数字农科院2.0

Performance of stacking machine learning and volume model for improving corn above ground biomass prediction

文献类型： 外文期刊

作者： Fu Xuan;Wei Su;Zhen Chen;Xianda Huang;Weiguang Zhai;Xuecao Li;Yelu Zeng;Zhi Li;Jingsuo Li;Jianxi Huang

作者机构：

关键词： AGB prediction;Multi-source UAV data;SHAP;Stacking ensemble learning;Volume model

期刊名称： Plant Phenomics

ISSN： 2643-6515

年卷期： 2025 年 7 卷 3 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版） ; ; CSCD（2025-2026年度）

摘要： The aboveground biomass (AGB) of crops is an essential metric for monitoring crop growth, making timely and accurate AGB forecasting critical for effective agricultural management. The introduction of Unmanned Aerial Vehicles (UAVs) and advanced sensor technologies has revolutionized traditional AGB prediction techniques. Currently, machine learning (ML) combined with UAV data are commonly utilized, along with the Vegetation Index Weighted Canopy Volume Model (CVMVI) for AGB prediction. Nevertheless, there is limited investigation into how these methods perform across different agricultural conditions. This study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments. We utilized LiDAR, multispectral (MS), thermal infrared (TIR), along with measured AGB and Leaf Area Index (LAI) data from various growth stages to develop a stacking ensemble learning model. This model effectively integrates data from multiple sources, resulting in a strong prediction performance with R2 of 0.86, Mean Absolute Error (MAE) of 1.54 ​t/ha, and Root Mean Square Error (RMSE) of 2.06 ​t/ha. Meanwhile, the analysis of the accuracy of CVMVI revealed its efficacy during the early-stage when corn is short, with its predictive capability diminishing as AGB increases. Consequently, we recommend the CVMVI for early-stage AGB prediction, which can streamline data collection and computational efforts. In contrast, the ML approach, which benefits from data fusion, is more appropriate for predicting AGB during the mid to late growth stages. This study enhances AGB prediction accuracy and speed, providing critical understanding of regional AGB dynamics and supporting better agricultural decision-making.

分类号：

  • 相关文献

[1]东北黑土区玉米产量时空演变规律及其驱动因素. 杨兆鹏,马秋梅,邬磊,李建兵,曲潇琳,崔萌,徐明岗,张文菊. 2025

[2]Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification. Sun, Ye,Zhao, Zhiyuan,Tong, Hailong,Sun, Baiming,Liu, Yanbiao,Ren, Nanqi,You, Shijie. 2023

[3]Identification of Cotton Leaf Mite Damage Stages Using UAV Multispectral Images and a Stacked Ensemble Method. Fan, Shifeng,He, Qiang,Chen, Yongqin,Xu, Xin,Guo, Wei,Lu, Yanhui,Liu, Jie,Qiao, Hongbo. 2025

作者其他论文 更多>>