Towards efficient IoT communication for smart agriculture: A deep learning framework

The integration of IoT (Internet of Things) devices has emerged as a technical cornerstone in the landscape of modern agriculture, revolutionising the way farming practises are viewed and managed. Smart farming, enabled by interconnected sensors and technologies, has surpassed traditional methods, giving farmers real-time, granular information into their farms. These Internet of Things devices are responsible for collecting and sending greenhouse data (temperature, humidity, and soil moisture) for the required destination, to provide a comprehensive awareness of environmental factors critical to crop growth. Therefore, ensuring that the received data are accurate is a challenge, thus this paper investigates the optimization of Agriculture IoT communication, proposing a complete strategy for improving data transmission efficiency within smart farming ecosystems. The proposed model intends to maximize energy efficiency and data throughput in the context of essential agricultural factors by using Lagrange optimization and a Deep Convolutional Neural Network (DCNN). The paper focus on the ideal communication required distance between IoT sensors that measure humidity, temperature, and water levels and central control systems. The investigation emphasizes the critical necessity of these data points in guaranteeing crop health and vitality. The proposed technique strives to improve the performance of agricultural IoT communication networks through the integration of mathematical optimization and cutting-edge deep learning. This paradigm change emphasizes the inherent link between precise achievable data rate and energy efficiency, resulting in resilient agricultural ecosystems capable of adjusting to dynamic environmental conditions for optimal crop output and health.