Plant Disease Identification in Ipomoea Batatas Leaf Images Using Color Space Features

DOI

10.2991/978-94-6463-618-5_27How to use a DOI?

Keywords

Ipomoea Batatas; Plant Disease Identification; Image Processing; Color Features; Image Segmentation

Abstract

In countries such as Indonesia, sweet potatoes are an important staple crop that plays a key role in global food security. However, sweet potato production faces significant challenges, with leaf diseases posing a major threat to crop yield and overall quality. These diseases, if not detected early, can cause significant losses to farmers. An innovative solution to this problem is color-based image segmentation technology, which provides a rapid and accurate method of identifying plant diseases by analyzing the differences in color between healthy and diseased leaves. This approach exploits the distinct color spectrum variations that occur when a leaf is infected, allowing early detection of disease. The method is also highly cost-effective and scalable, making it suitable for large-scale farming operations. It can also be integrated with mobile devices and web-based monitoring systems, enabling real-time disease detection in the field without the need for expensive equipment. This paper presents a method based on image processing techniques using color space features to detect leaf diseases in sweet potatoes. By capturing high-resolution images and applying color segmentation techniques, the algorithm can detect early disease symptoms. Using spectral clustering and t-SNE visualization, the images are effectively classified into distinct groups. The method proves to be accessible, efficient and suitable for real-time, large-scale agricultural applications.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Adi Purnama
AU  - Yenie Syukriyah
PY  - 2024
DA  - 2024/12/29
TI  - Plant Disease Identification in Ipomoea Batatas Leaf Images Using Color Space Features
BT  - Proceedings of the Widyatama International Conference on Engineering 2024 (WICOENG 2024)
PB  - Atlantis Press
SP  - 255
EP  - 265
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-618-5_27
DO  - 10.2991/978-94-6463-618-5_27
ID  - Purnama2024
ER  -