Deep Learning and Optimization Approaches in IoT based soil nutrition and plant disease detection system for smart agriculture using Multi-Layer Stacked Residual Coordinate Boosted Sooty Tern Attention Network: A Review

Abstract

The integration of Internet of Things (IoT) and Artificial Intelligence (AI) has significantly transformed modern agriculture by enabling intelligent monitoring and data-driven decision-making for soil nutrition management and plant disease detection. Traditional farming practices often face limitations such as delayed disease identification, inefficient nutrient management, and lack of real-time analysis. To overcome these issues, recent research has focused on advanced deep learning and optimization techniques for precision agriculture. This review highlights approaches centered on a novel Multi-Layer Stacked Residual Coordinate Boosted Sooty Tern Attention Network, which combines residual learning and attention mechanisms to enhance feature extraction and classification accuracy. Attention mechanisms help focus on critical regions in plant images and soil data, while residual connections support deeper and more efficient model training. The study also examines various models, including CNNs, CNN-LSTM hybrids, Vision Transformers, and Graph Neural Networks, along with optimization techniques like genetic algorithms and swarm intelligence. These methods improve detection accuracy and enable real-time monitoring through IoT systems. However, challenges such as computational complexity, scalability, and energy constraints remain, highlighting the need for efficient and deployable solutions.