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Oilseed Rape Yield Prediction from UAVs Using Vegetation Index and Machine Learning: A Case Study in East China

文献类型： 外文期刊

作者： Hu, Hao 1 ; Ren, Yun 3 ; Zhou, Hongkui 1 ; Lou, Weidong 1 ; Hao, Pengfei 4 ; Lin, Baogang 4 ; Zhang, Guangzhi 5 ; Gu, Qing 1 ; Hua, Shuijin 4 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Peoples R China

2.Minist Agr China, Key Lab Informat Traceabil Agr Prod, Hangzhou 310021, Peoples R China

3.Huzhou Agr Sci & Technol Dev Ctr, Huzhou 313000, Peoples R China

4.Zhejiang Acad Agr Sci, Inst Crops & Nucl Technol Utilizat, Hangzhou 310021, Peoples R China

5.Zhejiang Inst Hydraul & Estuary, Hangzhou 310020, Peoples R China

关键词： oilseed rape; UAV; yield; vegetation index; machine learning

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 8 期

页码：

收录情况： SCI

摘要： Yield prediction is an important agriculture management for crop policy making. In recent years, unmanned aerial vehicles (UAVs) and spectral sensor technology have been widely used in crop production. This study aims to evaluate the ability of UAVs equipped with spectral sensors to predict oilseed rape yield. In an experiment, RGB and hyperspectral images were captured using a UAV at the seedling (S1), budding (S2), flowering (S3), and pod (S4) stages in oilseed rape plants. Canopy reflectance and spectral indices of oilseed rape were extracted and calculated from the hyperspectral images. After correlation analysis and principal component analysis (PCA), input spectral indices were screened to build yield prediction models using random forest regression (RF), multiple linear regression (MLR), and support vector machine regression (SVM). The results showed that UAVs equipped with spectral sensors have great potential in predicting crop yield at a large scale. Machine learning approaches such as RF can improve the accuracy of yield models in comparison with traditional methods (e.g., MLR). The RF-based training model had the highest determination coefficient (R2) (0.925) and lowest relative root mean square error (RRMSE) (5.91%). In testing, the MLR-based model had the highest R2 (0.732) and lowest RRMSE (11.26%). Moreover, we found that S2 was the best stage for predicting oilseed rape yield compared with the other growth stages. This study demonstrates a relatively accurate prediction for crop yield and provides valuable insight for field crop management.

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