Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology

编号 040029103

推送时间 20210517

研究领域 森林培育 

年份 2021 

类型 期刊 

语种 英语

标题 Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology

来源期刊 Plant Reproduction

期 第291期

发表时间 20210316

关键词 Deep learning;  Computer vision;  Neural network;  Phenotyping;  Reproduction; 

摘要 High-throughput phenotyping systems are becoming critical for answering biological questions on a large scale. These systems have historically relied on traditional computer vision techniques. However, neural networks and specifically deep learning are rapidly becoming more powerful and easier to implement. Here, we examine how deep learning can drive phenotyping systems and be used to answer fundamental questions in reproductive biology. We describe previous applications of deep learning in the plant sciences, provide general recommendations for applying these methods to the study of plant reproduction, and present a case study in maize ear phenotyping. Finally, we highlight several examples where deep learning has enabled research that was previously out of reach and discuss the future outlook of these methods.

服务人员 孙小满

服务院士 尹伟伦

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