Abstract
With the rapid development of improved breeding equipment and information technology, computer-aided decision-making in plant breeding evaluation can help solve the problems associated with high-throughput demand and insufficient experience of breeders in modern large-scale field breeding experiments. Many linear models have made great contributions to the development of breeding evaluation although they are based on a wrong assumption of attribute independence. This paper proposes a unified coupled representation that integrates intra-coupled and inter-coupled relationships to capture the interdependence among quantitative traits by addressing coupling context and coupling weights. Moreover, a hybrid scheme of the linear correlation and ordinal relation is introduced to express the coupling relationship with a preset parameter that balances the contributions so as to capture both relative and absolute performance in cultivar selection and breeding evaluation. A framework that includes data preprocessing, coupled data representation, feature selection, prediction model construction, and assisted decision-making is our overall solution for the plant breeding evaluation task. Experiments on real plant breeding data sets demonstrated the effectiveness of coupled representation for elucidating the quantitative phenotypic traits and the advantages of the proposed plant breeding evaluation algorithm compared with benchmark algorithms.
Highlights
F OOD security is a key issue worldwide because feeding the several billion people on this planet is a serious challenge, especially with the pressure of global environmental change
The effectiveness of our proposed plant breeding evaluation framework was verified because recursive feature elimination (RFE)-C improved both accuracy and stability compared with the baseline SVMO, with about 6.0% accuracy improvement and 56% standard deviation reduction
Experiments on several real breeding data sets were conducted to analyze the effect of preset parameters and show the effectiveness of our proposed coupled representation and plant breeding evaluation algorithm
Summary
F OOD security is a key issue worldwide because feeding the several billion people on this planet is a serious challenge, especially with the pressure of global environmental change. A variety of statistical analyses have been introduced, such as variance analysis, selection index theory, best linear unbiased prediction, principal component analysis, association analysis, analytic hierarchy process, grey breeding science, and similarity-difference theory [14], [15] These methods have made great contributions to the development of breeding evaluation by effectively improving the degree of dataization and informationization of plant breeding evaluation technology through the analysis and utilization of quantitative trait data [16]. Linear models are the most commonly applied statistical approaches to analyze phenotype data based on the assumption of attribute independence [7] This assumption may not be satisfied in breeding programs, in which the traits generally interact and are coupled via explicit or implicit relationships.
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