GF-CNN: A Hybrid Approach for Pollen Recognition Combining Gabor Filters and Convolutional Neural Networks

Abstract

Pollen identification is a critical task across various scientific disciplines, including geology, ecology, evolutionary biology and botany. However, existing methods for pollen identification are often labour-intensive, time-consuming and dependent on highly skilled experts, highlighting the need for an automated and precise system. This study introduces an innovative approach that combines Gabor Filters (GF) with Convolutional Neural Networks (CNN) to enhance the accuracy of pollen classification. The Gabor filters are applied to high-resolution images of diverse pollen species, accentuating texture-specific details essential for differentiation. These pre-processed images are subsequently analysed using a CNN architecture with multiple layers designed to discern hierarchical features critical for precise classification. The proposed GF-CNN model demonstrates exceptional proficiency, achieving remarkable accuracy rates of 99.85% for the Malaysian Pollen Dataset (MPD) and 99.43% for the New Zealand Pollen Dataset (NPD). These results underscore the model's ability to balance precision and recall effectively. Additionally, the model exhibits high sensitivity, indicating an increased true-positive rate, which is essential for detailed ecological studies. Furthermore, the model's improved specificity scores highlight its success in minimizing false positives, emphasizing its relevance for precision-focused research.