Artificial Intelligence, and especially deep learning technologies, hold tremendous promise for characterizing and understanding plant biodiversity. These technologies are increasingly being applied to detect, identify, count, or measure key biological features including leaf shape and size, species identity and distribution, and reproductive characteristics. Increasingly, such investigations have led us to rethink how we can leverage digitized natural history collections and observations by applying convolutional neural networks (CNNs) to automate large scale phenotyping and assess biodiversity distributions. In this Research Topic, we welcome paper submissions that focus on methods development and the application of novel machine learning techniques to a variety of data layers to investigate plant biodiversity. This Research Topic aims to stimulate plant biologists to embrace these emerging technologies and promote expanding applications in this domain via collaboration with data and computer scientists.
Data documenting biological and ecological information on plant species are numerous and diverse. These data span specimens observed in culture, in the field, and across natural history collections. They contain rich information often linked with textual annotations. Automating the extraction and analysis of this information offers new opportunities to explore trait diversity, trait plasticity, and the spatiotemporal dynamics of species. However, a large number of methodological and algorithmic limitations need to be overcome to speed the exploration of these data and to conduct studies on unprecedented taxonomic, temporal, and spatial scales. Although promising results have been obtained from recent advancements, we are far from unleashing the full potential of these technologies.
In our proposed Research Topic, we are seeking original scientific contributions that apply deep learning technologies to characterize and scale plant biodiversity data from the individual, to the species, to the ecosystem. Contributions will cover, but are not limited to, the following topics:
- Development, integration, and evaluation of novel deep learning methods for detection, characterization, and classification of plant communities, plant species, and plant traits;
- Automated visual analysis of digitized natural history collections and related biodiversity visual datasets;
- AI-based automation of field plant image analysis for landscape management, including conservation planning and ecological restoration;
- Machine learning and AI approaches for the interpretation and analysis of botanical visual data from autonomous or mobile sensors;
- Plant mapping and features extraction from large scale visual datasets;
- AI applications in plant ecology, conservation and agriculture.
Artificial Intelligence, and especially deep learning technologies, hold tremendous promise for characterizing and understanding plant biodiversity. These technologies are increasingly being applied to detect, identify, count, or measure key biological features including leaf shape and size, species identity and distribution, and reproductive characteristics. Increasingly, such investigations have led us to rethink how we can leverage digitized natural history collections and observations by applying convolutional neural networks (CNNs) to automate large scale phenotyping and assess biodiversity distributions. In this Research Topic, we welcome paper submissions that focus on methods development and the application of novel machine learning techniques to a variety of data layers to investigate plant biodiversity. This Research Topic aims to stimulate plant biologists to embrace these emerging technologies and promote expanding applications in this domain via collaboration with data and computer scientists.
Data documenting biological and ecological information on plant species are numerous and diverse. These data span specimens observed in culture, in the field, and across natural history collections. They contain rich information often linked with textual annotations. Automating the extraction and analysis of this information offers new opportunities to explore trait diversity, trait plasticity, and the spatiotemporal dynamics of species. However, a large number of methodological and algorithmic limitations need to be overcome to speed the exploration of these data and to conduct studies on unprecedented taxonomic, temporal, and spatial scales. Although promising results have been obtained from recent advancements, we are far from unleashing the full potential of these technologies.
In our proposed Research Topic, we are seeking original scientific contributions that apply deep learning technologies to characterize and scale plant biodiversity data from the individual, to the species, to the ecosystem. Contributions will cover, but are not limited to, the following topics:
- Development, integration, and evaluation of novel deep learning methods for detection, characterization, and classification of plant communities, plant species, and plant traits;
- Automated visual analysis of digitized natural history collections and related biodiversity visual datasets;
- AI-based automation of field plant image analysis for landscape management, including conservation planning and ecological restoration;
- Machine learning and AI approaches for the interpretation and analysis of botanical visual data from autonomous or mobile sensors;
- Plant mapping and features extraction from large scale visual datasets;
- AI applications in plant ecology, conservation and agriculture.
