中国马铃薯2025，Vol.39Issue(3)：186-195,10.DOI:10.19918/j.cnki.1672-3635.2025.03.004
马铃薯关键表型性状的高通量鉴定
High-throughput Characterization of Key Phenotypic Traits in Potato
摘要
Abstract
Potato(Solanum tuberosum L.)is the fourth most important food crop in China and is of great significance for ensuring national food security.Efficient phenotyping systems and the mining of genes underlying key traits are fundamental for breeding high-yielding and high-quality potato varieties.With the advancement of unmanned aerial vehicle(UAV)remote sensing technology,efficient,precise,and non-destructive phenotyping has become feasible.Using 40 mid-to-late maturing potato varieties from Northeast China as plant materials,this study aimed to efficiently and non-destructively acquire field imagery data during five critical growth stages using UAVs equipped with visible-light and multispectral cameras.Combined with ground-measured data,four machine learning models were employed to establish an efficient assessment framework for canopy height,SPAD,leaf area index,and tuber yield,using the 2023 phenotyping data as the training set and the 2024 data as the validation set.The canopy height inversion model achieved the highest R2 of 0.91,the SPAD inversion model reached a maximum R2 of 0.97,the leaf area index inversion model attained a peak R2 of 0.96,and the yield inversion prediction model recorded the highest R2 of 0.96.The potato yield estimation model showed that random forest(RF)had the best estimation results at the starch accumulation stage,with R2 and RMSE of 0.86 and 82.6 g/plant.This research enables the rapid and efficient acquisition of key phenotypic traits during critical growth phases of potato,providing theoretical support for efficient potato breeding programs.关键词
马铃薯/高通量表型/机器学习/关键表型/无人机遥感Key words
potato/high-throughput phenotyping/machine learnin/key phenotypic traits/UAV remote sensing分类
农业科技引用本文复制引用
佟青松,刘勇,倪响,魏峭嵘,尹燕斌,唐海涛,石瑛..马铃薯关键表型性状的高通量鉴定[J].中国马铃薯,2025,39(3):186-195,10.基金项目
国家现代农业产业技术体系专项(CARS-09) （CARS-09）
北大荒信息有限公司处方图研发服务项目. （）
