Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks.

ABSTRACT:

Background

Camellia oleifera, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types.

Methods

This study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of C.oleifera, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, ci

SUBMITTER: Yang F 

PROVIDER: S-EPMC10840285 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature