@article{Patil2026, 
author = {Sahebgouda Patil and Sumana Maradithaya},
title = {Precision agriculture application of a GCLH-based ensemble deep learning model for pomegranate disease identification},
year = {2026},
journal = {AIMS Agriculture and Food},
volume = {11},
number = {1},
pages = {228-253},
keywords = {computer vision, convolutional neural networks, deep learning, ensemble learning, pomegranate disease classification, precision agriculture},
url = {https://www.sciopen.com/article/10.3934/agrfood.2026012},
doi = {10.3934/agrfood.2026012},
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.}
}