The past few years have witnessed significant progress in emerging disease detection techniques foraccurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focuson image proc...The past few years have witnessed significant progress in emerging disease detection techniques foraccurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focuson image processing techniques using machine learning (ML) and deep learning (DL) models related tomulti-scale rice diseases. Furthermore, we summarize applications of different detection techniques,including genomic, physiological, and biochemical approaches. In addition, we also present the state-ofthe-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. Thisreview 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 cropsthrough ML and DL models.展开更多
基金supported by the Key R&D Plan of Zhejiang Province(2021C02057,2020C02002)the National Key R&D Program of China(2021YFE0113700)+2 种基金the International S&T Cooperation Program of China(2019YFE0103800)Fundamental Research Funds for the Zhejiang Provincial Universities[2021XZZX024]Zhejiang University Global Partnership Fund.We also appreciate Prof.Zhonghua Ma(Institute of Biotechnology,Zhejiang University)for his insightful advice on this work.
文摘The past few years have witnessed significant progress in emerging disease detection techniques foraccurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focuson image processing techniques using machine learning (ML) and deep learning (DL) models related tomulti-scale rice diseases. Furthermore, we summarize applications of different detection techniques,including genomic, physiological, and biochemical approaches. In addition, we also present the state-ofthe-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. Thisreview 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 cropsthrough ML and DL models.
