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Journal : International Journal of Multidisciplinary Approach Research and Science

Implementing PSO-based Image Segmentation for Detecting Sweet Potato Leaf Disease Purnama, Adi; Fauzi, Esa; Prasetyo, Bagus Alit
International Journal of Multidisciplinary Approach Research and Science Том 3 № 02 (2025): International Journal of Multidisciplinary Approach Research and Science
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/ijmars.v3i02.1482

Abstract

Sweet potato (Ipomoea batatas) is an important global crop, but its production is threatened by various leaf diseases, requiring accurate and efficient disease detection methods. Traditional manual inspection is labor-intensive and error-prone, making automated image processing techniques a promising alternative. This study implements Particle Swarm Optimization (PSO)-based image segmentation to detect diseased leaf regions by optimizing threshold selection in HSV color space. In the classification phase, leaves are classified into healthy and diseased classes using a Euclidean distance-based classifier. The proposed method achieved an average classification accuracy of 88.1%, with an accuracy of 95.8% for diseased leaves and 80.4% for healthy leaves, demonstrating its effectiveness in discriminating infected regions. The results confirm that PSO is a robust and efficient segmentation technique that improves the accuracy of disease detection. This research highlights the potential of PSO-based segmentation in smart agriculture, enabling early disease detection to help farmers take timely action and minimize crop losses. Compared to traditional methods, PSO reduces computational complexity while maintaining high segmentation accuracy, making it a valuable tool for agricultural disease monitoring. Future work can integrate deep learning models to refine disease classification and expand datasets to improve system performance under different environmental conditions.
Image Segmentation for Sweet Potato Leaf Disease Detection using U-Net Syukriyah, Yenie; Purnama, Adi
International Journal of Multidisciplinary Approach Research and Science Том 3 № 03 (2025): International Journal of Multidisciplinary Approach Research and Science
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/ijmars.v3i03.1848

Abstract

The detection and management of sweet potato leaf diseases play a vital role in ensuring sustainable crop yields and reducing agricultural losses. This study proposes an automated segmentation approach using the U-Net convolutional neural network to detect disease regions on sweet potato leaves. The dataset, consisting of leaf images and corresponding masks, underwent a structured preprocessing pipeline including resizing, normalization, and reshaping. The U-Net architecture, comprising an encoder-decoder structure with skip connections, was trained on 70% of the dataset and evaluated using accuracy, Intersection over Union (IoU), and Dice coefficient. Experimental results show that the model achieved an accuracy of 94.6%, IoU of 0.88, and a Dice coefficient of 0.92, indicating strong segmentation performance. Visual comparison between predictions and ground truth masks further confirms the model’s effectiveness in isolating disease regions. This research demonstrates the potential of U-Net as a reliable deep learning framework for plant disease detection and contributes to the development of intelligent agricultural monitoring systems.
Evaluation of Use of Linear Regression to Predict Profit, Selling Price, and Stock on HSR Wheels Platform Fauzi, Esa; Prasetyo, Bagus Alit; Purnama, Adi; Pangestu, Rizky Bagus
International Journal of Multidisciplinary Approach Research and Science Том 3 № 03 (2025): International Journal of Multidisciplinary Approach Research and Science
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/ijmars.v3i03.1967

Abstract

In the ever-evolving digital era, the e-commerce sector faces significant challenges in efficiently managing sales, selling prices, and inventory. This study aims to evaluate the effectiveness of a linear regression model in predicting sales, selling prices, and stock levels on the HSR Wheels e-commerce platform. A quantitative method was used by analyzing daily transaction data to identify the relationship between the time variable and sales, profit, and stock. The results showed that linear regression has limitations in modeling data complexity, with low R² scores and high Mean Absolute Error (MAE) values. These findings indicate the need for more advanced predictive models, such as machine learning algorithms, to improve prediction accuracy. This research is expected to contribute to developing more efficient and relevant sales strategies for e-commerce platforms.