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MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification

IEEE Access 2025年 13卷 93977-94008页

作者： Rayed, Md Eshmam Jim, Jamin Rahman Islam, Md Juniadul Mridha, M.F. Kabir, Md Mohsin Hossen, Md. Jakir American International University-Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh Mälardalens University Division of Computer Science and Software Engineering Västerås722 20 Sweden Multimedia University Faculty of Engineering and Technology Department of Robotics and Automation Melaka Malaysia

Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture

关键词： Fruits

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Early Detection and Treatment of Skin Diseases Using Advanced Image Preprocessing and Machine Learning Techniques 4

Early Detection and Treatment of Skin Diseases Using Advance...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Pandian, Jeswin Paul Jegan, R. Jerlinkasmir, R. Jose, P. Subha Hency Raj, P. Anantha Christu Karunya Institute of Technology and Science Division of Biomedical Engineering Coimbatore India Karunya Institute of Technology and Science Department of Mathematics Coimbatore India Karunya Institute of Technology and Science Division of Robotics and Automation Engineering Coimbatore India

ISBN: (纸本)9798331529635

This research aims to develop a more accurate and efficient system for classifying skin diseases, including both malignant and non-cancerous conditions. The study utilizes a comprehensive dataset encompassing various skin diseases, including melanomas, basal cell carcinomas, and benign conditions like nevi and warts. To address the challenge of class imbalance with a higher number of melanoma images, the researchers employ techniques like SMOTE (Synthetic Minority Oversampling Technique) to create synthetic data for under-represented classes. The pre-processing stage involves crucial steps like image scaling, augmentation, normalization, and data cleaning to ensure the quality and consistency of the data. Following pre-processing, feature extraction and model training will be conducted. This research has the potential to significantly contribute to the field of dermatology by enabling faster and more precise diagnosis of skin diseases through the use of advanced pre-processing techniques and a diversified dataset. © 2024 IEEE.

关键词： Lung cancer

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Collaborating for Success: Optimizing System Efficiency and Resilience Under Agile Industrial Settings 23

Collaborating for Success: Optimizing System Efficiency and ...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Katyara, Sunny Sharma, Suchita Damacharla, Praveen Garcia, Carlos O'Farrell, Francis Long, Philip Irish Manufacturing Research Company Ltd Department of Robotics & Automation Ireland Software Engineering Division Shutterfly United States KineticAI Inc WoodlandsTX United States

ISBN: (纸本)9798350373578

Designing an efficient and resilient human-robot collaboration strategy that not only upholds the safety and ergonomics of shared workspace but also enhances the performance and agility of collaborative setup presents significant challenges concerning environment perception and robot control. In this research, we introduce a novel approach for collaborative environment monitoring and robot motion regulation to address this multifaceted problem. Our study proposes novel computation and division of safety monitoring zones, adhering to ISO 13855 and TS 15066 standards, utilizing 2D lasers information. These zones are not only configured in the standard three-layer arrangement but are also expanded into two adjacent planes, thereby enhancing system uptime and preventing unnecessary deadlocks. Moreover, we also leverage 3D visual information to track dynamic human articulations and extended intrusions. Drawing upon the fused sensory data from 2D and 3D perceptual spaces, our proposed hierarchical controller stably regulates robot velocity, validated using Lasalle in-variance principle. Empirical evaluations demonstrate that our approach significantly reduces task execution time and system response delay, resulting in improved efficiency and resilience within collaborative settings. © 2024 IEEE.

关键词： Efficiency

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Extraction of Weld Seam Centerline Based on Improved Canny Algorithm

Extraction of Weld Seam Centerline Based on Improved Canny A...

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automation, robotics and Computer engineering (ICARCE), International Conference on

作者： Yuxuan Yang Bing Wei Xiaoqing Cao Pengtao Zhu Yiran Guo Robotics Technology Engineering Division Beijing Research Institute of Automation for Machinery Industry Beijing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

Based on the analysis of characteristics in weld seam images, this paper proposes an improved method for detecting the centerline position using an adapted Canny algorithm, addressing issues such as susceptibility to noise interference and poor connection of strong and weak edges. Initially, adaptive median filtering and enhanced bilateral filtering replace traditional Gaussian filtering in the Canny algorithm. Subsequently, histogram analysis and Otsu's method are employed to adaptively select thresholds for the Canny operator. Finally, the Canny algorithm is utilized for extracting weld seam edges. Experimental results demonstrate that compared to Gaussian filtering, employing adaptive median and bilateral filtering improves Peak Signal-to-Noise Ratio (PSNR) by 9.2145 dB and reduces Mean Squared Error (MSE) by 33.32819, indicating superior effectiveness in preserving image details and eliminating noise. Pratt's Figure of Merit (PFOM) and Structural Similarity Index (SSIM) values approaching 1 further underscore the algorithm's enhanced precision.

关键词： Histograms PSNR Filtering Image edge detection Welding Robot kinematics Noise Solids Data mining Soldering

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Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models

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Petroleum Science 2025年第1期22卷 296-323页

作者： Shadfar Davoodi Hung Vo Thanh David A.Wood Mohammad Mehrad Sergey V.Muravyov Valeriy S.Rukavishnikov School of Earth Sciences&Engineering Tomsk Polytechnic UniversityLenin AvenueTomskRussia Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Applied Science Research Center Applied Science Private UniversityAmmanJordan DWA Energy Limited LincolnUK Division for Automation&Robotics Tomsk Polytechnic UniversityLenin AvenueTomskRussia

To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is *** study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional *** does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both *** distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water *** models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and *** LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training *** trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction *** paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil *** also establishes a new standard for such forecasting,which can

关键词： Hybrid machine learning Least-squares support vector machine Grey wolf optimization Feature selection Carbon dioxide storage Enhanced oil recovery

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Real-time sub-assembly identification through IMU data fusion with vision sensor for an inspection system

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International Journal of Nuclear Energy Science and Technology 2023年第2期16卷 80-96页

作者： Thirumalaesh, A. Nibarkavi, N.A. Winston, S. Joseph Jose, Joel Rathika, P.D. Robotics and Automation Engineering PSG College of Technology Tamil Nadu Coimbatore India Remote Handling and Irradiation Experiments Division Indira Gandhi Centre for Atomic Research Tamil Nadu Kalpakkam India Steam Generator Inspection Devices Section Indira Gandhi Centre for Atomic Research Tamil Nadu Kalpakkam India

The Prototype Fast Breeder Reactor (PFBR) is a two loop, sodium cooled, pool type reactor. The PFBR reactor core is made up of sub-assemblies holding core material in a hexagonal lattice. The Reactor Core Viewing System in Room Temperature (RCVS-RT) aids in the process of inspection of the reactor core components. It introduces a vision probe into the extracted subassembly slot to reach the grid plate top for inspection. This work explores fusing non-contact sensors to a vision sensor to achieve orientation recognition of the RCVS-RT since the orientation is completely lost during the deployment of camera probe. We achieve this by overlaying generated orientation information from non-contact sensors onto the vision data. We use the orientation of the RCVS-RT and the core geometry to generate a numbering scheme of the adjacent sub-assemblies. This allows for a straightforward identification of the subassemblies in the core while maintaining sterility of core components. © 2023 Inderscience Enterprises Ltd.

关键词： Probes

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Early Detection and Treatment of Skin Diseases Using Advanced Image Preprocessing and Machine Learning Techniques

Early Detection and Treatment of Skin Diseases Using Advance...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Jeswin Paul Pandian R. Jegan R. Jerlinkasmir P. Subha Hency Jose P. Anantha Christu Raj Division of Biomedical Engineering Karunya Institute of Technology and Science Coimbatore Department of Mathematics Karunya Institute of Technology and Science Coimbatore Division of Robotics and Automation Engineering Karunya Institute of Technology and Science Coimbatore

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

关键词： Training Technological innovation Accuracy Malignant tumors Machine learning Ubiquitous computing Skin Medical diagnosis Cancer Synthetic data

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Collaborating for Success: Optimizing System Efficiency and Resilience Under Agile Industrial Settings

Collaborating for Success: Optimizing System Efficiency and ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sunny Katyara Suchita Sharma Praveen Damacharla Carlos Garcia Francis O’Farrell Philip Long Department of Robotics & Automation Irish Manufacturing Research Company Ltd Ireland Software Engineering Division Shutterfly USA KineticAI INC Woodlands Texas USA

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

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PV Generation Monitoring Using Calculated Power Flow from μPMUS

PV Generation Monitoring Using Calculated Power Flow from μ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Hussain, K. Kaliappan, S. Arul Joseph Amalraj, M. Saini, Parvesh Nandha Kumar, S.K. Dhanraj, Joshuva Arockia Sharad Institute of Technology College of Engineering Department of Electrical Engineering Ichalkaranji416121 India Lovely Professional University Division of Research and Development Jalandhar144411 India Veltech Multitech Dr Rangarajan Dr Sakunthala Engineering College Department of Chemistry Chennai600109 India Graphic Era Deemed to Be University Department of Electrical Engineering Dehradun248002 India Psna College of Engineering & Technology Department of Electrical and Electronics Engineering Dindigul624622 India Hindustan Institute of Technology and Science Centre for Automation and Robotics Department of Mechatronics Engineering Chennai603103 India

ISBN: (纸本)9798350383348

The ability of PMUs to provide precise, synchronized readings of voltage, current and frequency has made them valuable for the observation of microgrids. In some microgrids, PMU s are utilized without a current transformer and only measure voltage phasor values. This research outlines a procedure to use μPMU (or micro-PMU) voltage readings to ascertain electric loads or photovoltaic (PV) production through gauging power flow (PF). The results of a study conducted at the Federal University of Paraná's Polytechnic School (UFPR) in Brazil demonstrated that utilizing the power flow calculated by a 'virtual CT' approach, as measured by a standard power meter and with a higher time resolution from a microPMU, is a reliable and efficient method for recognizing events, monitoring PV generation, and non-intrusively monitoring load (NILM). © 2024 IEEE.

关键词： microgrids power flow PV generation μPMU

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Collaborating for Success: Optimizing System Efficiency and Resilience Under Agile Industrial Settings

arXiv

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arXiv 2024年

作者： Katyara, Sunny Sharma, Suchita Damacharla, Praveen Garcia, Carlos O’Farrell, Francis Long, Philip Department of Robotics & Automation Irish Manufacturing Research Company Ltd Ireland Software Engineering Division Shutterfly United States KineticAI INC. WoodlandsTX United States

Designing an efficient and resilient human-robot collaboration strategy that not only upholds the safety and ergonomics of shared workspace but also enhances the performance and agility of collaborative setup presents significant challenges concerning environment perception and robot control. In this research, we introduce a novel approach for collaborative environment monitoring and robot motion regulation to address this multifaceted problem. Our study proposes novel computation and division of safety monitoring zones, adhering to ISO 13855 and TS 15066 standards, utilizing 2D lasers information. These zones are not only configured in the standard three-layer arrangement but are also expanded into two adjacent quadrants, thereby enhancing system uptime and preventing unnecessary deadlocks. Moreover, we also leverage 3D visual information to track dynamic human articulations and extended intrusions. Drawing upon the fused sensory data from 2D and 3D perceptual spaces, our proposed hierarchical controller stably regulates robot velocity, validated using Lasalle in-variance principle. Empirical evaluations demonstrate that our approach significantly reduces task execution time and system response delay, resulting in improved efficiency and resilience within collaborative settings. © 2024, CC BY.

关键词： Ergonomics

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