Seed yield prediction of sesame using artificial neural network
| 作者： | Samad Emamgholizadeh, M. Parsaeian, Mehdi Baradaran |
| 作者单位： |
1Associate professor, Department of Water and Soil Engineering, Shahrood University, Shahrood, Iran 2Assistant Professor, Department of Agronomy and Plant Breeding, Shahrood University, Shahrood, Iran |
| 刊名： | European Journal of Agronomy, 2015, Vol.68 , pp.89-96 |
| 来源数据库： | Elsevier Journal |
| DOI： | 10.1016/j.eja.2015.04.010 |
| 关键词： | Seed yield estimation; Sesame; Artificial neural networks; Multiple regression model; |
| 原始语种摘要： |
Abstract(#br)The prediction of seed yield is one of the most important breeding objectives in agricultural research. So, in this study, two methods namely artificial neural network (ANN) and multiple regression model (MLR) were employed to estimate the seed yield of sesame (SYS) from readily measurable plant characters (e.g., flowering time of 100% (days), the plant height (cm), the capsule number per plant, the 1000-seed weight (g) and the seed number per capsule). The ANN and MLR were tested using field data. Results showed that the ANN predicts the SYS accurately with a root-mean-square-error (RMSE) of 0.339 |
