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Estimation of Maize Yield in Plastic Film Mulched Field Using UAV Multispectral Imagery

文献类型： 外文期刊

作者： Wang, Yue 1 ; Hou, Min 2 ; Zhao, Zeyu 1 ; Zhang, Kaiping 1 ; Huang, Jie 4 ; Zhang, Li 5 ; Zhang, Feng 1 ;

作者机构： 1.Lanzhou Univ, Coll Ecol, Lanzhou 730000, Peoples R China

2.Yuncheng Yellow River Basin Ecol Protect & High Qu, Yuncheng 040000, Peoples R China

3.Nanjing Agr Univ, Sanya Inst, Nanjing 210095, Peoples R China

4.Gansu Acad Agr Sci, Anim Husb Pasture & Green Agr Inst, Lanzhou 730000, Peoples R China

5.Yangzhou Univ, Agr Coll, Jiangsu Key Lab Crop Genet & Physiol, Jiangsu Key Lab Crop Cultivat & Physiol, Yangzhou 225009, Peoples R China

6.Yangzhou Univ, Jiangsu Coinnovat Ctr Modern Prod Technol Grain Cr, Yangzhou 225009, Peoples R China

7.Yangzhou Univ, Res Inst Rice Ind Engn Technol, Yangzhou 225009, Peoples R China

关键词： multispectral camera; vegetation indices (VIs); crop growth parameters; yield prediction; sustainability

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 6 期

页码：

收录情况： SCI

摘要： In precision agriculture research, it is important to monitor maize growth conditions in real time for effective field diagnosis, management, and accurate yield prediction. This study aimed to develop an inverse model of vegetation index and maize yield using Unmanned Aerial Vehicle (UAV) multispectral imagery. The objective was to investigate how the vegetation index, maize growth stages, and growth parameters respond to plastic film mulching on the Loess Plateau. Annual field trials (2019-2020) employed a factorial design to evaluate mulch and nitrogen regimes. The results show that vegetation index long-time series curves, combined with maize phenological growth stages, can be used to monitor maize growth and yield estimation (R-2 > 0.9). The 13 vegetation indices (VIs) obtained by UAVs had a good regression relationship with the leaf area index, with the enhanced vegetation index 2 (EVI2) performing the best. The VIs obtained by UAVs at different stages of growth and development predicted yields, finding that EVI2 performed best with an R-2 of 0.92 and an RMSE of 0.52 t ha(-1) when maize entered the heading stage in 2019. The regression effect of VIs and yield based on maize without plastic film mulching management entering the heading stage was the best in 2020, with an R2 of 0.94 and an RMSE of 0.44 t ha(-1). When maize enters the heading stage, the best simulation results can be obtained by using the VIs to establish a yield prediction model. Spectral signatures during reproductive transition (VT-R1) proved most indicative of the final yield. Convergence of UAV-based spectral phenotyping with crop developmental physiology enables high-resolution growth diagnostics, providing empirical support for precision farming adaptations.

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