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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...

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Autores principales: Jin, Xin, Tang, Lumei, Li, Ruoshi, Ji, Jiangtao, Liu, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9355090/
https://www.ncbi.nlm.nih.gov/pubmed/35937327
http://dx.doi.org/10.3389/fpls.2022.893357
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author Jin, Xin
Tang, Lumei
Li, Ruoshi
Ji, Jiangtao
Liu, Jing
author_facet Jin, Xin
Tang, Lumei
Li, Ruoshi
Ji, Jiangtao
Liu, Jing
author_sort Jin, Xin
collection PubMed
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.
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spelling pubmed-93550902022-08-06 Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network Jin, Xin Tang, Lumei Li, Ruoshi Ji, Jiangtao Liu, Jing Front Plant Sci Plant Science 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. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9355090/ /pubmed/35937327 http://dx.doi.org/10.3389/fpls.2022.893357 Text en Copyright © 2022 Jin, Tang, Li, Ji and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Jin, Xin
Tang, Lumei
Li, Ruoshi
Ji, Jiangtao
Liu, Jing
Selective transplantation method of leafy vegetable seedlings based on ResNet 18 network
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 Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9355090/
https://www.ncbi.nlm.nih.gov/pubmed/35937327
http://dx.doi.org/10.3389/fpls.2022.893357
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AT jijiangtao selectivetransplantationmethodofleafyvegetableseedlingsbasedonresnet18network
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