Multi-Stage Neural Network-Based Ensemble Learning Approach for Wheat Leaf Disease Classification

Abstract

Prompt and accurate classification of wheat leaf diseases and their severity is essential for precise diagnosis, effective pesticide application, efficient disease control, and enhanced wheat production and quality. Nevertheless, the wide range of wheat diseases poses a significant challenge in terms of their identification, especially in intricate agricultural landscapes. The utilization of conventional models has major limitations in wheat disease detection, including dataset-specific performance, overfitting due to limited data, and high computational needs, making deployment in resource-constrained situations difficult. To address these challenges, this research proposes a multi-stage Convolutional Neural Network (CNN) based Ensemble Learning (EL) approach, which utilizes several concurrent CNN models with a bagged EL method to classify wheat leaf diseases. The various stages in the EL approach employ a multi-level framework to enhance feature extraction and capture complex data patterns. This improves the model’s emphasis on diseases while reducing the influence of complex backgrounds on disease identification. The proposed method, integrating pretrained CNNs and a bagging ensemble technique, achieved an accuracy of 86.78%, 98.28%, and 99.16% on the three publicly available datasets, outperforming the state-of-the-art models. These results demonstrate the potential of the proposed model for real-time disease diagnosis.