## 1 Introduction
The model uses novel loss functions, Exponentially Enhanced Binary Cross Entropy (EBCE) and Double-Enhanced Mean Square Error (DEMSE) to address the class imbalance. Experimental results show DSE-YOLO's superiority over existing methods, achieving a mean average precision (mAP) of 86.58% and an F1 score of 81.59%. This model effectively detected all stages of strawberry fruits in natural scenes, providing a solid theoretical foundation for automated harvesting and monitoring systems. [PERSON] et al. [14] enhanced Tiny YOLO-v4 for detecting pineapple ripeness, achieving a recognition rate of 98.26% and demonstrating applicability in large-scale plantations. The following innovative studies have leveraged YOLO-based algorithms to enhance precision agriculture. [PERSON] et al. [15] enhanced YOLOv5 to detect unopened cotton bolls, addressing challenges like occlusions and varying growth stages, outperforming YOLOv3, SSD, and Faster R-CNN. [PERSON] et al. [15] introduced MD-YOLO for pest detection in precision agriculture, achieving an email_1 of 86.2%, F1 score of 79.1%, and IoU of 88.1% using DenseNet blocks and adaptive attention modules. [PERSON] et al. [16] developed YOLOWeeds for multi-class weed detection in cotton production and identified YOLOv5n and YOLOv5s as effective for weed detection. [PERSON] et al. [17] investigated the YOLO-FT deep learning algorithm for UAV-based smart agriculture, emphasizing the role of drones in data acquisition and improved pollination through advanced object detection technology. Additionally, [PERSON] et al. [18] conducted a comprehensive literature review highlighting YOLO's significance in agricultural object detection, focusing on its performance characteristics and potential for advancing knowledge in the agricultural sector. As a result, effectively classifying strawberry products and implementing defect detection strategies emerge as potential issues that could be solved by applying machine vision within agricultural enterprises engaged in strawberry processing and cultivation [19]. To address the challenges of strawberry classification and defect mitigation, these enterprises can utilize web technologies and YOLO-integrated technologies for quality control activities. With the increasing availability of Convolutional Neural Network (CNN) variations in computer vision, various network architectures built on them have been used for defect detection, producing significant results [20]. This approach not only reduces overall costsbut also enhances the efficiency of agricultural operations, aligning seamlessly with the principles of smart agriculture (SA). This study aims to showcase the applicability of the SA concept within the strawberry processing industry, marking a stride toward overall agricultural productivity. To accomplish this objective, the authors have devised and implemented edge devices, web applications and machine vision YOLOv8 n, s, m, l, and x and YOLOv9 m, c, t, s and e architectures for small strawberry processing enterprises, offering several benefits. The motivation for this article stems from the need for efficient and accurate classification of strawberry defects during processing. Given the shortage of human labor and the critical timing required for processing, machine vision and automation are necessary to enhance productivity and quality control. The primary objective of this study is to develop a strawberry defects classification model capable of identifying the location of defective strawberries and distinguishing between different classes of strawberries based on their condition. The main contributions of this work are two-fold:  
* It introduces a new dataset aimed at advancing research in object detection systems, specifically for the classification of strawberries. This dataset is used with YOLO version 8 (YOLOv8) n, s, m, l, and x, as well as with YOLO version 9 (YOLOv9) c, m, s, t, and e architectures.