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Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging
- Citation Author(s):
- Submitted by:
- MINJUAN MA
- Last updated:
- Fri, 12/24/2021 - 03:58
- DOI:
- 10.21227/7eck-8z89
- License:
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Abstract
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 and metabolic disorders, 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 (SPA), Iteratively Retains Informative Variables (IRIV) and Variable Iterative Space Shrinkage Approac (VISSA) were used to choose important parameters. The common parameters extracted by the three algorithms are the actual light quantum efficiency at L2 during the light adaptation process(QY_L2), the non-actinic fluorescence quenching at L3 during the light adaptation process(NPQ_L3), and the light adaptation photochemical quenching at L2 time(qL_L2), steady-state light adaptation photochemical quenching(qL_Lss), and the light adaptation photochemical quenching at D3 time during the dark relaxation process(qL_D3). Then, 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
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 and metabolic disorders, 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 (SPA), Iteratively Retains Informative Variables (IRIV) and Variable Iterative Space Shrinkage Approac (VISSA) were used to choose important parameters. The common parameters extracted by the three algorithms are the actual light quantum efficiency at L2 during the light adaptation process(QY_L2), the non-actinic fluorescence quenching at L3 during the light adaptation process(NPQ_L3), and the light adaptation photochemical quenching at L2 time(qL_L2), steady-state light adaptation photochemical quenching(qL_Lss), and the light adaptation photochemical quenching at D3 time during the dark relaxation process(qL_D3). Then, 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
