Highlights A hybrid CNN–WE model markedly improves the accuracy of barley disease classification. Adaptive weighting enhances decision stability by reducing the influence of weaker classifiers. The proposed ensemble achieves 98.72% accuracy, outperforming individual models. The framework supports accurate barley disease detection and contributes to the next generation of smart agriculture. ABSTRACT. Rhynchosporium commune and Pyrenophora teres are two of the most prevalent barley diseases, and their highly similar visual symptoms make accurate diagnosis and differentiation particularly challenging. Artificial intelligence (AI) and machine learning (ML) methods have been widely applied to plant disease detection; however, their performance often degrades under diverse field conditions and noisy image data. Moreover, reliance on a single model increases vulnerability to its specific limitations, especially when diseases exhibit overlapping visual characteristics. To address these challenges, this study proposes a hybrid model that integrates deep feature extraction using a Convolutional Neural Network (CNN) with a weighted ensemble (WE) of ML classifiers. The CNN-extracted features are fed into four classifiers: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Random Forest (RF), and Decision Tree (DT), each leveraging distinct strengths in handling complex, nonlinear, and interpretable data. A dynamic weighting strategy is then applied, assigning higher weights to classifiers with superior validation accuracy, and the final prediction is obtained through weighted majority voting. Unlike conventional ensemble methods that treat all classifiers equally, this adaptive weighting enhances decision stability, reduces the influence of weaker models, and significantly improves overall accuracy. Experimental results demonstrate classification accuracies of 82.5% for CNN, 87.3% for k-NN, 83.7% for DT, 91.6% for SVM, and 93.5% for RF, while the proposed WE achieves 98.72% accuracy. Furthermore, the ensemble model attains class-wise precision of 98.5% for R. commune, 97.5% for P. teres, and 98.5% for healthy samples, marking a substantial improvement over individual classifiers. These findings highlight the effectiveness of combining automatic deep feature extraction with adaptive ensemble learning, offering a robust framework for accurate barley disease detection and contributing to the advancement of intelligent plant health monitoring and precision agriculture systems. Keywords: Barley disease classification, Convolutional neural networks, Deep feature extraction, Ensemble classifiers, Machine learning, Weighted ensemble.
Liu et al. (Thu,) studied this question.