Arvind Jagtap

@mituniversity.edu.in

Associate Professor
MIT Art Design and Technology University, Pune

Arvind Jagtap


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https://researchid.co/arvind.jagtap82

EDUCATION

BE,ME, PHD

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Networks and Communications, Computer Science Applications, Human-Computer Interaction
21

Scopus Publications

72

Scholar Citations

6

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • A Blockchain-Based Hybrid Hunger Game Search Archimedes Optimization Enabled Deep Learning for Multiclass Plant Disease Detection Using Leaf Images
    Yogesh Manohar Gajmal, Arvind M. Jagtap, Kiran Dhanaji Kale, Jawahar Sambhaji Gawade, Pranav More
    International Journal of Image and Graphics, 2026
    Plants are susceptible to a wide range of diseases when they are growing. One of the crucial difficulties in agriculture is the earlier finding of plant diseases. If the diseases are not detected at the beginning, it may have an undesirable effect on the entire production. To avoid these issues, a blockchain-based hybrid optimized deep learning (DL) approach is devised in this work. The plant leaf images are stored in the blockchain network and the noise level of the images is minimized by the Kalman filter. In image segmentation, the Deep Joint segmentation technique is employed to segment the disease-affected portion of the image. The position and color augmentation are carried out to enhance the size and clarity of the image. Moreover, the statistical and speeded-up robust features (SURF) are extracted in the feature extraction stage. In the first level classification process, the developed hunger game search Archimedes optimization (HGSAO) enabled SpinalNet is employed for classifying the plant type and the second level classification is carried out for multiclass disease identification using the proposed HGSAO optimized SpinalNet. Moreover, the proposed HGSAO with SpinalNet outperformed the accuracy of 0.972, True positive rate (TPR) of 0.963, true negative rate (TNR) of 0.951, false negative rate (FNR) of 0.936 and false positive rate (FPR) of 0. 942.
  • Simulation and virtual reality applications in medical training for tuberculosis diagnosis
    Sudhanshu Gonge, Arvind Jagtap, V.C. Patil, Shirish Jaysing Navale
    Indian Journal of Tuberculosis, 2026
  • Digital case-based learning for improving clinical decision-making in tuberculosis care
    Sudhanshu Gonge, Rahul Subhash Gaikwad, Nitin N. Jadhav, Arvind Jagtap, Jyoti S. Raghatwan, Balkrishna K. Patil
    Indian Journal of Tuberculosis, 2026
  • Advancements in Plant Disease Classification Using Deep Learning: Trends, Hybrid Models, and Future Directions
    Aoudumber D. Londhe, Namdev M. Sawant, Ganesh Shivaji Pise, Sonali D. Mali, Arvind Jagtap, Sandip Y. Bobade
    Lecture Notes in Networks and Systems, 2026
  • AI-Driven Visual Navigation for Smart Lab Tour Guide Robot
    Vinod Chandrakant Todkari, Avinash P. Kaldate, Shrikrishna Kolhar, Arvind Jagtap, Nilesh P. Sable
    Journal Europeen Des Systemes Automatises, 2025
    A self-contained guidance system is required in robotics and automation laboratories for autonomous navigation purposes.In laboratory conditions where conditions are constantly changing, regular fixed-path solutions will not work.In this paper, a comprehensive framework for a tour guide robot is developed.An AI-driven visual navigation system is used to guide the robot.Simultaneous localization and mapping (SLAM) is implemented instead of the traditional line following approach.Deep learning-based obstacle detection is optimized for robot path planning.ORB-SLAM2 (monocular version) is considered for real-time localization and mapping.A specific data set is taken from the laboratory for fine-tuning of YOLOv5s for dynamic obstacle detection.The algorithm is extended for real-time path planning to avoid obstacles in the robot's path.Raspberry Pi 4 and Arduino Uno are used for the development of the embedded system so that it compares both for practical deployment feasibility.In this research, a 40% reduction in tour completion time and a 95% obstacle avoidance success rate are achieved.This investigation has achieved an average path deviation accuracy of 1.1 cm.A sensor fusion architecture is used to combine visual SLAM feature with deep learning detection for robust navigation.This research considers and impacts the architecture contrasts on hardware.Extensive performance characteristics are studied under different environmental conditions.This proposed AI-driven robot navigation in lab operations has set a new benchmark for intelligent robotics in academia and the public sector.
  • SugarcaneGAN: A U-RSwinT and StyleGAN3-Driven Deep Learning Framework for High-Accuracy Sugarcane Leaf Disease Detection and Classification
    , Ramesh S. Lavhe, Satyajit Pangaonkar, , Parikshit N. Mahalle, , Sarika Vasantrao Bodake, , Tushar Jadhav, , Deepali S. Jadhav, , Sumit Arun Hirve, , Snehlata Wankhade, , Dattatray G Takale, , Arvind Jagtap, and
    Es Food and Agroforestry, 2025
    This integrated approach enables a scalable, real-time solution for intelligent sugarcane disease monitoring, aligning with the broader goals of precision agriculture, smart farming, and global food security.
  • Predictive Modelling of Bone Mineral Density: An ANN and Regression-based Approach
    Anurag Ashokkumar Nema, Gulab Dattrao Siraskar, Arvind Jagtap, Puja Gholap, Kirti Wanjale, et al.
    Journal of Scientific and Industrial Research, 2025
    In recent years, the development of predictive models using Multi-Variable Regression (MVR) and Artificial Neural Networks (ANN) has become a focal point in health research, particularly in predicting Bone Mineral Density (BMD) for the early detection of osteoporosis. This study compares the performance of MVR and ANN models using a clinical dataset comprising patient attributes such as age, weight, height, and BMD. The primary objective is to predict BMD values of the femur bone and evaluate the potential risks of osteoporosis. ANN demonstrated superior predictive accuracy with a correlation coefficient (R²) of 0.8823 compared to 0.6087 for MVR, highlighting its capability to capture data linearity and complex patterns effectively. The study used filtered and validated datasets, including results from BMD tests on two dry intact femurs, sourced from Kaggle. Performance metrics such as regression accuracy and Mean Square Error (MSE) were calculated, showing that ANN with a hidden layer of 12 neurons provided the best results. The findings indicate that ANN not only outperforms MVR in predictive accuracy but also avoids the need for experiments on real human femurs, providing a non-invasive, data-driven alternative for medical diagnostics. A secondary goal was to develop a practical model for clinical use in predicting bone density. The study also explores the integration of ANN outputs with Genetic Algorithms (GA) to optimize the prediction process. This hybrid strategy reduces the number of simulations and computation time, offering a robust framework for global optimization. The combination of ANN and GA demonstrates the potential to enhance diagnostic precision and streamline decision-making processes in orthopedic and medical technology. In conclusion, this study emphasizes the applicability of ANN for accurate BMD predictions, paving the way for advanced diagnostic tools in healthcare. Future research could focus on expanding datasets and exploring hybrid optimization techniques to further improve prediction accuracy and clinical utility.
  • Plant disorder recognition and classification with segmentation
    Aarti P. Pimpalkar, Arvind Jagtap
    Progressive Computational Intelligence Information Technology and Networking, 2025
    Plant diseases affect crop yield and food security, posing a serious threat to global agriculture. Timely identification and acurate classification are necessary for the optimal management of many illnesses. This research presents a novel method for segmentation-based Improved Balanced Iterative Reducing & Clustering utilising Hierarchies (BIRCH)-based plant disease detection & classification. Plant pictures are divided into discrete parts using the Modified BIRCH algorithm, which helps to distinguish between healthy and sick areas. In order to precisely detect and categorize the plant illnesses, a classification model then undergoes training on the segmented regions. The suggested strategy is effective in obtaining excellent precision and reliability for plant disease detection as well as classification tasks, as shown by the experimental findings. Large-scale plant image processing is made efficient while segmentation quality is maintained thanks to using modified BIRCH based segmentation. The development of automatic plant disease detection systems is facilitated by this research, which improves agricultural output by allowing prompt disease control actions.
  • Plant Disease Detection: PyramidNet-ICNN Architecture With Modified BIRCH Segmentation
    Aarti P. Pimpalkar, Arvind M. Jagtap
    Journal of Phytopathology, 2025
    Agriculture stands as the primary occupation in India, yet it faces a substantial annual loss of 35% in crop productivity due to plant diseases. These diseases pose a significant task in the sector of agriculture, emphasising the critical need for their automatic identification to efficiently monitor plant health. The conventional technique of analysis by specialists in laboratories is costly and time‐consuming, even though the signs of the majority of diseases appear in plant leaves. Recognising the vital importance of early issue identification, this research proposes a novel hybrid Architecture, a hybrid of PyramidNet and ICNN models (Py‐ICNN) for plant disease detection and classification with an Improved BIRCH (I‐BIRCH) segmentation model, which uses an image as input. This framework follows a systematic approach, comprising preprocessing, segmentation, extraction of features and detection and classification of diseases. Using median and Contrast Limited Adaptive Histogram Equalisation (CLAHE) filtering, the input image first undergoes enhanced preprocessing. The preprocessed outcome is then subjected to I‐Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH) segmentation. Then, features including IPHOG, multi‐texton features and MBP‐based features are extracted from the segmented image. These extracted features are then individually processed using PyramidNet and improved convolutional neural network (ICNN) to detect and classify the plant disease. Furthermore, the proposed Py‐ICNN model is evaluated and compared with traditional methods. The findings demonstrate that the Py‐ICNN framework obtained an accuracy of 93.70% and a specificity of 95.82%. These results demonstrate how well the Py‐ICNN approach detects and classifies plant diseases.
  • Computer–Aided Faulty Solar Panel Detection using Deep Learning
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Hunger Game Search Archimedes Optimization Enhanced Blockchain Enabled Deep Learning for Multiclass Plant Disease Detection Using Leaf Images
    Yogesh Manohar Gajmal, Arvind M. Jagtap, Pranav More, Kiran Dhanaji Kale
    Metaheuristics in Engineering Applications, 2025
  • Automated Structural Integrity Assessment of Wind Turbines Using YOLOv8
    Deepali S. Jadhav, Rushikesh Gaikwad, Arvind Jagtap, Veena M. Kadam
    Proceedings 2025 2nd International Conference on Electronic Circuits and Signaling Technologies Icecst 2025, 2025
  • Machine Learning Based Monitoring System for Elderly Home Alone
    Deepali Jadhav, Vedant Patil, Savitri Chougule, Devata R Anekar, Veena M Kadam, Arvind Jagtap
    Proceedings 2025 2nd International Conference on Electronic Circuits and Signaling Technologies Icecst 2025, 2025
  • A Survey on a Novel Cryptojacking Covert Attack
    Yogesh M. Gajmal, Pranav More, Kiran Dhanaji Kale, Arvind Jagtap
    2nd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2024, 2024
  • Access control and data sharing mechanism in decentralized cloud using blockchain technology
    Yogesh Gajmal, Pranav More, Arvind Jagtap, Kiran Kale
    Journal of Autonomous Intelligence, 2024
  • A Discrete Firefly Algorithm Applied to Structural Bridge Truss Optimization
    Nayar Cuitláhuac Gutiérrez Astudillo, Dinesh Bhagwan Hanchate, Arvind M. Jagtap
    Lecture Notes in Networks and Systems, 2023
  • Comprehensive analysis for fraud detection of credit card through machine learning
    Parth Roy, Prateek Rao, Jay Gajre, Kanchan Katake, Arvind Jagtap, Yogesh Gajmal
    2021 International Conference on Emerging Smart Computing and Informatics Esci 2021, 2021
  • Energy efficient sensor deployment with TCOV and NCON in wireless sensor networks: Energy efficient sensor deployment with TCOV
    Arvind Madhukar Jagtap, Gomathi N.
    International Journal of Embedded and Real Time Communication Systems, 2020
  • Optimal sensor deployment in internet of things based wireless sensor network for irrigation management system
    Arvind Madhukar Jagtap, , N. Gomathi, and
    International Journal of Engineering and Advanced Technology, 2019
  • Minimizing movement for network connectivity in mobile sensor networks: an adaptive approach
    Arvind Madhukar Jagtap, N. Gomathi
    Cluster Computing, 2019
  • Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence
    A. M. Jagtap, N. Gomathi
    Bulletin of the Polish Academy of Sciences Technical Sciences, 2017

RECENT SCHOLAR PUBLICATIONS

  • A blockchain-based hybrid hunger game search archimedes optimization enabled deep learning for multiclass plant disease detection using leaf images
    YM Gajmal, AM Jagtap, KD Kale, JS Gawade, P More
    International Journal of Image and Graphics 26 (03), 2650018 , 2026
    2026
    Citations: 3
  • MESHMIND: DECENTRALIZED LAN-BASED COORDINATION FOR LARGE LANGUAGE MODELS: DESIGN, IMPLEMENTATION, AND EVALUATION
    VU Rathod, AD Londhe, SY Bobade, A Jagtap, S Dhamdhere, VC Todkari
    Nigerian Journal of Technology 45 (S1) , 2026
    2026
  • Simulation and Virtual Reality Applications in Medical Training for Tuberculosis Diagnosis
    S Gonge, A Jagtap, VC Patil, SJ Navale
    Indian Journal of Tuberculosis , 2026
    2026
  • Digital Case-Based Learning for Improving Clinical Decision-Making in Tuberculosis Care
    S Gonge, RS Gaikwad, NN Jadhav, A Jagtap, JS Raghatwan, BK Patil
    Indian Journal of Tuberculosis , 2026
    2026
  • AI-Driven Visual Navigation for Smart Lab Tour Guide Robot
    VCTAPKSKAJNP Sable
    Journal Européen des Systèmes Automatisés 58 (12), 2609-2616 , 2025
    2025
  • SugarcaneGAN: A U-RSwinT and StyleGAN3-Driven Deep Learning Framework for High-Accuracy Sugarcane Leaf Disease Detection and Classification
    RS Lavhe, S Pangaonkar, PN Mahalle, SV Bodake, T Jadhav, DS Jadhav, ...
    ES Food and Agroforestry 22, 1916 , 2025
    2025
  • Advancements in Plant Disease Classification Using Deep Learning: Trends, Hybrid Models, and Future Directions
    AD Londhe, NM Sawant, GS Pise, SD Mali, A Jagtap, SY Bobade
    International Conference on Sustainable Computing and Intelligent Systems … , 2025
    2025
  • Machine Learning Based Monitoring System for Elderly Home Alone
    D Jadhav, V Patil, S Chougule, DR Anekar, VM Kadam, A Jagtap
    2025 2nd International Conference on Electronic Circuits and Signaling … , 2025
    2025
  • Automated Structural Integrity Assessment of Wind Turbines Using YOLOv8
    DS Jadhav, R Gaikwad, A Jagtap, VM Kadam
    2025 2nd International Conference on Electronic Circuits and Signaling … , 2025
    2025
  • Artificial Intelligence in Predicting Performance of Sustainable Marine Materials.
    GC Sarode, A Jagtap, T Pawase, S Jaiswal, N Joshi, K Mannepalli, ...
    Journal of Mines, Metals and Fuels 73 (10), 3035-3053 , 2025
    2025
  • Predictive Modelling of Bone Mineral Density: An ANN and Regression-based Approach
    AA Nema, GD Siraskar, A Jagtap, P Gholap, K Wanjale, ...
    Journal of Scientific & Industrial Research (JSIR) 84 (8), 862-870 , 2025
    2025
    Citations: 1
  • Plant disorder recognition and classification with segmentation
    AP Pimpalkar, A Jagtap
    Progressive Computational Intelligence, Information Technology and … , 2025
    2025
    Citations: 5
  • Plant Disease Detection: PyramidNet‐ICNN Architecture With Modified BIRCH Segmentation
    AP Pimpalkar, AM Jagtap
    Journal of Phytopathology 173 (3), e70068 , 2025
    2025
    Citations: 6
  • A Survey on a Novel Cryptojacking Covert Attack
    YM Gajmal, P More, KD Kale, A Jagtap
    2024 2nd International Conference on Intelligent Data Communication … , 2024
    2024
    Citations: 1
  • Original Research Article Access control and data sharing mechanism in decentralized cloud using blockchain technology
    Y Gajmal, P More, A Jagtap, K Kale
    Journal of Autonomous Intelligence 7 (3) , 2024
    2024
    Citations: 4
  • Soil Health Monitoring System using Random Forest Algorithm
    International Journal of Research in Engineering, Science and Management 5 … , 2022
    2022
    Citations: 2
  • Comprehensive analysis for fraud detection of credit card through machine learning
    P Roy, P Rao, J Gajre, K Katake, A Jagtap, Y Gajmal
    2021 international conference on emerging smart computing and informatics … , 2021
    2021
    Citations: 15
  • Smart agriculture system towards Iot based wireless sensor network
    N Gomathi, AM Jagtap
    Turkish Journal of Computer and Mathematics Education 12 (6), 4133-4150 , 2021
    2021
    Citations: 6
  • Artificial Intelligence Based Intimation of Houses for Tenants Landlords and Other Folks
    S Motiwala, V Sutar, A Nandan, A Moharkar, A Jagtap, Y Gajmal
    IRJET 8 (7) , 2021
    2021
    Citations: 1
  • Energy efficient sensor deployment with tcov and ncon in wireless sensor networks: Energy efficient sensor deployment with tcov
    AM Jagtap, N Gomathi
    International Journal of Embedded and Real-Time Communication Systems … , 2020
    2020
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Comprehensive analysis for fraud detection of credit card through machine learning
    P Roy, P Rao, J Gajre, K Katake, A Jagtap, Y Gajmal
    2021 international conference on emerging smart computing and informatics … , 2021
    2021
    Citations: 15
  • Minimizing sensor movement in target coverage problem: A hybrid approach using Voronoi partition and swarm intelligence
    AM Jagtap, N Gomathi
    Bulletin of the polish academy of sciences technical sciences, 263-272-263-272 , 2017
    2017
    Citations: 10
  • Improved salp swarm algorithm for network connectivity in mobile sensor network
    AM Jagtap, N Gomathi
    Journal of Networking and Communication Systems 2 (3), 11-19 , 2019
    2019
    Citations: 8
  • Minimizing movement for network connectivity in mobile sensor networks: an adaptive approach
    AM Jagtap, N Gomathi
    Cluster Computing 22 (Suppl 1), 1373-1383 , 2019
    2019
    Citations: 7
  • Plant Disease Detection: PyramidNet‐ICNN Architecture With Modified BIRCH Segmentation
    AP Pimpalkar, AM Jagtap
    Journal of Phytopathology 173 (3), e70068 , 2025
    2025
    Citations: 6
  • Smart agriculture system towards Iot based wireless sensor network
    N Gomathi, AM Jagtap
    Turkish Journal of Computer and Mathematics Education 12 (6), 4133-4150 , 2021
    2021
    Citations: 6
  • Plant disorder recognition and classification with segmentation
    AP Pimpalkar, A Jagtap
    Progressive Computational Intelligence, Information Technology and … , 2025
    2025
    Citations: 5
  • Original Research Article Access control and data sharing mechanism in decentralized cloud using blockchain technology
    Y Gajmal, P More, A Jagtap, K Kale
    Journal of Autonomous Intelligence 7 (3) , 2024
    2024
    Citations: 4
  • A blockchain-based hybrid hunger game search archimedes optimization enabled deep learning for multiclass plant disease detection using leaf images
    YM Gajmal, AM Jagtap, KD Kale, JS Gawade, P More
    International Journal of Image and Graphics 26 (03), 2650018 , 2026
    2026
    Citations: 3
  • Soil Health Monitoring System using Random Forest Algorithm
    International Journal of Research in Engineering, Science and Management 5 … , 2022
    2022
    Citations: 2
  • Extracting application model from restful web services for client stub generation
    A Menkudle, S Sonawane, A Jagtap
    Int. J. Comput. Technol. Appl.(IJCTA) 5 (1), 226-232 , 2014
    2014
    Citations: 2
  • Predictive Modelling of Bone Mineral Density: An ANN and Regression-based Approach
    AA Nema, GD Siraskar, A Jagtap, P Gholap, K Wanjale, ...
    Journal of Scientific & Industrial Research (JSIR) 84 (8), 862-870 , 2025
    2025
    Citations: 1
  • A Survey on a Novel Cryptojacking Covert Attack
    YM Gajmal, P More, KD Kale, A Jagtap
    2024 2nd International Conference on Intelligent Data Communication … , 2024
    2024
    Citations: 1
  • Artificial Intelligence Based Intimation of Houses for Tenants Landlords and Other Folks
    S Motiwala, V Sutar, A Nandan, A Moharkar, A Jagtap, Y Gajmal
    IRJET 8 (7) , 2021
    2021
    Citations: 1
  • Energy efficient sensor deployment with tcov and ncon in wireless sensor networks: Energy efficient sensor deployment with tcov
    AM Jagtap, N Gomathi
    International Journal of Embedded and Real-Time Communication Systems … , 2020
    2020
    Citations: 1
  • MESHMIND: DECENTRALIZED LAN-BASED COORDINATION FOR LARGE LANGUAGE MODELS: DESIGN, IMPLEMENTATION, AND EVALUATION
    VU Rathod, AD Londhe, SY Bobade, A Jagtap, S Dhamdhere, VC Todkari
    Nigerian Journal of Technology 45 (S1) , 2026
    2026
  • Simulation and Virtual Reality Applications in Medical Training for Tuberculosis Diagnosis
    S Gonge, A Jagtap, VC Patil, SJ Navale
    Indian Journal of Tuberculosis , 2026
    2026
  • Digital Case-Based Learning for Improving Clinical Decision-Making in Tuberculosis Care
    S Gonge, RS Gaikwad, NN Jadhav, A Jagtap, JS Raghatwan, BK Patil
    Indian Journal of Tuberculosis , 2026
    2026
  • AI-Driven Visual Navigation for Smart Lab Tour Guide Robot
    VCTAPKSKAJNP Sable
    Journal Européen des Systèmes Automatisés 58 (12), 2609-2616 , 2025
    2025
  • SugarcaneGAN: A U-RSwinT and StyleGAN3-Driven Deep Learning Framework for High-Accuracy Sugarcane Leaf Disease Detection and Classification
    RS Lavhe, S Pangaonkar, PN Mahalle, SV Bodake, T Jadhav, DS Jadhav, ...
    ES Food and Agroforestry 22, 1916 , 2025
    2025