Advancing plant disease classification using an attention-based CNN for intra-dataset and cross- dataset training Prateek Mahapatra, Madhumita Panda, Santanu Kumar Dash, Umesh Kumar Sahu Scientific Reports, 2026 The precise classification of plant diseases is crucial for ensuring food security for all people and boosting agricultural productivity. Although there has been significant progress in this field using deep learning approaches, cross-dataset training hasn’t drawn as much attention from researchers as intra-dataset training has. Moreover, very few models have successfully blended intra-dataset and cross-dataset training approaches. This paper proposes a novel attention-based Convolutional Neural Network (CNN) to overcome these limitations. The model improves feature extraction and classification accuracy across multiple datasets by using attention mechanisms. It was tested on five datasets (Digipathos, Northern Leaf Blight (NLB), PlantVillage, PlantDoc, and the CD&S dataset) that covered leaf diseases of both corn and potatoes. During intra-dataset training, the model achieved the highest classification accuracy of 99.38% when trained on images of potato leaves from the PlantVillage dataset. During cross-dataset training, the model exhibited the highest average classification accuracy of 82.93% for corn leaf diseases when trained on images from the CD&S dataset with their backgrounds removed. When compared to the techniques taken into consideration in this study under comparable experimental conditions, the results demonstrate improved performance. This study shows how the model may be flexible for both intra- and cross-datasets, offering a flexible way to categorize diseases that affect plants. Because of its ability to generalize across different datasets, it may be helpful in real-world agricultural applications with a wide variety of image quality and situations. This encourages the advancement of precision farming techniques and disease control.
Robust lane detection in autonomous driving using gated convolutional networks with spatial attention Rahul Surya M, Santanu Kumar Dash Results in Engineering, 2026 • Proposed Gated CNN with Spatial Attention enhances lane feature focus. • Hybrid Dice-Focal-Tversky Loss improves thin lane boundary segmentation. • Outperforms six methods on BDD100K, CULane, and TUSimple benchmarks. Lane detection plays an important role in modern self-driving systems, directly bearing on vehicle control, navigation stability, and road safety. All autonomous steering, adaptive cruise control, and lane-keeping assistance were reliant on lane detection. CNN-based models, particularly with encoder-decoder architectures like UNet and SegNet, have inherent limitations in their performance. These limitations become apparent in challenging conditions such as varying lighting, occlusions, faded markings, tight corners, and urban clutter, which can negatively impact their accuracy and robustness. Thus, the present study, introduce a novel deep learning method that utilizes a spatial attention mechanism in combination with Gated Convolutional Neural Networks (Gated CNNs) to mitigate these challenges. Gated convolutional layers enable adaptive feature selection by suppressing irrelevant background information, while the spatial attention module enhances localization of lane-relevant regions. Moreover, a hybrid dice-focal-Tversky loss function is employed to attenuate class imbalance and improve thin lane boundary segmentation. The proposed model was evaluated on three extensive benchmark datasets: BDD100K, CULane, and TUSimple, which collectively represent a wide range of real-world circumstances, including nighttime driving, lane occlusions, and crowded driving conditions. As a result, the model consistently outperformed multiple baseline approaches, in terms of accuracy, F1-score, and intersection over union (IoU) across segmentation experiments. The current framework demonstrates that Gated CNN with Spatial Attention provides a more robust, accurate, and generalizable lane detection approach, applicable in real-time autonomous driving scenarios.
Autonomous object tracking with vision based control using a 2DOF robotic arm Umesh Kumar Sahu, Mebin K. S., Abhinav K., Muhammed Muzammil P, Ankur Jaiswal, Umesh Kumar Yadav, Santanu Kumar Dash Scientific Reports, 2025 The tracking of moving object by implementing robot manipulator is one of the challenging task for many applications such as manufacturing, agriculture, logistics, healthcare, space, military, entertainment, etc. In the deployment of robotic manipulators with real-time object tracking for aforementioned important applications, the proper sensor surveillance and ensuring stability are major challenges. The purpose of this study is to design a precise and responsive object-tracking system by eliminating the complexities related to tedious mechanisms, rigidity, requirement of multiple sensors, etc. which are commonly associated with traditional systems. The robotic arms can be effectively designed to track moving objects autonomously with vision-based control. In comparison with different classical and traditional servoing approaches, the image-based visual servoing (IBVS) is more advantageous in vision-based control. The present article describes a new approach for IBVS-based tracking control of 2-degree-of-freedom (DOF) robotic arm by including object identification and trajectory tracking based crucial components. To solve the issues associated with IBVS, an accurate deep learning-based object detection framework is employed. The presented framework is utilized to detect and locate the objects in real-time. Further, an effective vision-based control technique is designed to control the 2-DOF robotic arm with the help of real-time response of object detection system. The validation of proposed control strategy is done by performing a simulation and experimental investigations with CoppeliaSim robot simulator and 2-DOF robotic arm, respectively. The findings reveal that the proposed deep learning controller for the vision-based 2-DOF robotic arm achieves good levels of accuracy and response time while performing visual servoing tasks. Furthermore, thorough discussion on possibility of using data-driven learning technique has been explored to improve the robustness and adaptability of the presented control scheme.
Optimizing air velocity for energy-efficient and sustainable rubberwood drying kilns Suratsavadee K. Korkua, Choosak Rittiphet, Siraporn Sakphrom, Santanu Kumar Dash, Chalearm Tesanu, Kamon Thinsurat Results in Engineering, 2025 The rubberwood industry is a key player in the global timber market, supplying sustainable wood products to meet increasing demands worldwide. Despite its importance, conventional drying processes are energy-intensive, accounting for a substantial portion of production costs while contributing significantly to carbon emissions. This study presents the development of a real-time monitoring and control system for rubberwood drying kilns, integrating smart infrastructure to enhance process efficiency and sustainability. Predicting moisture content accurately during drying is crucial, as it directly impacts wood quality and process optimization. Traditional methods often struggle with dynamic variations in drying behavior, making precise control challenging. To address this, an artificial neural network-based forecasting model was employed to predict changes in moisture content, enabling more effective process adjustments. Experimental investigations were conducted under controlled laboratory conditions on 75 mm thick rubberwood lumber, with a constant dry-bulb temperature of 75°C and wet-bulb temperature of 50°C. Two air velocities—1 m/s and 3 m/s—were employed consecutively until a final moisture content of 10% was achieved. Results highlight the effectiveness of a two-stage drying strategy, starting with a higher air velocity to accelerate drying, followed by a lower velocity as the drying rate diminishes. Notably, reducing air velocity from 3 m/s to 1 m/s when moisture content fell below 40% resulted in approximately 50% energy savings, primarily through reduced motor speed. This strategic approach minimizes energy consumption while maintaining product quality, offering factories significant operational cost reductions and the potential to claim carbon credits. These findings underscore the scalability of this technology, contributing to more sustainable and efficient production practices in the global timber industry.
Energy management with control parameter optimization for a PV/FC/battery islanded DC microgrid Harin M Mohan, Santanu Kumar Dash Engineering Research Express, 2025 The integration of renewable energy resources (RERs) and the need for reliable and efficient power distribution in emerging power systems has driven growing interest in microgrids. This has led to challenges like uneven load distribution, overloading, less usage of power, and unstable operations that may lead to damage to power electronic devices connected. The existing metaheuristic-based control mechanisms struggle to manage fluctuating sources and demands effectively, often leading to suboptimal energy management. Therefore, with such constraints, this research work proposes a method utilizing Sparrow Search Optimization (SSA) and optimizes the control parameters for more effective EMS functionality. To support efficient power management and balancing of generation versus load demands, the proposed system integrates dispatchable distributed generators such as fuel cells (FC) and battery storage systems (BSS) with non-dispatchable distributed generators such as solar photovoltaic (PV) source. The method uses SSA optimization of controller parameter estimation ensuring better stability and reliability, compared to conventional PI and other metaheuristic methods. The intended system was tested under various operating scenarios with the use of MATLAB Simulink, to validate the efficacy of this system in the regulation of renewable energy fluctuations and optimization of battery performance. The performance of the developed system was compared with systems optimized through Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Harris Hawk Optimization (HHA). Simulation results show that the system optimized using the SSA method results in better efficiency with faster execution time than other optimization-based systems.
Meta model approach for real time and short-term forecasting of wind turbine power generation with interactive 24 h dashboard Gaurav Chauhan, Sachin Jakhar, Priyamvad Singh, Santanu Kumar Dash, Muchenedi Hari Kishor Engineering Research Express, 2025 This study evaluates the performance of various machine learning algorithms (Linear Regression, SVR, AdaBoost, XGBoost, Gradient Boosting, Decision Tree, Random Forest, Extra Trees, CatBoost) for predicting wind power generation. We investigate their strengths and weaknesses through extensive experimentation using data from Kaggle and ENTSO-E. To enhance accuracy, we employ a meta-model approach and incorporate data cleaning techniques. We integrate statistical methods, artificial neural networks, and deep learning for improved short-term forecasting. A key outcome is the development of a real-time GUI dashboard that utilizes the OpenWeather API to fetch wind data and display predictions. This user-friendly interface features visualizations, alerts, and real-time data updates. Our results demonstrate that the selected meta-model significantly surpasses traditional methods, achieving superior metrics like R-squared and RMSE. This research showcases the potential of hyperparameter-tuned machine learning for precise wind power prediction, contributing to increased renewable energy utilization and reduced greenhouse gas emissions.
Implementation of adaptive control strategies for PV system integrated unified conditioner for optimal power quality regulation Santanu Kumar Dash, Pravat Kumar Ray, Pragnyashree Ray Results in Engineering, 2025 • PV-UPQC is with advance new hybrid adaptive controllers for performance enhancement. • ANF-ISC for series compensation has been employed. • AHTF-MAF for shunt compensation has been proposed. • Adaptive controllers have been analyzed at dynamic environmental conditions. • Reduction in THD, from 33.45 % to 2.45 % with the adaptive controllers. This research is to examine the performance and utilization of Solar Photovoltaic based hybrid system to supply regulated power to the local consumers. Accurate power generation by the proposed Solar Photovoltaic based system is desired to maintain power quality under various distorted and weak grid scenarios. The considered system utilizes an adaptive notch filter based Instantaneous Symmetrical Component controller for series compensation of SPV-UPQC to eliminate dynamic voltage conditions to protect the consumer loads. For the mitigation of current quality issues in presence of non linear loads, Adaptive Hyperbolic tangent function based controller has been employed. Initially, simulation based approach for verification of proposed ANF-ISC Controller for series compensation and HTF controller for current regulation are utilized to evaluate the SPV-UPQC performance. Under weak grid voltage conditions such as voltage sag and voltage swell, proposed ANF-ISC Controller is used for voltage regulation to protect the consumer loads. The validation of the simulation based performance through MATLAB/Simulink platform has been achieved through experimental prototype development. Efficiency of the system under abnormal current scenarios, distorted grid voltage conditions has been validated by utilization of proposed adaptive controllers. Moreover, under the dynamic harmonic content scenario, the proposed system has shown effective reduction in THD, from 33.45 % to 2.45 % .The results obtained, that are presented in the research confirms the regulation of the power quality.
A novel reduced-switch multilevel inverter topology for DC microgrid interfacing with V2G/G2V functionality and advanced FO-PID energy management Shashi Bhushan Mohanty, Satyajit Mohanty, Mrutunjaya Panda, Santanu Kumar Dash Results in Engineering, 2025 This paper proposes an enhanced energy management framework for DC microgrids by integrating a reduced-switch asymmetrical Multilevel Inverter (MLI) and bidirectional Vehicle-to-Grid/Grid-to-Vehicle (V2G/G2V) operations to improve system performance and resilience. A novel State Machine–Fractional Order PID (FO-PID) controller is introduced, with both its tuning parameters and the MLI switching angles optimized using Particle Swarm Optimization (PSO). The hybrid control enables precise load sharing among fuel cells, batteries, supercapacitors, and EVs while maintaining stable DC-link voltage and minimizing hydrogen consumption. Simulation results demonstrate a 32.7% reduction in fuel cell stress, a 33% decrease in hydrogen usage, and an efficiency improvement of up to 6% compared to conventional systems. Furthermore, a detailed stress analysis methodology is introduced to quantify instantaneous power variations across sources, directly linked to their lifespan. Comparative simulation studies between the proposed PSO-optimized FO-PID-MLI framework and conventional AC-DC systems validate the superior performance of the proposed approach in terms of reduced power stress, enhanced efficiency, and improved load-side stability. This work provides a comprehensive solution for future-ready microgrid energy management systems by combining advanced control techniques with next-generation inverter and EV technologies. • A Novel Reduced-Switch Multilevel Inverter Topology for DC Microgrid Interfacing with V2G/G2V Functionality and Advanced FO-PID Energy Management. • Designing a PSO-optimized State Machine–FPID energy management controller enhancing load sharing, system stability, and dynamic performance across fuel cell, battery, and supercapacitor units in a DC microgrid. • Integration of a reduced-switch Multilevel Inverter (MLI) in place of the traditional AC–DC conversion stage, yielding improved waveform quality, lower Total Harmonic Distortion (THD), and enhanced load power smoothing. • Incorporating bidirectional V2G/G2V electric vehicle operation allows for dynamic energy storage and exchange, therefore supporting grid stability and enhancing general SoC performance of energy storage systems. • Formulation of a novel dual-layer optimization framework using PSO to simultaneously determine optimal FOPID control parameters and MLI switching angles, ensuring energy-efficient operation and minimal fuel cell hydrogen consumption. • Design and simulation of an advanced hybrid DC microgrid model, validated through waveform analysis, stress reduction evaluation, and SoC stability assessment under various dynamic load conditions. • Comprehensive comparative analysis of the proposed system with conventional PID, classical state machine control, and non-MLI-based architectures to validate its superiority in terms of power quality, efficiency, and fuel economy.
Performance Analysis of MAF-Based Control Strategy in Hybrid Renewable Energy System Mohammed Shijas V, Saichol Chudjuarjeen, Santanu Kumar Dash, Muhammed Inamu Rahman Thekkedath, Harin M Mohan, Panithan Chakkuchan Proceedings of the 2025 3rd International Conference on Cyber Physical Systems Power Electronics and Electric Vehicles Icpeev 2025, 2025
Study of Class E Inverter for Induction Heating in High Temperature Paradorn Techasakolrat, Saichol Chudjuarjeen, Panithan Chakkuchan, Santanu Kumar Dash, Pinit Boonla, Sahatsawat Thongduang Proceeding 12th International Electrical Engineering Congress Smart Factory and Intelligent Technology for Tomorrow Ieecon 2024, 2024
Mitigation of power quality issues using UPQC Pravat Kumar Ray, Santanu Kumar Dash, Bidyadhar Subudhi, Suratsavadee K. Korkua International Journal of Emerging Electric Power Systems, 2020
Development and Analysis of Pothole detection and Alert based on NodeMCU Etukala Jaswanth Reddy, Padhuri Navaneeth Reddy, Govindula Maithreyi, M. Bharath Chandra Balaji, Santanu Kumar Dash, K. Aruna Kumari International Conference on Emerging Trends in Information Technology and Engineering Ic Etite 2020, 2020
