Field-scale yield prediction of winter wheat under different irrigation regimes based on dynamic fusion of multimodal UAV imagery
文献类型: 外文期刊
第一作者: Ma, Juncheng
作者: Ma, Juncheng;Ji, Lin;Zhu, Zhicheng;Wu, Yongfeng;Liu, Binhui;Zhu, Zhicheng;Jiao, Weihua;Wu, Yongfeng
作者机构:
关键词: Yield prediction; Winter wheat; Multimodal UAV imagery; Dynamic fusion; Deep learning
期刊名称:INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION ( 影响因子:7.5; 五年影响因子:7.2 )
ISSN: 1569-8432
年卷期: 2023 年 118 卷
页码:
收录情况: SCI
摘要: Field-scale crop yield prediction is critical to site-specific field management, which has been facilitated by recent studies fusing unmanned aerial vehicles (UAVs) based multimodal data. However, these studies equivalently stacked multimodal data and underused canopy spatial information. In this study, multimodal imagery fusion (MIF) attention was proposed to dynamically fuse UAV-based RGB, hyperspectral near-infrared (HNIR), and thermal imagery. Based on the MIF attention, a novel model termed MultimodalNet was proposed for field-scale yield prediction of winter wheat. To compare multimodal imagery-based and multimodal features-based methods, a stacking-based ensemble learning model was built using UAV-based canopy spectral, thermal, and texture features. The results showed that the MultimodalNet achieved accurate results at the reproductive stage and performed better than any single modality in the fusion. The MultimodalNet performed best at the flowering stage, with a coefficient of determination of 0.7411 and a mean absolute percentage error of 6.05%. The HNIR and thermal imagery were essential in yield prediction of winter wheat at the reproductive stage. Compared to equivalent stacking fusion, dynamic fusion through adaptively adjusting modality attention improved the model accuracy and adaptability across winter wheat cultivars and water treatments. Equivalently stacking more modalities did not necessarily yield improved performance than dynamically fusing fewer modalities. Methods using multimodal UAV imagery with rich spatial information were more applicable than methods using multi -modal features to field-scale yield prediction. This study indicates that the MultimodalNet makes a powerful tool for field-scale yield prediction of winter wheat.
分类号:
- 相关文献
作者其他论文 更多>>
-
Estimating daily minimum grass temperature to quantify frost damage to winter wheat during stem elongation in the central area of Huang-Huai plain in China
作者:Wu, Yongfeng;Ji, Lin;Ma, Juncheng;Gong, Zhihong
关键词:Minimum grass temperature; Environmental variables; Winter wheat; Stem elongation; Frost damage
-
Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples
作者:Ma, Juncheng;Wu, Yongfeng;Liu, Binhui;Zhang, Wenying;Wang, Bianyin;Chen, Zhaoyang;Wang, Guangcai;Guo, Anqiang;Liu, Binhui;Zhang, Wenying;Wang, Bianyin;Chen, Zhaoyang;Wang, Guangcai;Guo, Anqiang
关键词:yield prediction; winter wheat; split-merge; convolutional neural network; UAV RGB imagery
-
Assessing the Spatial-Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index
作者:Wang, Yihao;Wu, Yongfeng;Ji, Lin;Wang, Yihao;Meng, Linghua;Zhang, Jinshui
关键词:northeast China; spring maize; spatial-temporal pattern; drought monitoring; random forest model; Sen plus Mann-Kendall test model
-
Identification method of vegetable diseases based on transfer learning and attention mechanism
作者:Zhao, Xue;Li, Kaiyu;Li, Yunxia;Zhang, Lingxian;Ma, Juncheng;Zhang, Lingxian
关键词:Vegetable disease; Attention mechanism; Transfer learning; Convolutional neural network
-
Towards improved accuracy of UAV-based wheat ears counting: A transfer learning method of the ground-based fully convolutional network
作者:Ma, Juncheng;Wu, Yongfeng;Li, Yunxia;Zhang, Lingxian;Liu, Hongjie
关键词:Number of wheat ears; UAV; Digital images; Convolutional neural network; Transfer learning
-
Trait Selection for Yield Improvement in Foxtail Millet (Setaria italica Beauv.) under Climate Change in the North China Plain
作者:Zhang, Wenying;Wang, Bianyin;Liu, Binhui;Chen, Zhaoyang;Lu, Guanli;Zhang, Wenying;Wang, Bianyin;Liu, Binhui;Chen, Zhaoyang;Lu, Guanli;Ge, Yaoxiang;Bai, Caihong;Ge, Yaoxiang;Bai, Caihong
关键词:foxtail millet; weather factor; yield variation
-
The recombinant swinepox virus expressing sseB could provide piglets with strong protection against Salmonella typhimurium challenge
作者:Ji, Lin;Yuan, Kenan;Li, Yue;Leghari, Ambreen;Lin, Huixing;Lin, Xisha;Lin, Xisha;Leghari, Ambreen;Yuan, Bingbing
关键词:Swinepox virus; Salmonella; sseB; Vaccine