Improved Butterfly Optimization and Feature Fusion for Apple Leaf Disease Classification

Abstract

In apple leaf disease classification, the health of apple trees plays a crucial role in crop care and productivity. Since leaves are responsible for photosynthesis, they are essential for nutrient transport and overall plant vitality. Therefore, accurate disease identification enables timely intervention and effective disease management. This confirms the optimal yield and crop quality. This research introduces a novel parallel priority feature fusion and improved Butterfly Optimization network for apple leaf disease classification. This method uses PlantVillage, Apple Tree Leaf Disease and PlantPathology Apple as three benchmark datasets. Images undergo preprocessing, in which resizing, normalisation, contrast enhancement using the Top‐Hat operation and data augmentation are used to improve strength and reduce data imbalance. High‐level and complementary deep features by using transfer learning, EfficientNet‐B3 and NasNetLarge are two pre‐trained Convolutional Neural Networks are extracted. These features are fused by the parallel priority strategy, which reduces redundancy and enhances discriminability representation. Then, for optimal feature selection, overfitting is reduced and generalisation is improved using an improved Butterfly Optimization Algorithm with adaptive crossover strategies. The selected features are then classified using Light Gradient‐Boosting Machine, which handles high‐dimensional data efficiently, and more robust classification is ensured. The experimental results demonstrate that the proposed method achieves superior performance, attaining an accuracy of 98.97% and an F1‐score of 97.57% confirming its effectiveness in feature representation and disease discrimination. The proposed method enables early and accurate detection of apple leaf diseases, providing an efficient and reliable solution. This contributes to sustainable agricultural management and enhanced productivity.

Plant phenotyping relevance

リンゴ葉の病害状態を画像から分類する深層学習・特徴融合・特徴選択手法を開発し、複数データセットで性能評価しているため、植物フェノタイピング手法が中心である。

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