Deep Learning in Image-Based Plant Phenotyping.

IF 21.3 1区 生物学 Q1 PLANT SCIENCES
Katherine M Murphy, Ella Ludwig, Jorge Gutierrez, Malia A Gehan
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引用次数: 0

Abstract

A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.

基于图像的植物表型分析中的深度学习
作物改良过程中的一个主要瓶颈是我们能否快速高效地对作物进行表型。基于图像的高通量表型技术具有许多优点,因为它是非破坏性的,而且减少了人力,但在从大量图像数据中提取有意义的信息方面出现了新的挑战。深度学习是人工智能的一种,是一种用于分析图像数据并对未见图像进行预测的方法,最终可减少计算中对人工输入的需求。在此,我们回顾了深度学习的基本原理、深度学习的成功评估、深度学习在植物表型组学中的应用实例、最佳实践和公开挑战。植物生物学年刊》第 75 卷的最终在线出版日期预计为 2024 年 5 月。修订后的预计日期请参见 http://www.annualreviews.org/page/journal/pubdates。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annual review of plant biology
Annual review of plant biology 生物-植物科学
CiteScore
40.40
自引率
0.40%
发文量
29
期刊介绍: The Annual Review of Plant Biology is a peer-reviewed scientific journal published by Annual Reviews. It has been in publication since 1950 and covers significant developments in the field of plant biology, including biochemistry and biosynthesis, genetics, genomics and molecular biology, cell differentiation, tissue, organ and whole plant events, acclimation and adaptation, and methods and model organisms. The current volume of this journal has been converted from gated to open access through Annual Reviews' Subscribe to Open program, with all articles published under a CC BY license.
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