Qi Liu , Shi-min Zuo , Shasha Peng , Hao Zhang , Ye Peng , Wei Li , Yehui Xiong , Runmao Lin , Zhiming Feng , Huihui Li , Jun Yang , Guo-Liang Wang , Houxiang Kang
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In this study, we evaluated eight different machine learning (ML) methods, including random forest classification (RFC), support vector classifier (SVC), light gradient boosting machine (lightGBM), random forest classification plus kinship (RFC_K), support vector classification plus kinship (SVC_K), light gradient boosting machine plus kinship (lightGBM_K), deep neural network genomic prediction (DNNGP), and densely connected convolutional networks (DenseNet), for predicting plant disease resistance. Our results demonstrate that the three plus kinship (K) methods developed in this study achieved high prediction accuracy. Specifically, these methods achieved accuracies of up to 95% for rice blast (RB), 85% for rice black-streaked dwarf virus (RBSDV), and 85% for rice sheath blight (RSB) when trained and applied to the rice diversity panel I (RDPI). Furthermore, the plus K models performed well in predicting wheat blast (WB) and wheat stripe rust (WSR) diseases, with mean accuracies of up to 90% and 93%, respectively. To assess the generalizability of our models, we applied the trained plus K methods to predict RB disease resistance in an independent population, rice diversity panel II (RDPII). Concurrently, we evaluated the RB resistance of RDPII cultivars using spray inoculation. Comparing the predictions with the spray inoculation results, we found that the accuracy of the plus K methods reached 91%. These findings highlight the effectiveness of the plus K methods (RFC_K, SVC_K, and lightGBM_K) in accurately predicting plant disease resistance for RB, RBSDV, RSB, WB, and WSR. The methods developed in this study not only provide valuable strategies for predicting disease resistance, but also pave the way for using machine learning to streamline genome-based crop breeding.</p></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":null,"pages":null},"PeriodicalIF":10.1000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2095809924002431/pdfft?md5=9dc8b3be287e67c94801e5fcfecf7209&pid=1-s2.0-S2095809924002431-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Development of Machine Learning Methods for Accurate Prediction of Plant Disease Resistance\",\"authors\":\"Qi Liu , Shi-min Zuo , Shasha Peng , Hao Zhang , Ye Peng , Wei Li , Yehui Xiong , Runmao Lin , Zhiming Feng , Huihui Li , Jun Yang , Guo-Liang Wang , Houxiang Kang\",\"doi\":\"10.1016/j.eng.2024.03.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly. 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引用次数: 0
摘要
筛选植物抗病表型的传统方法既费时又费钱。基因组选择为提高效率提供了潜在的解决方案,但准确预测植物的抗病性仍是一项挑战。在这项研究中,我们评估了八种不同的机器学习(ML)方法,包括随机森林分类法(RFC)、支持向量分类器(SVC)、光梯度提升机(lightGBM)、随机森林分类法加亲缘关系(RFC_K)、支持向量分类法加亲缘关系(SVC_K)、光梯度提升机加亲缘关系(lightGBM_K)、深度神经网络基因组预测法(DNGP)和密集连接卷积网络(DenseNet),用于预测植物抗病性。我们的研究结果表明,本研究中开发的三种加亲缘关系(K)方法实现了较高的预测准确性。具体来说,这些方法经过训练并应用于水稻多样性面板 I(RDPI)时,对稻瘟病(RB)的预测准确率高达 95%,对水稻黑条矮缩病病毒(RBSDV)的预测准确率高达 85%,对水稻鞘枯病(RSB)的预测准确率高达 85%。此外,加 K 模型在预测小麦稻瘟病(WB)和小麦条锈病(WSR)方面表现良好,平均准确率分别高达 90% 和 93%。为了评估模型的普适性,我们将训练好的加 K 方法用于预测独立种群水稻多样性面板 II(RDPII)的 RB 抗病性。同时,我们使用喷雾接种法评估了 RDPII 栽培品种的 RB 抗性。将预测结果与喷雾接种结果进行比较,我们发现加 K 方法的准确率达到 91%。这些发现凸显了加 K 方法(随机森林分类加亲缘关系(RFC_K)、支持向量分类加亲缘关系(SVC_K)和光梯度提升机加亲缘关系(lightGBM_K))在准确预测 RB、RBSDV、RSB、WB 和 WSR 植物抗病性方面的有效性。本研究开发的方法不仅为预测抗病性提供了有价值的策略,还为利用机器学习简化基于基因组的作物育种铺平了道路。
Development of Machine Learning Methods for Accurate Prediction of Plant Disease Resistance
The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly. Genomic selection offers a potential solution to improve efficiency, but accurately predicting plant disease resistance remains a challenge. In this study, we evaluated eight different machine learning (ML) methods, including random forest classification (RFC), support vector classifier (SVC), light gradient boosting machine (lightGBM), random forest classification plus kinship (RFC_K), support vector classification plus kinship (SVC_K), light gradient boosting machine plus kinship (lightGBM_K), deep neural network genomic prediction (DNNGP), and densely connected convolutional networks (DenseNet), for predicting plant disease resistance. Our results demonstrate that the three plus kinship (K) methods developed in this study achieved high prediction accuracy. Specifically, these methods achieved accuracies of up to 95% for rice blast (RB), 85% for rice black-streaked dwarf virus (RBSDV), and 85% for rice sheath blight (RSB) when trained and applied to the rice diversity panel I (RDPI). Furthermore, the plus K models performed well in predicting wheat blast (WB) and wheat stripe rust (WSR) diseases, with mean accuracies of up to 90% and 93%, respectively. To assess the generalizability of our models, we applied the trained plus K methods to predict RB disease resistance in an independent population, rice diversity panel II (RDPII). Concurrently, we evaluated the RB resistance of RDPII cultivars using spray inoculation. Comparing the predictions with the spray inoculation results, we found that the accuracy of the plus K methods reached 91%. These findings highlight the effectiveness of the plus K methods (RFC_K, SVC_K, and lightGBM_K) in accurately predicting plant disease resistance for RB, RBSDV, RSB, WB, and WSR. The methods developed in this study not only provide valuable strategies for predicting disease resistance, but also pave the way for using machine learning to streamline genome-based crop breeding.
期刊介绍:
Engineering, an international open-access journal initiated by the Chinese Academy of Engineering (CAE) in 2015, serves as a distinguished platform for disseminating cutting-edge advancements in engineering R&D, sharing major research outputs, and highlighting key achievements worldwide. The journal's objectives encompass reporting progress in engineering science, fostering discussions on hot topics, addressing areas of interest, challenges, and prospects in engineering development, while considering human and environmental well-being and ethics in engineering. It aims to inspire breakthroughs and innovations with profound economic and social significance, propelling them to advanced international standards and transforming them into a new productive force. Ultimately, this endeavor seeks to bring about positive changes globally, benefit humanity, and shape a new future.