Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging
Drought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9760457/ |
| _version_ | 1798040442081640448 |
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| author | Yan Long Minjuan Ma |
| author_facet | Yan Long Minjuan Ma |
| author_sort | Yan Long |
| collection | DOAJ |
| description | Drought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress in tomato seedlings using chlorophyll fluorescence imaging. In this study, chlorophyll fluorescence parameters and there corresponding chlorophyll fluorescence images of 4 different drought stress levels were collected. Then three feature optimization algorithms which were Successive Projections Algorithm, Iteratively Retains Informative Variables and Variable Iterative Space Shrinkage Approac were used to choose important parameters. A total of five common parameters were obtained, and the corresponding chlorophyll fluorescence images of the five common parameters were selected. And two types of image features were used to study and analyze drought stress classes: histogram features and texture features. The Pearson correlations of the features were calculated and the high correlated features were input into three models, which were Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and k-Nearest Neighbor (KNN), to identify drought stress classes. The recognition accuracy rate of LDA, SVM and KNN were 86.8%, 87.1% and 76.5% respectively. Our experiment results showed that the five common fluorescence parameters and there corresponding image features could be used to evaluate the drought stress classes of tomato seedlings, and had a good evaluation effect. This research provideed a new method for monitoring drought stress classes and had considerable prospects for non-destructive diagnosis of plant drought stress. |
| first_indexed | 2024-04-11T22:08:38Z |
| format | Article |
| id | doaj.art-1234b69f78d34d209f737e897571e7f1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:08:38Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1234b69f78d34d209f737e897571e7f12022-12-22T04:00:38ZengIEEEIEEE Access2169-35362022-01-0110486334864210.1109/ACCESS.2022.31688629760457Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence ImagingYan Long0Minjuan Ma1https://orcid.org/0000-0002-0710-6380College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ChinaDrought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress in tomato seedlings using chlorophyll fluorescence imaging. In this study, chlorophyll fluorescence parameters and there corresponding chlorophyll fluorescence images of 4 different drought stress levels were collected. Then three feature optimization algorithms which were Successive Projections Algorithm, Iteratively Retains Informative Variables and Variable Iterative Space Shrinkage Approac were used to choose important parameters. A total of five common parameters were obtained, and the corresponding chlorophyll fluorescence images of the five common parameters were selected. And two types of image features were used to study and analyze drought stress classes: histogram features and texture features. The Pearson correlations of the features were calculated and the high correlated features were input into three models, which were Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and k-Nearest Neighbor (KNN), to identify drought stress classes. The recognition accuracy rate of LDA, SVM and KNN were 86.8%, 87.1% and 76.5% respectively. Our experiment results showed that the five common fluorescence parameters and there corresponding image features could be used to evaluate the drought stress classes of tomato seedlings, and had a good evaluation effect. This research provideed a new method for monitoring drought stress classes and had considerable prospects for non-destructive diagnosis of plant drought stress.https://ieeexplore.ieee.org/document/9760457/Chlorophyll fluorescence imagingdrought stressfeature extractionmachine learningtomato seedlings |
| spellingShingle | Yan Long Minjuan Ma Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging IEEE Access Chlorophyll fluorescence imaging drought stress feature extraction machine learning tomato seedlings |
| title | Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging |
| title_full | Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging |
| title_fullStr | Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging |
| title_full_unstemmed | Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging |
| title_short | Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging |
| title_sort | recognition of drought stress state of tomato seedling based on chlorophyll fluorescence imaging |
| topic | Chlorophyll fluorescence imaging drought stress feature extraction machine learning tomato seedlings |
| url | https://ieeexplore.ieee.org/document/9760457/ |
| work_keys_str_mv | AT yanlong recognitionofdroughtstressstateoftomatoseedlingbasedonchlorophyllfluorescenceimaging AT minjuanma recognitionofdroughtstressstateoftomatoseedlingbasedonchlorophyllfluorescenceimaging |