom genus in the Thalassiosirales supported by morphology and molecular data

Based on a combination of morphology and molecular data of ribosomal DNA genes,a new diatom genus Lineaperpetua gen.nov.Yu,You,Kociolek&Wang is described.The features that help define Lineaperpetua at the level of genus include:a tangentially undulated valve face;continuous cribra areolae on the valve interior consisting of pores arranged as strips;single rimoportula located inside the ring of marginal fultoportulae.Additionally,phylogenetic analysis based on nuclear small subunit(SSU)rDNA sequences and nuclear large subunit(LSU)rDNA gene placed the three strains of L.lacustris in a single,monophyletic clade at a considerable sequence distance from the other genera(Thalassiosira,Conticribra,Planktoniella,Shinodiscus,and other genera)belonging to Thalassiosirales.Despite the similarities with some species of Thalassiosira,Conticribra,and Spicaticribra,the suite of features found in Lineaperpetua differentiate it from these other genera.These molecular data and morphological characters suggest an affinity of the new genus to the Thalassiosiraceae.

MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

Multi-scale Modeling and Finite Element Analyses of Thermal Conductivity of 3D C/SiC Composites Fabricating by Flexible-Oriented Woven Process

Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites.An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture.The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions.Three RVE models of cuboid,hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale.Besides,the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models.The prediction results at the mesoscale correspond closely to the experimental values.The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration.The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Few-shot image recognition based on multi-scale features prototypical network

In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.

Leaf Morphology Genes SRL1 and RENL1 Co-Regulate Cellulose Synthesis and Affect Rice Drought Tolerance

The morphological development of rice(Oryza sativa L.)leaves is closely related to plant architecture,physiological activities,and resistance.However,it is unclear whether there is a co-regulatory relationship between the morphological development of leaves and adaptation to drought environment.In this study,a drought-sensitive,roll-enhanced,and narrow-leaf mutant(renl1)was induced from a semi-rolled leaf mutant(srl1)by ethyl methane sulfonate(EMS),which was obtained from Nipponbare(NPB)through EMS.Map-based cloning and functional validation showed that RENL1 encodes a cellulose synthase,allelic to NRL1/OsCLSD4.The RENL1 mutation resulted in reduced vascular bundles,vesicular cells,cellulose,and hemicellulose contents in cell walls,diminishing the water-holding capacity of leaves.In addition,the root system of the renl1 mutant was poorly developed and its ability to scavenge reactive oxygen species(ROS)was decreased,leading to an increase in ROS after drought stress.Meanwhile,genetic results showed that RENL1 and SRL1 synergistically regulated cell wall components.Our results revealed a theoretical basis for further elucidating the molecular regulation mechanism of cellulose on rice drought tolerance,and provided a new genetic resource for enhancing the synergistic regulation network of plant type and stress resistance,thereby realizing simultaneous improvement of multiple traits in rice.

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.