International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Li-Hua Li 1, Radius Tanone 1,2*

1 Department of Information Management, Chaoyang University of Technology, Taichung City, Taiwan

2 Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Indonesia

 

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ABSTRACT

Indonesia, a country heavily dependent on agriculture, continues to grow potatoes. However, the presence of plant diseases, manifested by the condition of the leaves, is a significant problem that requires attention. Agriculture offers extensive opportunities to explore computer vision applications, including tasks like object detection. In this paper, we present a method that increases the YOLOv7 tiny model's accuracy to assist farmers in identifying diseases in potato leaves. Our study employed multi-scale and MixUp augmentation techniques to process input images when training using the YOLOv7 tiny model. Based on our experiment, the model can be enhanced using multi-scale training instead of fixed-scale training. After implementing our proposed technique, the mAP metric significantly improved over the original model, achieving a range of 0.94325 to 0.96975 for fixed-scale training and a range of 0.9620 to 0.97525 for multi-scale training with the MixUp approach. In addition, we have developed the YOLOv7 tiny model, which aims to enable seamless use of mobile devices in real-time applications. To assess the current state of potato leaves on land in real-time, we convert the results of our extended model into a compact format called TF Lite. Future potato production can be improved by using these findings to help farmers combat leaf diseases.


Keywords: MixUp, Multi-scale, Potato leaf diseases, TF Lite, YOLOv7 tiny.


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ARTICLE INFORMATION

Received: 2024-01-22
Revised: 2024-02-02
Accepted: 2024-02-15


Cite this article:

Li, L.H., Tanone, R. 2024. Lightweight model based on improved YOLOv7 tiny for potato leaf diseases detection. International Journal of Applied Science and Engineering, 21, 2024033. https://doi.org/10.6703/IJASE.202406_21(2).010

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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