# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 2 Related Work  
Computer vision is a branch of artificial intelligence that enables machines to interpret and analyze images to make decisions. This process utilizes cameras and computers to identify, track, and measure targets, followed by image processing [17]. This technology has increased automation and efficiency in various agricultural fields. Critical applications include crop growth monitoring, disease control, product identification and classification, automated harvesting, quality testing, and modern farm management automation. Unmanned aerial vehicles (UAVs) equipped with computer vision technology are used for aerial surveillance of agricultural lands, providing valuable data for decision-making. Automating identification processes through computer vision offers significant benefits, including increased efficiency and productivity, enhanced accuracy and consistency, early problem detection, cost reduction, and continuous monitoring [17]. Deep learning (DL), a subfield of machine learning (ML) and artificial intelligence (AI), is considered a core technology of the Fourth Industrial Revolution, also known as Industry 4.0 [18; 19]. The word \"deep\" refers to the multiple layers through which data is processed to build a hierarchical data-driven model. DL models can automatically extract features from data, enabling them to handle complex tasks accurately. These models are widely applied in healthcare, cybersecurity, business intelligence, and especially agricultural visual recognition tasks. According to [PERSON] et al. [20], convolutional neural networks (CNNs) represent a fundamental class of deep learning models, particularly significant in the field of computer vision. The architecture of CNNs is designed to process grid-structured data, such as images. This process is achieved through convolutional layers, which apply convolution operations to extract local features from images, followed by pooling layers that reduce the dimensionality of the features while retaining essential representations. These networks have shown remarkable capabilities in learning complex hierarchical representations directly from data, making them highly effective in pattern recognition tasks such as image classification, object detection, and semantic segmentation. The capacity of CNNs to capture detailed and abstract visual patterns makes them ideal for identifying various visual characteristics of agricultural products, such as shape, color, and texture, which are critical for distinguishing between different pepper varieties [21]. Recent studies show that deep learning methods substantially enhance the detection and classification of agricultural diseases and renewable energy sites. [PERSON] and [PERSON] [22] used convolutional neural networks (CNNs) with high-resolution satellite imagery to automatically and accurately detect photovoltaic farms, offering a scalable tool for energy monitoring. In agriculture, [PERSON] et al. [23] found that ResNet50 outperformed other deep-learning models in classifying cauliflower diseases, achieving an accuracy of 90.85%. [PERSON] and [PERSON] [24] applied vision transformers (ViTs) for rice leaf disease detection and severity estimation, surpassing some limitations of traditional CNNs and achieving strong performance in both classification and severity assessment. Together, these findings highlight the value of advanced AI models in both environmental monitoring and precision agriculture. [PERSON] [25] explains that the YOLO (you only look once) model represents an innovative approach to real-time object detection, using a single convolutional neural network that simultaneously predicts bounding boxes and class probabilities from an image divided into a grid. This architecture enables the model to process the entire image simultaneously, resulting in high frame-per-second rates suitable for critical applications such as surveillance, autonomous driving, and industrial automation.