One of the central goals in plant and human biology is to understand how the phenotype of an organism is encoded in its genome. Agricultural and medical genetics share a dependence on the phenotype-genotype map (G-P map). Phenomics and genomics, the comprehensive study of phenotypes and genotypes, are therefore essential to understanding biology. Despite the advances in knowledge that sequencing technologies and analysis platforms have brought to plant and human biology, awareness is growing that many phenotypes are highly polygenic and susceptible to genetic and environmental interactions. Prime examples are human diseases and plant responses to environmental stress. Therefore, our understanding of all the genetic factors that influence these traits remains incomplete. The integration of phenomic data is critically needed, yet it adds a new level of complexity.
To overcome these barriers and integrate genomic and phenomic big data, new Artificial intelligence (AI) tools will take center stage. AI, which encompasses machine learning (ML), deep learning (DL) reinforcement learning (RL) and ensemble learning (EL), is the scientific discipline that uses computer algorithms to learn from large, highly heterogenous and complex data, to help identify patterns in data, and make predictions and interpretations.
The use of AI is becoming increasingly attractive to the plant and forest tree breeding, precision agriculture/forestry and precision medicine industry. AI and exascale biology are no longer concepts confined to the pages of futuristic science fiction novels; they are here now and are advancing rapidly. Globally, researchers must work collectively to provide insights into the genetic factors that influence common and rare human diseases and plant environmental stress responses and ensure breakthroughs in research and innovation that will help reach agriculture and health Sustainable Development Goals.
This Research Topic addresses these challenges from a multitude of perspectives. This includes insights into the genetic bases underlying quantitative phenotypic differences in plant and humans and what is needed to understand the genotype-to-phenotype problem on a broader scale by applying AI.
The following topics are therefore covered here:
- Plant/tree breeding and intelligent agriculture/forestry
- ML, DL, RL and EL for image and sequence analysis.
- ML, DL, RL and EL for big datasets integration to accelerate crop breeding and monitoring of biotic and abiotic stress in the field.
- AI to connect the genome with the phenome.
- AI implementation in a farmer's field.
- AI infrastructure, analytics, and applications tailored to plant and forest tree breeding and precision farming.
- Explainable AI applications where interpretable models could potentially replace black box models in breeding and farming.
One of the central goals in plant and human biology is to understand how the phenotype of an organism is encoded in its genome. Agricultural and medical genetics share a dependence on the phenotype-genotype map (G-P map). Phenomics and genomics, the comprehensive study of phenotypes and genotypes, are therefore essential to understanding biology. Despite the advances in knowledge that sequencing technologies and analysis platforms have brought to plant and human biology, awareness is growing that many phenotypes are highly polygenic and susceptible to genetic and environmental interactions. Prime examples are human diseases and plant responses to environmental stress. Therefore, our understanding of all the genetic factors that influence these traits remains incomplete. The integration of phenomic data is critically needed, yet it adds a new level of complexity.
To overcome these barriers and integrate genomic and phenomic big data, new Artificial intelligence (AI) tools will take center stage. AI, which encompasses machine learning (ML), deep learning (DL) reinforcement learning (RL) and ensemble learning (EL), is the scientific discipline that uses computer algorithms to learn from large, highly heterogenous and complex data, to help identify patterns in data, and make predictions and interpretations.
The use of AI is becoming increasingly attractive to the plant and forest tree breeding, precision agriculture/forestry and precision medicine industry. AI and exascale biology are no longer concepts confined to the pages of futuristic science fiction novels; they are here now and are advancing rapidly. Globally, researchers must work collectively to provide insights into the genetic factors that influence common and rare human diseases and plant environmental stress responses and ensure breakthroughs in research and innovation that will help reach agriculture and health Sustainable Development Goals.
This Research Topic addresses these challenges from a multitude of perspectives. This includes insights into the genetic bases underlying quantitative phenotypic differences in plant and humans and what is needed to understand the genotype-to-phenotype problem on a broader scale by applying AI.
The following topics are therefore covered here:
- Plant/tree breeding and intelligent agriculture/forestry
- ML, DL, RL and EL for image and sequence analysis.
- ML, DL, RL and EL for big datasets integration to accelerate crop breeding and monitoring of biotic and abiotic stress in the field.
- AI to connect the genome with the phenome.
- AI implementation in a farmer's field.
- AI infrastructure, analytics, and applications tailored to plant and forest tree breeding and precision farming.
- Explainable AI applications where interpretable models could potentially replace black box models in breeding and farming.
