利用无人机高光谱图像结合基于变换器的语义分割模型检测蟹塘中的水生植物

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-11-18 DOI:10.1016/j.compag.2024.109656
Zijian Yu , Tingyu Xie , Qibing Zhu , Peiyu Dai , Xing Mao , Ni Ren , Xin Zhao , Xinnian Guo
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引用次数: 0

摘要

水生植物为中华绒螯蟹的生长提供了栖息地和食物,对水生植物种类的鉴定和覆盖范围的监测可为水生植物的管理提供基础信息,有助于提高养殖效率。本研究针对传统蟹塘水生植物监测依赖人工观测耗时耗力的特点,首次报道了利用无人机和高光谱成像(UAV-HSI)技术,结合改进的语义分割模型 SpectralUFormer,对水生植物种类进行分类的方法。无人机-高光谱成像数据为水生植物的自动检测提供了高质量的数据源,而所提出的 SpectralUFormer 则集成了混合注意力块和混合级联上采样器。具体来说,混合注意力块在编码器中汇聚了丰富的光谱特征。在解码器部分,混合级联上采样器的设计结合了 PixelShuffle 和 G-L MLP 模块,共同完成重要度计算和特征权重的校准。实验结果表明,SpectralUFormer 实现了高精度的水生植物物种分类,总体准确率为 93.15%,Kappa 系数为 89.14%。这项研究为自动识别蟹塘中的水生植物物种和估计其覆盖范围提供了一种可行的方法。
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Aquatic plants detection in crab ponds using UAV hyperspectral imagery combined with transformer-based semantic segmentation model
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 labour-intensive 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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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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