数字农科院2.0

Accelerating Wild Rice Disease-Resistant Germplasm Exploration: Artificial Intelligence (AI)-Powered Wild Rice Blast Disease Level Evaluation and Disease-Resistance Identification

文献类型： 外文期刊

作者： Pan, Pan;Guo, Wenlong;Li, Hengbo;Shao, Yifan;Guo, Zhihao;Jin, Ye;Cheng, Yanrong;Yu, Guoping;Fu, Zhenshi;Hu, Lin;Zheng, Xiaoming;Guomin, Zhou;Jianhua, Zhang

作者机构：

关键词： wild rice;germplasm resource;rice blast;crop disease level evaluation;disease-resistance identification

期刊名称： RICE SCIENCE

ISSN： 1672-6308

年卷期： 2025 年 32 卷 5 期

页码：

收录情况： SCIE（2025版） ; ; CSCD（2025-2026年度） ; ; 科技核心（2024版） ; ; 农林核心（2024版）

摘要： Accurate evaluation of disease levels in wild rice germplasm and identification of disease resistance are critical for developing rice varieties resistant to blast disease. However, existing evaluation methods face limitations that hinder progress in breeding. To address these challenges, we proposed an AIpowered method for evaluating blast disease levels and identifying resistance in wild rice. A lightweight segmentation model for diseased leaves and lesions was developed, incorporating an improved federated learning approach to enhance robustness and adaptability. Based on the segmentation results and resistance identification technical specifications, wild rice materials were evaluated into 10 disease levels (L0 to L9), further enabling disease-resistance identification through multiple replicates of the same materials. The method was successfully implemented on augmented reality glasses for real-time, first-person evaluation. Additionally, high-speed scanners and edge computing devices were integrated to enable continuous, precise, and dynamic evaluation. Experimental results demonstrate the outstanding performance of the proposed method, achieving effective segmentation of diseased leaves and lesions with only 0.22 M parameters and 5.3 G floating-point operations per second (FLOPs), with a mean average precision (mAP@0.5) of 96.3%. The accuracy of disease level evaluation and disease-resistance identification reached 99.7%, with a practical test accuracy of 99.0%, successfully identifying three highly resistant wild rice materials. This method provides strong technical support for efficiently identifying wild rice materials resistant to blast disease and advancing resistance breeding efforts.

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