# CEMB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments
## 1 Introduction  
Corn is one of the world's major cereal crops, second only to wheat and rice in terms of cultivation area, and it serves a vital role as an essential feed and industrial raw material [1]. Northern maize leaf blight (NLB), caused by the phytopathogenic fungus Setosphaeria turcica, occurs frequently in northern China and greatly restricts photosynthesis and the transport of nutrients in the maize leaves, seriously affecting the yield and quality of the maize. As a result, the most critical task for maize producers is to detect whether maize is contaminated with NLB in a timely and accurate manner, thereby preventing the spread of the disease and the resulting decrease in maize production.  
Currently, the primary method for detecting NLB is still visual identification, but it is difficult for inexperienced growers to identify similar diseases with the naked eye, leading to inappropriate pesticide applications that affect maize yield and quality, while relying on plant pathologists to identify disease types on site is time-consuming, inefficient and prone to subjective errors, especially in large field environments, significantly increasing labour costs. Many researchers have increasingly utilized machine vision and image-processing techniques to overcome the limitations of manual detection [2, 3]. The idea of these studies is often based on the analysis of the colour, texture and spatial structure of the image, using edge arithmetic, threshold segmentation clustering and other methods [4, 5, 6, 7], but it is difficult to meet the natural conditions of complex background images; there are poor adaptability, weak anti-interference ability and other problems, leading to serious limitations in the practical application [8].  
Compared to the detection of other crop diseases, the small size of the disease area spots on the leaves of maize leaf blight in the early stages of the pathology, coupled with interfering factors such as the growth chain, lighting, climatic conditions and shading, poses a huge challenge to the visual detection of maize leaf blight. This requires that the algorithm model should have the ability to accurately detect small targets and understandcomplex scenes. To overcome these challenges, we explore the visual detection of maize leaf blight from the perspective of digital image analysis, proposing the lightweight, robust model named CEMLB-YOLO based on YOLOV5. The specific contributions of the research are as follows:  
1. We introduced a key information position attention mechanism into our model to enhance critical information representation in the feature map, reducing information loss during the downsampling process.
2. To aggregate global context data more effectively and affordably, the MobileBit is added to the feature extraction network to improve the model's ability to understand complex scenarios.
3. To exploit the deep feature map's potential for semantic information, FRAFM is incorporated into the model to reorganize and up-sample the semantic information of the deep feature map while adaptively adjusting the proportion of cross-scale feature map information for efficient feature aggregation.