About this Research Topic
In recent years, the agriculture sector has witnessed a significant transformation due to the integration of digital technologies and data-driven methodologies, leading to the emergence of smart agriculture. Advanced agricultural technologies, notably high-throughput phenotyping and crop modeling, have fundamentally altered our understanding and management of crops. Phenotyping allows for precise characterization of plant traits, while crop modeling provides predictive insights into crop growth and yield based on a diverse set of environmental parameters. In many contexts, phenotyping and modeling are closely intertwined; phenotypic data forms the foundation for modeling, and models offer quantifiable tools for analyzing complex traits. The convergence of these domains presents an exciting opportunity to optimize agricultural practices, enhance resource efficiency, and make substantial contributions to global food security.
This research topic aims to seamlessly integrate phenotyping and modeling, essential components in smart agriculture, to address urgent challenges like sustainable food production amidst a growing global population and to optimize resource utilization. The key challenge is the fragmented use and insufficient integration of high-throughput phenotyping and advanced crop modeling. The cohesive fusion of these technologies can revolutionize crop management, offering predictive analytics for optimized resource allocation, enhanced productivity, and environmental sustainability. Recent strides in sensor tech, machine learning, and computational modeling provide a strong foundation for a refined integration of phenotyping and modeling, enabling real-time, data-driven decisions for farmers. This research strives to bridge the gap between phenotyping and crop modeling, aiming for a transformative approach in smart agriculture to ensure sustainability and food security.
The scope of this research topic encompasses a broad range of subjects, including but not limited to:
- Cutting-edge phenotyping techniques and methodologies in agricultural research.
- Integrating phenotypic data into crop models to enhance prediction accuracy and decision support.
- Applying machine learning and AI algorithms in crop modeling for increased accuracy and adaptability.
- Utilizing the Internet of Things (IoT), sensors, and drones for real-time data collection and monitoring in smart agriculture.
- Promoting sustainable agricultural practices and optimizing resources through the seamless integration of phenotyping and crop modeling.
We invite authors to contribute original research articles, perspectives, and reviews, providing valuable insights into the utilization of phenotyping and crop modeling to advance the field of smart agriculture.
This research topic aims to seamlessly integrate phenotyping and modeling, essential components in smart agriculture, to address urgent challenges like sustainable food production amidst a growing global population and to optimize resource utilization. The key challenge is the fragmented use and insufficient integration of high-throughput phenotyping and advanced crop modeling. The cohesive fusion of these technologies can revolutionize crop management, offering predictive analytics for optimized resource allocation, enhanced productivity, and environmental sustainability. Recent strides in sensor tech, machine learning, and computational modeling provide a strong foundation for a refined integration of phenotyping and modeling, enabling real-time, data-driven decisions for farmers. This research strives to bridge the gap between phenotyping and crop modeling, aiming for a transformative approach in smart agriculture to ensure sustainability and food security.
The scope of this research topic encompasses a broad range of subjects, including but not limited to:
- Cutting-edge phenotyping techniques and methodologies in agricultural research.
- Integrating phenotypic data into crop models to enhance prediction accuracy and decision support.
- Applying machine learning and AI algorithms in crop modeling for increased accuracy and adaptability.
- Utilizing the Internet of Things (IoT), sensors, and drones for real-time data collection and monitoring in smart agriculture.
- Promoting sustainable agricultural practices and optimizing resources through the seamless integration of phenotyping and crop modeling.
We invite authors to contribute original research articles, perspectives, and reviews, providing valuable insights into the utilization of phenotyping and crop modeling to advance the field of smart agriculture.
Keywords: High-throughput phenotyping, Crop modeling, smart agriculture, physiological process, complex traits
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.
