Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
Irrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral...
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MDPI AG
2020-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/13/4665 |
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| author | Tiejun Zhao Akimasa Nakano Yasunaga Iwaski Hiroki Umeda |
| author_facet | Tiejun Zhao Akimasa Nakano Yasunaga Iwaski Hiroki Umeda |
| author_sort | Tiejun Zhao |
| collection | DOAJ |
| description | Irrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral imaging has been widely used as a nondestructive measurement method in agriculture to obtain plant biological information. The objective of this research was to establish an approach to obtain the tomato leaf water status—specifically, the relative water content (WC) and equivalent water thickness (MC)—for five different tomato cultivars in real time by using hyperspectral imaging. The normalized difference vegetation index (NDVI) and two-band vegetation index (TBI) analyses were performed on the tomato leaf raw relative reflection (RAW), the inversion-logarithm relative reflection (LOG), and the first derivative of relative reflection (DIFF) from wavelengths of 900 nm to 1700 nm. The best regression model for WC assessment was obtained by TBI regression using DIFF at wavelengths of 1410 nm and 1520 nm, and the best regression model for MC assessment was obtained by NDVI regression using RAW at wavelengths of 1300 nm and 1310 nm. Higher model performance was obtained with MC assessment than with WC assessment. The results will help improve our understanding of the relationship between hyperspectral reflectance and leaf water status. |
| first_indexed | 2024-03-10T18:39:17Z |
| format | Article |
| id | doaj.art-34aea2dae4e9458f8cfdc580a4c037b7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:39:17Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-34aea2dae4e9458f8cfdc580a4c037b72023-11-20T05:59:37ZengMDPI AGApplied Sciences2076-34172020-07-011013466510.3390/app10134665Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant FactoriesTiejun Zhao0Akimasa Nakano1Yasunaga Iwaski2Hiroki Umeda3Department of Agro-Food Science, Niigata Agro-Food University, Faculty of Agro-Food Science, Tainai Campus, Hiranedai 2416, Tainai, Niigata 959-2702, JapanChiba University Innovation Management Organization, Chiba University, Kashiwano-ha Campus 6-2-1, Kashiwano-ha, Kashiwa-shi, Chiba 277-0882, JapanTohoku Agricultural Research Center, NARO, 4 Akahira, Shimo-kuriyagawa, Morioka, Iwate 020-0198, JapanCollege of Bioresource Sciences, Nihon University, 1866 Kameino, Fujisawa, Kanagawa 252-0880, JapanIrrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral imaging has been widely used as a nondestructive measurement method in agriculture to obtain plant biological information. The objective of this research was to establish an approach to obtain the tomato leaf water status—specifically, the relative water content (WC) and equivalent water thickness (MC)—for five different tomato cultivars in real time by using hyperspectral imaging. The normalized difference vegetation index (NDVI) and two-band vegetation index (TBI) analyses were performed on the tomato leaf raw relative reflection (RAW), the inversion-logarithm relative reflection (LOG), and the first derivative of relative reflection (DIFF) from wavelengths of 900 nm to 1700 nm. The best regression model for WC assessment was obtained by TBI regression using DIFF at wavelengths of 1410 nm and 1520 nm, and the best regression model for MC assessment was obtained by NDVI regression using RAW at wavelengths of 1300 nm and 1310 nm. Higher model performance was obtained with MC assessment than with WC assessment. The results will help improve our understanding of the relationship between hyperspectral reflectance and leaf water status.https://www.mdpi.com/2076-3417/10/13/4665hyperspectral imagingtomato leafwater statusnormalized difference vegetation indextwo-band index |
| spellingShingle | Tiejun Zhao Akimasa Nakano Yasunaga Iwaski Hiroki Umeda Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories Applied Sciences hyperspectral imaging tomato leaf water status normalized difference vegetation index two-band index |
| title | Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories |
| title_full | Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories |
| title_fullStr | Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories |
| title_full_unstemmed | Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories |
| title_short | Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories |
| title_sort | application of hyperspectral imaging for assessment of tomato leaf water status in plant factories |
| topic | hyperspectral imaging tomato leaf water status normalized difference vegetation index two-band index |
| url | https://www.mdpi.com/2076-3417/10/13/4665 |
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