Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications
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Published:2023-07-21
Issue:14
Volume:15
Page:3653
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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, Mao Bohan134, Li Zongpeng134, Li Yafeng1234, Ding Fan134, Qin Siqing134, Fei Shuaipeng5, Chen Zhen134ORCID
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 5. College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Abstract
Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology.
Funder
Central Public-Interest Scientific Institution Basal Research Fund Intelligent Irrigation Water and Fertilizer Digital Decision System and Regulation Equipment Key Grant Technology Project of Henan National Innovation and Entrepreneurship Training Program for College Students Key Project of Science and Technology of the Henan Province
Subject
General Earth and Planetary Sciences
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