# Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
## 1 Introduction  
Wheat powdery mildew, caused by the fungal pathogen _Blumeria graminis tritici_, is a prevalent airborne disease that poses a persistent and global problem. This obligate biotrophic pathogen relies on living host organisms for survival and propagation [1]. The pathogen spreads rapidly and adapts due to its short life. Powdery mildew symptoms appear first as white powdery spots on wheat plant leaves. Spots can be enlarged and spread to cover the entire wheat leaf. As a result, crop loss may occur as the plant becomes more suitable for lodging. Furthermore, since it limits photosynthesis, it can also cause kernel quality loss [2]. Researchers have shown that it causes yield losses of up to 34% and, in extreme cases, up to 50% when appropriate protection methods are not used [3]. There are cost-effective approaches to controlling powdery mildew, such as planting resistant varieties, using fungicides, rotating crops, and maintaining excellent sanitation practices. However, fungicides are most commonly used since new virulent pathogen races are continuously emerging [4]. In today's approach to precision agriculture, proper agricultural input utilization, such as pesticides, is crucial for sustainability [5]. The goal is to apply these inputs in the right amount, maximize their benefits, and minimize their negative effects on the environment and human health [6]. Selective spraying (spraying only diseased areas) is an important precision agriculture approach [7; 8; 9]. In selective spraying applications, diseased areas are detected with various types of sensors. Digital camera (2D) sensors are commonly used as primary sensors [10; 11; 12]. A significant precision agriculture technique is selective spraying, which involves targeting only diseased areas [7; 8; 9]. To achieve this, various sensors, the most common being computer vision-based, are used to detect diseased regions [10; 11; 12]. Object detection is a computer vision method that identifies and locates objects in images or videos. This task is challenging because objects can vary in shape, size, and color and be partially occluded by other objects [13]. Object detection is used in various fields, including self-driving cars, security cameras, facial recognition, and medical imaging for tumor identification [14; 15; 16; 17; 18]. Computer vision-based object detection is used to identify and locate objects in agricultural fields such as pests, diseases, weeds, and crop damage [19; 20; 21; 22; 23; 24; 25]. This technology has the potential to reduce pesticide use, leading to improved crop yields and reduced environmental pollution. In addition, object detection can monitor crop growth and yield [26; 27]. The field of object detection in agriculture has progressed significantly, as computer vision techniques are continuously improved, resulting in more precise and efficient object recognition [28; 29; 30]. Object detection can be approached using two basic methods: classical and deep learning-based techniques. Conventional detection methods are manually designed by individuals for a given purpose. However, deep learning techniques employ deep neural networks to autonomously acquire features from data. Research has shown that deep learning-based approaches are superior in terms of accuracy to conventional methods [13; 31]. Deep learning methods are classified into one-stage and two-stage detectors [19]. Two-stage detectors, such as R-CNN and its variations (Fast R-CNN, Faster R-CNN), initially generate proposals for regions of interest and subsequently classify these regions. Although they possess high accuracy levels, they tend to be slower at processing speed. On the contrary, one-stage detectors prioritize speed by performing localization and classification simultaneously [32; 33]. You Only Look Once (YOLO) is a prominent one-stage object detection algorithm that has revolutionized real-time object detection. YOLO employs a single neural network to simultaneously predict bounding boxes and class probabilities for multiple objects within an image [34].