# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 2 Literature Review  
Several studies have explored the application of CNNs to image analysis in plants, highlighting the efficiency and precision of these techniques compared to traditional methods. In the area of coffee-leaf disease detection, approaches based on computer vision have shown promising results. Models such as YOLO have stood out for their ability to perform detections in real time, and are widely used in tasks that require quick and accurate identification of complex patterns. Recent research has investigated the use of different versions of YOLO, taking advantage of the improvements in efficiency and robustness introducedthroughout the updates. For example, versions such as YOLOv5 and YOLOv8 have been employed to identify pests and perform post-harvest production control [23; 24], while more recent studies explore YOLOv10 and YOLOv11 in complex agricultural scenarios [25; 26]. This section presents an analysis of the main studies related to the detection of crop diseases using neural networks and computer vision, with an emphasis on the contributions that use the YOLO architecture and its variations. The techniques used, the results achieved, and the limitations observed will be discussed, with the aim of contextualizing and substantiating the approach proposed in this paper. In the study by [PERSON] et al. (2024) [12], an improved model based on YOLOv7-Tiny was proposed for identifying rice-leaf diseases, addressing specific detection challenges in agricultural environments. To mitigate interference from complex backgrounds and improve identification accuracy, the model incorporated the CBAM (Convolutional Block Attention Module) attention module into the backbone network, as well as using the RepGhost module to deal with the irregularity of the regions affected by the diseases. To separate the classification and localization tasks, a lightweight head based on the YOLOX decoupled head was introduced. The experimental results showed that the model outperformed other architectures, including YOLOv3-Tiny and YOLOv5-S, achieving a [EMAIL_ADDRESS] of 0.922 and an inference time of 26.4 ms per image, making it suitable for use in embedded devices. Similarly, the research by [PERSON] and [PERSON] (2024) [25] explored the application of YOLOv10 for the advanced detection of rice-leaf diseases, with the aim of improving crop health management and minimizing production losses. The model was developed to overcome the limitations of traditional methods, such as SVM (Support Vector Machine), KNN (K-Nearest Neighbors) and CNNs, which, although effective in some tasks, present difficulties in real-time application and scalability for field scenarios. With its optimized architecture, YOLOv10 demonstrated high precision and speed in identifying leaf diseases, allowing for rapid diagnosis and early intervention. The proposed methodology contributes significantly to smart agriculture, enabling the more efficient monitoring of plantations and reducing the need for time-consuming manual inspections. The results indicated that the model not only outperformed conventional techniques, but also showed promise for implementation in automated systems, promoting more sustainable and efficient agricultural practices. In the paper by [PERSON] et al. (2023) [27], the authors introduce an artificial intelligence-based solution to quickly and accurately diagnose and identify diseases in tea leaves. The approach uses the YOLOv7 model, known to be one of the fastest and most efficient object-detection networks, trained with a dataset of 4000 images of tea leaves collected from four prominent gardens in Bangladesh. This dataset was carefully annotated and augmented with data augmentation techniques to overcome the limitation of the number of samples. The experimental results demonstrate the superiority of YOLOv7 compared to other architectures, with metrics such as detection accuracy of 97.3% and mAP of 98.2%, among others.