Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests.In most previous studies,the utilization of the whole spectrum or a large number of bands as well as ...Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests.In most previous studies,the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice.If a detection system can be established with a few bands and a relatively simple logic,it would be of great significance for application.This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat,i.e.,powdery mildew,yellow rust and aphid with a few specific bands.Through a comprehensive spectral analysis,only three bands at 570,680 and 750 nm were selected.A novel vegetation index namely Ratio Triangular Vegetation Index(RTVI)was developed for detecting anomalous areas on leaves.Then,the Support Vector Machine(SVM)method was applied to construct the discrimination model based on the spectral ratio analysis.The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy(OA)of 83%.With three bands from the hyperspectral imaging data,the three wheat diseases and pests were successfully detected and discriminated.A stepwise strategy including background removal,damage lesions recognition and stresses discrimination was proposed.The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.展开更多
Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and evaluation.The application of artificial intelligence technology to deep-sea mining project...Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and evaluation.The application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of mining.The existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly longer.In order to improve the segmentation performance of underwater mineral images,this paper uses the Pix2PixHD(Pixel to Pixel High Definition)algorithm based on Conditional Generative Adversarial Network(CGAN)to segment deep-sea mineral images.The model uses a coarse-to-fine generator composed of a global generation network and two local enhancement networks,and multiple multi-scale discriminators with same network structures but different input pictures to generate highquality images.The test results on the deep-sea mineral datasets show that the Pix2PixHD algorithm can identify more target minerals under certain other conditions.The evaluation index shows that the Pix2PixHD algorithm effectively improves the accuracy rate and the recall rate of deep-sea mineral image segmentation compared with the CGAN algorithm and the U-Net algorithm.It is important for expanding the application of deep learning techniques in the field of deep-sea exploration and mining.展开更多
Person re-identification(Re-ID)is a fundamental subject in the field of the computer vision technologies.The traditional methods of person Re-ID have difficulty in solving the problems of person illumination,occlusion...Person re-identification(Re-ID)is a fundamental subject in the field of the computer vision technologies.The traditional methods of person Re-ID have difficulty in solving the problems of person illumination,occlusion and attitude change under complex background.Meanwhile,the introduction of deep learning opens a new way of person Re-ID research and becomes a hot spot in this field.This study reviews the traditional methods of person Re-ID,then the authors focus on the related papers about different person Re-ID frameworks on the basis of deep learning,and discusses their advantages and disadvantages.Finally,they propose the direction of further research,especially the prospect of person Re-ID methods based on deep learning.展开更多
Two-dimensional(2D) materials have recently received a great deal of attention due to their unique structures and fascinating properties,as well as their potential applications.2D hexagonal boron nitride(2D hBN),an in...Two-dimensional(2D) materials have recently received a great deal of attention due to their unique structures and fascinating properties,as well as their potential applications.2D hexagonal boron nitride(2D hBN),an insulator with excellent thermal stability,chemical inertness,and unique electronic and optical properties,and a band gap of 5.97 e V,is considered to be an ideal candidate for integration with other 2D materials.Nevertheless,the controllable growth of high-quality 2D h-BN is still a great challenge.A comprehensive overview of the progress that has been made in the synthesis of 2D h-BN is presented,highlighting the advantages and disadvantages of various synthesis approaches.In addition,the electronic,optical,thermal,and mechanical properties,heterostructures,and related applications of 2D h-BN are discussed.展开更多
基金subsidized by National Natural Science Foundation of China(Grant No.42071420)External Cooperation Program of the Chinese Academy of Sciences(183611KYSB20200080)+1 种基金National Key R&D Program of China(2019YFE0125300)Beijing Nova Program of Science and Technology(Z191100001119089).
文摘Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests.In most previous studies,the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice.If a detection system can be established with a few bands and a relatively simple logic,it would be of great significance for application.This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat,i.e.,powdery mildew,yellow rust and aphid with a few specific bands.Through a comprehensive spectral analysis,only three bands at 570,680 and 750 nm were selected.A novel vegetation index namely Ratio Triangular Vegetation Index(RTVI)was developed for detecting anomalous areas on leaves.Then,the Support Vector Machine(SVM)method was applied to construct the discrimination model based on the spectral ratio analysis.The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy(OA)of 83%.With three bands from the hyperspectral imaging data,the three wheat diseases and pests were successfully detected and discriminated.A stepwise strategy including background removal,damage lesions recognition and stresses discrimination was proposed.The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.
基金This work was supported in part by national science foundation project of P.R.China under Grant No.52071349,No.U1906234 Partially Supported by the Open Project Program of Key Laboratory of Marine Environmental Survey Technology and ApplicationMinistry of Natural Resource MESTA-2020-B001+1 种基金the cross discipline research project of Minzu University of China(2020MDJC08)the Graduate Research and Practice Projects of Minzu University of China.
文摘Deep-sea mineral image segmentation plays an important role in deep-sea mining and underwater mineral resource monitoring and evaluation.The application of artificial intelligence technology to deep-sea mining projects can effectively improve the quality and efficiency of mining.The existing deep learning-based underwater image segmentation algorithms have problems such as the accuracy rate is not high enough and the running time is slightly longer.In order to improve the segmentation performance of underwater mineral images,this paper uses the Pix2PixHD(Pixel to Pixel High Definition)algorithm based on Conditional Generative Adversarial Network(CGAN)to segment deep-sea mineral images.The model uses a coarse-to-fine generator composed of a global generation network and two local enhancement networks,and multiple multi-scale discriminators with same network structures but different input pictures to generate highquality images.The test results on the deep-sea mineral datasets show that the Pix2PixHD algorithm can identify more target minerals under certain other conditions.The evaluation index shows that the Pix2PixHD algorithm effectively improves the accuracy rate and the recall rate of deep-sea mineral image segmentation compared with the CGAN algorithm and the U-Net algorithm.It is important for expanding the application of deep learning techniques in the field of deep-sea exploration and mining.
基金supported by the Natural Science Foundation of China No.61703119,61573114Natural Science Fund of Heilongjiang Province of China No.QC2017070Fundamental Research Funds for the Central Universities of China No.HEUCFM180405.
文摘Person re-identification(Re-ID)is a fundamental subject in the field of the computer vision technologies.The traditional methods of person Re-ID have difficulty in solving the problems of person illumination,occlusion and attitude change under complex background.Meanwhile,the introduction of deep learning opens a new way of person Re-ID research and becomes a hot spot in this field.This study reviews the traditional methods of person Re-ID,then the authors focus on the related papers about different person Re-ID frameworks on the basis of deep learning,and discusses their advantages and disadvantages.Finally,they propose the direction of further research,especially the prospect of person Re-ID methods based on deep learning.
基金Project supported by the National Natural Science Foundation of China(Nos.61376007,61674137)the National Key Research and Development Program of China(No.2016YFB0400802)
文摘Two-dimensional(2D) materials have recently received a great deal of attention due to their unique structures and fascinating properties,as well as their potential applications.2D hexagonal boron nitride(2D hBN),an insulator with excellent thermal stability,chemical inertness,and unique electronic and optical properties,and a band gap of 5.97 e V,is considered to be an ideal candidate for integration with other 2D materials.Nevertheless,the controllable growth of high-quality 2D h-BN is still a great challenge.A comprehensive overview of the progress that has been made in the synthesis of 2D h-BN is presented,highlighting the advantages and disadvantages of various synthesis approaches.In addition,the electronic,optical,thermal,and mechanical properties,heterostructures,and related applications of 2D h-BN are discussed.
