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2024 International Conference on Information Technology, Communication Ecosystem and Management, ITCEM 2024

作者： Yang, Yimei Yang, Yujun Shu, Huixia Zhao, Changhua Deng, Shaowei Huaihua418008 China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua418008 China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Huaihua Hunan418000 China

ISBN: (纸本)9798331542696

Recently, with the increasing complexity of agricultural systems and climate change, agricultural pest and disease surveillance and early detection have become critical tasks for guaranteeing food stability and promoting sustainable farming growth. Agricultural pest and disease surveillance is a key component of agricultural production, directly affecting crop yield and quality. As big data advances and image processing technologies rapidly, their application in crop pest and disease surveillance has become increasingly widespread. By integrating multi-source data, such as remote sensing data, meteorological data, ecological data, and pest and disease survey data, researchers can build more accurate and efficient pest and disease surveillance models. This paper reviews the research progress of big data and image processing technologies in crop pest and disease surveillance, analyzes the advantages and challenges of current technologies, and proposes future research directions to enhance the automation and intelligence of systems. Additionally, it discusses the integration of deep learning and other techniques to achieve efficient information mining and accurate retrieval of disaster-related data, aiming to provide scientific guidance and technical support for agricultural production. © 2024 IEEE.

关键词： Big Data Analysis Image Processing Technology Pest and Disease Detection

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Personalized Learning Path Recommendation Driven by Multi-Modal Knowledge Graphs and Large Models Collab.ration

Personalized Learning Path Recommendation Driven by Multi-Mo...

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2024 International Conference on Information Technology, Communication Ecosystem and Management, ITCEM 2024

作者： Zhao, Changhua Yang, Yimei Yang, Yujun Tang, Bo Mi, Chunqiao Huaihua418008 China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua418008 China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Hunan Huaihua418000 China

ISBN: (纸本)9798331542696

This study examines ways to create personalized learning pathways that are influenced by large collab.rative models and multi-modal knowledge graphs. By integrating multi-modal data to construct a comprehensive knowledge network, this study utilizes the generative and comprehension capabilities of large models to achieve personalized customization of learning resources. The focus of the research is on accurately depicting learner characteristics through multi-modal profiling techniques, ensuring the transparency and credibility of the content generated by large models. The personalized learning route suggestion model proposed in this paper includes two parts: recommendations for learning paths within courses and recommendations for learning paths within learning tasks, aiming to enhance learning efficiency and outcomes through the collab.rative effects of multi-modal knowledge graphs and large models. Experimental results indicate that the learning path recommendation model driven by multi-modal knowledge graphs and large collab.rative models performs excellently in improving learning outcomes and recommendation performance. © 2024 IEEE.

关键词： Collab.rative learning

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Design of a Crop Disease and Pest Identification System Based on Deep Learning 3

Design of a Crop Disease and Pest Identification System Base...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Zhou, Chengling Zhou, Lingfang Liu, Siyi Deng, Wen Xiao, Jing Hang, Shiyang Zhang, Shunru School of Physics Electronics and Intelligent Manufacturing Huaihua University Hunan Huaihua China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua University Hunan Huaihua China Huaihua Key Laboratory of Agricultural Ecology Prevention and Control Huaihua University Hunan Huaihua China

ISBN: (纸本)9798400716775

Traditional conventional methods for identifying pests and diseases often rely depended on manual experience, making it difficult to ensure accuracy and low efficiency. Deep learning technology Learning technology deeply can automatically extract useful features from a large amount of image data, classify and recognize them, and improve enhance accuracy and efficiency This approach can help assist farmers detect pests and diseases in a timely manner, thus taking timely prevention and control measures to reduce the spread and loss of pests and diseases taking immediate steps to minimize its diffusion and loss as much as possible. This article in this paper, designs a deep learning based crop pest identification system designing a crop pest and disease recognition system which is based on deep learning, aiming to improve the accuracy and efficiency of crop pest identification. The system uses utilizes three classic convolutional neural networks (CNNs) as training models to achieve recognition of various common crop pest pests and disease types. After system testing and analysis, the results show demonstrate that the system has significant advantages in accuracy, real-time performance, and can meet fulfil practical application requirements. Future research directions Future studies can further optimize models, expand datasets, reduce false fake positives, and provide more accurate and efficient pest identification support for agricultural production. © 2023 ACM.

关键词： Convolutional neural networks

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intelligent Mobile Power Sharing and Rental System Based on BeiDou Navigation 2

Intelligent Mobile Power Sharing and Rental System Based on ...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Hu, Hui Li, Xinhu Wu, Jiaxin Zeng, Qingli Zhang, Renmin College of Communication and Electronic Engineering Jishou University Hunan Jishou416000 China Hunan Provincial Key Lab. of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Hunan Huaihua418000 China

ISBN: (纸本)9781665464017

Aiming at the problems that the mobile power supply in the market cannot output AC voltage and manually adjustable DC voltage at the same time, expensive and cannot be accurately located, an intelligent mobile power sharing and rental system based on BeiDou navigation is designed. The device mainly consists of three major parts: power circuit module, controller module and mobile APP, which solves a series of problems such as location finding, rental use, visual power management and positioning return of shared power by users. The power circuit module converts the battery voltage into 220V AC output and adjustable DC precision output. The controller module realizes the functions of power supply DC output voltage collection, Battery power Detection, Beidou positioning and data uploading to the server. The mobile APP realizes the functions such as displaying the location, voltage, power and other data of multiple power supply units and sharing the rental under the shared rental mode by the APP. The system realized in this paper can greatly promote the development of China's shared energy storage industry and effectively change the current dilemma that people have difficulties in using electricity outdoors. © 2022 IEEE.

关键词： Digital storage

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Fault Analysis of Bearings in Various States Based on Continuous Wavelet and Improved Residual Network 2

Fault Analysis of Bearings in Various States Based on Contin...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Wang, Qingnan Tu, Jihui School of Intelligent Manufacturing Huaihua University China Key Lab. of Intelligent Control Technology for Wuling Mountain Ecological Agriculture Hunan Province Huaihua China School of Electronic and Information Yangtze University Jingzhou China

ISBN: (纸本)9781665464017

The bearing fault signals are complex and difficult to collect, which leads to a limited number of fault samples, and it is difficult to analyze the changeable bearing faults with conventional methods. In this paper, an automatic signal feature extraction method by improved residual network and wavelet transform is presented, which is suitable for bearing fault analysis in various states. Continuous wavelet transform (CWT) can obtain enough time-frequency images by superposing the original vibration signals. Improve the residual network, increase the attention mechanism and better deal with complex features. Transfer learning is applied to various working states, and the common features of bearing faults concealment are extracted and learned, so as to improve the applicability of diagnosis. The prediction ability of the bearing is tested in various working states, which proves the applicability of our fault analysis method. The problem of low accuracy of small sample identification caused by lack of actual fault data is solved. © 2022 IEEE.

关键词： Wavelet transforms

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Determination of birefringence of myocardial tissue based on PS-OCT with circularly polarized light as reference 16

Determination of birefringence of myocardial tissue based on...

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16th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Li, Wangbiao Li, Ke Li, Dezi Chen, Haiyu Cao, Wenliang Zhuo, Shufeng Li, Hui Li, Zhifang Key Laboratory of Optoelectronic Science and Technology for Medicine Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application College of Photonic and Electronic Engineering Fujian Normal University Fuzhou Fujian350117 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua University Huaihua Hunan418000 China Fujian Medical University Department of Cardiovascular Surgery Fujian Provincial Hospital Fuzhou Fujian350001 China The Internet of Thing and Artificial Intelligence College Fujian Polytechnic of Information Technology Fuzhou Fujian350001 China

ISBN: (纸本)9781510667341

In this study, the modified spectral domain polarization-sensitive optical coherence tomography (SD PS-OCT) is proposed for determining the birefringence of the myocardial tissue. In this modified SD PS-OCT, the circular polarization state of light was generated before entering the beam splitter. Thus, the polarization states in the reference and sample arms are both circular, and the symmetry between them is good without using additional quarter-wave plate (QWP), which reduce the dispersion effect. The results demonstrated that theoretical analysis for determination of birefringence including the phase retardance and the fast axis orientation based on Stokes parameters of backscattered from biological tissue, which is different from the traditional SD PS-OCT. In addition, the phase retardance and the fast axis orientation was used to differentiate the myocaridal tissue in the diastole of the cardiac cycle the from that in the systole of the cardiac cycle. The findings suggest that the SD PS-OCT be a potential tool for the real-Time monitoring the change of the myocardial wall during the cardiac cycle. © 2023 SPIE.

关键词： Birefringence

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intelligent Mobile Power Sharing and Rental System Based on BeiDou Navigation

Intelligent Mobile Power Sharing and Rental System Based on ...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Hui Hu Xinhu Li Jiaxin Wu Qingli Zeng Renmin Zhang College of Communication and Electronic Engineering Jishou University Jishou Hunan P.R. China Hunan Provincial Key Lab. of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Huaihua Hunan P.R. China

ISBN: (纸本)9781665464024

关键词： Smart agriculture Visualization Technological innovation Costs Power supplies Navigation Voltage

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YOLO v 3-based Crop Health Detection and Design of a Cargo Transport Trolley

YOLO v 3-based Crop Health Detection and Design of a Cargo T...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Qin Zhang Qingyu Li Jiaxin Wu Yingjing Qian Xin Liu School of Communication and Electronic Engineering Jishou University Jishou Hunan P.R. China Hunan Provincial Key Lab of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Huaihua Hunan P.R. China

ISBN: (纸本)9781665464024

In the environment of the gradual prosperity of intelligent agriculture, in order to prevent the decline of branches and leaves in the growth of crops caused by the reduction of yield, take remedial measures; This paper presents a truck based on neural network identification and detection, which can monitor the growth of crops in real time, identify the health state of crops, assist farm workers to inspect the growth of crops, transport field crops and other articles. By integrating Raspberry PI camera into YOLO v3 convolutional neural network, it can judge the features of crops and identify the health status of target objects The information collected by the camera controls the tracking of the steering gear, motor and trolley, and the position information and health status can be viewed on the OneNet platform. Real-time detection and growth judgment of agricultural products can be realized without increasing the energy and financial cost of manual inspection.

关键词： Smart agriculture Training Target tracking Protocols Crops Cameras Real-time systems

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Research on Adaptive Navigation System of Mountain Orchard Plant Protection Unmanned Aerial Vehicle Based on Simultaneous Localization and Mapping and Global Navigation Satellite System Fusion

Research on Adaptive Navigation System of Mountain Orchard P...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xuan Ouyang Xujun Liu Xiangkai Xu Yingjing Qian Qingli Zeng College of Communication and Electronic Engineering Jishou University Jishou Hunan P.R. China College of Automation and Electronic Information Xiangtan University Xiangtan Hunan P.R. China Hunan Provincial Key Lab. of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Huaihua Hunan P.R. China

ISBN: (纸本)9781665464024

China's western terrain is dominated by mountains, plateaus and basins, and the east is dominated by plains and hills. The mountainous area occupies 2/3 of the total national area. Due to the restriction of terrain conditions, the mechanization degree of orchard management is not high, and the lab.r intensity of fruit farmers is large and the cost is high. The desire of fruit farmers for orchard mechanization management and protection is very urgent. This system in view of the four rotor unmanned aerial vehicle developed a terrain adaptive landing gear, analysis of the terrain recognition algorithm, combined with mountain orchard plant protection drone operation requirements, the plant protection drones take operation line extraction method, the optimal path for homework, according to the characteristics of the fruit tree distribution research combines visual simultaneous localization and mapping technology and global navigation satellite system technology level of unmanned aerial vehicle navigation control method, It is of great research and economic value to combine the above methods and build an adaptive navigation control system suitable for quadrotor plant protection unmanned aerial vehicle operation in mountain orchards, and to establish an adaptive navigation system for mountain orchard plant protection unmanned aerial vehicle.

关键词： Solid modeling Global navigation satellite system Adaptive systems Simultaneous localization and mapping Gears Autonomous aerial vehicles control systems

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