# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 1 Introduction  
The international trade of spicy peppers (_Capsicum_ species) constitutes a vibrant and economically significant sector within global agriculture and commerce [1]. Worldwide, the demand for spice peppers continues to rise, fueled by their role in ethnic cuisines, food preservation, and the growing recognition of their health-promoting properties, such as capsaicin content [1, 2, 3]. Major producing countries, including Brazil, India, China, and Mexico, utilize pepper cultivation as a vital agricultural activity that supports rurallivelihoods, exports, and local economies [1; 4]. The stability of the pepper production chain ensures a constant market supply. The Food and Agriculture Organization (FAO) reported that global pepper production in 2022 reached 812,673 tons, with Brazil producing approximately 128,331 tons [4]. Peppers are essential spices and widely used horticultural crops with global cultivation origins in South America [1]. Peppers, commonly used in the culinary, pharmaceutical, and cosmetic industries, vary in color, heat, size, and shape. In Brazil, varieties such as Malagueta (_Capsicum frutscens_), Dedo-de-Moca (_Capsicum bacatum_), and Bode (_Capsicum chinense_) exhibit distinct traits, with seed variation influencing their characteristics [5]. Consumer satisfaction with spicy peppers is a multifaceted concept that significantly influences market demand and product differentiation. This satisfaction is not solely derived from heat level but also encompasses flavor profiles, aroma, freshness, and perceived health benefits [6; 7]. Accurate identification of spicy varieties is crucial for optimizing production, enhancing quality, and minimizing errors in post-harvest management and market. Traditional post-harvest and market management screening methods are manual, time-consuming, and prone to errors in peppers (_Capsicum_ spp. ), which are widely cultivated and valued for their diverse flavors, shapes, and heat levels. These limitations underscore the need to adopt advanced technologies, such as computer vision, to streamline post-harvest practices [8; 9]. The accurate identification of pepper (_Capsicum_ spp.) varieties is a critical concern in agricultural production, as misclassification can result in substantial economic losses for producers, diminished product quality, and compromised supply chain efficiency. Given the increasing demand for specific pepper varieties in both domestic and international markets, failure to correctly identify and classify peppers can result in reduced market value, increased post-harvest losses, and a loss of consumer trust [1; 4; 5]. The complexity of distinguishing between morphologically similar varieties, particularly in large-scale operations, further worsens the inherent risks of errors associated with manual sorting and screening processes. Consequently, there is a need for advanced, automated identification methods to improve both the accuracy and efficiency of varietal discrimination in pepper production systems [6]. Recent developments in image processing have sparked significant interest in agricultural research [7]. The development and implementation of robust computer vision-based detection systems hold considerable potential for transforming post-harvest handling, quality control, and overall agricultural productivity by reducing human error, lowering operational costs, and ensuring consistent product standards [10; 11]. Adopting computer vision methods provides greater accuracy and speed in the identification process. Recent advances in object detection methods, particularly those based on deep learning, have shown remarkable progress in agriculture, with several models producing positive results in identification tasks [12]. These technologies automate processes, reduce errors, enhance agricultural operations, and facilitate research advancements, as well as the integration of monitoring systems, contributing to more precise and sustainable agriculture.