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

作     者：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 

作者机构：State Key Laboratory for Biology of Plant Diseases and Insect PestsInstitute of Plant ProtectionChinese Academy of Agricultural SciencesBeijing 100193China Zhongshan Biological Breeding Laboratory&Jiangsu Key Laboratory of Crop Genomics and Molecular BreedingAgricultural College of Yangzhou UniversityYangzhou 225009China Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and College of AgronomyHunan Agricultural UniversityChangsha 410128China State Key Laboratory of Crop Gene Resources and BreedingInstitute of Crop SciencesChinese Academy of Agricultural SciencesBeijing 100081China College of Plant ProtectionHunan Agricultural UniversityChangsha 410128China Key Laboratory of Green Prevention and Control of Tropical Plant Diseases and Pests Ministry of EducationCollege of Plant ProtectionHainan UniversityHaikou 570228China MARA Key Laboratory of Surveillance and Management for Plant Quarantine PestsDepartment of Plant BiosecurityCollege of Plant ProtectionChina Agricultural UniversityBeijing 100193China Department of Plant PathologyOhio State UniversityColumbusOH 43210USA 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2024年第40卷第9期

页      面：100-110页

核心收录：

学科分类：09[农学] 0901[农学-作物学] 

基　　金：supported by the National Natural Science Foundation of China(32261143468) the National Key Research and Development(R&D)Program of China(2021YFC2600400) the Seed Industry Revitalization Project of Jiangsu Province(JBGS(2021)001) the Project of Zhongshan Biological Breeding Laboratory(BM2022008-02) 

主　　题：Predicting plant disease resistance Genomic selection Machine learning Genome-wide association study 

摘      要：The traditional method of screening plants for disease resistance phenotype is both time-consuming and *** selection offers a potential solution to improve efficiency,but accurately predicting plant disease resistance remains a *** 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 *** results demonstrate that the three plus kinship(K)methods developed in this study achieved high prediction ***,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%,*** 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 *** 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 *** 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.