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Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network

文献类型： 外文期刊

作者： Feng, Guoqing 1 ; Wang, Cheng 1 ; Wang, Aichen 1 ; Gao, Yuanyuan 1 ; Zhou, Yanan 2 ; Huang, Shuo 2 ; Luo, Bin 1 ;

作者机构： 1.Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： UAV; wheat lodging; lightweight; deep learning; improved U2NetP

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

ISSN：

年卷期： 2024 年 14 卷 2 期

页码：

收录情况： SCI

摘要： Crop lodging is an important cause of direct economic losses and secondary disease transmission in agricultural production. Most existing methods for segmenting wheat lodging areas use a large-volume network, which poses great difficulties for annotation and crop monitoring in real time. Therefore, an ultra-lightweight model, Lodging-U2NetP (L-U2NetP), based on a novel annotation strategy which crops the images before annotating them (Crop-annotation), was proposed and applied to RGB images of wheat captured with an unmanned aerial vehicle (UAV) at a height of 30 m during the maturity stage. In the L-U2NetP, the Dual Cross-Attention (DCA) module was firstly introduced into each small U-structure effectively to address semantic gaps. Then, Crisscross Attention (CCA) was used to replace several bulky modules for a stronger feature extraction ability. Finally, the model was compared with several classic networks. The results showed that the L-U2NetP yielded an accuracy, F1 score, and IoU (Intersection over Union) for segmenting of 95.45%, 93.11%, 89.15% and 89.72%, 79.95%, 70.24% on the simple and difficult sub-sets of the dataset (CA set) obtained using the Crop-annotation strategy, respectively. Additionally, the L-U2NetP also demonstrated strong robustness in the real-time detection simulations and the dataset (AC set) obtained using the mainstream annotation strategy, which annotates images before cropping (Annotation-crop). The results indicated that L-U2NetP could effectively extract wheat lodging and the Crop-annotation strategy provided a reliable performance which is comparable with that of the mainstream one.

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