Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees
Abstract Field-grown peach trees are large and have a complex branch structure; therefore, detection of water deficit stress from images is challenging. We obtained large datasets of images of field-grown peach trees with continuous values of stem water potential (Ψstem) through partial secession tr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49980-8 |
| _version_ | 1827581709331202048 |
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| author | Takayoshi Yamane Harshana Habaragamuwa Ryo Sugiura Taro Takahashi Hiroko Hayama Nobuhito Mitani |
| author_facet | Takayoshi Yamane Harshana Habaragamuwa Ryo Sugiura Taro Takahashi Hiroko Hayama Nobuhito Mitani |
| author_sort | Takayoshi Yamane |
| collection | DOAJ |
| description | Abstract Field-grown peach trees are large and have a complex branch structure; therefore, detection of water deficit stress from images is challenging. We obtained large datasets of images of field-grown peach trees with continuous values of stem water potential (Ψstem) through partial secession treatment of the base of branches to change the water status of the branches. The total number of images as frames extracted from videos of branches was 23,181, 6743, and 10,752, in the training, validation, and test datasets, respectively. These datasets enabled us to precisely model water deficit stress using a deep-learning-regression model. The predicted Ψstem of frames belonging to a single branch showed a Gaussian distribution, and the coefficient of determination between the measured and predicted values of Ψstem increased to 0.927 by averaging the predicted values of the frames in each video. This method of averaging the predicted values of frames in each video can automatically eliminate noise and summarize data into the representative value of a tree and is considered to be robust for the diagnosis of water deficit stress in large field-grown peach trees with a complex branch structure. |
| first_indexed | 2024-03-08T22:39:16Z |
| format | Article |
| id | doaj.art-4ddb9c5b598a4e12aa0d8cff5ac442df |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T22:39:16Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4ddb9c5b598a4e12aa0d8cff5ac442df2023-12-17T12:13:56ZengNature PortfolioScientific Reports2045-23222023-12-011311910.1038/s41598-023-49980-8Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach treesTakayoshi Yamane0Harshana Habaragamuwa1Ryo Sugiura2Taro Takahashi3Hiroko Hayama4Nobuhito Mitani5Institute of Fruit Tree and Tea Science, NAROResearch Center for Agricultural Information Technology, NAROResearch Center for Agricultural Information Technology, NAROInstitute of Fruit Tree and Tea Science, NAROInstitute of Fruit Tree and Tea Science, NAROInstitute of Fruit Tree and Tea Science, NAROAbstract Field-grown peach trees are large and have a complex branch structure; therefore, detection of water deficit stress from images is challenging. We obtained large datasets of images of field-grown peach trees with continuous values of stem water potential (Ψstem) through partial secession treatment of the base of branches to change the water status of the branches. The total number of images as frames extracted from videos of branches was 23,181, 6743, and 10,752, in the training, validation, and test datasets, respectively. These datasets enabled us to precisely model water deficit stress using a deep-learning-regression model. The predicted Ψstem of frames belonging to a single branch showed a Gaussian distribution, and the coefficient of determination between the measured and predicted values of Ψstem increased to 0.927 by averaging the predicted values of the frames in each video. This method of averaging the predicted values of frames in each video can automatically eliminate noise and summarize data into the representative value of a tree and is considered to be robust for the diagnosis of water deficit stress in large field-grown peach trees with a complex branch structure.https://doi.org/10.1038/s41598-023-49980-8 |
| spellingShingle | Takayoshi Yamane Harshana Habaragamuwa Ryo Sugiura Taro Takahashi Hiroko Hayama Nobuhito Mitani Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees Scientific Reports |
| title | Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees |
| title_full | Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees |
| title_fullStr | Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees |
| title_full_unstemmed | Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees |
| title_short | Stem water potential estimation from images using a field noise-robust deep regression-based approach in peach trees |
| title_sort | stem water potential estimation from images using a field noise robust deep regression based approach in peach trees |
| url | https://doi.org/10.1038/s41598-023-49980-8 |
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