Variable Segments Research Articles

A Comprehensive Analysis of the Impact of Viral Marketing on Goods and Services and Its Correlation with Product Cannibalization

When COVID-19 spread across the globe, people everywhere are learning to cope up with a new way of living. Meanwhile, people are spending way more time online in their mobile phone/laptop/pc etc., than ever before, which still exists even after the downfall of COVID-19. Digital means has become a solution, a means to satisfy an individual’s basic needs such as food delivery from restaurant, buying products via e-commerce online shopping etc. but also to be in contact with friends and family, entertainment and acquisition of information, goods and services normally accessed physically and in person are now possessed by digitalized ways. However, the advent of lockdown has dramatically increased the size, availability, and profile of the digital audience, now bringing in those people more traditionally accessed offline. Not only that, it has also changed their buying behaviours, expectations, their consumption of content and the way they expect to be interacted with as customers. As more businesses enter the online marketplace, the competition for traffic becomes even greater. For this reason, digital marketing is critical in building brand recognition and increasing traffic to business websites a comprehensive digital marketing strategy starts with creating a search-optimized website and includes an active social media presence. The pandemic and lockdown of 2020 has strong-armed even diehard traditionalists into embracing digital, simply to remain connected to the outside world. The phenomenon of “Product cannibalization” also enters into the scenario secretly in order to exist and develop due to these strong digitalized usage of people across the globe. To “cannibalize” means to “eat its own flesh” but in the context of marketing it refers to the tremendous change in the goods/service on its own for its survivability, growth and to excellent profit.
 In the contemporary business landscape, characterized by the omnipresence of digital technologies and the aftermath of global disruptions, this research delves into the intricate dynamics between viral marketing strategies and the phenomenon of product cannibalization within the framework of the "New Normal." As organizations increasingly adopt viral marketing techniques to amplify their reach and engage with diverse consumer segments, understanding the nuanced implications on the product lifecycle becomes imperative.
 This study employs a multidimensional approach, combining qualitative and quantitative methodologies to scrutinize the intricate interplay between viral marketing campaigns and product cannibalization.
 The research aims to provide a comprehensive understanding of the evolving consumer preferences and purchasing behaviours in the digital era, with a focus on the unintended consequences of viral marketing, such as product cannibalization. By examining case studies, conducting surveys, and utilizing data analytics, the study seeks to unearth patterns and trends that can inform strategic decision-making for businesses navigating the complexities of the modern marketplace.
 Key findings are anticipated to contribute not only to academic literature but also offer actionable insights for practitioners in marketing, branding, and product management. The implications of this research extend beyond immediate business concerns, touching upon societal changes, technological advancements, and the ongoing transformations in consumer expectations, thereby providing a holistic perspective on the evolving dynamics of commerce in the New Normal

Are we ready to use new endoscopic scores for ulcerative colitis?

For ulcerative colitis (UC), the variability in inflammatory activity along the colon poses a challenge in management. The focus on achieving endoscopic healing in UC is evident, where the UC Endoscopic Index of Severity and Mayo Endoscopic Subscore are commonly used for evaluation. However, these indices primarily consider the most severely affected region. Liu et al recent study validates the Toronto Inflammatory Bowel Disease Global Endoscopic Reporting (TIGER) score offering a comprehensive assessment of inflammatory activity across diverse segments of the colon and rectum and a reliable index correlating strongly with UC Endoscopic Index of Severity and moderately with Mayo Endoscopic Subscore (MES). Despite recommendation, certain aspects warrant further investigation. Fecal calprotectin, an intermediate target, correlates with TIGER and should be explored. Determining TIGER scores defining endoscopic remission and response, evaluating agreement with histological activity, and assessing inter-endoscopist agreement for TIGER require scrutiny. Exploring the correlation between TIGER and intestinal ultrasound, akin to MES, adds value.

Dealcoholized Wine: A Scoping Review of Volatile and Non-Volatile Profiles, Consumer Perception, and Health Benefits

AbstractThe dealcoholization technique has gained more attention in the wine industry as it can significantly influence wine compositions and quality beyond alcohol level reduction. However, the changes occurring in the various processes of dealcoholization are not fully understood yet. This work summarizes the literature published in the last 10 years (2013–2023) about changes in color, sulfur dioxide (SO2), phenolic composition, losses of desirable volatile aroma compounds, and sensory characteristics of wine after the removal of ethanol by different processes/techniques. Several factors can influence the final characteristics of wine during the process, including physico-chemical parameters such as the initial alcohol level, the retention properties of the wine non-volatile matrix, and the characteristics of aroma components. Additionally, the quality of dealcoholized wine can be affected by the choice of dealcoholization techniques, distillation temperature, operating pressure, and membrane properties, including filtration and pore size. Low- and zero-alcohol products have the potential to expand the market and cater to diverse consumer segments. This comprehensive review would help winemakers in choosing the best techniques to produce dealcoholized wine, limiting the adverse effects, and meeting the needs of consumers.

Evaluating the quality of visual explanations on chest X-ray images for thorax diseases classification

AbstractDeep learning models are extensively used but often lack transparency due to their complex internal mechanics. To bridge this gap, the field of explainable AI (XAI) strives to make these models more interpretable. However, a significant obstacle in XAI is the absence of quantifiable metrics for evaluating explanation quality. Existing techniques, reliant on manual assessment or inadequate metrics, face limitations in scalability, reproducibility, and trustworthiness. Recognizing these issues, the current study specifically addresses the quality assessment of visual explanations in medical imaging, where interpretability profoundly influences diagnostic accuracy and trust in AI-assisted decisions. Introducing novel criteria such as informativeness, localization, coverage, multi-target capturing, and proportionality, this work presents a comprehensive method for the objective assessment of various explainability algorithms. These newly introduced criteria aid in identifying optimal evaluation metrics. The study expands the domain’s analytical toolkit by examining existing metrics, which have been prevalent in recent works for similar applications, and proposing new ones. Rigorous analysis led to selecting Jensen–Shannon divergence (JS_DIV) as the most effective metric for visual explanation quality. Applied to the multi-label, multi-class diagnosis of thoracic diseases using a trained classifier on the CheXpert dataset, local interpretable model-agnostic explanations (LIME) with diverse segmentation strategies interpret the classifier’s decisions. A qualitative analysis on an unseen subset of the VinDr-CXR dataset evaluates these metrics, confirming JS_DIV’s superiority. The subsequent quantitative analysis optimizes LIME’s hyper-parameters and benchmarks its performance across various segmentation algorithms, underscoring the utility of an objective assessment metric in practical applications.

Towards Climate Resilience: A Comprehensive Study on Eco-Friendly Alternatives for Single-Use Plastic Sheets in Somalia

This research critically examines the recent policy intervention by the Somali government, focusing on the prohibition of single-use plastic sheets. The primary aim is to safeguard the environment and enhance public safety, aligning with global efforts to address plastic pollution and promote sustainable practices. The government's proactive approach urges manufacturers to explore alternative materials that prioritize community well-being and minimize environmental impact. This research, in response to the government's call for action, seeks to contribute comprehensive strategies that complement existing efforts. One key facet of the proposed initiatives involves research and development to identify and promote eco-friendly substitutes for single-use plastic sheets. This includes investigating materials with minimal environmental impact throughout their lifecycle. By encouraging innovation in materials science, the study aims to support the government's commitment to sustainable practices and reduce dependence on harmful substances. In addition to material innovation, the research emphasizes the importance of awareness campaigns to educate the public about the environmental and social implications of plastic usage. Community awareness is crucial for fostering a collective understanding of the need for change and garnering support for the government's policies. This aspect of the study aims to develop targeted communication strategies to engage diverse segments of the population and promote a culture of responsible consumption. Furthermore, the research advocates for the implementation of recycling programs to manage existing plastic waste effectively. Developing robust waste management infrastructure is essential for ensuring that discarded plastic materials are recycled efficiently, reducing their impact on the environment. Recognizing the economic aspects of the transition, the research proposes incentives for businesses that adopt sustainable materials. These incentives can include financial support, tax breaks, or preferential treatment in government procurement processes. By aligning economic interests with ecological responsibility, the study aims to facilitate a smooth transition and encourage widespread adoption of sustainable practices. Through a comprehensive analysis of various initiatives, the research aims to provide actionable insights for policymakers. Recommendations will be formulated to guide the government in implementing effective measures that align with its commitment to environmental sustainability and the overall safety of the Somali population. In conclusion, this research aspires to contribute practical strategies for Somalia, balancing economic interests with ecological responsibility. By fostering a harmonious coexistence between human activities and the environment, the study aims to support the government's vision of a resilient and sustainable future.

Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance

Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance

Adoption of Online Home Services an Empirical Study of Consumer Behaviour in Ludhiana City

The advent of technology has revolutionized the service industry, particularly in the realm of home services. This study aims to investigate the nuanced relationship between demographic factors and the adoption of online home services by customers. Recognizing the increasing prevalence of digital platforms offering services ranging from home maintenance to professional consultations, understanding the demographic influences on consumer adoption becomes imperative for service providers and marketers. This research employs a mixed-methods approach, combining quantitative surveys and qualitative interviews, to gather comprehensive insights into the preferences and behaviors of consumers in the online home services sector. The study focuses on key demographic variables such as age, income, education, and geographic location to discern patterns and correlations that may influence the likelihood of customer adoption. The findings are expected to contribute to both academic literature and industry practices by shedding light on the factors that drive or inhibit the adoption of online home services among different demographic groups. Moreover, implications for marketing strategies and platform design may emerge from understanding how customer demographics intersect with their preferences, trust levels, and technological literacy. As the digital landscape evolves, this research aims to provide actionable insights for businesses aiming to tailor their services to diverse customer segments. The outcomes of this study will contribute to a deeper understanding of the socio-economic and cultural factors influencing the adoption of online home services, fostering a more informed and effective approach for businesses operating in this dynamic and rapidly growing sector.

Variability and influence of fisheries acoustic echogram annotations on machine learning applications

High-frequency echosounders are the workhorse in fisheries and marine ecological surveys. Due to the inherent complexity of biological aggregations and ambiguity in interpreting echoes from species of similar size and anatomical compositions, echogram annotation typically requires combining spectral information referencing scattering physics, biological ground-truth from nearby net-trawls, and empirical school morphology of specific fish species. Here, we investigate the variability of echogram annotations and its influence on machine learning applications using data from the biennial Pacific hake acoustic-trawl survey. Compared to many other fish species, hake tend to possess less defined school boundaries with variable acoustic features and often form mixed-species aggregations in the mesopelagic. Nonnegative matrix factorization and hierarchical clustering of volume backscattering strength (Sv) distributions across the 18, 38, and 120 kHz channels revealed a spectrum of annotation region types that reflect differences in morphological and acoustic features as well as differences in annotator style. This variability likely contributes to the observed variable segmentation behavior of deep learning models trained using this dataset. These results highlight the importance of considering the diversity of echogram annotation, its connection to scattering physics and the underlying aggregation composition, and the incorporation of such information in developing machine learning models.

Analyzing the Influence of Demographic Factors on Consumer Preferences in the Tourism Industry in Bali

This research investigates the influence of demographic factors on consumer preferences within the tourism industry in Bali. 250 tourists examined their accommodation choices, activity preferences, and overall satisfaction. The study employs Structural Equation Modeling with Partial Least Squares (SEM-PLS) to analyze the data. Results reveal nuanced patterns: younger tourists favor budget accommodations, older tourists opt for luxury, and higher-income individuals prefer upscale options. Gender and education impact preferences, emphasizing the need for tailored strategies. The measurement model assessment confirms the reliability and validity of the constructs. Findings contribute to understanding Bali's tourism dynamics, guiding businesses and policymakers in optimizing offerings for diverse demographic segments.

Instagram Marketing Strategies on Fashion Consumer’s Purchasing Behaviour: A Literature Review Study

This literature review is an attempt to examine the impact of Instagram marketing strategies on consumer purchasing behaviour within the fashion industry. While existing studies offered insights into specific aspects such as influencer credibility and viral marketing, there was a lack of a combined framework that integrated these factors to provide analysis. The study addresses this gap by analyzing 20 research papers spanning from 2015 to 2023, sourced from academic databases and scholarly journals. Findings revealed that brand equity is a crucial determinant of consumer behaviour, alongside influencer credibility, creative brand aesthetics, and interactive features. Despite Instagram's dominance, the study also brought forward insights into how influencers are sometimes perceived as passive sources of fashion inspiration. This review thus underscores the multifaceted nature of Instagram's influence on consumer behaviour and highlights the need for further research to develop adaptive marketing strategies that can resonate across diverse consumer segments.

Acoustic emission-based assessment of weld effects on the health monitoring of the rail section: an experimental study

Railways are a vital lifeline for both passengers and freight, with an extensive network connected by robust welded joints. This study explores the potential of Acoustic Emission (AE) techniques in rail health monitoring, particularly in the context of rail sections and their relationship with welds. While AE-based health monitoring has gained attention, a significant gap exists in understanding welds’ influence. This research bridges this gap by examining the interplay between welds and health monitoring. Welds introduce complexities in AE wave propagation, crucial for precise health assessments. Through artificial AE source simulation using the Pencil Lead Break (PLB) method across diverse rail segments, specialized sensors capture AE signals. Rigorous analysis, involving signal processing and statistical methods, reveals how welds impact AE wave behaviour and health assessment accuracy. Importantly, this research underscores how welds alter AE wave propagation, ensuring accurate health evaluations. This study’s insights hold far-reaching implications, not only for enhancing current rail system maintenance but also for advancing our understanding of the critical interrelationship between welds and health monitoring, thus contributing to the longevity and reliability of rail systems.

Investigating the e-WOM Features Influencing Purchase Intention at Korean Restaurants based on SOR Theory

The growing fascination among Indonesians with Korean cuisine and the escalating usage of social media platforms has led to a significant reliance on electronic Word-of-Mouth (e-WOM) as a pivotal determinant for restaurant selection. This research aims to investigate the influence of e-WOM features, including Celebrity Endorsements, Influencer Recommendations, and Customer Reviews, on the purchase intentions of Indonesian consumers concerning Korean restaurants. Employing a deductive framework and quantitative methodologies, primary data collection was conducted. The findings underscore the paramount importance of Customer Reviews in shaping Indonesian consumers' decision-making processes when selecting Korean restaurants. These reviews are valued for their perceived authenticity and representation of diverse viewpoints, distinguishing them as credible and influential sources. In contrast, while Celebrity Endorsements and Influencer Recommendations influence consumer attitudes and perceptions, they do not significantly impact purchase intentions. This study provides valuable insights into the dynamics of e-WOM's influence on the Indonesian culinary landscape, with a specific emphasis on Korean restaurants. These findings hold practical significance for restaurant operators and marketers who leverage e-WOM to effectively engage and cater to diverse consumer segments within the Indonesian market.

Enhancing Marketing Transparency and Trust through Blockchain Technology

This research delved into the intersection of blockchain technology and marketing, aiming to investigate its transformative potential in enhancing transparency, trust, and reducing ad fraud. Employing a mixed-methods research design, both quantitative and qualitative approaches were employed to comprehensively address the research objectives. In the quantitative phase, a stratified random sampling technique was utilized to select participants from diverse demographic segments, including both consumers and marketing professionals. Surveys and digital analytics tools were employed to collect data on trust levels, perceptions, and customer engagement in traditional and blockchain-based marketing initiatives. Statistical analyses, including chi-square tests and ANOVA, were conducted to determine the significance of blockchain's impact. The qualitative phase involved purposive sampling of key informants for in-depth interviews, providing nuanced insights into experiences and perceptions regarding blockchain technology in marketing. Content analysis and thematic analysis were applied to marketing materials and qualitative data, respectively. The integration of findings from both phases allowed for a comprehensive understanding of blockchain's impact on marketing, emphasizing its transformative potential. Limitations, such as the challenge of capturing the rapidly evolving nature of blockchain technology, were acknowledged.

The Use of Digital Payment Methods and its Implications on Financial Inclusion: A Survey Study

In the digital era, the advent of electronic payment systems has revolutionized financial transactions, with significant implications for financial inclusion. This research paper delves into the dynamics of digital payment methods and their impact on financial inclusion in India, focusing on user demographics, adoption rates, perceptions, and barriers to usage.
 The study was underpinned by several research objectives aimed at understanding the nuances of digital payment adoption across different demographic segments, assessing the perception of security and privacy, evaluating the role of digital payments in reducing cash dependency, identifying the adoption barriers, and recommending inclusive strategies for broader access. A set of research questions and hypotheses were formulated in line with these objectives.
 A quantitative research approach was employed, involving a survey disseminated across multiple Indian states. The survey, comprising structured questionnaires, targeted a diverse population segment, accumulating a total of 500 responses. The data collected was rigorously analyzed using descriptive statistics, inferential tests (t-tests and ANOVA), and thematic analysis for open-ended responses.
 Key findings of the study indicated a higher propensity for digital payment adoption among younger individuals, with notable hesitancy among older demographics. Security and privacy concerns were prevalent, significantly influencing user behavior, particularly among females. The research also highlighted a substantial reduction in cash dependency among digital payment users, indicating an increase in financial autonomy. However, significant barriers, including digital literacy, infrastructure inadequacies, and language barriers, especially in rural areas, impede widespread adoption.
 These findings have profound implications for financial inclusion, emphasizing the need for a multifaceted approach to address the digital divide. The study underscores the necessity for stakeholder collaboration in enhancing digital infrastructure, literacy, and trust, thereby fostering an inclusive financial environment. The insights derived are of paramount importance for policymakers, financial institutions, and fintech companies aiming to innovate and expand the reach of digital financial services.

Tissue-Matched IgH Gene Rearrangement of Circulating Tumor DNA Shows Significant Value in Predicting the Progression of Diffuse Large B Cell Lymphoma.

Evidence has demonstrated that monitoring of the variable, diversity, and joining gene segments (VDJ) rearrangement of the immunoglobulin (Ig) genes in the circulating tumor DNA (ctDNA) is of value in predicting the outcomes of diffuse large B cell lymphoma (DLBCL). In this study, we investigated the role of VDJ rearrangement proportion in ctDNA for predicting DLBCL progression. Patients diagnosed with newly diagnosed DLBCL were included in this study. The VDJ sequences of IgH were detected using next-generation sequencing (NGS) in formalin-fixed paraffin-embedded tissue and/or peripheral blood. The clonotype of the highest proportion in the peripheral blood was defined as the "dominant circulating clonotype," whilst the clonotype of the highest proportion in matched tissue that is detected in peripheral blood was defined as the "dominant tissue-matched clonotype." The decision tree, a machine learning-based methodology, was used to establish a progression-predicting model through a combination of "dominant tissue-matched clonotype" proportion or "dominant circulating clonotype" proportion, and the clinicopathological information, including age, sex, cell of origin, stage, international prognostic index, lactate dehydrogenase, number of extranodal involvements and β2-microglobulin. A total of 55 patients with eligible sequencing data were used for prognosis analysis, among which 36 patients had matched tissue samples. The concordance rate of "dominant circulating clonotype" and "dominant tissue-matched clonotype" was 19.44% (7/36). The decision tree model showed that the combination of extranodal involvement event and "dominant circulating clonotype" proportion (≥37%) had a clinical value in predicting the prognosis of DLBCL following combined chemotherapy (sensitivity, 0.63; specificity, 0.81; positive prediction value (PPV), 0.59; negative prediction value, 0.83; kappa value, 0.42). Noticeably, the combination of the "dominant tissue-matched clonotype" and extranodal involvement event showed a higher value in predicting the progression (sensitivity, 0.85; specificity, 0.78; PPV, 0.69; kappa value, 0.64). IgH proportion detected in the ctDNA samples traced from tissue samples has a high clinical value in predicting the progression of DLBCL.

Two-layer Ensemble of Deep Learning Models for Medical Image Segmentation

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification.

The Effect of Electronic Service Quality on Increasing Customer Loyalty with a Focus on Adding Diverse Product Segments

Using e-commerce platforms to improve product marketing is the right choice for every business in the digital era. According to recent research, online shopping is increasingly popular among customers who prefer to shop without physically visiting a store. This study aims to investigate the quality of electronic services provided by online e-commerce stores to encourage customer satisfaction and loyalty. This study measures the quality of electronic services in terms of website design, customer service, security/privacy, and fulfillment using the most recent WebQual model. The Partial Least Square method (SmartPLS-v3) was utilized to analyze the sample data collected using 159 respondents who met the specified criteria as the research sample. The research demonstrates that all hypotheses are found to have significant positive results. Positive correlations have been found between the four dimensions of the model and the quality of electronic services, as well as their impact on customer satisfaction and intentions to stay loyal. This study is beneficial for determining the electronic service model customers in online e-commerce stores want to gain customer loyalty.

Challenges and Opportunities in Implementing Intellectual Property Rights Protection System for Creative Industry Development in Indonesia

The Indonesian creative industry stands at the intersection of cultural richness, technological advancements, and economic potential. This research undertakes a quantitative analysis to unravel the challenges and opportunities in implementing an Intellectual Property Rights (IPR) protection system for creative industry development in Indonesia. The study involves 100 respondents representing diverse segments of the creative industry. Findings reveal a nuanced landscape, with varying perceptions of the current IPR protection system, significant concerns about challenges such as piracy, and optimistic views on growth opportunities through digital platforms, collaborations, and global markets. Recommendations from creative professionals emphasize the need for legal reforms, increased awareness campaigns, and collaborative efforts. This research contributes valuable insights to inform policymakers, industry practitioners, and stakeholders in fostering a robust and dynamic creative industry in Indonesia.

The investment choices of different investors for energy companies

This research delves into the dynamic field of energy investment by investigating how different types of investors make choices regarding energy companies, employing a combination of literature analysis, data collection, and quantitative methods to explore the preferences of distinct investor categories. The central research question revolves around discerning the investment inclinations of diverse investor segments and constructing tailored investment portfolios based on their preferences, using a systematic framework encompassing data collection, data analysis, and interpretation stages. The study's main findings reveal distinct investor preferences within the energy sector, shedding light on the types of enterprises that align with different investor profiles, and thus, enabling the construction of bespoke investment portfolios that cater to these preferences. The conclusions drawn from this research hold significant application value in guiding investors, both individual and institutional, in optimizing their energy investments, fostering sustainable energy practices, and contributing to the continued evolution of the global energy landscape.

Tree Crown Segmentation and Diameter at Breast Height Prediction Based on BlendMask in Unmanned Aerial Vehicle Imagery

The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method’s performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm’s 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes.