气候变化领域集成服务门户(CCPortal): Deep learning: as the new frontier in high-throughput plant phenotyping

CCPortal

DOI	10.1007/s10681-022-02992-3
	Deep learning: as the new frontier in high-throughput plant phenotyping
	Arya, Sunny; Sandhu, Karansher Singh; Singh, Jagmohan; Kumar, Sudhir
发表日期	2022
ISSN	0014-2336
EISSN	1573-5060
卷号	218 期号:4
英文摘要	With climate change and ever-increasing population growth, the pace of varietal development needs to be accelerated in order to feed a population of 10 billion by 2050. Non-invasive high-throughput plant phenotyping (HTP) using advanced imaging technology has capabilities to boost the varietal development process. The tremendous data generated with sensor aided HTP have created the big data and problem in the downstream data analysis pipeline. The higher-level abstraction achieved on high dimensional data by multiple hidden layers for function approximation have made deep learning applications in HTP of significant interest. Application of deep learning models to enhance image-based throughput in phenotyping is an emerging and dynamic area of research in plant phenomics. In this comprehensive review we highlighted the recent developments in the field of deep learning application for HTP. The deep learning principles are described and contextualized relative to machine learning and conventional computer vision algorithms. Novel and emerging deep learning applications are identified. Recommendations are provided with the intent of choosing the most suitable models and training strategy for the capturing and predicting sensor-based phenotyping traits. It also includes steps and suggestions for the development and eventual deployment of such models for multi-task phenotyping. Public datasets have been identified and these datasets are reported which can be used for model training and benchmarking. Overall, this study provided a comprehensive overview of deep learning, it's application in plant phenomics, potential barriers and scope of improvement.
英文关键词	Deep learning; High-throughput plant phenotyping; Convolution neural network (CNN); Field phenotyping; Unmanned aerial vehicle (UAV)
语种	英语
WOS研究方向	Agronomy ; Plant Sciences ; Horticulture
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000770312900001
来源期刊	EUPHYTICA
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281019
作者单位	Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; Washington State University; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute
推荐引用方式 GB/T 7714	Arya, Sunny,Sandhu, Karansher Singh,Singh, Jagmohan,et al. Deep learning: as the new frontier in high-throughput plant phenotyping[J],2022,218(4).
APA	Arya, Sunny,Sandhu, Karansher Singh,Singh, Jagmohan,&Kumar, Sudhir.(2022).Deep learning: as the new frontier in high-throughput plant phenotyping.EUPHYTICA,218(4).
MLA	Arya, Sunny,et al."Deep learning: as the new frontier in high-throughput plant phenotyping".EUPHYTICA 218.4(2022).