Estimating the frost damage index in lettuce using UAV-based RGB and multispectral images

文献类型： 外文期刊

第一作者： Liu, Yiwen

作者： Liu, Yiwen;Li, Linyi;Liu, Yiwen;Ban, Songtao;Li, Linyi;Tian, Minglu;Hu, Dong;Yuan, Tao;Liu, Yiwen;Ban, Songtao;Li, Linyi;Tian, Minglu;Hu, Dong;Yuan, Tao;Wei, Shiwei;Liu, Weizhen

作者机构： Shanghai Ocean Univ, Coll Informat Technol, Shanghai, Peoples R China;Shanghai Acad Agr Sci, Inst Agr Sci & Technol Informat, Shanghai, Peoples R China;Minist Agr & Rural Affairs, Key Lab Smart Agr Technol Yangtze River Delta, Nanjing, Peoples R China;Shanghai Agrobiol Gene Ctr, Jinshan Expt Stn, Shanghai, Peoples R China;Wuhan Univ Technol, Sch Comp & Artificial Intelligence, Wuhan, Peoples R China

关键词： lettuce; frost damage; unmanned aerial vehicle; high-throughput detection; multisource data

期刊名称：FRONTIERS IN PLANT SCIENCE （ 2022影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2024 年 14 卷

页码：

收录情况： SCI

摘要： IntroductionThe cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce.MethodsWe proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed to select FDI-sensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively.Results and discussionDigital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce group were generally higher. The MULTISURCE-GREEN-NN model with R2 of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序