文献类型：期刊文献

英文题名：Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

作者：Yang, Fan[1] Zhou, Yuhuan[1,2] Du, Jiayi[1] Wang, Kailiang[2] Lv, Leyan[3] Long, Wei[2]

第一作者：Yang, Fan

通信作者：Long, W[1]

机构：[1]Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China;[2]Res Inst Subtrop Forestry, Chinese Acad Forestry, Zhejiang Prov Key Lab Tree Breeding, Hangzhou 311400, Zhejiang, Peoples R China;[3]Coll Hydraul Engn, Zhejiang Tongji Vocat Coll Sci & Technol, Hangzhou 311231, Zhejiang, Peoples R China

年份：2024

卷号：20

期号：1

外文期刊名：PLANT METHODS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001157383300001)】；

基金：Data summarized in this paper have been generated through work of several authors and we would like to thank them for their continuous efforts which contribute to the study of propagation of the Camellia oleifea.

语种：英文

外文关键词：Camellia Oleifera; Grafting; Artificial neural network; Fruit characteristics

摘要：BackgroundCamellia 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. MethodsThis 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, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation R-2 and and time consumption. ResultsWhen using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum R-2 improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of C. oleifera, providing a valuable tool for predicting the impact of grafting combinations on the fruit of C. oleifera.