# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 1 Introduction  
Coffee, one of the most widely consumed beverages in the world, is grown mainly in tropical regions and is an important source of income for millions of farmers [1]. In Brazil, the cultivation of coffee, especially the Arabica variety, plays a vital role in the economy, with the country being one of the largest global producers and exporters [2]. However, coffee production faces several challenges, one of the most significant being the diseases and pests that affect the plants, compromising the quality and quantity of production [3]. Among the most common diseases are rust, miner, phoma, and cercospora, which, if not detected and controlled early, can cause irreparable damage to plantations [4]. The early detection of these diseases and pests is essential for implementing effective control and management strategies, reducing the economic and environmental impact [5]. Traditionally, the identification of these conditions is carried out by phytopathology specialists, through visual inspections and laboratory samples, which can be time consuming, cost a lot of money, and be limited by the specialist's ability to reach [3, 6]. In view of this, faster and more accurate alternatives are emerging, such as the application of deep learning techniques, which have proven to be highly effective in image analysis, providing an automated and high-performance way to detect diseases and pests in real time [7, 8, 9]. The use of CNNs (convolutional neural networks) in computer-vision tasks has advanced considerably in recent years, enabling the accurate detection of complex patterns in images and the diagnosis of plant diseases [10]. Models such as the YOLO (You Only Look Once) [11] family are especially popular due to their efficiency and accuracy in image object detection. These models are able to identify and classify diseases and pests on leaves or plants with a high accuracy rate, even in variable lighting conditions, and capture angles, as proposed in [12]. YOLO is a neural network architecture that processes the entire image at once, optimizing detection to be carried out in real time. Other methods, such as the R-CNN [13], which extracts proposed regions for analysis, or the SSD (Single-Shot MultiBox Detector) [14], which uses multiple feature maps for detection at different scales, can also achieve real-time detection. However, R-CNN tends to be slower due to its region-selection process, while SSD can be less accurate on small objects due to its multi-scale approach. From its first version, YOLO has evolved rapidly, with continuous improvements in subsequent versions, becoming more robust, faster, and more accurate. YOLO version 8 [15], for example, introduced significant improvements in architecture and training, optimizing performance in various detection tasks. Version 9 [16] improved the ability to adapt to different types of images and considerably reduced inference time. Version 10 of YOLO [17] made even more significant advances, with the introduction of new regularization techniques and better performance in complex environments, making it better suited to dealing with images of plants and agricultural diseases. Version 11 [18], the most recent, brought significant refinements in detection accuracy and the ability to classify multiple objects in a single image, which is crucial for the simultaneous identification of different diseases and pests on coffee leaves. This improvement was made possible by innovations in training techniques and deep learning algorithms, allowing YOLO 11 to become one of the most advanced networks for real-time detection [11]. These innovations have allowed YOLO to be successfully applied in a variety of areas, including precision agriculture, where the automated detection of diseases and pests can mean a faster and more effective response to the threats that compromise agricultural production [12; 19; 20].