Streamlining Digital Agriculture: Advances in Sensing, Processing, and Modeling for Accessible Solutions

Digital agriculture represents a revolutionary phase in agriculture, harnessing the power of proximal and remote sensing, Internet of Things (IoT), and artificial intelligence (AI) to revolutionize crop monitoring, resource optimization, and productivity enhancement. Despite these advances, a notable challenge persists: integrating these various tools into a user-friendly system accessible to researchers, agronomists, and farmers with limited technical expertise. Current impediments include generalized predictive model limitations and a lack of cohesive hardware-software systems tailored to agriculture's specific needs. While there are remarkable advancements in AI models, remote sensing platforms, and data handling algorithms, a streamlined approach that integrates these components into an easily accessible framework is underdeveloped. Moreover, issues like managing large datasets, ensuring data security, and the absence of farmer-friendly technological ecosystems underscore the need for innovative, integrative solutions.

This Research Topic aims to address these gaps by seeking innovative solutions that prioritize usability, adaptability, and the seamless integration of current technologies into a cohesive system. By focusing on reducing the barriers faced by end users in agriculture, this research topic wishes to expose how the integration could support effective decision-making, improved data security, and real-world applicability. Contributions should aim to demonstrate solutions to specific challenges like generalizability of models, quality of data, AI-driven user-friendly platforms, and integrated hardware-software ecosystems.

To gather further insights in the integration and application of these advanced technologies, we welcome articles addressing, but not limited to, the following themes:

• Automated processing and analysis pipelines for remote sensing data

• Enhancements in remote sensing data quality and noise reduction

• AI models with high generalizability across diverse agricultural contexts

• Decision-support systems simplifying data interpretation for end users

• Integrative platforms combining sensing, processing, and user-friendly modeling

• Comprehensive reviews of existing challenges and innovations in digital agriculture

We invite original research articles, reviews, and framework proposals aiming to integrate technologies and streamline digital agriculture, tackling the challenges and seizing the opportunities for technological progress in the sector.

Dr. Jing Zhou is the co-founder, Secretary, and shareholder of Digital Seed Technology Inc. The other Topic Editors declare no competing interests with regard to the Research Topic subject.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Digital Agriculture, Sensing, Predictive Models, Accessible systems, Artificial intelligence

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.