Fusion Driven Apple leaf Disease Classification using a Siamese Squeeze and Excitation Residual Network with Feature Pyramid Learning

Abstract

Abstract The classification of apple leaf diseases is a complex task that requires specialized expertise and is a major contributor to crop losses in agriculture worldwide. The effective earlier diagnosis of automatic apple leaf disease classification model is crucial to maintain the crop's healthy growth. The existing research cannot provide adequate classification results for apple leaf diseases due to the sophisticated classification models. This research proposed the novel Feature pyramid Siamese Squeeze and Excitation Residual neural network (FPS2ENet) model for precise categorization of apple leaf diseases. Initially, the proposed research employed the bilateral guided filter (BGF) which is composed of median and fast guided filter to clean up the apple leaf images. Furthermore, the color, textural and neighboring pixel relationship features are extracted from the apple leaf image with aid of windmill graph-based feature extraction technique. After that, the proposed model is employed with two blocks namely Feature pyramid neural network block (FPNN) for deep feature selection as well as Squeeze and Excitation Residual neural network block for disease classification. Moreover, the Siamese layer and fully connected layer are employed for fusion and classifying process respectively. In experimental analysis, there are four datasets are utilized including Kashmiri Apple Plant Disease Dataset (KAPD), New Plant Diseases Dataset (NPD), Plant Pathology Apple (PPA) dataset, plant village-apple-color (PVAC) dataset. In the performance analysis scenario, the various kinds of analysis are evaluated with different existing methods such as Bi-GRU, Bi-LSTM, DenseNet, and ResNet for accessing the proposed model effectiveness. The numerous effective analyses are performance metric analysis, loss and accuracy curve analysis, ROC curve analysis and confusion matrix analysis. In addition to this, the proposed method can attain 98.81%, 99.43%, 99.88% and 99.61% accuracy in KAPD, NPD, PPA and PVAC dataset correspondingly.

Plant phenotyping relevance

リンゴ葉画像から病害状態を分類する画像ベースの表現型推定手法を提案・評価しており、病害分類モデルの開発が研究の中心である。

Code and data availability

The paper uses four apple leaf image datasets (KAPD, NPD, PPA, PVAC) and a custom FPS2ENet model, but provides no public repository, code deposit, or URL for its implementation, trained models, or analysis. The Data availability statement only offers datasets 'from the corresponding author on reasonable request,' and K

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