Detection of tomato water stress based on terahertz spectroscopy
China’s tomato cultivation area is nearly 15 thousand km2, and its annual tomato output is about 55 million tons, accounting for 7% of its total vegetable production. Because of the high drought sensitivity of tomatoes, water stress inhibits their nutrient uptake, leading to a decrease in tomato qua...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-01-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1095434/full |
| _version_ | 1811175365292326912 |
|---|---|
| author | Yixue Zhang Xinzhong Wang Yafei Wang Lian Hu Pei Wang |
| author_facet | Yixue Zhang Xinzhong Wang Yafei Wang Lian Hu Pei Wang |
| author_sort | Yixue Zhang |
| collection | DOAJ |
| description | China’s tomato cultivation area is nearly 15 thousand km2, and its annual tomato output is about 55 million tons, accounting for 7% of its total vegetable production. Because of the high drought sensitivity of tomatoes, water stress inhibits their nutrient uptake, leading to a decrease in tomato quality and yield. Therefore, the rapid, accurate and non-destructive detection of water status is important for scientifically and effectively managing tomato water and fertilizer, improving the efficiency of water resource utilization, and safeguarding tomato yield and quality. Because of the extreme sensitivity of terahertz spectroscopy to water, we proposed a tomato leaf moisture detection method based on terahertz spectroscopy and made a preliminary exploration of the relationship between tomato water stress and terahertz spectral data. Tomato plants were grown at four levels of water stress. Fresh tomato leaves were sampled at fruit set, moisture content was calculated, and spectral data were collected through a terahertz time-domain spectroscope. The raw spectral data were smoothed using the Savitzky–Golay algorithm to reduce interference and noise. Then the data were divided by the Kennard–Stone algorithm and the sample set was partitioned based on the joint X-Y distance (SPXY) algorithm into a calibration set and a prediction set at a ratio of 3:1. SPXY was found to be the better approach for sample division. On this basis, the stability competitive adaptive re-weighted sampling algorithm was used to extract the feature frequency bands of moisture content, and a multiple linear regression model of leaf moisture content was established under the single dimensions of power, absorbance and transmittance. The absorbance model was the best, with a prediction set correlation coefficient of 0.9145 and a root mean square error of 0.1199. To further improve the modeling accuracy, we used a support vector machine (SVM) to establish a tomato moisture fusion prediction model based on the fusion of three-dimensional terahertz feature frequency bands. As water stress intensified, the power and absorbance spectral values both declined, and both were significantly and negatively correlated with leaf moisture content. The transmittance spectral value increased gradually with the intensification of water stress, showing a significant positive correlation. The SVM-based three-dimensional fusion prediction model showed a prediction set correlation coefficient of 0.9792 and a root mean square error of 0.0531, indicating that it outperformed the three single-dimensional models. Hence, terahertz spectroscopy can be applied to the detection of tomato leaf moisture content and provides a reference for tomato moisture detection. |
| first_indexed | 2024-04-10T19:35:59Z |
| format | Article |
| id | doaj.art-d048a6d01bb242bc91798496f440b72b |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-10T19:35:59Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-d048a6d01bb242bc91798496f440b72b2023-01-30T07:50:05ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-01-011410.3389/fpls.2023.10954341095434Detection of tomato water stress based on terahertz spectroscopyYixue Zhang0Xinzhong Wang1Yafei Wang2Lian Hu3Pei Wang4Basic Engineering Training Center, Jiangsu University, Zhenjiang, ChinaCollege of Agricultural Engineering, Jiangsu University, Zhenjiang, ChinaCollege of Agricultural Engineering, Jiangsu University, Zhenjiang, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou, ChinaCollege of Agricultural Engineering, Jiangsu University, Zhenjiang, ChinaChina’s tomato cultivation area is nearly 15 thousand km2, and its annual tomato output is about 55 million tons, accounting for 7% of its total vegetable production. Because of the high drought sensitivity of tomatoes, water stress inhibits their nutrient uptake, leading to a decrease in tomato quality and yield. Therefore, the rapid, accurate and non-destructive detection of water status is important for scientifically and effectively managing tomato water and fertilizer, improving the efficiency of water resource utilization, and safeguarding tomato yield and quality. Because of the extreme sensitivity of terahertz spectroscopy to water, we proposed a tomato leaf moisture detection method based on terahertz spectroscopy and made a preliminary exploration of the relationship between tomato water stress and terahertz spectral data. Tomato plants were grown at four levels of water stress. Fresh tomato leaves were sampled at fruit set, moisture content was calculated, and spectral data were collected through a terahertz time-domain spectroscope. The raw spectral data were smoothed using the Savitzky–Golay algorithm to reduce interference and noise. Then the data were divided by the Kennard–Stone algorithm and the sample set was partitioned based on the joint X-Y distance (SPXY) algorithm into a calibration set and a prediction set at a ratio of 3:1. SPXY was found to be the better approach for sample division. On this basis, the stability competitive adaptive re-weighted sampling algorithm was used to extract the feature frequency bands of moisture content, and a multiple linear regression model of leaf moisture content was established under the single dimensions of power, absorbance and transmittance. The absorbance model was the best, with a prediction set correlation coefficient of 0.9145 and a root mean square error of 0.1199. To further improve the modeling accuracy, we used a support vector machine (SVM) to establish a tomato moisture fusion prediction model based on the fusion of three-dimensional terahertz feature frequency bands. As water stress intensified, the power and absorbance spectral values both declined, and both were significantly and negatively correlated with leaf moisture content. The transmittance spectral value increased gradually with the intensification of water stress, showing a significant positive correlation. The SVM-based three-dimensional fusion prediction model showed a prediction set correlation coefficient of 0.9792 and a root mean square error of 0.0531, indicating that it outperformed the three single-dimensional models. Hence, terahertz spectroscopy can be applied to the detection of tomato leaf moisture content and provides a reference for tomato moisture detection.https://www.frontiersin.org/articles/10.3389/fpls.2023.1095434/fullterahertz spectroscopyspectroscopyterahertztomatowater stress |
| spellingShingle | Yixue Zhang Xinzhong Wang Yafei Wang Lian Hu Pei Wang Detection of tomato water stress based on terahertz spectroscopy Frontiers in Plant Science terahertz spectroscopy spectroscopy terahertz tomato water stress |
| title | Detection of tomato water stress based on terahertz spectroscopy |
| title_full | Detection of tomato water stress based on terahertz spectroscopy |
| title_fullStr | Detection of tomato water stress based on terahertz spectroscopy |
| title_full_unstemmed | Detection of tomato water stress based on terahertz spectroscopy |
| title_short | Detection of tomato water stress based on terahertz spectroscopy |
| title_sort | detection of tomato water stress based on terahertz spectroscopy |
| topic | terahertz spectroscopy spectroscopy terahertz tomato water stress |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1095434/full |
| work_keys_str_mv | AT yixuezhang detectionoftomatowaterstressbasedonterahertzspectroscopy AT xinzhongwang detectionoftomatowaterstressbasedonterahertzspectroscopy AT yafeiwang detectionoftomatowaterstressbasedonterahertzspectroscopy AT lianhu detectionoftomatowaterstressbasedonterahertzspectroscopy AT peiwang detectionoftomatowaterstressbasedonterahertzspectroscopy |