  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 MCFFA-Net, which is composed of three
pre-trained architectures named MobileNetV2, DenseNet201, and InceptionResNetV2
as backbone networks. We also propose a novel multi-scale dilated residual
convolution module to capture multi-scale contextual information with several
dilated receptive fields from the extracted features. Channel-based attention
mechanism is provided through squeeze and excitation networks to make the
MCFFA-Net focused on the relevant information in the multi-receptive fields.
The proposed MCFFA-Net achieves a classification accuracy of 90.86%.
