A Predictive Model for Abnormal Conditions in Smart Farming using IoT Sensors

Abstract

By combining sensors and devices, the Internet of Things (IoT) technology revolutionizes smart farming by enabling real-time monitoring and data-driven decision-making. However, challenges persist, particularly in managing crop diseases and unusual weather patterns. Innovative solutions, such as neural network-based modules, provide early warning systems, empowering farmers to address these concerns proactively, and ensuring sustainable farming practices. The proposed research work incorporates a customized neural network module to detect unusual crop diseases and abnormal weather patterns, acting as an early warning system. The proposed technology delivers critical insights for timely interventions, allowing farmers to be proactive. Farmers can increase crop output and encourage sustainable agricultural methods by quickly detecting these uncommon challenges. This technique not only improves agricultural output, but it also improves the resilience and durability of farming ecosystems, assuring agriculture's long-term viability. The simulation results demonstrated that the proposed model outperforms other state-of-the-art smart farming models.