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Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

文献类型： 外文期刊

作者： Ma, Juncheng 1 ; Wu, Yongfeng 2 ; Liu, Binhui 3 ; Zhang, Wenying 3 ; Wang, Bianyin 3 ; Chen, Zhaoyang 3 ; Wang, Guangcai 3 ; Guo, Anqiang 3 ;

作者机构： 1.China Agr Univ, Coll Water Resources & Civil Engn, Beijing 100083, Peoples R China

2.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing 100081, Peoples R China

3.Hebei Acad Agr & Forestry Sci, Dryland Farming Inst, Hengshui 053000, Peoples R China

4.Key Lab Crop Drouht Tolerance Res Heibei Prov, Hengshui 053000, Peoples R China

关键词： yield prediction; winter wheat; split-merge; convolutional neural network; UAV RGB imagery

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 23 期

页码：

收录情况： SCI

摘要： Low-cost UAV RGB imagery combined with deep learning models has demonstrated the potential for the development of a feasible tool for field-scale yield prediction. However, collecting sufficient labeled training samples at the field scale remains a considerable challenge, significantly limiting the practical use. In this study, a split-merge framework was proposed to address the issue of limited training samples at the field scale. Based on the split-merge framework, a yield prediction method for winter wheat using the state-of-the-art Efficientnetv2_s (Efficientnetv2_s_spw) and UAV RGB imagery was presented. In order to demonstrate the effectiveness of the split-merge framework, in this study, Efficientnetv2_s_pw was built by directly feeding the plot images to Efficientnetv2_s. The results indicated that the proposed split-merge framework effectively enlarged the training samples, thus enabling improved yield prediction performance. Efficientnetv2_s_spw performed best at the grain-filling stage, with a coefficient of determination of 0.6341 and a mean absolute percentage error of 7.43%. The proposed split-merge framework improved the model ability to extract indicative image features, partially mitigating the saturation issues. Efficientnetv2_s_spw demonstrated excellent adaptability across the water treatments and was recommended at the grain-filling stage. Increasing the ground resolution of input images may further improve the estimation performance. Alternatively, improved performance may be achieved by incorporating additional data sources, such as the canopy height model (CHM). This study indicates that Efficientnetv2_s_spw is a promising tool for field-scale yield prediction of winter wheat, providing a practical solution to field-specific crop management.

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