## 1 Introduction
### Related Works
#### 1.1.2 Leaf Diseases Detection  
Conventional object detection approaches can be adopted for leaf disease detection [25]. Unlike leaf disease identification, leaf disease detection requires both the category and location of leaf diseases. Conventionally, two approaches have been adopted for object detection. One is a sliding-window detector [26], and the other is a region proposal [27]. The sliding-window detector moves a window along a raster scanning direction, and determines whether the window contains leaf diseases. During the training phase, handcrafted features such as histogram of gradients (HOG) [26], LBP [3], or SIFT [2] are extracted from positive and negative samples. Subsequently, SVM [9; 10] is trained for leaf disease classification. In the test phase, the same feature extractor is applied repeatedly for each sliding window, and the learned SVM determines whether leaf diseases are contained in the window.  
The region proposal is a technique for generating candidates (i.e., bounding boxes) where leaf diseases may exist. It starts by generating superpixels [28], and merging them with similar colors, sizes, and textures. Here, a superpixel is defined as a group of pixels that share common characteristics. Through bottom-up grouping, approximately a thousand bounding boxes surrounding the superpixels before and after merging are generated. Similarly to the sliding-window detector, handcrafted features such as SIFT and its variants are generated for each bounding box, and the SVM classifier is trained. Selective Search [27] and EdgeBoxes [29] are popular methods for region proposals.  
Recent object detection technologies use pretrained models such as VGG [13] and ResNet [14] as backbones for feature extractors. For each bounding box generated by a region proposal, the pretrained models extract deep features through a layer-by-layer transformation. The region-based CNN (RCNN) [30], which is the earliest model, adopts this approach. Two stages are required: region proposal and classifier learning. In RCNN, training is a multi-stage pipeline, and object detection is slow. To overcome these drawbacks, a Fast RCNN [31] has been proposed. In this model, a multi-task loss is utilized to jointly minimize class and bounding-box losses, and an ROI pooling layer is utilized to transform each candidate into a fixed-sized feature map. This enables training to be in a single stage. However, the region proposal is not separated from the Fast RCNN. To conduct the region proposal in the feature domain, a region proposal network (RPN) [32] has been proposed.  
In addition to the RCNN series, there is another single-shot detector that treats object detection as a single regression problem. YOLO [33] and SSD [34] are popular models. Unlike the RCNN series, YOLO directly predicts bounding boxes from a full image. This creates a high-speed and high-accuracy detector. Similar to YOLO, SSD is a single-shot detector; however, it utilizes multiple features with different scales to handle various objects in size. In addition, the SSD defines a set of default boxes with different aspect ratios, which correspond to anchors in a Faster RCNN [32].  
Recently, Mask RCNN [35] and Cascaded RCNN [36] have been introduced. Mask RCNN extends the Faster RCNN by adding a new branch for instance segmentation. Therefore, the Mask RCNN can simultaneously achieve localization and instance segmentation. The Cascaded RCNN deals with a noisy detection problem, due to an intersection over union (IOU) threshold. To address the limitation, the Cascaded RCNN trains a sequence of detectors with increasing IOU thresholds. More details are provided in the literature [36].