Utilizing AI to enhance the effectiveness of plant breeding for the development of climate-resilient smart food crops

Document Type : Research Paper

Authors

1 Department of CS & IT, Kalinga University, Raipur, India.

2 Research Scholar, Department of CS & IT, Kalinga University, Raipur, India.

Abstract

Objectives
Growing crops is always the primary goal of agricultural operations. Still, worldwide agricultural systems are under increasing stress because of climate change and the growing number of people worldwide who need food. Dealing with climate change, making crops that produce more, protecting the environment, and adapting to changing conditions have become difficult to ensure that the world's population can keep growing. Climate-resilient smart Food Crops (CRSFC) are also needed to control biomass output, a crucial part of keeping the environment working properly globally. Pure-line selections, mass selection, back cross breeding, recurrent selection for improving agricultural CRSFC are limited and time-consuming. Careful selection processes are needed to grow new and better crop types. There is an urgent need to accelerate the process of CRSFC breeding by using artificial intelligence to replicate some features of human intelligence using technology. AI offers significant computing capabilities and a wide range of novel instruments and methods for foreseeable plant breeding (PB) due to the neural network training and classification module.

Results
This review will discuss the use of AI technology in current breeding practices to address challenges in large-scale phenotyping and gene functionality analysis. AI algorithms make it easy for researchers to quickly look at genetic data, find complicated trends, and build predictive models that help with crop breeding and selecting the most beneficial features. It will also explore how advancements in AI technologies create fresh possibilities for subsequent breeding by promoting the widespread utilization of envirotyping information. It is hard to connect gene to trait with the breeding methods we have now. This makes it harder to use high-volume field phenotyping, genomics, and enviromics effectively.

Conclusions
This paper discusses the use of AI as the preferred a method for improving the reliability of high-volume crop phenotyping, genotyping, and envirotyping information. Additionally, we examine the emerging methodologies and obstacles in integrating large multi-omics computational data. Hence, combining AI with "omics" might facilitate swift gene discovery and ultimately expedite agricultural enhancement initiatives.

Keywords

References

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Agricultural Biotechnology Journal
Volume 16, Issue 3
September 2024
Pages 171-188

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