The integration of machine learning, computer vision, and Big Data analytics in conjunction with life sciences has opened new opportunities for plant science research. Multifactorial models of specific phenotypes can be dynamically generated from large biological datasets to characterize phenotypic traits and predict complex trends as plants interact with their environment. Such methodological and technical advances have enabled plant scientists to unravel the genetics of otherwise complicated plant phenotypes at the cell, organ, tissue, plant, and population levels. Together with the increasing whole genome sequencing of many plant species, large-scale, high-throughput plant phenotyping and associated phenotypic analysis has become a bottleneck that urgently needs to be addressed. Plant genetics and plant breeding studies can therefore be accelerated by several recent rapid technological advances, from sensors to data extraction, combined with system integration and decreasing costs. Now, plant morphological and physiological traits can be assessed non-destructively and repeatedly in large populations and throughout growth and development, for which current technologies are still at an early stage and are being actively developed.
In conjunction with regular surveys of crop phenotyping trends by International Plant Phenotyping Network (IPPN), we believe that future challenges include, in particular, dynamic quantification of growth at the organ level to provide new insights into crop development, innovations in root phenotyping, cost-effective and flexible phenotyping in the field, the use of 3D imaging techniques during the growing season and in postharvest phenotyping (X-ray imaging CT, 3D laser scanning etc.) and novel phenotypic analysis techniques (computer vision, machine learning, etc.).
The goal of this Research Topic is to provide an overview of the latest methodological improvements in crop phenotyping.
In this Research Topic, we encourage the submissions describing state-of-the-art phenotyping techniques and applications in crop-related studies, with a particular focus on these areas:
1) Dynamic organ phenotyping in crop science;
2) Root system architectures phenotyping: the innovation below ground;
3) Field phenotyping: unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV), distributed phenotyping, pocket phenotyping, etc.;
4) Post-harvest phenotyping: 3D grain phenotyping, seed germination, etc;
5) Machine learning and deep learning in phenotypic analysis.
The integration of machine learning, computer vision, and Big Data analytics in conjunction with life sciences has opened new opportunities for plant science research. Multifactorial models of specific phenotypes can be dynamically generated from large biological datasets to characterize phenotypic traits and predict complex trends as plants interact with their environment. Such methodological and technical advances have enabled plant scientists to unravel the genetics of otherwise complicated plant phenotypes at the cell, organ, tissue, plant, and population levels. Together with the increasing whole genome sequencing of many plant species, large-scale, high-throughput plant phenotyping and associated phenotypic analysis has become a bottleneck that urgently needs to be addressed. Plant genetics and plant breeding studies can therefore be accelerated by several recent rapid technological advances, from sensors to data extraction, combined with system integration and decreasing costs. Now, plant morphological and physiological traits can be assessed non-destructively and repeatedly in large populations and throughout growth and development, for which current technologies are still at an early stage and are being actively developed.
In conjunction with regular surveys of crop phenotyping trends by International Plant Phenotyping Network (IPPN), we believe that future challenges include, in particular, dynamic quantification of growth at the organ level to provide new insights into crop development, innovations in root phenotyping, cost-effective and flexible phenotyping in the field, the use of 3D imaging techniques during the growing season and in postharvest phenotyping (X-ray imaging CT, 3D laser scanning etc.) and novel phenotypic analysis techniques (computer vision, machine learning, etc.).
The goal of this Research Topic is to provide an overview of the latest methodological improvements in crop phenotyping.
In this Research Topic, we encourage the submissions describing state-of-the-art phenotyping techniques and applications in crop-related studies, with a particular focus on these areas:
1) Dynamic organ phenotyping in crop science;
2) Root system architectures phenotyping: the innovation below ground;
3) Field phenotyping: unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV), distributed phenotyping, pocket phenotyping, etc.;
4) Post-harvest phenotyping: 3D grain phenotyping, seed germination, etc;
5) Machine learning and deep learning in phenotypic analysis.
