What does this research mean for the field?

A framework integrating deep feature extraction, composite filter-based feature selection, and ensemble learning achieves highly accurate (up to 0.9916 accuracy) and lightweight olive leaf disease detection with substantially reduced feature dimensionality. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop an efficient framework for detecting olive leaf diseases using deep learning and feature selection.

May 29, 2026Open Access

Efficient Olive Leaf Disease Detection Using Composite Feature Selection and Ensemble Learning

Key Points

The aim is to develop an efficient framework for detecting olive leaf diseases using deep learning and feature selection.
Employ composite filter-based feature selection strategies including mutual information, Chi-square, F-score, and devised selectors.
Use transfer learning with ResNet101 and MobileNet for deep feature extraction from olive leaf images.
Evaluate selected features using k-NN, SVM, and LightGBM classifiers under stratified 5-fold cross-validation.
The proposed approach achieves an accuracy of up to 0.988 and a Matthews correlation coefficient (MCC) of 0.976 using 128 selected features.
Voting-based ensemble strategies yield a mean accuracy of 0.9916 with various configurations.
The method provides effective and lightweight disease detection for implementation in precision agriculture.

Abstract

Early and reliable detection of plant diseases is critical for sustaining agricultural productivity and reducing economic losses. In olive cultivation, peacock eye disease poses a significant threat by adversely affecting leaf health and crop yield. While deep learning models have demonstrated strong performance in plant disease detection, their reliance on high-dimensional feature representations often leads to increased computational cost and limited deployability in real-world agricultural settings. This study proposes an efficient and robust olive leaf disease classification framework that integrates deep feature extraction, devised composite filter-based feature selection, and ensemble learning. Deep features are extracted from olive leaf images using transfer learning with ResNet101 and MobileNet architectures. To address feature redundancy and computational inefficiency, multiple filter-based selection strategies—including mutual information, Chi-square, F-score, and five devised composite selectors (score fusion, union, intersection, hybrid, and class-wise filtering)—are employed to generate compact and informative feature subsets of fixed sizes (32, 64, and 128 features). The selected features are evaluated using k-NN, SVM, and LightGBM classifiers under stratified 5-fold cross-validation. Experimental results demonstrate that competitive and near-baseline performance can be achieved with substantially reduced feature dimensionality. In particular, using only 128 selected features, the proposed approach attains up to 0.988 accuracy and 0.976 MCC, closely matching the performance obtained with full deep feature vectors. Furthermore, voting-based ensemble strategies, including iterative majority voting and hybrid GA–BO fusion, further enhance robustness, achieving the highest mean accuracy of 0.9916 among the evaluated ensemble configurations. These findings highlight the effectiveness of the proposed composite filter-based selection and ensemble framework as a practical, lightweight, and accurate solution for olive leaf disease detection, suitable for deployment in precision agriculture and resource-constrained environments.

Efficient Olive Leaf Disease Detection Using Composite Feature Selection and Ensemble Learning

Key Points

Abstract

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