High-throughput phenotyping platform for analyzing drought tolerance in rice
- PDF / 7,282,006 Bytes
- 18 Pages / 595.276 x 790.866 pts Page_size
- 91 Downloads / 205 Views
ORIGINAL ARTICLE
High‑throughput phenotyping platform for analyzing drought tolerance in rice Song Lim Kim1 · Nyunhee Kim1 · Hongseok Lee1,2 · Eungyeong Lee1,3 · Kyeong‑Seong Cheon1 · Minsu Kim1 · JeongHo Baek1 · Inchan Choi1 · Hyeonso Ji1 · In Sun Yoon1 · Ki‑Hong Jung4 · Taek‑Ryoun Kwon1 · Kyung‑Hwan Kim1 Received: 4 April 2020 / Accepted: 29 July 2020 © The Author(s) 2020
Abstract Main conclusion A new imaging platform was constructed to analyze drought-tolerant traits of rice. Rice was used to quantify drought phenotypes through image-based parameters and analyzing tools. Abstract Climate change has increased the frequency and severity of drought, which limits crop production worldwide. Developing new cultivars with increased drought tolerance and short breeding cycles is critical. However, achieving this goal requires phenotyping a large number of breeding populations in a short time and in an accurate manner. Novel cutting-edge technologies such as those based on remote sensors are being applied to solve this problem. In this study, new technologies were applied to obtain and analyze imaging data and establish efficient screening platforms for drought tolerance in rice using the drought-tolerant mutant osphyb. Red–Green–Blue images were used to predict plant area, color, and compactness. Near-infrared imaging was used to determine the water content of rice, infrared was used to assess plant temperature, and fluorescence was used to examine photosynthesis efficiency. DroughtSpotter technology was used to determine water use efficiency, plant water loss rate, and transpiration rate. The results indicate that these methods can detect the difference between tolerant and susceptible plants, suggesting their value as high-throughput phenotyping methods for short breeding cycles as well as for functional genetic studies of tolerance to drought stress. Keywords Drought stress · RGB · NIR · IR · Fluorescence · Parameter
Communicated by Anastasios Melis. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00425-020-03436-9) contains supplementary material, which is available to authorized users. * Kyung‑Hwan Kim [email protected] 1
The National Institute of Agricultural Sciences, 370 Nongsaengmyeong‑ro, Wansan‑gu, Jeonju‑si, Jeollabuk‑do, Republic of Korea
2
Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
3
Department of Crop Science and Biotechnology, Jeonbuk National University, Jeonju 54896, Republic of Korea
4
Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin, Republic of Korea
Abbreviations RGB Red–Green–Blue NIR Near-infrared IR Infrared WUE Water use efficiency TR Transpiration rate ChIF Chlorophyll fluorescence DSP Drought stress phase RWP Re-watering phase PWLR Plant water loss rate
Introduction The worldwide population is projected to increase to 90 billion people by 2050, and the availability of water is an important problem
Data Loading...
