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 g...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-024-01145-y |
| _version_ | 1797274478843002880 |
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| author | Fan Yang Yuhuan Zhou Jiayi Du Kailiang Wang Leyan Lv Wei Long |
| author_facet | Fan Yang Yuhuan Zhou Jiayi Du Kailiang Wang Leyan Lv Wei Long |
| author_sort | Fan Yang |
| collection | DOAJ |
| description | 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, 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. Results When 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. |
| first_indexed | 2024-03-07T14:59:56Z |
| format | Article |
| id | doaj.art-eaaa228561d1434cb8a25780e1249d89 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-03-07T14:59:56Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-eaaa228561d1434cb8a25780e1249d892024-03-05T19:15:15ZengBMCPlant Methods1746-48112024-02-0120111310.1186/s13007-024-01145-yPrediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networksFan Yang0Yuhuan Zhou1Jiayi Du2Kailiang Wang3Leyan Lv4Wei Long5College of Computer and Information Engineering, Central South University of Forestry & TechnologyCollege of Computer and Information Engineering, Central South University of Forestry & TechnologyCollege of Computer and Information Engineering, Central South University of Forestry & TechnologyZhejiang Provincial Key Laboratory of Tree Breeding, Research Institute of Subtropical Forestry, Chinese Academy of ForestryCollege of Hydraulic Engineering, Zhejiang Tongji Vocational College of Science and TechnologyZhejiang Provincial Key Laboratory of Tree Breeding, Research Institute of Subtropical Forestry, Chinese Academy of ForestryAbstract 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, 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. Results When 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.https://doi.org/10.1186/s13007-024-01145-yCamellia OleiferaGraftingArtificial neural networkFruit characteristics |
| spellingShingle | Fan Yang Yuhuan Zhou Jiayi Du Kailiang Wang Leyan Lv Wei Long Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks Plant Methods Camellia Oleifera Grafting Artificial neural network Fruit characteristics |
| title | Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks |
| title_full | Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks |
| title_fullStr | Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks |
| title_full_unstemmed | Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks |
| title_short | Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks |
| title_sort | prediction of fruit characteristics of grafted plants of camellia oleifera by deep neural networks |
| topic | Camellia Oleifera Grafting Artificial neural network Fruit characteristics |
| url | https://doi.org/10.1186/s13007-024-01145-y |
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