About this Research Topic
With the global climate changing and the dramatic shrinking of agricultural labor forces, the agricultural industry is facing unprecedented challenges, which has left the industry in a dilemma of who will farm and how. The rapid development of smart agriculture, especially the application of robotics and intelligent equipment in agriculture, has proved to be a breakthrough in solving bottlenecks in the agricultural industry. Typically, agricultural workers get exhausted in severe agricultural operation scenarios, however, intelligent robots can replace humans to perform tedious operation tasks around the clock, which saves time and labor costs for agricultural production, and potentially improves the operation efficiency. Utilizing robot technologies for automated monitoring and phenotyping in unstructured and semi-structured cultivation scenarios in a contact or non-contact manner is an important topic that needs to be discussed urgently.
Robotic systems equipped with multi-sensors have initially shown research potential in the field of smart agriculture with their excellent intelligent algorithms. However, limited by highly complex agricultural scenarios, existing agricultural robots still have limitations in terms of measurement accuracy, real-time performance and robustness. To fully explore the related technologies, this Research Topic focuses on the application of intelligent robotics in crop growth monitoring and high-throughput phenotyping. This is a unique attempt to simultaneously tackle phenotypic robot development, sensor information perception, phenotypic data parsing, and crop dynamics monitoring, including robotic system design and development, robotic field SLAM and autonomous navigation, multi-sensor (e.g., LiDAR, RGB-D camera, GNSS, etc.) data fusion, 3D reconstruction of field scenes, crop imaging techniques, and crop morphological and physiological feature extraction (e.g., plant height, plant counts, leaf area index, canopy cover, etc.). These techniques facilitate the development of modern agriculture while also helping to expand the potential application for intelligent robots.
This Research Topic aims to demonstrate the latest technical developments in smart agriculture from an intelligent robotics point of view. We welcome innovative contributions of original research articles, review articles, opinion papers, and papers on perspectives and on novel methods of crop growth monitoring and high-throughput phenotyping. Contributions include, but are not limited to, the following:
(i) ag-robot system design and development;
(ii) SLAM and autonomous navigation for mobile robots in field scenarios;
(iii) multi-sensor data fusion for agricultural scene perception;
(iv) crop 3D reconstruction;
(v) intelligent imaging of field crops;
(vi) high-throughput phenotyping; and
(vii) crop growth dynamics monitoring.
Robotic systems equipped with multi-sensors have initially shown research potential in the field of smart agriculture with their excellent intelligent algorithms. However, limited by highly complex agricultural scenarios, existing agricultural robots still have limitations in terms of measurement accuracy, real-time performance and robustness. To fully explore the related technologies, this Research Topic focuses on the application of intelligent robotics in crop growth monitoring and high-throughput phenotyping. This is a unique attempt to simultaneously tackle phenotypic robot development, sensor information perception, phenotypic data parsing, and crop dynamics monitoring, including robotic system design and development, robotic field SLAM and autonomous navigation, multi-sensor (e.g., LiDAR, RGB-D camera, GNSS, etc.) data fusion, 3D reconstruction of field scenes, crop imaging techniques, and crop morphological and physiological feature extraction (e.g., plant height, plant counts, leaf area index, canopy cover, etc.). These techniques facilitate the development of modern agriculture while also helping to expand the potential application for intelligent robots.
This Research Topic aims to demonstrate the latest technical developments in smart agriculture from an intelligent robotics point of view. We welcome innovative contributions of original research articles, review articles, opinion papers, and papers on perspectives and on novel methods of crop growth monitoring and high-throughput phenotyping. Contributions include, but are not limited to, the following:
(i) ag-robot system design and development;
(ii) SLAM and autonomous navigation for mobile robots in field scenarios;
(iii) multi-sensor data fusion for agricultural scene perception;
(iv) crop 3D reconstruction;
(v) intelligent imaging of field crops;
(vi) high-throughput phenotyping; and
(vii) crop growth dynamics monitoring.
Keywords: Agricultural robot, System integration SLAM, Autonomous navigation, LiDAR sensor, RGB-D camera, GNSS, Multi-sensor data fusion, Information perception, 3D reconstruction, Intelligent imaging, Phenotyping platform, Dynamic monitoring, Field crop
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.
