Applications of Unmanned Aerial Vehicle (UAV) and Ground-Based Platforms for High Throughput Crop Phenotyping
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".
Deadline for manuscript submissions: closed (31 January 2021)
Special Issue Editors
Interests: functional genomics and ‘-omics’ data in field trials; induced resistance in potato varieties; potato late blight; transcriptomics and proteomics; plant defense; biofortification of cassava; botanicals and plant resistance
Special Issues, Collections and Topics in MDPI journals
Interests: Image processing and analysis; hyperspectral and multispectral imaging; spectroscopy, computer vision for plant phenotyping; 3D imaging; agricultural robotics; machine vision; machine learning
Special Issue Information
Dear Colleagues,
High-throughput plant phenotyping is a rapidly emerging research area concerned with quantitative measurements of structural and functional plant properties as well as plant performance and health. The use of unmanned aerial vehicles (UAVs) and ground-based platforms, such as robots, that can be equipped with a variety of sensors enables a faster and more efficient method to collect a large amount of data. Crop phenotyping analysis is the foundation for crop breeding. Traditionally, crop phenotypes are measured manually, which is time-consuming and labor-intensive and sometimes requires destructive sampling for certain readouts. Currently, advances in artificial intelligence, such as machine learning and deep learning algorithms, big data analysis, and computer vision techniques, will further assist the crop phenotyping community to address limitations of using conventional approaches to phenotype analysis.
In this context, we welcome papers that present primary research, novel analytical methods, and applications including standard plant trait measurements, experimental technologies, hardware development for data collection, and software development, as well as reviews and opinion pieces in the field of high-throughput crop phenotype analysis both in field and controlled environments.
Dr. Erik Alexandersson
Dr. Gerrit Polder
Dr. Junfeng Gao
Guest Editors
Manuscript Submission Information
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Keywords
- Remote sensing
- Machine learning
- Image analysis
- Data analytics
- Plant traits
- Automation and robotics
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