1National Key Laboratory of Tropical Crop Breeding, Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Xueyuan Road, Longhua District, Haikou, 571101, China 2National Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Buxin Road, Dapeng New District, Shenzhen, 518000, China 3Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Southern End of Weilai Road, Guancheng District, Zhengzhou, 450009, China 4State Key Laboratory of Genetic Improvement and Germplasm Innovation of Crop Resistance in Arid Desert Regions (Preparation), Key Laboratory of Genome Research and Genetic Improvement of Xinjiang Characteristic Fruits and Vegetables, Institute of Horticultural Crops, Xinjiang Academy of Agricultural Sciences, Nanchang Road, Urumqi, 830091, China 5Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 646420, USA 6School of Life Sciences, Henan University, Minglun Street, Kaifeng, 475004, China 7Department of Prenatal Diagnosis Center, Women and Children’s Hospital of Chongqing Medical University, Chongqing, 401147, China 8Institute of Life and Health, China Resources Research Institute of Science and Technology, Pak Shek Kok Road, Sha Tin District, Hong Kong, 999077, China *Corresponding author. E-mail: wangyiwen@caas.cn,liuzhongjie@caas.cn,zhouyongfeng@caas.cn †Yu Gan and Zhenya Liu,Fan Zhang,Qi Xu contributed equally to the study.
Received: 15 Dec 2024 Accepted: 28 Apr 2025 Published online: 07 May 2025
Abstract
Crop pests significantly reduce crop yield and threaten global food security. Conventional pest control relies heavily on insecticides, leading to pesticide resistance and ecological concerns. However, crops and their wild relatives exhibit varied levels of pest resistance, suggesting the potential for breeding pest-resistant varieties. This study integrates deep learning (DL)/machine learning (ML) algorithms, plant phenomics, quantitative genetics, and transcriptomics to conduct genomic selection (GS) of pest resistance in grapevine. Building deep convolutional neural networks (DCNNs), we accurately assess pest damage on grape leaves, achieving 95.3% classification accuracy (VGG16) and a 0.94 correlation in regression analysis (DCNN-PDS). The pest damage was phenotyped as binary and continuous traits, and genome resequencing data from 231 grapevine accessions were combined in a Genome-Wide Association Studies, which maps 69 quantitative trait locus (QTLs) and 139 candidate genes involved in pest resistance pathways, including jasmonic acid, salicylic acid, and ethylene. Combining this with transcriptome data, we pinpoint specific pest-resistant genes such as ACA12 and CRK3, which are crucial in herbivore responses. ML-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting pest resistance as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facilitating genomic breeding of pest-resistant grapevine.
, Zhongjie Liu1,8 ,
