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使用不同规模的先进技术预测米病:目前的状况和未来的前景

Ruyue Li^1,2, Sishi Chen¹, Haruna Matsumoto³

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China.

|December 18, 2023

概括

这篇综述强调了先进的机器学习 (ML) 和深度学习 (DL) 用于早期检测水疾病. 这些方法通过图像处理和其他技术来改进对作物健康的跟踪和解决方案.

科学领域:

农业科学农业科学
计算机科学计算机科学
植物病理学植物病理学

背景情况:

准确和快速跟踪病对全球粮食安全至关重要.
新兴技术对于应对农作物监测高通量数据分析的挑战至关重要.
传统的方法往往难以应对现代农业数据的复杂性和规模.

研究的目的:

审查使用机器学习 (ML) 和深度学习 (DL) 的图像处理技术,用于多规模检测水疾病.
总结各种检测方法,包括基因组,生理和生化方法.
介绍目前的光学传感在病原体和植物相互作用现象型分析中的现状.

主要方法:

专注于基于ML和DL模型的图像处理技术.
对基因组,生理和生化检测策略的审查.
对病原体与植物相互作用的当代光学传感应用的分析.

主要成果:

ML和DL模型显示出对准确和快速检测病的显著前景.
将图像处理与ML/DL集成为复杂数据集提供了有效的解决方案.
光学传感为表型层面的病原体-植物相互作用提供了洞察力.

结论:

ML和DL是早期检测作物疾病的重要工具.
本综述为农业技术和植物病理学研究人员提供了全面的资源.
未来的研究应该专注于改进高通量数据分析和模型识别,以提高作物管理.

关键词:

人工智能的人工智能是人工智能.高通量数据数据的高通量数据.图像技术技术的成像技术模型算法模型算法植物病原体相互作用病是指大米的疾病.

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