{"title":"Predicting rice diseases using advanced technologies at different scales: present status and future perspectives","authors":"Ruyue Li, Sishi Chen, Haruna Matsumoto, Mostafa Gouda, Yusufjon Gafforov, Mengcen Wang, Yufei Liu","doi":"10.1007/s42994-023-00126-4","DOIUrl":null,"url":null,"abstract":"<div><p>The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.</p></div>","PeriodicalId":53135,"journal":{"name":"aBIOTECH","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10721578/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"aBIOTECH","FirstCategoryId":"1091","ListUrlMain":"https://link.springer.com/article/10.1007/s42994-023-00126-4","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.
过去几年中,用于准确、快速跟踪水稻病害并预测潜在解决方案的新兴病害检测技术取得了重大进展。在这篇综述中,我们重点介绍了与多尺度水稻病害相关的使用机器学习(ML)和深度学习(DL)模型的图像处理技术。此外,我们还总结了不同检测技术的应用,包括基因组学、生理学和生物化学方法。此外,我们还介绍了病原体与植物相互作用表型的当代光学传感应用的最新进展。本综述为研究人员提供了宝贵的资源,帮助他们寻求有效的解决方案,以应对高通量数据和模型识别方面的挑战,从而通过 ML 和 DL 模型及早发现影响水稻作物的问题。