Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network
To solve the problem of low survival rate caused by unscreened transplanting of seedlings. This study proposed a selective transplanting method of leafy vegetable seedlings based on the ResNet 18 network. Lettuce seedlings were selected as the research object, and a total of 3,388 images were obtain...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.893357/full |
| _version_ | 1818477971270270976 |
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| author | Xin Jin Xin Jin Xin Jin Lumei Tang Ruoshi Li Jiangtao Ji Jiangtao Ji Jing Liu |
| author_facet | Xin Jin Xin Jin Xin Jin Lumei Tang Ruoshi Li Jiangtao Ji Jiangtao Ji Jing Liu |
| author_sort | Xin Jin |
| collection | DOAJ |
| description | To solve the problem of low survival rate caused by unscreened transplanting of seedlings. This study proposed a selective transplanting method of leafy vegetable seedlings based on the ResNet 18 network. Lettuce seedlings were selected as the research object, and a total of 3,388 images were obtained in the dataset. The images were randomly divided into the training set, validation set, and test set in the ratio of 6:2:2. The ResNet 18 network was used to perform transfer learning after tuning, identifying, and classifying leafy vegetable seedlings, and then establishing a model to screen leafy vegetable seedlings. The results showed that the optimal detection accuracy of the presence and health of seedlings in the training data set was above 100%, and the model loss remained at around 0.005. Nine hundred seedlings were selected for the validation test, and the screening accuracy rate was 97.44%, the precision rate of healthy seedlings was 97.56%, the recall rate was 97.34%, the precision rate of unhealthy seedlings was 92%, and the recall rate was 92.62%, which was better than the screening model based on the physical characteristics of seedlings. If they were identified as unhealthy seedlings, the manipulator would remove them during the transplanting process and perform the seedling replenishment operation to increase the survival rate of the transplanted seedlings. Moreover, the seedling image is extracted by background removal technology, so the model processing time for a single image is only 0.0129 s. This research will provide technical support for the selective transplantation of leafy vegetable seedlings. |
| first_indexed | 2024-12-10T09:42:48Z |
| format | Article |
| id | doaj.art-0c7de394efd34302915561678a8660d6 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-10T09:42:48Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-0c7de394efd34302915561678a8660d62022-12-22T01:53:56ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-07-011310.3389/fpls.2022.893357893357Selective transplantation method of leafy vegetable seedlings based on ResNet 18 networkXin Jin0Xin Jin1Xin Jin2Lumei Tang3Ruoshi Li4Jiangtao Ji5Jiangtao Ji6Jing Liu7College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang, ChinaScience & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaTo solve the problem of low survival rate caused by unscreened transplanting of seedlings. This study proposed a selective transplanting method of leafy vegetable seedlings based on the ResNet 18 network. Lettuce seedlings were selected as the research object, and a total of 3,388 images were obtained in the dataset. The images were randomly divided into the training set, validation set, and test set in the ratio of 6:2:2. The ResNet 18 network was used to perform transfer learning after tuning, identifying, and classifying leafy vegetable seedlings, and then establishing a model to screen leafy vegetable seedlings. The results showed that the optimal detection accuracy of the presence and health of seedlings in the training data set was above 100%, and the model loss remained at around 0.005. Nine hundred seedlings were selected for the validation test, and the screening accuracy rate was 97.44%, the precision rate of healthy seedlings was 97.56%, the recall rate was 97.34%, the precision rate of unhealthy seedlings was 92%, and the recall rate was 92.62%, which was better than the screening model based on the physical characteristics of seedlings. If they were identified as unhealthy seedlings, the manipulator would remove them during the transplanting process and perform the seedling replenishment operation to increase the survival rate of the transplanted seedlings. Moreover, the seedling image is extracted by background removal technology, so the model processing time for a single image is only 0.0129 s. This research will provide technical support for the selective transplantation of leafy vegetable seedlings.https://www.frontiersin.org/articles/10.3389/fpls.2022.893357/fulldeep learningtransfer learningseedling screeningseedling characteristicsmachinery automation |
| spellingShingle | Xin Jin Xin Jin Xin Jin Lumei Tang Ruoshi Li Jiangtao Ji Jiangtao Ji Jing Liu Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network Frontiers in Plant Science deep learning transfer learning seedling screening seedling characteristics machinery automation |
| title | Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network |
| title_full | Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network |
| title_fullStr | Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network |
| title_full_unstemmed | Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network |
| title_short | Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network |
| title_sort | selective transplantation method of leafy vegetable seedlings based on resnet 18 network |
| topic | deep learning transfer learning seedling screening seedling characteristics machinery automation |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.893357/full |
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