High-throughput phenotypic traits estimation of faba bean based on machine learning and drone-based multimodal data

文献类型： 外文期刊

第一作者： Ji, Yishan

作者： Ji, Yishan;Liu, Zehao;Liu, Rong;Wang, Zhirui;Zong, Xuxiao;Yang, Tao;Ji, Yishan

作者机构：

关键词： Unmanned aerial vehicle; Sensors; Plant height; Above-ground biomass; Yield

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2024 年 227 卷

页码：

收录情况： SCI

摘要： Faba bean is a global food legume crop, and it is essential to accurately and timely determine its plant height, above-ground biomass (fresh and dry weight) and yield for enhancing cultivation practices and planning the next planting season. Traditional ground sampling is a time-consuming and labor-intensive approach. However, the utilization of an unmanned aerial vehicle (UAV) as a high-throughput technique offers a promising alternative strategy for estimating crop phenotypic traits. In this study, a two-year experiment was conducted from 2020 to 2022, where UAV-based multimodal data were collected using red-green-blue, multispectral and thermal infrared sensors. The variables derived from these three sensors and their combinations were used to estimate the fresh weight, dry weight and yield of faba bean based on extreme gradient boosting (XGBoost), random forest, multiple linear regression and k-nearest neighbor algorithms. The following findings were obtained: (1) The use of the maximum percentile crop surface model resulted in the highest estimation accuracy for faba bean plant height. (2) Fusion data from multiple sensors increased the estimation accuracy of faba bean fresh weight, dry weight and yield, the coefficient of determination (R2) improved by 14.22%, 1.45%, and 18.76%, respectively, compared with the best estimation accuracy of a single sensor. (3) The XGBoost algorithm outperformed the other algorithms in estimating fresh weight, dry weight and yield of faba bean. These results demonstrate that multiple sensors and appropriate algorithms can be used to effectively estimate faba bean phenotypic traits and provide valuable insights for agricultural remote sensing research.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序