An Integrated IoT and Machine Learning Framework for Sustainable Crop Recommendation and Disease Detection

Metaheuristic Algorithms are an efficient approach for handling data management issues in IoT-WSNs. They are suitable for evolving and resource-limited networks because of their adaptability and capacity to detect almost optimal approaches in challenging conditions. Metaheuristic techniques have the ability to substantially enhance the performance and sustainability of future IoT-WSN deployments with additional improvements in hybridization and computational effectiveness. Data aggregation, routing, clustering, and various data management techniques benefit from the high optimization capabilities of metaheuristic algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Firefly Algorithm (FA). In the case of highly dimensional and complex search spaces, these algorithms provide a flexible structure that can find optimal solutions in manageable computing timeframes. WSNs can achieve improved energy efficiency, prolonged network lifetime, decreased data redundancy, and more accurate data by applying the research and utilization capabilities of metaheuristics. Additionally, new possibilities for context-aware, real-time data processing and intelligent decision-making have been made possible by hybrid metaheuristic techniques integrating with machine learning models.