On to the next chapter for crop breeding: Convergence with data science

Abstract

AbstractCrop breeding is as ancient as the invention of cultivation. In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. Predictive breeding has been demonstrated in the agricultural industry and in public breeding programs for over a decade. The massive stores of data that have been generated by industry, farmers, and scholars through several decades have finally been recognized as a potential asset that can be brought to bear on specific breeding decisions. A wide range of analytical methods that were initially developed for various other quantitative disciplines, such as machine learning, deep learning, and artificial intelligence, are now being adapted for application in crop breeding to support analytics and decision making processes. This convergence between data science and crop breeding analytics is expected to address long‐standing gaps in crop breeding analytics, and realize the potential of applying advanced analytics to multidimensional data such as geospatial variables, a multitude of phenotypic responses, and genetic information. Here, we summarize the few existing examples followed by perspectives on where else these technologies would have applications to accelerate operational aspects of crop breeding and agricultural product development efforts.