Attention enhanced hybrid deep learning architecture with PCA-based feature fusion for banana leaf disease detection.

Abstract

Banana is a key staple crop in the world, but its yield is very desperately affected by the leaf diseases like Sigatoka, Fusarium Wilt, and Cordana, which bring serious losses of yield and economy. The manual disease detection methods deployed in the past are time and labor-intensive and cannot be effective in the field, so powerful automated solutions are sought. In this paper, we have presented a hybrid deep learning architecture which combines MobileNetV2 and ResNet101 with attention, dilated convolutions, multi-scale feature pooling and PCA-based feature fusion to classify banana leaf disease accurately and efficiently. The Banana and Banana Leaf, Banana Disease Recognition, and Banana LSD three benchmark datasets have been trained and evaluated following an innovative preprocessing pipeline that consists of illumination correction, background suppression, denoising, and hi-tech data augmentation. The experimental findings have shown that the presented hybrid model is always better than ten state-of-the-art deep learning models, such as VGG16, ResNet50, DenseNet121, EfficientNet-B0, and Vision Transformer (ViT). It has reached an optimal accuracy of 98.28, precision of 98.18, recall of 98.77 and F1-score of 98.43, with only 12.7 M trainable parameters and convergence rate of only 15 epochs, which makes the model both high-accuracy and computationally efficient.

Plant phenotyping relevance

バナナ葉画像から病害状態を推定する深層学習手法の開発・評価が中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper uses three public Mendeley Data banana leaf image datasets (with DOIs) as inputs, but no authors' code, models, or paper-specific derived data deposits are stated. Moreover, the only permitted URL in this audit is the Creative Commons license link, so no qualifying asset can be linked to an allowed URL; the M

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