• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.791-798
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• 2024

The purpose of this study is to diagnose diseases in pepper crops using YOLOv8, ResNet50, and Faster R-CNN models and compare their performance. The first model utilizes YOLOv8 for disease diagnosis, the second model uses ResNet50 alone, the third model combines YOLOv8 and ResNet50, and the fourth model uses Faster R-CNN. The performance of each model was evaluated using metrics such as accuracy, precision, recall, and F1-Score. The results show that the combined YOLOv8 and ResNet50 model achieved the highest performance, while the YOLOv8 standalone model also demonstrated high performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.911-918
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• 2024

This study developed and evaluated the performance of a machine learning-based model for predicting cucumber fruit set using cucumber growth data. In this study, plant height, node number, internode length, stem thickness, leaf length, leaf width, leaf count, and female flower count were used as independent variables, and the fruit set was set as the dependent variable to develop a prediction model. Various machine learning algorithms, including Linear Regression, Random Forest, XGBoost, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), were applied, and model performance was evaluated based on Mean Squared Error (MSE) and the coefficient of determination (R²). As a result, the Random Forest algorithm demonstrated the best performance, with an MSE of 3.91 and an R² of 0.828, effectively capturing the non-linear relationships in the cucumber growth data. In particular, the Random Forest model showed robustness against outliers and proved to be highly effective in predicting fruit set.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.89-96
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• 2024

In this paper, we design an improved weed detection model using YOLOv10, a deep learning-based object detection algorithm. YOLOv10 improves its performance compared to previous versions by adding an attention module, the PSA module. PSA is strong at recognising complex patterns in large areas because it uses some features of its own attention to reduce computation and learn global information. However, it may be inefficient for certain problems, such as weeds, which are generally small objects. Therefore, in this paper, we propose an improved YOLOv10 by applying another attention module, SENet, instead of the PSA module. Since, SENet learns the importance between channels, it can learn the features of weeds in more detail than the PSA module. In addition, SENet is lighter, less computationally intensive, and faster than the PSA module, so we conducted experiments by replacing the PSA module with SENet, which is suitable for weed detection. The experiment consisted of 200 training runs with a total of 14 classes, and we compared the performance through various performance evaluations. The experimental results showed that the FPS increased from 476.19 to 526.32, which is about 9.52% processing speed improvement. The mAP50-95 value increased from 88.7% to 88.3%, which shows that the proposed model is lighter than the existing model and performs similarly to the existing model.