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.

MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases

“…In a significant number of studies, new CNN architectures such as ResNet [30], AlexNet [33], and DenseNet [28], [34], with transfer learning are used to detect crop diseases. Few studies [35], [36], [37], [38], [39] suggested novel CNN architectures for the detection and classification of crop disorders. These approaches can yield accurate results with minimal preprocessing and computing costs.…”