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Development of Machine Learning Methods for Accurate Prediction of Plant Disease Resistance

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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 classifi-cation plus kinship(SVC_K),light gradient boosting machine plus kinship(lightGBM_K),deep neural net-work 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 accura-cies 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.

Predicting plant disease resistanceGenomic selectionMachine learningGenome-wide association study

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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State Key Laboratory for Biology of Plant Diseases and Insect Pests,Institute of Plant Protection,Chinese Academy of Agricultural Sciences,Beijing 100193,China

Zhongshan Biological Breeding Laboratory & Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding,Agricultural College of Yangzhou University,Yangzhou 225009,China

Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and College of Agronomy,Hunan Agricultural University,Changsha 410128,China

State Key Laboratory of Crop Gene Resources and Breeding,Institute of Crop Sciences,Chinese Academy of Agricultural Sciences,Beijing 100081,China

College of Plant Protection,Hunan Agricultural University,Changsha 410128,China

Key Laboratory of Green Prevention and Control of Tropical Plant Diseases and Pests Ministry of Education,College of Plant Protection,Hainan University,Haikou 570228,China

MARA Key Laboratory of Surveillance and Management for Plant Quarantine Pests,Department of Plant Biosecurity,College of Plant Protection,China Agricultural University,Beijing 100193,China

Department of Plant Pathology,Ohio State University,Columbus,OH 43210,USA

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National Natural Science Foundation of ChinaNational Key Research and Development(R&D)Program of ChinaSeed Industry Revitalization Project of Jiangsu ProvinceProject of Zhongshan Biological Breeding Laboratory

322611434682021YFC2600400JBGS2021001BM2022008-02

2024

10.1016/j.eng.2024.03.014

工程(英文)

工程(英文)

CSTPCDEI
ISSN：2095-8099
年,卷(期)：2024.40(9)