Design of an Iterative Method for Crop Disease Analysis Incorporating Graph Attention with Spatial-Temporal Learning and Deep Q-Networks

Existing approaches often fail to capture the complex spatial-temporal interactions among crop conditions, environmental factors, and disease progression, leading to suboptimal diagnosis and treatment strategies.This work introduces innovative methodologies integrating advanced machine learning techniques to overcome these limitations.Specifically, we propose a novel Graph Attention Mechanism with Spatial-Temporal Graph Convolutional Networks (ST-GCN) model to analyze crop diseases.This model uniquely combines graph attention mechanisms with spatialtemporal modeling, enabling precise identification and tracking of disease progression over time and space.This allows for high-accuracy predictions of disease presence, severity, and spatial distribution, achieving over 90% accuracy and an Intersection over Union (IoU) score of at least 0.8.Furthermore, we introduce the Deep Q-Network with Attention-Based Feature Selection (DQN-AFS) model, which innovatively applies deep Q-networks integrated with attention mechanisms to optimize feature selection in crop images.This approach significantly enhances the model's ability to discern between varying types and severity levels of crop diseases, ensuring an 85% or higher disease classification accuracy and an 80% feature selection rate in different use case scenarios.Lastly, we propose a Swarm Intelligence-Based Multiple Agent Reinforcement Learning (SI-MARL) framework for adaptive treatment recommendation.This approach demonstrates superior treatment efficacy and resource utilization efficiency compared to conventional methods.