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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2016.1
- 수록면
- 21 - 29 (9page)
이용수
초록· 키워드
오류제보하기
Remote Sensing (RS) is a technique to obtain necessary information in a non-contact and non-destructive method by using various sensors on the surface, water or atmospheric phenomena. These techniques combine elements such as sensors, and platform and information communication technology (ICT) for mounting the sensor. ICT has contributed significantly to the success of smart agriculture through quantification and measurement of environmental factors and information such as weather, crop and soil management to distribution and consumption stage, as well as the production stage by the cloud computer. Remote sensing techniques, including non-destructive non-contact bioimaging (remote imaging) is required to measure the plant function. In addition, bioimaging study in plant science is performed at the gene, cellular and individual plant level. Recently, bioimaging technology is considered the latest phenomics that identifies the relationship between the genotype and environment for distinguishing phenotypes. In this review, trends in remote sensing in plants, plants diagnostics and response to environment and status of plants phonemics research were presented.
목차
등록된 정보가 없습니다.
참고문헌 (24)
참고문헌 신청
Berger B / 2010 / High-throughput shoot imaging to study drought responses / Journal of Experimental Botany 61:3519~3528
Furbank RT / 2009 / Plant phenomics / Funct Plant Biol 36:845~1026
Granier C / 2006 / PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit / New Phytologist 169:623~635
Hartmann A / 2011 / HTPheno: An image analysis pipeline for high-throughput plant phenotyping / BMC Bioinformatics 12:148
Ishimura A / 2011 / Remote sensing of Japanese beech forest decline using an improved Temperatur Vegetation Dryness Index(iTVDI) / iForest 4:195~199
Furbank RT / 2009 / Plant phenomics / Funct Plant Biol 36:845~1026
Granier C / 2006 / PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit / New Phytologist 169:623~635
Hartmann A / 2011 / HTPheno: An image analysis pipeline for high-throughput plant phenotyping / BMC Bioinformatics 12:148
Ishimura A / 2011 / Remote sensing of Japanese beech forest decline using an improved Temperatur Vegetation Dryness Index(iTVDI) / iForest 4:195~199
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