FGDCC: Fine-Grained Deep Cluster Categorization -- A Framework for Intra-Class Variability Problems in Plant Classification

The article introduces a new framework, FGDCC, designed to address the challenges of intra-class variability in plant classification. This suggests a focus on improving the accuracy and robustness of plant identification systems, which is a valuable contribution to the field of computer vision and potentially to botany and agriculture. The use of deep clustering indicates an application of advanced machine learning techniques.