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

TL;DR

This paper introduces MCFFA-Net, a transfer learning ensemble model with multi-scale context and attention mechanisms, significantly improving apple foliar disease classification accuracy despite complex backgrounds and symptom variations.

Contribution

The paper presents a novel ensemble architecture with multi-scale dilated residual convolution and attention modules for improved disease classification.

Findings

Achieved 90.86% classification accuracy.

Effectively captures multi-scale contextual information.

Outperforms existing methods in accuracy.

Abstract

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%.