Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods

Yiyang Shen, Ziyi Yan, Yongjie Yang, Wei Tang, Jinqiu Sun and Yanchao Zhang ()
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Yiyang Shen: School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Ziyi Yan: School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Yongjie Yang: State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 311400, China
Wei Tang: State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 311400, China
Jinqiu Sun: State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 311400, China
Yanchao Zhang: School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China

Sustainability, 2024, vol. 16, issue 2, 1-19

Abstract: Rice yield estimation is vital for enhancing food security, optimizing agricultural management, and promoting sustainable development. However, traditional satellite/aerial and ground-based/tower-based platforms face limitations in rice yield estimation, and few studies have explored the potential of UAV-borne hyperspectral remote sensing for this purpose. In this study, we employed a UAV-borne push-broom hyperspectral camera to acquire remote sensing data of rice fields during the filling stage, and the machine learning regression algorithms were applied to rice yield estimation. The research comprised three parts: hyperspectral data preprocessing, spectral feature extraction, and model construction. To begin, the preprocessing of hyperspectral data involved geometric distortion correction, relative radiometric calibration, and rice canopy mask construction. Challenges in geometric distortion correction were addressed by tracking linear features during flight and applying a single-line correction method. Additionally, the NIR reflectance threshold method was applied for rice canopy mask construction, which was subsequently utilized for average reflectance extraction. Then, spectral feature extraction was carried out to reduce multicollinearity in the hyperspectral data. Recursive feature elimination (RFE) was then employed to identify the optimal feature set for model performance. Finally, six machine learning regression models (SVR, RFR, AdaBoost, XGBoost, Ridge, and PLSR) were used for rice yield estimation, achieving significant results. PLSR showed the best R 2 of 0.827 with selected features, while XGBoost had the best R 2 of 0.827 with full features. In addition, the spatial distribution of absolute error in rice yield estimation was assessed. The results suggested that this UAV-borne imaging hyperspectral-based approach held great potential for crop yield estimation, not only for rice but also for other crops.

Keywords: UAV; imaging hyperspectral; rice; yield estimation; spectral feature extraction; regression models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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