Agricultural productivity and food security are heavily impacted by plant diseases, and thus there is a high demand for accurate and automated plant disease detection that can be achieved by applying deep learning techniques. This research proposes a Multi-Model Ensemble Method Based on Deep Learning for multi-plant disease detection using ResNet50 to improve classification performance across multiple crop varieties. The proposed framework takes five important categories of plants into consideration including tomato, potato, grape, apple and maize, and 10 classes of healthy and diseased plants are generated from the PlantSeg dataset. The Anaconda platform was used along with Python to create a development environment that allows data preprocessing, augmentation, training and testing to be implemented efficiently. The proposed ensemble framework combines the feature extraction power of ResNet50 with several deep learning classifiers so as to obtain a good identification performance at different resolutions and environments. The proposed model performance is tested with the following metrics Accuracy, Precision, Inference Time, and Resolution quality and compared with MobileNetV2, YOLOv8 and the baseline CNN models. Experimental results show that the proposed ensemble ResNet50 framework achieves an accuracy of 98.7% and precision of 98.3%, which is about 6.4%, 4.8%, and 9.2% higher than that of MobileNetV2, YOLOv8, and CNN respectively. Moreover, the proposed method achieves high resolution disease localization capability with an inference time improvement of almost 18% compared with YOLOv8. The proposed system greatly improves the detection accuracy of the early stage and the calculation speed of the system, which is very suitable for smart agriculture applications and real-time monitoring of the health status of crops.
This paper proposes an Intelligent Link Failure Prediction and QoS-Aware Routing framework for Mobile Ad Hoc Networks (MANETs) using the Harris Hawk Optimization (HHO) algorithm to achieve reliable and efficient data transmission under highly dynamic network conditions. The proposed model integrates proactive link failure prediction with HHO-based multi-objective route optimization to select stable, energy-efficient, and QoS-compliant paths. The framework is implemented and evaluated using the SimPy simulation environment under a realistic node mobility and traffic workload generated using Random Waypoint mobility with CBR and VBR traffic patterns, which is widely adopted for MANET performance evaluation. The proposed method is compared with Multi-Agent Deep Learning (MADL), Multi-Agent Deep Reinforcement Learning (MADRL), and the Communication-Aware Hierarchical Routing Framework (CAHRF). Experimental results demonstrate that the HHO-based approach significantly improves network performance by increasing the Packet Delivery Ratio (PDR) by 9.8-15.6%, reducing Link Failure Recovery Time by 21.4-34.7%, extending Network Lifetime by 18.2-27.9%, and decreasing Control Packet Cost by 16.5-25.3% compared to the benchmark methods. These improvements confirm that the proposed HHO-driven intelligent routing framework provides a robust, scalable, and QoS-aware solution for reliable communication in highly dynamic MANET environments.
K. Helini, Malleswari Lakkapogu, Suryateja Kothuru et al.· 2026 7th International Confe...· 0 citations
Task scheduling in multi-cloud-based IoT is a very relevant issue as the demand on energy-efficient and environmentally conscious computing is growing. The proposed study suggests that a Quantum-Inspired Metaheuristic-based scheduling method based on a Quadratic Unconstrained Binary Optimization (QUBO) model could be used to optimize the task allocation among IoT devices over distributed cloud resources. The main goal is to reduce key performance parameters, such as temperature, cost, energy consumption, and carbon emissions, which are essential in sustainable cloud operations. The suggested approach is assessed on the CEA-Curie workload data set in the CloudSim simulation platform and compared to such traditional algorithms as Genetic Algorithm (GA), Deep Reinforcement Learning (DRL), and Asynchronous Advantage Actor-Critic (A3C). The experimental findings show that the QUBO-based algorithm is much better than the current method with an average temperature, operational cost, energy consumption, and carbon reduction of 21.8%, 19.5%, 24.3%, and 22.7% respectively relative to baseline algorithms. These enhancements indicate that quantum-inspired optimization is effective in solving more complicated multi-objective scheduling issues. The results indicate that the suggested model offers a scalable and sustainable solution to next-generation IoT-cloud ecosystems, which helps increase the impact of reduced environmental impact and improved resource efficiency in multi-cloud systems.
Ritu Singh, Srinivasa Reddy Arekuti, Sai Mounika Chintalapudi et al.· International Conference Com...· 0 citations
Minimizing energy consumption and carbon emissions while maintaining system performance is a critical challenge in cloud task scheduling. This paper presents a multi-objective scheduling framework based on a Memetic Algorithm (MA) designed to optimize task-to-VM mapping with respect to energy efficiency, carbon footprint, and throughput. The algorithm employs a weighted fitness function that integrates actual and idle energy usage, simulated time-varying carbon intensity, and task throughput. To enhance solution quality, MA combines global evolutionary operations (selection, crossover, mutation) with local search heuristics that adaptively refine candidate solutions based on workload characteristics and green energy opportunities. The carbon emission model incorporates dynamic emission factors (γ) derived from location- and time-sensitive datasets, reflecting real-world variability in grid carbon intensity. The proposed method is evaluated using the NASA Ames iPSC/860 workload under both low and high resource utilization scenarios. Comparative results demonstrate that the proposed MA approach achieves reduces the carbon emission by 20.2%, minimizes energy consumption by 17.7%, and enhances throughput by 21.2% over conventional techniques such as HDDPGTS and RAPTS, while also ensuring competitive performance in terms of makespan and resource utilization. These improvements underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures. The findings highlight the importance of integrating eco-aware intelligence into task scheduling policies, particularly for mission-critical and energy-intensive cloud applications.
Chennoji Sandhya, Mandla Alphonsa, Vankudoth Biksham et al.· International Journal of Int...· 0 citations