Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions

The Internet of Things (IoT) stands as a revolutionary technology which impacts smart agriculture and different sectors beyond it. The research describes an intelligent IoT-enabled monitoring system which uses sensors to bring improved agricultural performance through environmental real-time observations combined with artificial intelligence decision systems. The proposed system completes its operation by executing sensor data acquisition followed by deep learning-based analysis and secure data transmission procedures. The initial stage of IoT-enabled operation utilizes environment-sensing devices which collect essential parameters for precision farming. A Recurrent Neural Network (RNN) processing system applies classification methods to collected data to generate useful insights. Secure cloud data transmission takes place through an encryption system which combines Substitution-Caesar cipher with Improved Elliptic Curve Cryptography (IECC). The system demonstrates 99.64% accuracy, and the proposed encryption method proves efficiency through its average execution times of encryption at 0.3904 ms and decryption at 0.1102 ms which exceeds current models in speed and security performance.