Difference between revisions of "IC4R009-Phenomics-2013-23578473"
(→Key technologies in plant phenomics) |
(→Plant Culture & Treatment) |
||
| Line 8: | Line 8: | ||
<br> | <br> | ||
| − | == | + | ==Current applications of phenomics in rice or other cereal crops== |
| + | *Using the plant age and plant area calculated by the Scanalyzer 3D, a modified model enables the more accurate high-throughput estimation of biomass for cereal plants under saline conditions [19��]. IR thermal imaging is also commonly used to quantify the osmotic stress response to salinity or drought in cereal crops [55].By precisely controlling the environments of each potgrown cereal plant in the greenhouse, these accurate,high-throughput phenotyping tools can overcome the limitations of current salinity-resistance or drought-resistance research. | ||
| + | *Both grain production and grain quality decrease as plants are damaged by insects and disease; it is, therefore,important to detect and classify the plant infestations at an early stage [58]. To identify rice blast disease at the seedling stage, a near-infrared hyperspectral imaging system was developed to scan clipped leaves, with an overall accuracy of classification (infected and healthy leaves) of approximately 92% [59]. In addition to these destructive measurement techniques, there are several approaches to achieve real-time and dynamic screening in vivo for pot-grown rice or field-grown cereals. With a color-based corner detection algorithm, visible imagebased methods can detect plant-hopper infestations on the stems of pot-grown rice [60]. Integrating hyper-spectral and fluorescence imaging enables the detection of yellow rust in a winter wheat field, and the overall | ||
| + | discrimination performance can reach 99% with a selforganizing map neural network [61]. | ||
==Research Findings== | ==Research Findings== | ||
Revision as of 10:51, 22 July 2016
Contents
Project Title
- Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies
Key technologies in plant phenomics
- Since the first digital camera was invented by Eastman Kodak in 1975 (www.letsgodigital.org/en/16859/ce-hallof-fame), visible light imaging technology has been widely adopted in plant science due to its low cost and ease of maintenance. With a similar wavelength (400–700 nm) perception as the human eye, two-dimensional(2D) photography can be used to analyze shoot biomass[18,19��], yield-related traits [20��], leaf morphology [23],panicle traits [24], and the system architecture traits of washed roots or roots grown in transparent media [25].
- Because of internal molecular movements, all objects emit characteristic infrared radiation [29]. Two popular infrared imaging devices can be used to screen radiation images: a near-infrared (NIR, wavelength of approximately 0.9–1.7 mm) imaging device and a far-infrared(Far-IR, wavelength of approximately 7.5–13.5 mm) imaging device. Healthy green plants reflect a large proportion of NIR light from 800 to 1400 nm, whereas the soil reflects little NIR light; moreover, soil and unhealthy plants reflect considerably more red wavelength light as compared with healthy plants. For these reasons, many studies have combined NIR imaging and visible imaging to detect vegetative indices. A Crop Phenology Recording System (CPRS) has been developed for monitoring rice growth. CPRS uses visible light imaging to derive the visible atmospherically resistant index and uses nearinfrared imaging (830 nm) to derive the night-time relative brightness index and then establishes the relationship between the camera-derived indices and the agronomic traits [30].
- In recent years, several modern optical imaging techniques, for instance, 3D structural tomography and functional imaging, have been developed and expanded to improve living plant visualization. The rice plants serve as ‘patients’ in a novel use of X-ray computed tomography(CT) scanners to estimate the tiller number [38��].Equipped with an acceleration algorithm using the adaptive minimum enclosing rectangle (AMER) and graphics processing unit (GPU), the entire tiller inspection time of one plant is less than 200 ms [39]. Moreover, the incorporation of various optical sensors, such as visible and infrared digital cameras, provides this system with the potential to achieve the advanced screening of multiple traits for pot-grown rice plants within one chamber [38��].
Current applications of phenomics in rice or other cereal crops
- Using the plant age and plant area calculated by the Scanalyzer 3D, a modified model enables the more accurate high-throughput estimation of biomass for cereal plants under saline conditions [19��]. IR thermal imaging is also commonly used to quantify the osmotic stress response to salinity or drought in cereal crops [55].By precisely controlling the environments of each potgrown cereal plant in the greenhouse, these accurate,high-throughput phenotyping tools can overcome the limitations of current salinity-resistance or drought-resistance research.
- Both grain production and grain quality decrease as plants are damaged by insects and disease; it is, therefore,important to detect and classify the plant infestations at an early stage [58]. To identify rice blast disease at the seedling stage, a near-infrared hyperspectral imaging system was developed to scan clipped leaves, with an overall accuracy of classification (infected and healthy leaves) of approximately 92% [59]. In addition to these destructive measurement techniques, there are several approaches to achieve real-time and dynamic screening in vivo for pot-grown rice or field-grown cereals. With a color-based corner detection algorithm, visible imagebased methods can detect plant-hopper infestations on the stems of pot-grown rice [60]. Integrating hyper-spectral and fluorescence imaging enables the detection of yellow rust in a winter wheat field, and the overall
discrimination performance can reach 99% with a selforganizing map neural network [61].
Research Findings
Labs working on this Project
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, PR China
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, PR China
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China
Corresponding Author
- Xiong, Lizhong:lizhongx@mail.hzau.edu.cn & Liu, Qian:qianliu@mail.hust.edu.cn
