Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing
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Published:2023-07-07
Issue:13
Volume:15
Page:3454
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Zhai Weiguang1234, Li Changchun2, Cheng Qian134, Ding Fan1234, Chen Zhen134
Affiliation:
1. Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China 2. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China 3. Key Laboratory of Water-Saving Irrigation Engineering, Ministry of Agriculture & Rural Affairs, Xinxiang 453002, China 4. Key Laboratory of Water-Saving Agriculture of Henan Province, Xinxiang 453002, China
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.
Funder
Intelligent Irrigation Water and Fertilizer Digital Decision System and Regulation Equipment Central Public-interest Scientific Institution Basal Research Fund Key projects of China National Tobacco Corporation Shandong Province Key Grant Technology Project of Henan the Research on Precision Irrigation for Nitrogen and Moisture Content Estimation Model Based on Deep Learning 2023 Henan Province Key R&D and Promotion Special Project the Henan Province Collaborative Innovation Centre Open Course the Henan Province Science and Technology Research Project
Subject
General Earth and Planetary Sciences
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