## 1 Introduction
### Proposed Approach  
Figure 2 depicts the difference between traditional approaches [5; 9; 39] and the proposed networks for leaf disease identification and detection. As shown in Figure 1, the traditional approach includes three steps, i.e., clustering, feature extraction, and classifier learning. First, the clustering step divides the input image into background and spot areas, and two types of features are extracted from the divided areas via a feature extraction step. Next, the classifier is trained using machine learning tools such as SVM and decision trees. However, this traditional approach has a few drawbacks. The first drawback is that the colors and textures used for clustering and feature extraction are handcrafted features, which are not better than deep features in terms of the discriminative power because deep learning can provide abundant features via a layer-by-layer nonlinear transformation. The second drawback is that the employed classifiers, such as SVM and decision trees, are not better than deep learning in terms of classification performance.  
To overcome the shortcomings of traditional approaches, the proposed method incorporates the three steps into a single deep learning architecture. Inspired by conventional approaches, the clustering and feature extraction are replaced by the ROI-aware feature  
Figure 1: Leaf image characteristics.  
Figure 2: Traditional approach [39] versus proposed networks for leaf disease identification and detection.  
extraction subnetwork (ROI-aware FES), and the classifier learning is replaced by the SAC-SubNet or YOLO detection subnetwork, as illustrated in Figure 2. For leaf disease identification, ROI-aware FES and SAC-SubNet are adopted. The entire network that comprises the ROI-aware FES and SAC-SubNet is referred to as LSA-Net. For leaf disease detection, the ROI-aware FES and YOLO detection subnetwork are adopted, and this detection network is referred to as the AE-YOLO network.  
The primary idea of the proposed networks is based on the observation that symptoms of leaf diseases can solely be detected in the leaf area. To realize this, the ROI-aware FES, which is a new spot feature extractor, is designed, and the ROI-aware feature fusion is proposed to model the leaf spot attention mechanism. The ROI-aware FES predicts a leaf segmentation map, and subsequently encodes spot information. The ROI-aware feature fusion combines two features provided by the feature extractors, that is, the backbone and ROI-aware FES. This feature fusion models the leaf spot attention mechanism, and increase the discriminative power. The ROI-aware FES and feature fusion can teach the SAC-SubNet and YOLO detection subnetwork regarding which areas and features have a decisive role in classifying and localizing leaf diseases. Therefore, the proposed ROI-aware FES and feature fusion can be viewed as attention mechanisms [22; 24]. Owing to the leaf spot attention enabled by the ROI-aware FES and feature fusion, the proposed networks can improve leaf disease identification and detection.  
Although deep learning approaches [15; 16; 17; 18; 19; 20; 37; 38] have been recently introduced, existing models, such as VGG and ResNet, have been directly utilized for leaf disease identification and detection. That is, TL dominates. However, this approach has limitations in improving the discriminative power, because these models exclude the leaf spot attention mechanism to extract informative features from leaf images. This is the major difference between the proposed network and conventional transfer learning-based deep learning.