Spatial attention-guided pre-trained networks for accurate identification of crop diseases

Hybrid Energy Storage Systems (HESS) can provide solutions for grid stThe maintenance of agricultural productivity is critically dependent on the efficient and accurate identification of plant diseases. As observed, the manual inspection to the illness is often inefficient and error-prone, particularly under conditions such as inconsistent lighting, leaf deformities, and subtle distinctions between disease symptoms. To address these challenges, we introduce an enhanced crop disease classification framework that incorporates EfficientNet-B3 with an ancillary convolutional layer and a spatial attention module (ACSA). EfficientNet-B3 offers a strong foundation for feature extraction due to its compound scaling and efficient computation, while the spatial attention module improves classification accuracy by directing the model to focus on critical regions of diseased leaves. Additionally, the integration of ancillary convolutional layer to this architecture enhances the ability of the model to detect subtle disease variations. To further improve the adaptability, the proposed method incorporates a preprocessing and data augmentation techniques. Together, these enhancements create a more effective process for identifying disease pattern in wide range of plant species. The model was evaluated using an extensive crop disease dataset and against state-of-the-art methods such as EffiNet-TS, PlantXViT, and MobileNet V2 to assess its effectiveness. The proposed approach achieved an accuracy of 99.89% and a recall rate of 99.87%, demonstrating its suitability for crop classification with minimal computational overhead. Ablation studies further validate the significant contributions of the spatial attention module and the ancillary convolutional layer to the overall performance of the proposed model.ability and integration of renewable energy sources. A novel model integrating Model Predictive Control (MPC), Particle Swarm Optimization (PSO), Mixed-Integer Linear Programming (MILP), and Artificial Neural Networks (ANN) is developed to optimize HESS performance. MPC is employed for its predictive capabilities, enabling real-time control under dynamic grid conditions, while PSO optimizes system configurations for cost and efficiency. In the real-time operations of the grid, the MPC framework successfully handles complicated constraints with the possibility of being adaptively predictive. PSO optimizes the system by obtaining optimal configurations in non-linear and multimodal conditions with balanced cost and performance. MILP ensures the operating strategy of the system by fulfilling capacity, efficiency, and stability conditions at the grid level. ANN provides high precision predictions for energy management, hence enhancing the process of decision making for various cases. Simulations show up to 15.8% reductions in energy losses, 21.4% enhancements in grid stability, and 90% improvements in the operation efficiency as compared to the current methods. The integration benefits highlight superior adaptability and cost effectiveness. System costs are reduced to $1.6 million. Conclusion scalable pathway for enhancing renewable energy penetration and addressing the demands of the grid. The proposed model, employing advanced control and optimization techniques, ensures resilient energy systems capable of assimilating various REs and dynamic grid conditions that ensure proper transition towards low-carbon energy grids.