Optimizing Greenhouse Gas Emission Predictions in Agriculture via Multi-Modal Data Integration Using Hyper-Node Hamiltonian Relational Quantum Graph Generative Adversarial Attention Networks

Agriculture is one of the major sources of greenhouse gas (GHG) emissions, and robust prediction models are required to overcome environmental hazards. Conventional techniques remain challenged in the integration of various environmental parameters, thus affecting prediction accuracy. Optimizing Greenhouse Gas Emission Predictions in Agriculture via Multi-Modal Data Integration Using Hyper-Node Hamiltonian Relational Quantum Graph Generative Adversarial Attention Networks (2HNR-Q2G-N2AN) is a suggested approach by this research to maximize predictive efficiency. The data set GHG emission data contains 5,000 records with 11 environmental factors impacting emissions. Pre-processing is conducted with entropy and τ-Kendall methods, and feature extraction uses the Multi-Axis Vision Transformer (MaxViT) to identify complex dependencies. This is achieved using a new 2HNR-Q2G-N2AN method, which blends Hamiltonian quantum generative adversarial networks (HQuGANs) with a Hyper-node relational graph attention network (HRGATN) and uses the Nutcracker optimizer algorithm (NOA) to optimize its parameters. Experimental results show superb outcomes, achieving a root mean square error (RMSE) that is much lower than current approaches and an accuracy of 99.9%. The proposed approach offers enhanced multi-modal data integration, leading to robust predictions and improved agricultural sustainability.