“ON TO THE NEXT CHAPTER FOR CROP BREEDING: CONVERGENCE WITH DATA SCIENCE”

 

  Crop 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.

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 information².

Integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning) can be proposed as a smart breeding scheme¹. The development of innovative modelling approaches, genotype -environment interaction and the complexity of multiple traits can be accommodated in genomic selection which would enable broad implementation of the technology along with speed breeding. Predictive breeding using genomic selection, BLUP and integrating genomic selection³ with speed breeding has further shortened the generation cycle of plants. AI algorithms are used to create expert systems for prediction or classification based on input data. Breeders’ experience and knowledge can be transferred into future AI-assisted breeding systems, and digitalization of breeding experience will promote the transition of breeding from empirically driven to AI-driven⁴.

 References:

¹ANSARIFAR, J., AKHAVIZADEGAN, F. AND WANG, L., 2020, Performance prediction of crosses in plant breeding through genotype by environment interactions. Sci. Rep, 10(1), 11533.

²BHATTA, M., SANDRO, P., SMITH, M. R., DELANEY, O., VOSS-FELS, K. P., GUTIERREZ, L. AND HICKEY, L. T. 2021, Need for speed: manipulating plant growth to accelerate breeding cycles. Current Opinion in Plant Biology, 60, 101986.

³VANRADEN, P. M., 2008, Efficient methods to compute genomic predictions. J. dairy cience, 91(11), 4414-4423.

⁴XU, Y., ZHANG, X., LI, H., ZHENG, H., ZHANG, J., OLSEN, M. S. AND QIAN, Q., 2022. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Mol. Plant, 15(11), 1664-1695.