# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 2 Literature Review
In addition to reducing manual effort and subjectivity in identification, the system has the potential to minimize economic losses in tea production, which faces challenges such as climate change and an increase in diseases. In the paper by [PERSON] et al. (2022) [19], an approach was proposed for detecting and locating pineapple fruit in natural environments, using binocular stereo vision and an improved model of YOLOv3. The improved model incorporates DenseNet into the Darknet-53 backbone network, optimizing the 13 \(\times\) 13 and 26 \(\times\) 26 resource layers, as well as integrating SPP-net (Spatial Pyramid Pooling) into the 52 \(\times\) 52 dimension-detection module to improve information representation capacity. Pineapple detection is based on images captured by a binocular camera, which acquires left and right images, where the left image is processed by the improved YOLOv3 model to identify the position of the fruit. The stereo-matching algorithm then calculates the parallax and stereo matching to determine the three-dimensional coordinates of the pineapples, based on the triangulation principle. Comparative tests showed that the proposed model outperformed other approaches, such as Faster-RCNN and Mobilenet-SSD, in terms of F1-Score and average precision (AP), standing out for its detection efficiency, even under occlusion conditions. In the research conducted by [PERSON] et al. (2021) [28], a model based on YOLOv3 combined with MobileNetv2 was developed to detect diseases in Robusta coffee leaves, one of the main agricultural crops in the Philippines. This model, aimed at identifying biotic agents present in the leaves, seeks to help farmers make the correct diagnosis and appropriate treatment, reducing significant losses caused by misdiagnosis. The integration of YOLOv3 with MobileNetv2 allows image processing to work well even on devices with low-performance graphics units. With an accuracy of 90% in detecting diseases, the system proved to be effective, but only under conditions of sufficient lighting and uniformity in the background of the images used for training. Other studies also aim to achieve sustainable pest management through the use of deep learning. However, they use the disease classification technique instead of real-time detection. This is the case of the paper by [PERSON] et al. (2024) [29], the ShuffleNet architecture was applied to the classification of diseases in coffee leaves, using data from the JMuBEN and JMuBEN2 sets. The methodology adopted included the use of convolutional neural networks and traditional computer-vision feature-extraction approaches. ShuffleNet, designed for mobile devices with limited resources, proved to be efficient, achieving good results even with a smaller volume of data and fewer parameters, compared to other architectures such as MobileNetV2 and VGG-16. External validation used other datasets to corroborate the robustness of the model, highlighting its superior performance in terms of computational cost and accuracy. Similarly, the paper by [PERSON] et al. (2023) [30] developed the CoffeeNet model, a deep learning-based approach for classifying coffee-leaf diseases. The research addresses common challenges in identifying these diseases, such as variations in lighting, differences in leaf coloration and the similarity between healthy and infected areas. To overcome these limitations, the authors proposed an improved version of the CenterNet architecture, incorporating a spatial- and channel-attention strategy based on the ResNet-50 model. This approach allowed for the extraction of more discriminative features from the images, improving the model's accuracy in classifying different types of infections. The experiments were conducted using a dataset of Arabica coffee leaves captured in realistic environmental conditions. The results obtained demonstrated the effectiveness of CoffeeNet, which achieved an accuracy of 98.54% and a mAP of 97%, standing out as a robust solution for the automated diagnosis of diseases in coffee plants.