Predicting rice diseases using advanced technologies at different scales: present status and future perspectives.

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.

Code and data availability

This is a review article summarizing literature on rice disease detection. The authors explicitly state no datasets were generated or analyzed, and no paper-specific phenotype datasets, images, code, or models are deposited or referenced with public availability.

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