Research Resources Research Articles

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

Advances in the Synthesis of 3‐Acyl‐2‐Pyridones: Strategies and Methods for Accessing the Versatile Scaffold

ABSTRACTThe syntheses and bioevaluation of 3‐acyl‐2‐pyridones have garnered significant interest in recent years as a novel class of compounds with diverse biological activities. The bioactivities cover a wide range of therapeutic areas including antimicrobial, antitumor, anti‐inflammatory, antifungal, antibiotic, neuritogenic, cytotoxic, and antiviral activities. Therefore, 3‐acyl‐2‐pyridone scaffold offers a versatile platform for the development of new molecules with potential applications in medicinal chemistry. The recent exploration on synthesis and bioevaluation of 3‐acyl‐2‐pyridones has yielded promising results, making them attractive targets for further optimization and development of new therapeutic agents. This review summarizes the available information on total synthesis of 3‐acyl‐2‐pyridone containing natural products, and the other synthetic strategies developed for accessing the 3‐acyl pyridones such as multicomponent synthesis, condensation reaction, microwave‐assisted synthesis, catalytic hydrogenation reactions, metalation reaction, Michael addition, and formylation reactions. The presented information may serve as a valuable resource for researchers working in the field of medicinal chemistry and drug discovery, stimulating further exploration and development of this intriguing scaffold.

A Survey of Multimodal Perception Methods for Human–Robot Interaction in Social Environments

Human–robot interaction (HRI) in human social environments (HSEs) poses unique challenges for robot perception systems, which must combine asynchronous, heterogeneous data streams in real time. Multimodal perception systems are well-suited for HRI in HSEs and can provide more rich, robust interaction for robots operating among humans. In this article, we provide an overview of multimodal perception systems being used in HSEs, which is intended to be an introduction to the topic and summary of relevant trends, techniques, resources, challenges, and terminology. We surveyed 15 peer-reviewed robotics and HRI publications over the past 10+ years, providing details about the data acquisition, processing, and fusion techniques used in 65 multimodal perception systems across various HRI domains. Our survey provides information about hardware, software, datasets, and methods currently available for HRI perception research, as well as how these perception systems are being applied in HSEs. Based on the survey, we summarize trends, challenges, and limitations of multimodal human perception systems for robots, then identify resources for researchers and developers and propose future research areas to advance the field.

Exploring Saffron's Therapeutic Potential: Insights on Phytochemistry, Bioactivity, and Clinical Implications.

Saffron, derived from the Crocus sativus plant, has been revered for centuries for its culinary, medicinal, and cultural significance. This review provides a comprehensive overview of saffron's chemical constituents and phytochemistry, elucidating its rich profile of bioactive compounds. Emphasis is placed on exploring the bio-accessibility, bioavailability, and bioactivity of saffron's phytochemicals, laying the foundation for understanding its pharmaceutical significance. The pharmaceutical importance of saffron and its phytochemicals is thoroughly examined, focusing on their diverse therapeutic properties. These include anticancer, antidiabetic, antioxidant, antimicrobial, anti-inflammatory, antinociceptive, anticonvulsant, antidepressant, learning and memory enhancement, cardiovascular, and antihypertensive properties. Such multifaceted pharmacological activities underscore saffron's potential as a valuable medicinal resource. Clinical studies investigating the efficacy and safety of saffron in various health conditions are synthesized, providing insights into its clinical applications. Moreover, toxicity assessments in animal models, encompassing acute, subacute, subchronic, and developmental toxicity, are discussed to delineate the safety profile of saffron and its bioactive constituents. Finally, recent advances and future perspectives in saffron research are highlighted, underscoring emerging trends and potential avenues for further exploration. This review serves as a comprehensive resource for researchers, clinicians, and stakeholders interested in harnessing the therapeutic potential of saffron while ensuring its safe and effective utilization in healthcare settings.

Heavy metal immobilisation with microbial-induced carbonate precipitation: a review

Microbial-induced carbonate precipitation (MICP) is a promising bioremediation technology for heavy metal immobilisation. This review explores the applications and efficacy of MICP in environmental challenges. It provides a comprehensive overview of the mechanism, primarily through ureolysis, detailing the process from urea hydrolysis to heavy metal precipitation as carbonate minerals. Alternative pathways like photosynthesis and nitrate reduction are also discussed, highlighting the broad applicability of MICP. The review covers the historical evolution and advancements of MICP as a sustainable solution for heavy metal contamination. Recent studies demonstrate the efficiency of MICP in achieving high removal rates in diverse environments. The sustainable operation, precise targeting of heavy metal species, and versatility of MICP are examined. Challenges such as high copper concentrations, acidic conditions, and cost considerations are addressed. The article provides future directions and solutions to these challenges, including leveraging machine learning for optimal performance and enhancing cost considerations through detailed analyses. This review improves understanding of MICP’s potential, provides a valuable resource for researchers in environmental engineering and the built environment, and encourages innovative approaches within these fields.

Application of salt-assisted liquid extraction in bioanalytical methods

This review provides a comprehensive analysis of bioanalytical methods employed for the quantification of drug molecules in various biological matrices, including human plasma, urine, breast milk, and mouse plasma. The study not only examines traditional sample preparation techniques such as protein precipitation (PP), liquid-liquid extraction (LLE), and solid-phase extraction (SPE), but also delves into the relatively new and innovative salting-assisted liquid-liquid extraction (SALLE). It offers a thorough comparison of analytical methods utilizing SALLE, focusing on key parameters such as analysis time, calibration range, and the type and quantity of salts and organic solvents used. This review aims to serve as an essential resource for researchers and practitioners in selecting the most suitable bioanalytical methods for pharmacokinetic studies and drug monitoring, ultimately enhancing data quality and analytical efficiency in both clinical and research settings.

Bibliometric analysis of stone column research trends: A Web of Science perspective

Abstract Stone columns have garnered significant interest in geotechnical engineering as an effective ground improvement technique for enhancing the load-bearing capacity and mitigating settlement issues in soft soils. This study presents a comprehensive bibliometric analysis of scholarly literature on stone columns, utilizing data from the Web of Science database spanning from 1975 to 2023. The analysis aims to provide insights into publication trends, influential journals, key research institutions and authors, geographical distributions, and emerging themes in this domain. Through a systematic data curation process, 505 relevant publications were analyzed using Excel and VOSviewer software. The findings reveal a notable emphasis on geosynthetic encasements, load-settlement behavior, and soft soil reinforcement, with China being a major contributor. Over the past 48 years, the research has evolved from static loading to dynamic loading, sustainable structures, and artificial intelligence applications, as evidenced by the highly cited publications. This study maps the development, current state, and potential future directions of stone column research, serving as a valuable resource for researchers and practitioners in geotechnical engineering.

Ocular Toxoplasmosis: Advances in Toxoplasma gondii Biology, Clinical Manifestations, Diagnostics, and Therapy.

Toxoplasma gondii, an obligate intracellular parasite, is a globally prevalent pathogen capable of infecting a wide range of warm-blooded animals, including humans. Ocular toxoplasmosis (OT), a severe manifestation of T. gondii infection, can lead to potentially blinding complications. This comprehensive review delves into the current understanding of T. gondii biology, exploring its complex life cycle, diverse transmission routes, and strain diversity. This article provides an in-depth analysis of the clinical manifestations of OT, which can result from both congenital and acquired infections, presenting a spectrum of signs and symptoms. The review examines various diagnostic strategies employed for OT, including clinical examination, multimodal imaging techniques such as fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA), as well as laboratory tests including serology and molecular methods. Despite extensive research, the specific mechanisms underlying ocular involvement in T. gondii infection remain elusive, and current diagnostic options have limitations. Moreover, the treatment of active and recurrent OT remains a challenge. While existing therapies, such as antimicrobial agents and immunosuppressants, can control active infections, they do not offer a definitive cure or completely prevent recurrence. The clinical endpoints for the management of active and recurrent OT are also not yet well-established, and the available treatment methods carry the potential for adverse effects. This article highlights the need for future research to elucidate the pathogenesis of OT, investigate genetic factors influencing susceptibility to infection, and develop more sensitive and specific diagnostic tools. Enhancing global surveillance, implementing robust prevention strategies, and fostering multidisciplinary collaborations will be crucial in reducing the burden of OT and improving patient outcomes. This comprehensive review aims to provide a valuable resource for clinicians, researchers, and policymakers, contributing to a better understanding of T. gondii infection and its impact on ocular health.

Optimizing sheep growth curves using a meta-heuristic algorithm.

Sheep were among the first animals domesticated by humans, and to this day, small ruminants are primarily raised for their meat, milk, and wool. This study evaluated the goodness of fit for growth curve models using observed age and weight data from crossbred lambs of various breeds based on the mean values between paired breeds. We employed a hybrid metaheuristic algorithm, combining a simulated annealing (SA) algorithm and a genetic algorithm (GA) called SAGAC, to determine the optimal parameter values for growth models, ensuring the best alignment between simulated and observed curves. The goodness of fit and model accuracy was assessed using the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Errors were measured by comparing the criteria differences between simulated and observed data. Thirty crossbreed combinations were simulated, considering the average weight. Analysis of the observed and simulated growth curves indicated that specific crossbreeding scenarios produced promising results. This simulation approach is believed to assist geneticists in predicting potential crossbreeding outcomes, thereby saving time and financial resources in field research.

CRISPR/Cas9-mediated generation of a human induced pluripotent stem cell line with PRPF6 c.2699 G > A mutation to model retinitis pigmentosa

PRPF6, located on chromosome 20, is required for the formation of the spliceosome. Mutations in the PRPF6 gene can lead to retinitis pigmentosa (RP), a common inherited retinal disease characterized by progressive degeneration of retinal pigment epithelium and photoreceptors. Here, we generated an induced pluripotent stem cell (iPSC) line carrying the PRPF6 c.2699 G > A mutation using CRISPR/Cas9 technology, which will provide a valuable resource for RP pathogenesis and treatment research.

An ecological vulnerability index to assess impacts of offshore wind facilities on migratory songbirds

Abstract As offshore wind (OSW) energy expands globally, migratory songbirds are at risk of mortality from collisions with turbine blades, though the magnitude of this threat and which species are most vulnerable, remains poorly understood. Ecological vulnerability indices are commonly used to assess species' susceptibility to harmful factors, with results used to direct scarce research and monitoring resources to species showing relatively high vulnerability. These indices are based on the traits that elevate a species risk to adverse impacts (sensitivity), the overlap in occurrence between a species and the potentially harmful agent (exposure) and the influence of this exposure on the species' local or global persistence (resilience). We modified ecological vulnerability indices for seabirds to assess vulnerability of migratory songbirds to OSW related mortality. As a pertinent case study, we considered songbirds that fly across the Northwest Atlantic during their autumn migration. We utilized readily available information on each species' migratory behaviour, life history, and conservation status to calculate an index score that could range from 1 (lowest vulnerability) to 125 (highest vulnerability). We found scores of 3 to 55.2 for the 101 songbird species evaluated, with New World warblers (Parulidae) over‐represented among the highest scoring species. We found the scores to be sensitive to uncertainty in index components, highlighting the importance of considering scoring uncertainty when evaluating ecological vulnerability indices. Finally, we found that for seven of the top 10 highest scoring species, modest improvements in population trends had the potential to lower the scores substantially. Synthesis and applications. Our methodology is readily applicable to other regions where offshore wind (OSW) development is planned and songbird migration is common, allowing research and monitoring activities to be targeted to species most likely to be negatively affected by OSW facility encounters.

Relationship between human capital and entrepreneurship orientation from the intellectual capital perspective of innovative literacy

PurposeThis study measures the relationship between human capital and innovative literacy from the perspective of intellectual capital. For this purpose, the relationship between human capital and innovative literacy was first investigated to measure intellectual capital. Subsequently, the moderating effect of entrepreneurial orientation on the influence of innovative literacy on human capital was also analysed.Design/methodology/approachThis study analyses the relationship between innovative literacy, human capital and entrepreneurial orientation through structural equation modelling – the study sample comprised 457 participants in Türkiye.FindingsThe results suggest a positive relationship between human capital, a sub-component of intellectual capital and innovative literacy. Innovative literacy can influence intellectual capital and increase value. Our finding further confirms that entrepreneurial orientation has no moderating effect on the same relationship.Research limitations/implicationsDespite the observed correlation between IL and HC growth, this study had some limitations. First, the generalisability of the findings is limited because the sample was selected from individuals with a high level of education in Türkiye. Despite these limitations, this study has important theoretical and practical implications for developing countries. Türkiye is a developing country, and the selected sample is the most critical database in the country for an Innovative Literacy Scale. However, future research could focus on whether there is a potential source of bias and collect and compare data from heterogeneous workers. In addition, research on existing research frameworks in other countries may consider varying levels of education and business sectors. This research aims to be a pioneering initiative towards the advancement of IL. The question of whether IL should be considered equivalent to or a subset of HC, which has been previously proposed as a dimension of IC, can be clarified through a thorough examination of what IL means.Practical implicationsIn a theoretical framework, transforming human capital into value through innovations derived from innovative literacy activities will significantly increase the company's intellectual capital. Organizations in which innovative literacy play an important role achieve organizational goals, strengthen commitment and contribute to sustainability through the emergence of new potential innovative individuals. This research also contributes significantly to the existing literature. Initially regarding theoretical implications, this study examines the concept of innovative literacy in the existing literature on intellectual capital, human capital and entrepreneurial orientation. Additionally, this study offers a new perspective on intellectual capital.Social implicationsThe findings of this research also provide managerial outcomes. Initially, the assessment of concepts, research and development capabilities and efficient knowledge management are indicators of innovative literacy proficiency at the individual level. Human capital is a subcomponent of intellectual capital and refers to knowledge, skills and experience. Innovative and literate team members should be developed in order to strengthen the intellectual capital structure of an organization.Originality/valueThis study contributes to understanding innovative literacy from an intellectual capital perspective using the Innovative Literacy Scale to reveal important dimensions that influence innovative literacy. Using a measurement tool with new sub-dimensions not included in the extant literature on intellectual capital is unique. This study suggests that scholars should include innovative literacy – a distinct subset of intellectual capital – in their body of knowledge on human capital. Our findings have the potential to help meet the needs of human resource departments, researchers, educational institutions, public institutions and enterprises.

Photonic Ising machines for combinatorial optimization problems

The demand for efficient solvers of complicated combinatorial optimization problems, especially those classified as NP-complete or NP-hard, has recently led to increased exploration of novel computing architectures. One prominent collective state computing paradigm embodied in the so-called Ising machines has recently attracted considerable research attention due to its ability to optimize complex problems with large numbers of interacting variables. Ising model-inspired solvers, thus named due to mathematical similarities to the well-known model from solid-state physics, represent a promising alternative to traditional von Neumann computer architectures due to their high degree of inherent parallelism. While there are many possible physical realizations of Ising solvers, just as there are many possible implementations of any binary computer, photonic Ising machines (PIMs) use primarily optical components for computation, taking advantage of features like lower power consumption, fast calculation speeds, the leveraging of physical optics to perform the calculations themselves, possessing decent scalability and noise tolerance. Photonic computing in the form of PIMs may offer certain computational advantages that are not easily achieved with non-photonic approaches and is nonetheless an altogether fascinating application of photonics to computing. In this review, we provide an overview of Ising machines generally, introducing why they are useful, what types of problems they can tackle, and how different Ising solvers can be compared and benchmarked. We delineate their various operational mechanisms, advantages, and limitations vis-à-vis non-photonic Ising machines. We describe their scalability, interconnectivity, performance, and physical dimensions. As research in PIMs continues to progress, there is a potential that photonic computing could well emerge as a way to handle large and challenging optimization problems across diverse domains. This review serves as a comprehensive resource for researchers and practitioners interested in understanding capabilities and potential of PIMs in addressing such complex optimization problems.

Challenges to Successful Thesis Completion among Postgraduate Students

This study utilized a descriptive quantitative approach to examine the key challenges that postgraduate students face in completing their thesis work. The target population for the research consisted of master’s, and doctoral students enrolled in various academic disciplines at Universities in Mogadishu, and a random sampling technique was employed to obtain a sample size of 124 participants. The findings of the study identified several major obstacles that graduate students encounter during the thesis process. The most significant factors were time management issues, limited access to critical research resources, and various institutional barriers that did not adequately support the thesis completion. Specifically, the study found that Institutional Factors (mean 3.49) and Time Management (mean 3.4) were considered "Highly Challenging" by the participants. Access to Resources (mean 3.38), Supervisor Support (mean 3.22), Discipline-Specific Factors (mean 3.31), and Language Factors (mean 3.25) were rated as "Moderately Challenging". Personal Obligations (mean 2.9) were also seen as "Moderately Challenging" by the graduate students. The results of this study underscore the need for universities to provide stronger support systems and address these multifaceted challenges to enable postgraduate students to successfully complete their thesis work. Recommendations for improving thesis support are discussed.

Operational Excellence: Process optimization for competitive edge

This study explores the concept of operational excellence and the role of process optimization in gaining a competitive edge for organizations. Operational excellence involves the continuous improvement of business processes to increase efficiency, reduce waste, enhance quality, and improve overall performance. The study reviews established methodologies such as Lean, Six Sigma, and Total Quality Management (TQM) that provide structured approaches to achieving operational efficiency. It also examines the impact of emerging technologies, including artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT), in driving data-driven decision-making and enhancing process optimization. Additionally, the study highlights the importance of leadership in fostering a culture of continuous improvement and collaboration across departments, ensuring alignment with strategic objectives. The integration of sustainability into operational strategies is discussed as a means to not only improve efficiency but also meet corporate social responsibility goals. The relationship between process optimization and innovation is also explored, showing how operational excellence can free up resources for research and development (R&D) and foster a culture of innovation. Despite the focus on these established practices and technologies, the study acknowledges limitations, particularly the challenges faced by small and medium-sized enterprises (SMEs) and service oriented sectors in implementing these strategies. The study concludes that operational excellence, supported by technological advancements and a strong organizational culture, is essential for organizations to navigate competitive markets, improve customer satisfaction, and achieve long-term success. This research offers valuable insights for businesses aiming to enhance operational performance and sustain their competitive advantage through continuous process optimization and innovation.

Computational Analysis of Calcium Flux Data Using R.

Calcium imaging has emerged as a powerful tool for studying cellular dynamics, with applications spanning neuroscience, cell biology, and beyond. In this chapter, we present a comprehensive guide to the computational analysis of calcium flux data using the R programming language. Using an example of in vivo live imaging of GCaMP signal in zebrafish hepatocytes, we demonstrate techniques for segmentation, normalization, and quantification of calcium transients. We provide a step-by-step code example showcasing extraction of meaningful information from calcium imaging datasets. The code allows insights into the number of oscillating cells, number of oscillations per cell within a time frame, and generation of publication-ready plots for showcasing calcium dynamics. This chapter serves as a valuable resource for researchers seeking to leverage freely available computational tools for analyzing calcium flux data at cellular resolution and uncovering novel insights into cellular physiology.

Advancements in Polymer Biomaterials as Scaffolds for Corneal Endothelium Tissue Engineering

Corneal endothelial dysfunction is a leading cause of vision loss globally, frequently requiring corneal transplantation. However, the limited availability of donor tissues, particularly in developing countries, has spurred on the exploration of tissue engineering strategies, with a focus on polymer biomaterials as scaffolds for corneal endotlhelium regeneration. This review provides a comprehensive overview of the advancements in polymer biomaterials, focusing on their role in supporting the growth, differentiation, and functional maintenance of human corneal endothelial cells (CECs). Key properties of scaffold materials, including optical clarity, biocompatibility, biodegradability, mechanical stability, permeability, and surface wettability, are discussed in detail. The review also explores the latest innovations in micro- and nano-topological morphologies, fabrication techniques such as electrospinning and 3D/4D bioprinting, and the integration of drug delivery systems into scaffolds. Despite significant progress, challenges remain in translating these technologies to clinical applications. Future directions for research are highlighted, including the need for improved biomaterial combinations, a deeper understanding of CEC biology, and the development of scalable manufacturing processes. This review aims to serve as a resource for researchers and clinician–scientists seeking to advance the field of corneal endothelium tissue engineering.

Coordination of smart inverter-enabled distributed energy resources for optimal PV-BESS integration and voltage stability in modern power distribution networks: A systematic review and bibliometric analysis

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

Machine learning-aided microRNA discovery for olive oil quality.

MicroRNAs (miRNAs) are key regulators of gene expression in plants, influencing various biological processes such as oil quality and seed development. Although, our knowledge about miRNAs in olive (Olea europaea L.) is progressing, with several miRNAs being identified in previous studies, but most of these reported miRNAs have been predicted without the aid of a reference genome, primarily due to limited genome accessibility at the time. However, significant knowledge gaps still need to be improved in this area. This study addresses the complexities of miRNA detection in olive, using a high quality reference genome and a combination of genomics and machine learning-based methods. By leveraging random forest and support vector machine algorithms, we successfully identified 56 novel miRNAs in olive, surpassing the limitations of conventional homology-based methods. Our subsequent analysis revealed that some of these miRNAs are implicated in the regulation of key genes involved in oil quality. Within the context of oil biosynthesis pathways, the novel miRNA Oeu124369 regulates fatty acid biosynthesis by targeting acetyl-CoA acyltransferase 1 and palmitoyl-protein thioesterase, thereby influencing the production of acetyl-CoA and palmitic acid, respectively. These findings underscore the power of machine learning in unraveling the complex miRNA regulatory network in olive and provide a high quality miRNA resource for future research aimed at improving olive oil production by exploring the target genes of the identified miRNAs to understand their role and their biological processes.

Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions

The global demand for button mushrooms has surged in recent years, driven by their health benefits, creating a significant challenge for the mushroom industry in meeting this increasing demand. The increasing reliance on human labor, which is becoming unsustainable due to labor shortages and rising wage costs, highlights the urgent need for automated harvesting solutions. This review examines the integration of automated systems in button mushroom harvesting, delving into the key components such as robots, mechanisms, machine elements, programming, and algorithms. It offers a thorough analysis of the performance, design, operational mechanisms, and advantages and limitations of robotic systems, comparing the different methods employed in automated harvesting. This paper compares the performance of all the mushroom harvesters, including the commercially available ones with manual harvesting, and identifies their potential and limitations. The commercial harvesters are shown to pick 2000 mushrooms per hour on average, which is similar to how much a skilled worker picks at the same time. However, commercial automation harvesting has a relatively low success rate, high initial cost, high operating cost, and energy consumption, identifying areas for future research and challenges. This paper serves as a valuable resource for researchers and industry professionals striving to advance automated harvesting technology and improve its efficiency in meeting the rising demand for button mushrooms.