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- Seven Research Topics on the emerging technologies disrupting the world of science
Seven Research Topics on the emerging technologies disrupting the world of science
The link between science, innovation, and technology is crucial: rapid technological advances create new opportunities for scientific progress in healthcare, communication, energy production, and more, essential for society's progress.
In an impactful partnership, Frontiers joined the World Economic Forum to identify the top 10 emerging technologies in 2024. The result is a report that highlights tech advancements with the potential to revolutionize how we connect, tackle climate change, and propel scientific discovery forward.
Frederick Fenter, Frontiers' Chief Executive Editor, emphasizes how the report draws on the expertise of a global network of field editors to offer deep insights into breakthrough technology and its transformative potential for improving societies, economies, and individual lives.
Inspired by this collaboration, we’ve curated seven Research Topics harnessing the power of transformative technologies. From AI-powered plant disease detection to the future of digital health and big data in medicine, these research communities are tackling critical worldwide challenges across diverse fields.
All articles are openly available to view and download.
1 | Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture
128,000 views | 22 articles
This Research Topic explores how big data, machine, and deep learning algorithms are being applied to precision agriculture and plant health. It also investigates how these tools can be used and improved in the future to aid food security, mainly involving the integration of state-of-the-art technologies.
This topic brings together researchers from diverse fields and specializations, such as plant bioinformatics, computer engineering, computer science, agricultural engineering, environmental engineering, food engineering, information technology, and mathematics.
2 | Artificial Intelligence and Bioinformatics Applications for Omics and Multi-Omics Studies
30,000 views | 13 articles
Researchers present new bioinformatics tools and computational approaches to the analysis of omics data, or the application of existing tools, toward a more complete interpretation of biological phenomena, with applications in personalized medicine and biotechnology.
The omics sciences have revolutionized research in areas such as biology, biotechnology, medicine, and agri-food sciences. At the same time, the production of large-scale data has led to strong demand for appropriate computational tools for their management, analysis, and interpretation. All these factors make this Research Topic highly relevant for omics and multi-omics studies.
3 | Remote Sensing for Field-based Crop Phenotyping
32,100 views | 18 articles
This Research Topic studies field-based crop phenotyping through different remote sensing platforms and sensors coupled with diversified algorithms. The topic investigates achievements for determining multi-sensor integration methods, image processing ways, and retrieval modeling algorithms to improve the accuracy and robustness of crop phenotype assessment, which can be used for accelerating crop research, breeding efficiency, and precise agricultural management.
The development of crop science requires more rapid and accurate access to field-based crop phenotypes. Remote sensing provides a novel solution to quantify crop structural and functional traits in a timely, rapid, non-invasive, and efficient manner.
4 | AI Empowered Cerebro-Cardiovascular Health Engineering
43,000 views | 18 articles
This Research Topic highlights significant advancements in AI applications for cerebrovascular and cardiovascular healthcare. It also showcases how AI technology can enhance diagnosis, treatment, risk prediction, and rehabilitation for these diseases through extensive data analysis.
Cerebrovascular and cardiovascular diseases remain major global health challenges, significantly contributing to disabilities and mortality. Advances in machine learning, deep learning, computational power, and algorithms have made swift dataset analysis possible. Consequently, AI integration into healthcare is gaining significant attention, with physicians increasingly relying on AI tools for improved diagnosis, intervention guidance, and therapy monitoring.
5 | Current Advances in Genomics and Gene Editing Tools for Crop Improvement in a Changing Climate Scenario
86,000 views | 20 articles
A Research Topic demonstrating the use of cutting-edge plant genomics and gene editing technologies to tackle a specific problem, such as improving a trait. Advances in plant genomics and gene editing technologies have revolutionized breeding programs globally and are taking agriculture to new heights.
Feeding the burgeoning population and ensuring global food security in a changing climate scenario have prompted scientists to explore adoption of the latest tools and technologies (genomics and gene editing) to increase food production.
6 | Perspectives in Digital Health and Big Data in Medicine: Current Trends, Professional Challenges, and Ethical, Legal, and Social Implications
89,900 views | 24 articles
This Research Topic provides a comprehensive overview of the current trends, scientific potential, regulatory and professional challenges, and ethical and social implications of digital health and big data in medicine, including prevention, clinical care, research, management, regulation, and health policy perspectives.
It gathers multi- and transdisciplinary contributions, particularly from the family medicine, primary health care, and regulatory science communities, including physicians, nurses, midwives, physiotherapists, and health policy professionals.
7 | Advanced AI Methods for Plant Disease and Pest Recognition
30,000 views | 22 articles
This Research Topic explores advanced AI methods for plant disease and pest recognition for real-world applications. Plant diseases and pests cause significant losses to farmers and threaten food security worldwide.
Monitoring the growing conditions of crops and detecting plant diseases is critical for sustainable agriculture. However, this activity is prone to errors, leading to possible incorrect decisions. In this context, deep learning has played a key role in solving complicated applications with increasing accuracy over time. Recent interest in this type of technology has encouraged its potential application to address complex problems in agriculture, such as plant disease and pest recognition.
