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Aquatic plants detection in crab ponds using UAV hyperspectral imagery combined with transformer-based semantic segmentation model

文献类型： 外文期刊

作者： Yu, Zijian 1 ; Xie, Tingyu 1 ; Zhu, Qibing 1 ; Dai, Peiyu 2 ; Mao, Xing 2 ; Ren, Ni 2 ; Zhao, Xin 1 ; Guo, Xinnian 3 ;

作者机构： 1.Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China

2.Jiangsu Acad Agr Sci, Inst Agr Informat Res, Nanjing 210014, Peoples R China

3.Suqian Univ, Jiangsu Prov Engn Res Ctr Smart Poultry Farming &, Suqian 223800, Peoples R China

关键词： Aquatic plants; Crab ponds; UAV hyperspectral imagery; Semantic segmentation

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2024 年 227 卷

页码：

收录情况： SCI

摘要： Aquatic plants provide habitat and food for Chinese mitten crab growth, the identification of aquatic plant species and monitoring of their coverage can provide basic information for the management of aquatic plants, which can help to improve the efficiency of aquaculture. In this study, to address the time-consuming and labourintensive nature of traditional aquatic plant monitoring in crab ponds relying on manual observation, a classification method for aquatic plant species using unmanned aerial vehicle and hyperspectral imagery (UAV-HSI) technology, combined with an improved semantic segmentation model named SpectralUFormer was reported for the first time. The UAV-HSI data provides a high-quality data source for automatic aquatic plants detection, and the proposed SpectralUFormer integrates hybrid attention block and hybrid cascaded upsampler. Specifically, the hybrid attention block aggregates abundant spectral features in the encoder. In the decoder part, the hybrid cascaded upsampler is designed by incorporating PixelShuffle and G-L MLP block, which together perform the importance calculation and alignment of feature weights. Experimental results show that the SpectralUFormer achieves high-precision classification of aquatic plant species, with an overall accuracy of 93.15% and a Kappa coefficient of 89.14%. This study offers a feasible approach for the automatic identification of aquatic plant species in crab ponds and the estimation of their coverage.

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