Wheat Leaf Disease Detection with ANOVA-Driven Feature Selection and Whale Optimization Algorithm

Key words:  wheat leaf disease classification, deep learning, ResNet50, VGG16, support vector machine, ANOVA feature selection, whale optimization algorithm

Abstract:

Early and precise identification of wheat leaf diseases is crucial for sustainable crop management and yield improvement. In this study, we propose a novel hybrid framework that combines deep feature extraction (using ResNet50 and VGG16) with ANOVA-driven feature selection and Whale Optimization Algorithm (WOA) for hyperparameter tuning of Support Vector Machine (SVM) classifiers. The dataset consists of 40285 wheat leaf images across eight classes (seven disease types and healthy leaves), including augmented samples to address class imbalance. The Analysis of Variance (ANOVA) method significantly reduced dimensionality by selecting the top 500 most relevant features, while the WOA fine-tuned the SVM to enhance classification performance. The proposed model achieved an impressive accuracy of 98.1%, along with a precision of 97.9%, a recall of 98.0%, and an F1-score of 98.0% on the independent test set. A comparative analysis shows that our method outperforms several state-of-the-art (SOTA) models, including standard CNN and ensemble approaches. This study demonstrates that combining statistical feature selection and bio-inspired optimization with deep learning can substantially advance automated wheat leaf disease detection, offering promising applications for precision agriculture.