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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Menaga, D. Shri Roshan, B.T. Shri Vikaas, S.R. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319019

One of the most important occupations in India is agriculture. Out of all the crops, cotton is the best and is crucial to the agricultural economy of the country. In India, 40-50 million people work in the cotton trade and processing, while six million farmers directly depend on the crop. The cotton leaf disease has grown in importance over the last few decades, resulting in losses to crops, farming operations, and financial resources. To achieve this aim, we first need to acquire different images of cotton plants. We can use image processing techniques to analyze dead leaf images and extract features like color, texture, and other characteristics with the deep CNN model's assistance. In addition to being less expensive and more straightforward, automatic disease detection supports machine vision, which offers image-based automated process control and inspection. To properly train the algorithm, we will be using a dataset of approximately 1752(approximately 440 images in each class) images classified into different categories according to the diseases. This model will be developed using tools present in Anaconda such as Jupyter Notebook, Spyder etc. The results of this project will demonstrate whether using it in real-time applications is feasible and whether traditional or manual disease and pest identification could benefit from the use of IT- based solutions. © 2024 IEEE.

关键词： Cotton

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Characterization of the in-focus droplets in shadowgraphy systems via deep learning-based image processing method

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PHYSICS OF FLUIDS 2022年第11期34卷 1-15页

作者： Wang, Zhibo He, Feng Zhang, Haixiang Hao, Pengfei Zhang, Xiwen Li, Xiangru Tsinghua Univ Dept Engn Mech Appl Mech Lab Beijing 100084 Peoples R China Tsinghua Univ Sch Mat Sci & Engn AV Aerodynam Res Inst Joint Res Ctr Adv Mat & Antiicing Beijing 100084 Peoples R China

It is important to accurately identify and measure in-focus droplets from shadowgraph droplet images that typically contain a large number of defocused droplets for the research of multiphase flow. However, conventional in-focus droplet identification methods are time-consuming and laborious due to the noise and background illumination in experimental data. In this paper, a deep learning-based method called focus-droplet generative adversarial network (FocGAN) is developed to automatically detect and characterize the focused droplets in shadow images. A generative adversarial network framework is adopted by our model to output binarized images containing only in-focus droplets, and inception blocks are used in the generator to enhance the extraction of multi-scale features. To emulate the real shadow images, an algorithm based on the Gauss blur method is developed to generate paired datasets to train the networks. The detailed architecture and performance of the model were investigated and evaluated by both the synthetic data and spray experimental data. The results show that the present learning-based method is far superior to the traditional adaptive threshold method in terms of effective extraction rate and accuracy. The comprehensive performance of FocGAN, including detection accuracy and robustness to noise, is higher than that of the model based on a convolutional neural network. Moreover, the identification results of spray images with different droplet number densities clearly exhibit the feasibility of FocGAN in real experiments. This work indicates that the proposed learning-based approach is promising to be widely applied as an efficient and universal tool for processing particle shadowgraph images.

关键词： Generative adversarial networks

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image processing and Visualization Technology Application of an Improved deep learning Algorithm

Image Processing and Visualization Technology Application of...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Yunsheng Deng Yinghui Huang Zhangbao Chen Yan Chen Bengbu University Bengbu Huainan City Anhui China

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In today’s world, technology is changing our way of life and work at an alarming rate. This paper studies the performance of an improved deep learning algorithm in image processing tasks, introduces the implementation principle of algorithm design, and puts forward an improved deep learning algorithm. In the experimental method part, a group of experiments are designed to evaluate the image quality performance of the improved deep learning algorithm, which is based on three key performance indicators: Peak Signal-to-Noise Ratio (PSNR), Interactive Response time (IRT) and Structural Similarity Index Measure (SSIM). The research conclusion shows that the peak signal-to-noise ratio (PSNR) of the improved image quality-preserving deep learning algorithm is as high as 58 dB. The maximum IRT measurement of the improved algorithm is only 95 ms, which provides users with faster response speed and enables users to experience a smoother interactive experience in real-time image processing applications.

关键词： deep learning image quality Visualization PSNR image processing Scalability real-time systems time measurement Robustness time factors

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Measuring Surface Characteristics in Sustainable Machining of Titanium Alloys Using deep learning-Based image processing

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IEEE SENSORS JOURNAL 2023年第12期23卷 13629-13639页

作者： Ross, Nimel Sworna Shibi, C. Sherin Mustafa, Sithara Mohamed Gupta, Munish Kumar Korkmaz, Mehmet Erdi Sharma, Vishal S. Li, Z. SIMATS Saveetha Sch Engn Dept Mech Engn Chennai India SRM Inst Sci & Technol Dept Computat Intelligence Kattankulathur Tamil Nadu India Inst Appl Technol Appl Engn Dept Mech Engn Ajman U Arab Emirates Opole Univ Technol Fac Mech Engn Opole Poland Karabuk Univ Karabuk Turkiye Dr BR Ambedkar Natl Inst Technol Ind & Prod Engn Jalandhar India

A crucial method of maintenance in the manufacturing industry is machine vision-based fault diagnostics and condition monitoring of machine tools. The friction that occurs between the tool and the workpiece has a greater influence on the surface properties of the material. Effective problem diagnosis is necessary for machine systems to continue operations safely. Data-driven approaches have recently exhibited great promise for intelligent fault diagnosis. Unfortunately, the data collected under real-world conditions may be imbalanced, making diagnosis difficult. In dry, minimum quantity lubrication (MQL), and cryogenic circumstances, the method of failure detection of the proposed design is novel. The purpose of this interrogation is to evaluate the roughness profiles obtained from the machined surfaces and class separation. Markov transition field (MTF) is adopted to encode the surface profiles. In addition to this, conditional generative adversarial network (CGAN) for augmentation and bidirectional long-short term memory (BLSTM), multilayer perceptron (MLP), and 2-D-convolutional neural network (CNN) models are used for surface profile classification and correlation with process parameters. According to the study's finding, the 2-D-CNN was significantly more accurate than the models in predicting surface profiles, with an average accuracy of above 99.6% in both training and testing. In the limelight, the suggested approach can demonstrate to be quite useful for categorizing and proposing appropriate machining circumstances, specifically in situations with minimal data.

关键词： Surface treatment Machining Surface morphology Markov processes Surface roughness Rough surfaces Predictive models Conditional generative adversarial network (CGAN) cryogenic deep learning (DL) machining Markov transition field (MTF)

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Supervised deep learning based Leaf Disease and Pest Detection using image processing 7

Supervised Deep Learning based Leaf Disease and Pest Detecti...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Isravel, Deva Priya Somasundaram, Kumaresan Jestin Josephraj, M. Christopher Paul, L. Johnson, J.P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Sivaganga India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Kumbakonam India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Dharmapuri India

ISBN: (纸本)9798350397253

The key problems that influence plant health and crop yield quality are leaf disease and pests. To improve crop production many advanced technologies are deployed. One of the predominant technologies is the incorporation of deep learning (DL) techniques. DL supports numerous training and testing models that are suitable for smart agriculture. real-time detection of plant diseases and pest detection is made simpler and more efficient by using DL models. This paper provides an extensive survey of the DL techniques associated with agriculture automation and discusses the latest models focusing on leaf disease detection and pest identification. To select the best features and to optimize the accuracy of the results, an optimizer is identified and an enhanced deep learning model is proposed. The VGG16 and YOLOV5s models are deployed with ADAM optimizer. The results illustrate that the proposed optimized approach achieves an accuracy of 98.71% for leaf disease detection and 97.52% for pest detection. © 2023 IEEE.

关键词： image processing

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A Novel Human Face Expression Recognition Based on image processing Assisted Convoluted deep learning Methodology

A Novel Human Face Expression Recognition Based on Image Pro...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Tamilselvi, M. Venkata Siva Prasad, Ch. Kumari, M. Varuna Lavanya, R. Sai Nandurkar, Y. Al-Mousa, Mohammad Rasmi Dept of Ece Chennai India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam Andhra Pradesh Prakasam District523272 India Qis College of Engineering and Technology Department of Mca Pondur Road Vengamukkapalem Ongole Prakasam Andhra Pradesh Prakasam District523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Facial expression recognition has become a critical component in applications involving human-computer interaction, security systems, and behavioral analysis. This paper presents a novel approach to human face expression recognition using a convoluted deep learning methodology supported by image processing techniques. The system is designed to accurately classify seven fundamental emotions - Anger, Disgust, Fear, Happy, Sadness, Surprise, and Neutral - using the FER2013 dataset. The methodology includes preprocessing steps such as noise reduction, grayscale conversion, and face registration. Feature extraction is performed using Gabor filters and Local Binary Patterns (LBP), while Convolutional Neural Networks (CNNs) are employed to learn deep hierarchical features. The system was optimized through hyperparameter tuning, achieving an overall accuracy of 96.29% on the test set. Precision, recall, and F1-scores for each emotion also exceeded 95%, with 'Happy' and 'Surprise' emotions showing the best performance. The results indicate that the model is effective for real-time applications requiring reliable emotion detection. The paper concludes with discussions on model performance, challenges in classifying similar emotions, and recommendations for future improvements. © 2024 IEEE.

关键词： Gabor filters

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Cotton Plant Disease Detection and Prevention Using deep learning and image processing 11

Cotton Plant Disease Detection and Prevention Using Deep Lea...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kesani, Ruthvija Sunkavalli, Jaya Prakash Shaik, Mahamamad Rafi Yadala, Kusuma Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350350357

One of the most important aspects of the agricultural economy is the production of cotton, which is threatened by diseases that lower crop quality and yield. Conventional techniques for diagnosing diseases are frequently subjective and labor-intensive. This paper presents a novel method for the automatic detection and prevention of cotton plant diseases that makes use of deep learning techniques. A convolutional neural network (CNN) model is trained using a dataset that includes various photos of both healthy and sick cotton plants. The suggested model offers a dependable and time-efficient solution by exhibiting high accuracy in differentiating between different diseases. Moreover, proactive disease detection is made possible by the integration of real-time monitoring systems, such as drones fitted with high-resolution cameras. Early detection lessens the need for broadspectrum antibiotics by enabling the prompt application of preventive measures, such as targeted therapies. Finally, we conduct a comprehensive computational analysis of eight cutting-edge object detection algorithms on the cotton plant dataset to identify diseases on the leaves and seven cutting-edge classification algorithms on the cotton plant datasets to determine if a leaf has a disease or not. computed results indicate that it has a high degree of object detection accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Quantum-Classical Hybrid deep learning for 6G image processing with Copyright Detection

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INFORMATION 2024年第11期15卷 727页

作者： Seol, Jongho Kim, Hye-Young Kancharla, Abhilash Kim, Jongyeop Middle Georgia State Univ Dept Comp Sci Warner Robins GA 31093 USA Hongik Univ Sch Games Game Software Seoul 121791 South Korea Univ Tampa Dept Comp Sci Tampa FL 33606 USA Georgia Southern Univ Dept Informat Technol Statesboro GA 30458 USA

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical-classical deep learning architectures within 6G environments.

关键词： copyright detection quantum computing hybrid deep learning image processing 6G networks

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real-time 2D/ 3D image processing with deep learning

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MULtimeDIA TOOLS AND APPLICATIONS 2021年第28-29期80卷 35771-35771页

作者： Kim, Soo Kyun Choi, Min-Hyung Chun, Junchul Jia, Xibin Jeju Natl Univ Jeju South Korea Univ Colorado Denver CO 80202 USA Kyonggi Univ Suwon South Korea Beijing Univ Technol Beijing Peoples R China

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Adaptive image processing embedding to make the ecological tasks of deep learning more robust on camera traps images

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ECOLOGICAL INFORMATICS 2024年 82卷

作者： Yang, Zihe Tian, Ye Zhang, Junguo Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Key Lab State Forestry & Grassland Adm Forestry E Beijing 100083 Peoples R China Beijing Forestry Univ Res Ctr Biodivers Intelligent Monitoring Beijing 100083 Peoples R China

Camera traps serve as a valuable tool for wildlife monitoring, generating a vast collection of images for ecologists to conduct ecological investigations, such as species identification and population estimation. However, the sheer volume of images poses a challenge, and the integration of deep learning into automated ecological investigation tasks remains complex, particularly when dealing with low-quality images in long-term monitoring programs. Existing approaches often struggle to strike a balance between image enhancement and deep learning for ecological tasks, thereby overlooking crucial information contained within low-quality images. This research introduces a pioneering adaptive image processing module (AIP) that seamlessly incorporates image processing into camera trap ecological tasks, elevating the performance of wildlife monitoring activities. Specifically, a differentiable image processing (DIP) module is presented to enhance low-quality images, with its parameters predicted by a Non-local based parameter predictor (NLPP). Additionally, an end-to-end approach based on hybrid data containing both original and synthetic data is proposed, encompassing adaptive image processing methods and downstream tasks for camera traps, adaptable to various scenarios. This approach effectively reduces the manual labor and time required for professional image processing. When applied to real-world camera trap images and synthetic image datasets, our method achieves an accuracy of 92.26% and 86.65% in classifying wildlife, respectively, demonstrating its robustness. By outperforming alternative methods under harsh conditions, the application of the adaptive image processing module instills greater confidence in deep learning applications within complex environments.

关键词： Adaptive image processing Camera traps deep learning Ecological tasks

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