# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 1 Introduction  
Plant diseases are one of the main factors that affect plant growth, and the detection and identification of plant diseases are the keys to early diagnosis and the precise control of pests and diseases. Maize is the world's top-producing food crop. When encountering diseases, maize plants infected with viruses and fungi produce physiological lesions. The infected parts of the leaves show characteristics such as deformation, discoloration, curling, or rotting, and discoloration. There is a need for the quick, easy, and accurate detection of plant disease areas and identification of the disease species. Traditional leaf detection methods by manual observation and judgment of maize leaves require extensive practical experience and professional knowledge, which are time-consuming and have a high cost and high false detection rate. In recent years, deep learning has been widely used in various fields such as face recognition, intelligent transportation, and automatic driving. Deep learning applied to plant disease detection and identification can overcome the drawbacks of traditional diagnostic methods and significantly improve the accuracy of disease detection and identification, and it has attracted widespread attention [1, 2]. [PERSON] et al. proposed R-CNN [3], which uses a convolutional neural network (CNN) to extract image features for plant disease detection. The proposed Fast R-CNN is based on R-CNN [4] to solve the problem of large numbers of overlapping boxes in the process ofcandidate region selection in R-CNN. Faster R-CNN is widely used in the disease detection of grapes [5] and rice [6] due to its outstanding detection accuracy. However, Faster R-CNN is a two-stage detector, and the computational effort of selecting candidate frames is heavy, which leads to its slow detection speed and makes it difficult to achieve real-time detection. [PERSON] et al. proposed the YoLo method [7], which is based on one-stage detection and does not require the generation of a proposal box, but it divides the image into a grid to determine target boundaries and classes, which improves the detection speed and alleviates the real-time detection problem compared with Faster R-CNN. YoLo-based methods are also widely used in plant disease detection, such as YoLov3-based tea disease detection [8] and YoLov4-based citrus disease detection [9], but YoLo is not accurate enough in the detection and localization of small targets. [PERSON] et al. [10] proposed CenterNet, a detection algorithm without anchor frames, which removes the operation of generating anchor frames and saves some time-consuming operations by estimating the loss from the heat map, thus improving the detection speed. [PERSON] et al. [11] improved CenterNet by extracting deep-seated key points based on DenseNet-77 and classifying and recognizing 26 kinds of plant diseases in 14 plants, such as tomatoes, apples, grapes, etc., but the detection effect of small plant diseases was not ideal. [PERSON] et al. performed potato disease detection [12] based on YoLov5 using multi-scale pooling and feature pyramid up- and down-sampling to enhance contextual linguistic features to accommodate multi-scale plant diseases and directly improve small plant disease accuracy. It also combined the advantages of the accuracy of the anchor-less frame algorithm and the detection speed of the single-segment algorithm, and the detection effect is remarkable. Scholars have undertaken a lot of research on the target detection and classification and recognition of plant leaf diseases based on deep learning technology, as shown in Table 1. In practical application scenarios, plant disease detection and recognition still face many challenges. The main reasons are as follows:  
1. The change of illumination makes it difficult to locate the target area accurately. Due to the change in light intensity and reflection, as well as other reasons, it is difficult to accurately locate the diseased area in some detection images.