# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 1 Introduction
The combination of high precision and speed of detection makes YOLO an ideal tool for helping farmers to identify problems in crops early on, improving crop management and reducing the excessive use of pesticides and other inputs. Some studies using YOLO in agriculture have demonstrated its effectiveness in detecting diseases and pests in various crops. For example, [PERSON] et al. (2024) [12] applied YOLO to detect rice diseases, managing to quickly identify fungal and bacterial infections, which allowed for early and more targeted intervention. [PERSON] et al. (2024) [20] used YOLO version 10 in tomato plantations in Bangladesh, achieving high accuracy rates in detecting pests such as whitefly and spider mite, with a significant impact on reducing pesticide use. On the other hand, [PERSON] et al. (2023) [19] implemented YOLO in pineapple plantations, identifying not only diseases, but also nutritional deficiencies that could affect plant growth, demonstrating the versatility of the network in agriculture. Therefore, the use of neural networks such as YOLO in the detection of diseases and pests not only improves the efficiency of the process, but also contributes to more sustainable agricultural practices by enabling more precise and conscious control of the inputs applied to crops. With the continuous improvement of these technologies, it is hoped that, in the future, YOLO will be even more integrated into automated monitoring systems, benefiting farmers and researchers in the fight against the diseases and pests that affect global agricultural production. Among the public datasets for disease detection and classification in Arabica coffee leaves are JMuBEN, JMuBEN2, and BRACOL (Brazilian Arabica Coffee Leaf). The JMuBEN [21] dataset has clippings of coffee leaves containing a single disease per clipping and is commonly used for the classification task. This dataset is composed of three compressed files containing images of affected coffee leaves: the first file contains 7682 images of leaves infected with cercospora, the second has 8337 images of leaves with rust, and the third includes 6572 images of leaves affected by phoma. JMuBEN2 [21] consists of two additional compressed files. The first contains 16,979 images of leaves with the miner pest, while the second brings together 18,985 images of healthy leaves. In total, the JMuBEN and JMuBEN2 datasets add up to 58,555 images of coffee leaves, distributed among five classes, including cercospora, rust, phoma, miner, and healthy. In addition to the images, the datasets include detailed annotations about the condition of the leaves and the names of the diseases. Due to the lack of images with whole leaves and their respective markings, the JMuBEN and JMuBEN2 datasets are not used for the detection task. On the other hand, the BRACOL [22] dataset originally proposed for the segmentation and classification tasks has 1747 images of whole leaves and disease clippings. Due to its name structure, which indicates the location of the disease clippings on the leaves, it was possible to create precise annotations, which facilitate its adaptation for object-detection models. This paper provides the first systematic evaluation of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures for the detection of diseases and pests in Arabica coffee leaves using a unified experimental protocol. The research found in the literature uses previous versions of YOLO applied to disease detection in other crops, making this paper uniquely relevant to both the coffee domain and more advanced object-detection architectures. The research explores the advanced capabilities of YOLO models, which stand out for their high detection speed and accuracy, making them fundamental for efficient plant monitoring. To ensure a high-quality database, the BRACOL dataset was revised and enhanced through an expert-guided annotation process, which included the correction of inconsistent labels and the addition of previously unmarked diseased regions.