Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Abstract

Maize leaf disease significantly reduces the quality and overall crop yield. Therefore, it is crucial to monitor and diagnose illnesses during the growth season to take necessary actions. However, accurate identification is challenging to achieve as the existing automated methods are computationally complex or perform well on images with a simple background. Whereas, the realistic field conditions include a lot of background noise that makes this task difficult. In this study, we presented an end-to-end learning CNN architecture, Efficient Attention Network (EANet) based on the EfficientNetv2 model to identify multi-class maize crop diseases. To further enhance the capacity of the feature representation, we introduced a spatial-channel attention mechanism to focus on affected locations and help the detection network accurately recognize multiple diseases. We trained the EANet model using focal loss to overcome class-imbalanced data issues and transfer learning to enhance network generalization. We evaluated the presented approach on the publically available datasets having samples captured under various challenging environmental conditions such as varying background, non-uniform light, and chrominance variances. Our approach showed an overall accuracy of 99.89% for the categorization of various maize crop diseases. The experimental and visual findings reveal that our model shows improved performance compared to conventional CNNs, and the attention mechanism properly accentuates the disease-relevant information by ignoring the background noise.

Plant phenotyping relevance

トウモロコシ葉の画像から病害状態を分類するCNN手法を開発・評価しており、植物病害表現型の取得・抽出が研究の中心である。

Code and data availability

The paper evaluates its EANet maize disease classification model on publicly available image datasets, explicitly citing the public Kaggle 'Corn or maize leaf disease dataset' as one of its data sources. This is a paper-specific, publicly accessible plant image dataset used directly for the paper's classificationex per