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The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning

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Plant Phenomics 2023年第1期5卷 47-59页

作者： Kaiyu Li Xinyi Zhu Chen Qiao Lingxian Zhang Wei Gao Yong Wang China Agricultural University Beijing100083China Key Laboratory of Agricultural Informationization Standardization Ministry of Agriculture and Rural AffairsBeijing100083China Tianjin Academy of Agricultural Sciences Institute of Plant ProtectionTianjin300384China

Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture.Traditional detection methods are time-consuming,laborious,and subjective,and Image processing methods mainly rely on manually designed features that are difficult to cope with pathogen spore detection in complex scenes.Therefore,an MG-YOLO detection algorithm(Multi-head self-attention and Ghost-optimized YOLO)is proposed to detect gray mold spores rapidly.Firstly,Multi-head self-attention is introduced in the backbone to capture the global information of the pathogen spores.Secondly,we combine weighted Bidirectional Feature Pyramid Network(BiFPN)to fuse multiscale features of different layers.Then,a lightweight network is used to construct GhostCSP to optimize the neck part.Cucumber gray mold spores are used as the study object.The experimental results show that the improved MG-YOLO model achieves an accuracy of 0.983 for detecting gray mold spores and takes 0.009 s per Image,which is significantly better than the state-of-the-art model.The visualization of the detection results shows that MG-YOLO effectively solves the detection of spores in blurred,small targets,multimorphology,and high-density scenes.Meanwhile,compared with the YOLOv5 model,the detection accuracy of the improved model is improved by 6.8%.It can meet the demand for high-precision detection of spores and provides a novel method to enhance the objectivity of pathogen spore detection.

关键词： Ghost Deep Image

Microsnoop: A generalist tool for microscopy Image representation

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The Innovation 2024年第1期5卷 109-117页

作者： Dejin Xun Rui Wang Xingcai Zhang Yi Wang Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State Key Lab of Computer-Aided Design&Computer Graphics Zhejiang UniversityHangzhou 310058China John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University Hangzhou 310018China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314100China

Accurate profiling of microscopy Images from small scale to high throughput is an essential procedure in basic and applied biological research.Here,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy Images using masked self-supervised learning.Microsnoop can process various complex and heterogeneous Images,and we classified Images into three categories:single-cell,full-field,and batch-experiment Images.Our benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 Images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy Image representation ability,surpassing existing generalist and even several custom algorithms.Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology Image and multimodal analysis.Furthermore,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms.We will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.

关键词： hardware Image representation

Applying Image clustering to phylogenetic analysis:A trial

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Plant Diversity 2023年第2期45卷 234-237页

作者： Li-Dan Tao Wei-Bang Sun Yunnan Key Laboratory for Integrative Conservation of Plant Species with Extremely Small Populations Kunming Institute of BotanyChinese Academy of SciencesKunmingYunnan 650201China University of Chinese Academy of Sciences 100049 BeijingChina Kunming Botanical Garden Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China

Phylogenetic studies have increased in recent years,largely due to rapid developments in sequencing techniques (Tucker et al.,2017).However,molecular phylogenetic studies rely on collecting biomaterials,which limits t... 详细信息

关键词： Image biomaterials limits

The effectiveness of emotion-focused art therapy on the resilience and self-Image of emergency physicians

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World Journal of Emergency Medicine 2022年第6期13卷 479-484页

作者： Soo Kyung Eo Chanwoong Kim Kyung Hye Park Eun Kyung Eo Graduate School of Art Therapy at CHA University Pocheon 11160Republic of Korea Department of Emergency Medicine Chung-Ang University College of MedicineSeoul 06974Republic of Korea Department of Medical Education Yonsei University Wonju College of MedicineWonju 26426Republic of Korea Department of Emergency Medicine Wonju Severance Christian HospitalWonju 26426Republic of Korea Department of Emergency Medicine Soonchunhyang University Bucheon HospitalBucheon 14584Republic of Korea

INTRODUCTION Resilience refers to the quality of being able to overcome adversity.[1]Studies conducted in the United States[2]and China[3]following the 2019 coronavirus disease(COVID-19)pandemic reported reduced resilience levels among medical residents.High resilience levels are associated with low burnout levels among residents.[4]Physician burnout affects patients’safety and quality of care.[5]In particular,emergency physicians(EPs)are more prone to exhaustion or poor resilience,as their turnover intention tends to be higher.[6]Therefore,improving EPs’resilience is imperative.

关键词： Image physician overcome

Automatic characterization of spherical metal powders by microscope Image analysis:a parallel computing approach

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Journal of Iron and Steel Research(International) 2023年第11期30卷 2293-2300页

作者： Xiang Li Linyi Cui Sara Shonkwiler Sara McMains University of California BerkeleyBerkeleyCA94720USA The Hong Kong Polytechnic University Hong KongChina

The microstructural characteristics of spherical metal powders play an important role in determining the quality of mechanical parts manufactured by powder metallurgy processes.Identifying the individual powder particles from their microscopic Images is one of the most convenient and cost-efficient methods for the analysis of powder characteristics.Although numerous Image processing algorithms have been developed for automating the powder particle identification process,they perform less accurately in identifying adjacent particles that are heavily overlapped in their Image regions.We propose an automatic algorithm to robustly and accurately identify spherical powder particles,especially heavily overlapped particles,from their microscope Images.A parallel computing framework is designed to further enhance the computational efficiency of the proposed algorithm.Powder characteristics such as particle size distribution and the location of potential satellite particles have been derived from our identification results.The accuracy and efficiency of our algorithm are validated by real-world scanning electron microscope Images,outperforming other existing methods and achieving both precision and recall above 99%.

关键词： computing Image overlapped

WRA-Net:Wide Receptive Field Attention Network for Motion Deblurring in Crop and Weed Image

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Plant Phenomics 2023年第2期5卷 174-194页

作者： Chaeyeong Yun Yu Hwan Kim Sung Jae Lee Su Jin Im Kang Ryoung Park Division of Electronics and Electrical Engineering Dongguk University30 Pildong-ro 1-gilJung-guSeoul 04620Republic of Korea

Automatically segmenting crops and weeds in the Image input from cameras accurately is essential in various agricultural technology fields,such as herbicide spraying by farming robots based on crop and weed segmentation information.However,crop and weed Images taken with a camera have motion blur due to various causes(e.g.,vibration or shaking of a camera on farming robots,shaking of crops and weeds),which reduces the accuracy of crop and weed segmentation.Therefore,robust crop and weed segmentation for motion-blurred Images is essential.However,previous crop and weed segmentation studies were performed without considering motion-blurred Images.To solve this problem,this study proposed a new motion-blur Image restoration method based on a wide receptive field attention network(WRA-Net),based on which we investigated improving crop and weed segmentation accuracy in motion-blurred Images.WRA-Net comprises a main block called a lite wide receptive field attention residual block,which comprises modified depthwise separable convolutional blocks,an attention gate,and a learnable skip connection.We conducted experiments using the proposed method with 3 open databases:BoniRob,crop/weed field Image,and rice seedling and weed datasets.According to the results,the crop and weed segmentation accuracy based on mean intersection over union was 0.7444,0.7741,and 0.7149,respectively,demonstrating that this method outperformed the state-of-the-art methods.

关键词： Net shaking Image

Advancing spinal cord injury research with optical clearing,light sheet microscopy,and artificial intelligence-based Image analysis

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Neural Regeneration Research 2023年第12期18卷 2661-2662页

作者： Qiang Li Alfredo Sandoval Jr Bo Chen Department of Neurobiology University of Texas Medical BranchGalvestonTXUSA

From the days of Ramon y Cajal's first sketches,neuroscientists have recognized the importance of visualizing the complex architecture of the central nervous system.In the past century,we have come to appreciate how the rich structural and functional complementarity of axons and cell types in the spinal cord make it uniquely suited for information transfer between the periphery and the brain.

关键词： artificial Image sketch

Reconstruction performance for Image transmission through multimode fibers

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Optoelectronics Letters 2023年第4期19卷 235-241页

作者： HU Shicheng LIN Wei LIU Haifeng ZHU Yan YANG Ling JIN Song Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology Institute of Modern OpticsNankai UniversityTianjin 300350China Foreign Cooperation Projects Department PetroChina Dagang Oilfield CompanyTianjin 300280China

Due to the applications in the fields of optical communication,neuronal imaging,and medical endoscopic imaging,the study of multimode fiber(MMF)wavefront transmission is crucial for Image reconstruction and wavefront generation in user terminals.State of art studies in this area focus on high-quality Image reconstruction and wavefront shaping.Besides the way of imaging reconstruction,the performances of Image reconstruction and wavefront shaping are also dependent on system and environment parameters.This paper numerically analyzes the influence of key factors,such as the numerical aperture(NA)of the near-end objective lens of the MMF imaging system,the charge coupled device(CCD)noise of the acquired Image,and the ambient temperature at close distances.This work would help to the optimization of the MMF-based imaging system and provide a theoretical basis for potential applications in optical communication systems,neuron imaging and endoscopic diagnosis.

关键词： multimode communication Image

Coordinated underwater dark channel prior for artifact removal of challenging Image enhancement

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光电子快报:英文版 2023年第7期19卷 416-424页

作者： ZHANG Jiaokuan LIU Hao YING Xiaoqing HUANG Rong College of Information Science and Technology Donghua UniversityShanghai 201620China

When dehazing underwater Images,the patch-by-patch dark channel prior(DCP) method is frequently used.After the DCP-based processing,there are still some drawbacks,such as patch artifacts,and these artifacts will seriously affect the subjective quality of some challenging Images.To remove the patch artifacts from the DCP-guided enhancement mechanism,this paper proposes a coordinated underwater dark channel prior(CUDCP) method.The proposed method considers the characteristics of the red-green-blue channels with different attenuation situations,and thus the attenuation ratios of the red-green-blue channels are adaptively coordinated in diverse Images.The requirement for color restoration is then assessed by an evaluation criterion,and the color restoration is carried out by using the compensated gray world(CGW) theory,which further coordinates the intensity of various red-green-blue channels.Our method next applies a patch-division average filter in accordance with the sub-patch classification.On the typical dataset,the enhanced Images of our CUDCP method have higher average underwater Image quality measure(UIQM) scores(about 2.274 8) when compared with the original Images and those of some state-of-the-art enhancement methods,while the computational cost of CUDCP(about 88.618 8 s) is slightly higher than that of the original DCP(about 87.493 8 s).The experimental results demonstrate that in comparison to state-of-the-art enhancement methods,the proposed method can significantly reduce patch artifacts in challenging Image enhancement,while maintaining the objective quality of such underwater Images,and also enhancing their subjective quality at a reasonable computational cost.

关键词： underwater patch Image