Precision agriculture application of a GCLH-based ensemble deep learning model for pomegranate disease identification

Abstract

Pomegranate diseases significantly threaten global fruit production, causing substantial yield losses and economic impacts. Traditional manual disease identification methods are time-consuming, subjective, and require specialized expertise. In this study, we present a novel GCLH (Grouped Convolutional Learning Hierarchy) ensemble model for automated pomegranate disease classification compared to individual baseline models, including VGG16 (92.0%), DenseNet (91.66%), and InceptionV3 (92.05%). The proposed GCLH-based ensemble framework achieved a classification accuracy of 99.31%, outperforming individual backbone models, such as VGG16, DenseNet121, and InceptionV3, which achieved approximately 92% accuracy. This represents an improvement of nearly 7% over standalone CNN models and demonstrates enhanced precision (0.9917), recall (0.9929), and F1-score (0.9922). The significant performance gain confirms the effectiveness of multi-backbone feature fusion and hierarchical attention mechanisms for robust pomegranate disease classification.

Keywords

computer vision / convolutional neural networks / deep learning / ensemble learning / pomegranate disease classification / precision agriculture

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