The agricultural sector has been adopting IoT technologies to improve efficiency and yield. IoT devices in agriculture must overcome power consumption, resource constraint, and communication issues. We propose a power-efficient clustering method to improve IoT-based farming infrastructure productivity. We searched the vast literature on IoT-based agricultural systems for proven approaches, tools, and tactics. Clustering methods reduce power consumption, improve network scalability, and extend network life, improving IoT-based farm systems. Energy-efficient clustering uses k-means and particle swarm optimization methods. k-means clustering creates initial clusters, then particle swarm optimization selects cluster leaders. Simulations show that the proposed clustering method outperforms current methods in network lifetime and energy consumption. Internet of Things-based farming requires sensor nodes, cluster heads, a base station, and a cloud server. Field sensor nodes send data to cluster heads for processing and aggregation before sending it to the base station. The cloud server stores and processes data from the IoT network via the base station. IoT-based agriculture systems also use machine learning for predictive analytics and decision-making, UAVs for precision agriculture, and blockchain technology for secure data management and traceability. Energy-efficient clustering improves IoT-based agricultural systems' efficiency and dependability. IoT devices, machine intelligence, UAVs, and blockchain can improve IoT-based farm systems. This field will explore advanced algorithm development, multi-technology integration, and Internet of Things-based farm systems.

Precision farming, wireless sensor networks, energy-efficient clustering algorithms, Internet of Things.