# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 5 Conclusions  
This paper presents an analysis of single-shot models for the problem of visible detection of visible diseases and pests in Arabica coffee leaves. The analysis compared the performance of the YOLOv8, YOLOv9, YOLOv10, and YOLOv11 versions in miner, rust, phoma, and cercospora diseases detection. The experiments were carried out using the BRACOL dataset, which was revised and improved, with precise markings carried out by an expert in plant pathology. The analysis considers a quantitative and qualitative evaluation, taking into account the mAP, recall, F1-Score, and inference-time metrics, as well  
Figure 10: The GT of sample 4 and YOLO results from the detection of diseases and pests on coffee leaves. (**a**) GT of sample 4. (**b**) Detection of YOLOv8s. (**c**) Detection of YOLOv9m. (**d**) Detection of YOLOv101. (**e**) Detection of YOLOv11n. as checking the visual precision of the detections made by the models. These metrics enabled a comprehensive assessment of the models' accuracy, efficiency, and generalization capacity, making them essential tools for disease detection. From the analysis process carried out, it can be seen that the enhancement of the BRACOL dataset, as detailed in Table 1, brought significant improvements in the quality of the annotations. The total number of disease markings increased from 1899 to 8226, with the correction of inaccurate annotations and the addition of new affected regions that had not previously been identified. These improvements, made with the help of an expert in plant pathology, provided a more accurate representation of the diseases and pests present on coffee leaves. The experimental results indicated that the models trained with the improved dataset achieved consistent mAP, recall, and F1-Score values in detecting the diseases rust, miner, phoma, and cercospora. Additionally, the comparative analysis of the improved BRACOL dataset with the original dataset showed a qualitative contrast in the performance of the models evaluated using the modification. The detection results achieved show that the YOLO models perform very well in detecting diseases and pests on coffee leaves, excelling both in quantitative metrics and in the visual evaluation of predictions. The comparison between the most recent versions of YOLO allowed for a detailed analysis of the differences in performance, highlighting their respective advantages and limitations in the scenario evaluated. The experimental results revealed that the YOLOv8 versions stand out significantly compared to the other models evaluated, both in terms of accuracy and prediction time. In particular, YOLOv8s showed the highest mAP with 54.5%, indicating its better ability to detect diseases. When evaluating the total detection time, the model reached 11.4 ms, managing to maintain an excellent prediction performance with significantly reduced times when compared to the other models. This attribute is fundamental for practical applications where the speed of object detection can be decisive for the efficiency of the system, such as autonomous or continuous monitoring systems. When evaluating the visual characteristics of detection, YOLOv8s demonstrates accurate performance in complex scenarios, where other models fail to identify diseases or present false detections. The continuation of this research, together with the development of new technologies and resources, proposes for future work the expansion of the dataset by collecting images from different regions and environmental conditions to ensure greater robustness and the ability to generalize the models, as well as future collaborations with experts in plant pathology and agronomy to experimentally validate the model's accuracy in diagnosing diseases. In addition, there is interest in implementing fine-tuning mechanisms and adapting the model as new data are entered, ensuring continuous improvement in detection.