外文摘要:Seed yield is influenced by the combined effects of genes, including additive and non-additive interactions. Therefore, accurately predicting seed yield holds significant importance in rapeseed breeding. Nonetheless, limited information exists regarding yield estimation for canola using neural networks. This study employs multi-layer perceptron (MLP) neural network, radial basis function neural network and support vector machine, to forecast rapeseed yield. The models are trained using phenological, morphological, yield and yield-related data, as well as molecular marker information from 8 genotypes and 56 hybrids. Comparative analysis of the models reveals that the MLP model effectively forecasts hybrid yield with root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R-2) values of 226, 183, and 92%, respectively. Among the 40 primers examined, the ISJ10 primer demonstrates superior discriminatory power compared to others. The use of molecular and phenotypic data as inputs in the model highlights the MLP model's superiority, presenting lower RMSE and MAE values, along with a higher R2, compared to direct crosses in predicting the performance of reciprocal crosses. The proposed neural network model enables performance estimation of hybrids prior to crossing parent studied, thereby enabling spring rapeseed breeders to focus on the most promising hybrids.
外文关键词:machine learning;Yield prediction;support vector machine;neural network;MLP;Genetic diversity
作者:Sabouri, Hossein;Sajadi, Sayed Javad;Norouzi, Mohamad Amin;Ahangar, Leila;Payghamzadeh, Kamal
作者单位:Agr Res Educ & Extens Org AREEO;Gonbad Kavous Univ
期刊名称:GENETIC RESOURCES AND CROP EVOLUTION
期刊影响因子:0.0
出版年份:2024
出版刊次:71(8)
原文传递申请:江苏省科技资源(工程技术文献)统筹服务平台