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Numerous diseases cause severe economic loss in the apple production-based industry. Early disease identification in apple leaves can help to stop the spread of infections and provide better productivity. Therefore, it is crucial to study the identification and classification of different apple foliar diseases. Various traditional machine learning and deep learning methods have addressed and investigated this issue. However, it is still challenging to classify these diseases because of their complex background, variation in the diseased spot in the images, and the presence of several symptoms of multiple diseases on the same leaf. This paper proposes a novel transfer learning-based stacked ensemble architecture named Multi-Contextual Feature Fusion Network (MCFFA-Net). MCFFA-Net incorporates three pre-trained architectures: MobileNetV2, DenseNet201, and In-ceptionResNetV2 as backbone networks for effective feature extraction and shorter training time. We integrate a novel multi-scale dilated residual convolution module in the network that captures multi-scale contextual information with several dilated receptive fields from the extracted features. Channel-based attention mechanism is provided through squeeze and excitation networks which provides improved channel dependency and access to global contextual information. The proposed MCFFA-Net achieves a classification accuracy of 90.86% and outperforms several previous architectures.

MCFFA-Net: Multi-Contextual Feature Fusion and Attention Guided Network for Apple Foliar Disease Classification