文献类型： 外文期刊
作者： Fan, Jie-jie 1 ; Qiu, Chun-xia 1 ; Fan, Yi-guang 2 ; Chen, Ri-qiang 2 ; Liu, Yang 2 ; Bian, Ming-bo 2 ; Ma, Yan-peng 2 ; Yang, Fu-qin 4 ; Feng, Hai-kuan 2 ;
作者机构： 1.Xian Univ Sci & Technol, Sch Surveying & Mapping Sci & Technol, Xian 710054, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Minist Agr & Rural Affairs, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China
3.Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, Nanjing 210095, Peoples R China
4.Henan Univ Engn, Coll Civil Engn, Zhengzhou 451191, Peoples R China
关键词： Continuous wavelet transform; Hyperspectral; Machine Learning; Wheat; Yield Forecast
期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.8； 五年影响因子：0.7 ）
ISSN： 1000-0593
年卷期： 2024 年 44 卷 10 期
页码：
收录情况： SCI
摘要： Timely and accurate crop yield estimation is crucial for making informed decisions regarding crop management and assessing food security. This study aims to develop a method that combines continuous wavelet transform (CWT) with machine learning to predict wheat yield accurately. This research is based on the spectral data of canopy height and yield data obtained from two-year field trials conducted during wheat growth's flowering and filling stages in 2020 2021, Initially. CWT is employed to extract three wavelet features (WFs), namely Hortus- WFs based on the Bortua method, 1% R-2-WFs representing WFs along with the top 1% determination coefficient for wheat yield, and SS-WFs encompassing all WFs under a single decomposition scale, Subsequently, three machine learning algorithms Random Forest (RF). K-nearest neighbor (KNN). and extreme gradient Lift (XGPoost) are utilized to construct the yield prediction model, Finally, optimal spectral features are selected using the same methodology for modeling and comparison purposes. The results demonstrate that: (1) all three WFs models combined with machine learning methods perform well, with higher accuracy and stability observed in the model built based on Boruta-WFs (2) Compared to the spectral characteristic model, improved accuracy was achieved by utilizing Bortua WFs at cach growth stager specifically, an increase in R' accuracy by 17.5%, 4% and 39.6% during flowering stage. well as an increase by 8.4%. 5.6%, and 16.9% during filling stage respectively were observed across different models, (3) The estimation model at the grouting stage outperformed that at the flowering stages particularly noteworthy was the performance of XGBoost when combined with Bortua-WFs, which yielded an R-2 value of 0. 83 accompanied by an RMSE value of 0.78 t. ha(-1). This study compared the performance of different characteristics and methods. It determined the best model accuracy under different schemes, which can provide technical references for the accurate wheat yield prediction by spectral technology.
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