# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 2 Related Work
In post-harvest agricultural contexts, YOLO's efficiency is emphasized by its ability to perform accurate real-time detections, supporting real-time monitoring and identification essential for dynamic agricultural environments, such as fields or conveyor systems sorting produce [26]. The YOLO architecture has evolved substantially since its inception, with each version introducing significant enhancements to improve object detection accuracy, speed, and computational efficiency. YOLOv1 marked the initial step toward real-time detection using a grid-based approach to predict bounding boxes. Building on this, YOLOv2 incorporated anchor boxes, significantly improving localization accuracy. YOLOv3 further refined the model by adopting the Darknet-53 architecture and integrating feature pyramid networks (FPNs), enabling better generalization across object scales. In YOLOv4, additional architectural innovations were introduced, including the Bag of Specials (BoS), which improved accuracy without sacrificing speed. A significant shift occurred with YOLOv5, implemented in PyTorch, where optimizations in training processes and the integration of Cross Stage Partial Network (CSPNet) and Path Aggregation Network (PANet) enhanced performance and modularity. YOLOv6 introduced reparameterization techniques and novel backbone designs for greater efficiency. Continuing the trend of architectural improvements, YOLOv7 employed Efficient Layer Aggregation Network (ELAN) blocks to improve feature learning. With YOLOv8m, the focus turned to computational optimization and more refined training strategies, making it suitable for resource-constrained environments. YOLOv9 introduced programmed gradient information (PGI) to further enhance detection precision and efficiency. Most recently, YOLOv10 eliminated the traditional non-maximum suppression (NMS) step, streamlining the detection of multiple overlapping objects and enhancing throughput for dense scenes. This progression reflects a continuous drive to balance speed,accuracy, and model simplicity, particularly in real-time agricultural applications such as pepper variety identification [11]. These developments position YOLO as a leading architecture for real-time visual classification systems in the agricultural sector. In parallel, other deep learning approaches, such as convolutional neural networks (CNNs) combined with transfer learning, have also indicated strong practical potential in the agricultural sector. Previous studies have shown their effectiveness, particularly in the pepper production chain. [PERSON] et al. [27] developed a model to classify bell peppers (_Capsicum annuum_) based on external quality, utilizing pre-trained neural networks and image capture tools. Their approach achieved high accuracy (98.14%) and low loss (0.0669), showing excellent performance and generalization without requiring manual feature extraction. [PERSON] et al. [28] investigated the classification of pepper seeds using computer vision and neural networks. Their study involved extracting color, shape, and texture features from the seeds and using a multilayer perceptron (MLP) for classification, achieving an accuracy of 84.94%. This advancement underscores the feasibility of applying CNNs at various stages of the pepper production chain, from seed to final product. Furthermore, a deep learning model utilizing convolutional neural networks (CNNs) for disease detection in pepper leaves was developed based on a dataset of 2478 images. Previous research [29] has emphasized the use of transfer learning to enhance diagnostic accuracy under cultivation conditions. Their results presented an accuracy of 99.55% in distinguishing between healthy and diseased leaves, highlighting the model's practical applicability for early disease detection in agriculture. Presenting additional application lines, [PERSON] et al. [30] developed an intelligent classification system for bell peppers using deep convolutional neural networks (DCNN).