Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing

Abstract

Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also are time-consuming, often resulting in obtaining monitoring results after the optimal growth period of crops. Unmanned aerial vehicle (UAV) remote sensing technology offers the potential for rapidly acquiring chlorophyll content estimations over large areas. Currently, most studies only utilize single features from UAV data and employ traditional machine learning algorithms to estimate chlorophyll content, while the potential of multisource feature fusion and stacking ensemble learning in chlorophyll content estimation research remains largely unexplored. Therefore, this study collected UAV spectral features, thermal features, structural features, as well as chlorophyll content data during maize jointing, trumpet, and big trumpet stages, creating a multisource feature dataset. Subsequently, chlorophyll content estimation models were built based on four machine learning algorithms, namely, ridge regression (RR), light gradient boosting machine (LightGBM), random forest regression (RFR), and stacking ensemble learning. The research results demonstrate that (1) the multisource feature fusion approach achieves higher estimation accuracy compared to the single-feature method, with R2 ranging from 0.699 to 0.754 and rRMSE ranging from 8.36% to 9.47%; and (2) the stacking ensemble learning outperforms traditional machine learning algorithms in chlorophyll content estimation accuracy, particularly when combined with multisource feature fusion, resulting in the best estimation results. In summary, this study proves the effective improvement in chlorophyll content estimation accuracy through multisource feature fusion and stacking ensemble learning. The combination of these methods provides reliable estimation of chlorophyll content using UAV remote sensing technology and brings new insights to precision agriculture management in this field.

Better Inversion of Wheat Canopy SPAD Values before Heading Stage Using Spectral and Texture Indices Based on UAV Multispectral Imagery

“…Therefore, RF achieves the best winter wheat SPAD value estimation based on VIs, consistent with previous research findings [65,80].…”

“…This observation somewhat implies its capability to manage peak information effectively [79]. Therefore, RF achieves the best winter wheat SPAD value estimation based on VIs, consistent with previous research findings [65,80]. Compared to LinearSVR, CNN, and BPNN, the underestimation of SPAD values during the green-up stage is somewhat alleviated in LSTM and GBDT, and RF achieves the best estimation performance for this stage.…”

“…On the other hand, after RFE feature selection, all extracted TIs retained a significant number of features for subsequent modeling, whereas VIs retained only a small subset of features. This suggests that the unscreened VIs might have severe linear correlations and data redundancy between them [80]. The combination of these factors led to the better performance of models based on TIs in constructing SPAD value estimation models for winter wheat pre-heading stages.…”

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

“…SEL is a type of integrated learning model that relies on diversity and complementarity among base models to become more accurate 34 . The framework of SEL consists of two layers: base models and meta‐models.…”

Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China

“…By analyzing remote sensing data, researchers can obtain growth parameters of crops, such as the Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (Fpar), which are crucial for estimating crop yields [12,13]. Additionally, the ability of remote sensing to integrate multi-source data enables researchers to utilize different sensors and wavelengths, enhancing the accuracy of yield predictions [14]. For instance, remote sensing indices like the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Normalized Difference Water Index (NDWI) have been extensively used for monitoring vegetation health and estimating crop yields [15,16].…”