Design and Implementation of a Graph Neural Network Framework for Crop Variety Classification Based on Complex Network Analysis

With the rapid advancement of smart agriculture and precision breeding, accurate classification of crop varieties has become a fundamental prerequisite for enhancing agricultural productivity and ensuring food security. This study develops a multidimensional complex network model that integrates multi-modal information, including crop genotypes, phenotypic traits, and environmental adaptability. By employing a combined approach of node classification and graph classification through graph neural networks, this research achieves precise classification of crop varieties. The innovation lies in the deep fusion of heterogeneous data within the complex network structure and the incorporation of agricultural knowledge graphs to improve model interpretability and generalization. This methodology not only strengthens the characterization and understanding of intricate inter-varietal relationships but also provides scientific support for breeding decision-making, field management, and pest control. Consequently, it advances smart agriculture towards greater intelligence and sustainability, demonstrating broad application potential and practical value.