Zijian Yu , Tingyu Xie , Qibing Zhu , Peiyu Dai , Xing Mao , Ni Ren , Xin Zhao , Xinnian Guo
{"title":"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","authors":"Zijian Yu , Tingyu Xie , Qibing Zhu , Peiyu Dai , Xing Mao , Ni Ren , Xin Zhao , Xinnian Guo","doi":"10.1016/j.compag.2024.109656","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109656"},"PeriodicalIF":7.7000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Aquatic plants detection in crab ponds using UAV hyperspectral imagery combined with transformer-based semantic segmentation model\",\"authors\":\"Zijian Yu , Tingyu Xie , Qibing Zhu , Peiyu Dai , Xing Mao , Ni Ren , Xin Zhao , Xinnian Guo\",\"doi\":\"10.1016/j.compag.2024.109656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":50627,\"journal\":{\"name\":\"Computers and Electronics in Agriculture\",\"volume\":\"227 \",\"pages\":\"Article 109656\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers and Electronics in Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168169924010470\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924010470","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.