Genomics, Phenomics and Machine Learning for Accelerated Crop Improvement and Enhanced Genetic Gain

Dear Colleagues,

Classical plant breeding has evolved considerably during the last century. This can be attributed to the combined action of molecular markers, improved experimental designs, statistical methods, understanding of the concepts of population and quantitative genetics, and integration of other disciplines such as entomology, pathology, soil science, engineering, agronomy, and physiology. The evolution and adoption of all these techniques and tools have pushed the annual genetic gain of grain yield to approximately 1% for major cereals; however, the rate of genetic gain in these crops is insufficient to cope with a 2% annual increase in the human population, which is expected to reach 9.8 billion by 2050. Most plant breeding programs adopt recent genomics tools to select parents, decide crosses and advancements, mapping, gene characterization, knockout, and expression studies. All these genomics tools require accurate phenotyping information, which is leveraged by adopting the high throughput phenotyping platforms under controlled and small-scale field conditions. However, the implementation of phenomics studies is limited at a large scale due to cost, lack of infrastructure, and data analytics issues. Here, we propose the merger of genomics, phenomics, and machine learning approaches to implement these tools at a vast scale into the breeding programs.
We welcome submissions of original research, comprehensive or mini-reviews, opinions, perspectives, and method papers dealing with the utilization of genomics and phenomics approaches in plant breeding programs to enhance genetic gain.

The scope of this topic includes the following themes (but not limited to):

1. Linkage and association mapping for different economical traits in plants
2. Implementation of genomic selection into breeding programs with practical implementation and focus on population improvement and product development
3. Phenomics selection for plant breeding with the utilization of low and high throughput phenotyping tools
4. Merging genomics, phenomics and machine learning in breeding programs to focus on prediction in the programs
5. Adoption of high throughput genotyping and phenotyping platforms in plant breeding programs
6. Current trends and challenges with the adoption of phenotyping platforms under field conditions at a large scale

Dr. Karen Sanguinet
Dr. Karansher Singh Sandhu
Guest Editors

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• genomics
• genomic selection
• high throughput phenotyping
• linkage and association mapping
• machine and deep learning
• phenomics selection
• speed breeding