Special Issue on Crop Phenotyping: The Application of Novel Technologies for Improving Crop Breeding

Crop yields need to be improved in a sustainable manner to accommodate a rising global population and anticipated climate change in the coming decades. Genomics-assisted breeding now makes a significant contribution to food security, but the crop breeding community needs more effective ways to study the relationships between phenotype and genotype. While high-throughput genotyping is feasible at a low cost, crop phenotyping and associated data analysis remains relatively expensive. High-throughput phenotyping offers powerful methods to assess agronomic and physiological phenotypes, monitoring and quantifying large genetically-defined populations in breeding nurseries and field experiments at multiple scales. To do this, it exploits high-tech sensors, advanced robotics, images and data processing systems. In addition, new bioinformatics platforms are embracing large-scale, multi-dimensional phenotypic datasets, genotypic and other -omics information. Environmental responses and gene functions can now be dissected with unprecedented resolution using combined phenotyping, genotyping and multi-omics data. This will help to combat the current limitations of incremental improvements in crop yields.

This special issue focuses on the latest innovative research in remote sensing technologies, sensor development and applications. It also explores the development of image and multi-dimensional machine learning algorithms and applications specifically addressing issues estimating crop phenotyping traits based on multisource data streams and imagery.

Topics Covered:

These will include, but not be limited to:

• Reviews and perspectives of novel crop phenotyping approaches
• Unmanned Ground Vehicle (UGV), Unmanned Aerial Vehicle (UAV), and microsatellite platforms and their application in crop phenotyping
• Algorithm development and application (data fusion, computer vision, image segmentation and classification, machine learning and deep learning, etc.)
• Integration and fusion of multi-source data
• Data assimilation of high-throughput phenotypic data into crop models
• Technological platforms in crop phenotyping
• Applications of phenotyping in crop breeding
• Management, sharing and re-use of phenotypic data

Important Deadlines:

• Submission deadline (for full papers): 31 December 2021
• Peer review: October 2021 to March 2022
• Author revisions due: January to May 2022
• Publication date: August 2022

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online; please select the special issue “Crop phenotyping”.

Manuscripts will be reviewed and published on a first-come, first-served basis. Once all accepted manuscripts are published, they will be gathered together in this special issue.

Guest Editors:

• Dr. Xiuliang Jin, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, China. Email: jinxiuliang@caas.cn
• Dr. Wanneng Yang, Huazhong Agricultural University, China. Email: ywn@mail.hzau.edu.cn
• Prof. John H. Doonan, National Plant Phenomics Centre, IBERS, Aberystwyth University, UK. Email: jhd2@aber.ac.uk
• Prof. Clement Atzberger, Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Austria. Email: clement.atzberger@boku.ac.at