Ersoz, E.S.; Martin, N.F.; Stapleton, A.E. On to the next Chapter for Crop Breeding: Convergence with Data Science. Crop Science 2020, 60, 639–655, doi:10.1002/csc2.20054.
Ersoz, E.S.; Martin, N.F.; Stapleton, A.E. On to the next Chapter for Crop Breeding: Convergence with Data Science. Crop Science 2020, 60, 639–655, doi:10.1002/csc2.20054.
Ersoz, E.S.; Martin, N.F.; Stapleton, A.E. On to the next Chapter for Crop Breeding: Convergence with Data Science. Crop Science 2020, 60, 639–655, doi:10.1002/csc2.20054.
Ersoz, E.S.; Martin, N.F.; Stapleton, A.E. On to the next Chapter for Crop Breeding: Convergence with Data Science. Crop Science 2020, 60, 639–655, doi:10.1002/csc2.20054.
Abstract
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. The applications of predictive breeding has been gaining momentum in agricultural industry and public breeding programs for the last decade, in the aftermath of genomic selection being recognized and widely applied for accelerating genetic gain in breeding programs. The massive amounts of data that has been generated by industry and farmers year after year through several decades has finally been recognized as an asset. A wide range of analytical methods such as machine learning, deep learning and artificial intelligence that were initially developed for diverse quantitative disciplines are now being adopted to crop breeding decision making processes. New technologies are currently being developed that would enable integration of data from various domains such as geospatial variables and a multitude of phenotypic responses as well as genetic information, in order to identify, develop and improve crop faster via partial or full automation of the decisions that pertain to variety development. Here we will discuss and summarize efforts from public and private domains for predictive analytics, and its applications to crop breeding and agricultural product development, and provide suggestions for future research.
Keywords
machine learning; agroclimactic modelling; crop breeding and genetics; GxE
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
