About this Research Topic
The advances in big data management and decision support systems have pushed the development of techniques to innovate the world of agriculture. Data are collected in real-time from a wide range of sources (from on-site IoT sensors and robots to drone and satellite imagery) and integrated into a data platform to provide a decision support system that supports farmers and managers in the management and optimization of agriculture-related processes. Applications range from simple real-time monitoring capabilities to prescriptive analytics that supports the decision-making process, leading to a higher quality of cultivated products, a smarter usage of resources, and a more sustainable approach to agriculture.
The candidate papers for this Research Topic are innovative and high-quality contributions positioned at the frontier of research on both theoretical and practitioner advancements in (big) data platforms for agriculture, with the goal to optimize and simplify the different aspects of agricultural data management and fruition. Besides addressing the Vs of big data, papers should focus on designing and engineering solutions that enable the interoperability between heterogeneous data sources and that enable analytics supporting farmers/technicians in the decision-making processes (e.g., smart watering or supervision of robotic weeding systems).
Theoretical works should provide innovative contributions in terms of architectural framework design, data and metadata modeling, algorithms and methodologies for data integration, processing, and analysis. Practical works should discuss in detail the solution implemented in a real-world scenario and demonstrate the improvements obtained from its application.
We welcome original papers and applications on topics pertaining to advances in big data solutions and smart agriculture, including agricultural production, soils, water, pests, and controlled environments. When applicable, we consider beneficial the usage of previously published agriculture-related datasets to prototype and evaluate the proposed contributions.
Relevant topics include (but are not limited to):
• Big data platform for smart agriculture
• Data and metadata management
• Optimization of agricultural resources, such as water and fertilizers
• IoT, robotics, and autonomous vehicles
• Big data processing and analysis
• Machine learning and artificial intelligence algorithms
The candidate papers for this Research Topic are innovative and high-quality contributions positioned at the frontier of research on both theoretical and practitioner advancements in (big) data platforms for agriculture, with the goal to optimize and simplify the different aspects of agricultural data management and fruition. Besides addressing the Vs of big data, papers should focus on designing and engineering solutions that enable the interoperability between heterogeneous data sources and that enable analytics supporting farmers/technicians in the decision-making processes (e.g., smart watering or supervision of robotic weeding systems).
Theoretical works should provide innovative contributions in terms of architectural framework design, data and metadata modeling, algorithms and methodologies for data integration, processing, and analysis. Practical works should discuss in detail the solution implemented in a real-world scenario and demonstrate the improvements obtained from its application.
We welcome original papers and applications on topics pertaining to advances in big data solutions and smart agriculture, including agricultural production, soils, water, pests, and controlled environments. When applicable, we consider beneficial the usage of previously published agriculture-related datasets to prototype and evaluate the proposed contributions.
Relevant topics include (but are not limited to):
• Big data platform for smart agriculture
• Data and metadata management
• Optimization of agricultural resources, such as water and fertilizers
• IoT, robotics, and autonomous vehicles
• Big data processing and analysis
• Machine learning and artificial intelligence algorithms
Keywords: Precision Agriculture, Smart Irrigation, Big Data, Smart Agriculture, Sustainability
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.
