计算机视觉在评估作物非生物压力的应用:系统性审查
Nabil Anan Orka1, Fardeen Md Toushique1, M Nazim Uddin2
1Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh.
PloS one
|August 23, 2023
概括
深度学习计算机视觉提供了一种实用,非侵入性的方法来评估作物非生物压力. 本综述强调了当前的趋势,并确定了这个正在发展的领域的研究缺口.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 无生物应激因素显著降低了作物产量和生长.
- 目前的作物压力评估方法有限.
- 深度学习 (DL) 计算机视觉为作物压力监测提供了一个有希望的非逃避性的替代方案.
研究的目的:
- 对作物非生物压力评估的深度学习应用进行全面的文献审查.
- 为了确定当前的趋势和该领域的研究差距.
主要方法:
- 系统的文献审查遵循PRISMA指导方针.
- 专注于基于DL的计算机视觉文章,分类作物非生物压力.
- 分析了2012年至2022年12月间发表的文章.
主要成果:
- 在严格选后,确定了14个相关出版物.
- 涵盖了7种不同的作物中的11种不同的非生物压力因素.
- 观察到预训练网络的主导地位,许多DL架构尚未被探索.
结论:
- 对于作物非生物应激评估的DL领域是新生的,但显示出潜力.
- 存在显著的研究缺口,特别是在探索各种DL架构方面.
- 未来的研究应该专注于解决这些差距,以推进作物管理战略.
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