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

被引:1
|
作者
Hu, Hao [1 ,2 ]
Ren, Yun [3 ]
Zhou, Hongkui [1 ,2 ]
Lou, Weidong [1 ,2 ]
Hao, Pengfei [4 ]
Lin, Baogang [4 ]
Zhang, Guangzhi [5 ]
Gu, Qing [1 ,2 ]
Hua, Shuijin [4 ]
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
来源
AGRICULTURE-BASEL | 2024年 / 14卷 / 08期
关键词
oilseed rape; UAV; yield; vegetation index; machine learning; LEAF-AREA INDEX; NEURAL-NETWORKS; TIME-SERIES; ASSIMILATION;
D O I
10.3390/agriculture14081317
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
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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页数:15
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