Research Article | Open Access

Volume 2025 |Article ID 100068 | https://doi.org/10.1016/j.plaphe.2025.100068

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

Fu Xuan,¹ Wei Su ,¹ Zhen Chen ,² Xianda Huang,¹ Weiguang Zhai,² Xuecao Li,¹ Yelu Zeng,¹ Zhi Li,³ Jingsuo Li,⁴ Jianxi Huang^1,5

¹College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
²Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, 453002, China
³State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, 475004, China
⁴College of Economics and Management, Qingdao Agricultural University, Qingdao, Shandong, 266109, China
⁵Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Sichuan, China

Abstract

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.