Classification of Betel Leaf Diseases Based on Convolutional Neural Network to Increase Production Herbal Spice Materials

https://doi.org/10.26594/register.v11i1.4653

Authors

  • Dr. Rima Tri Wahyuningrum Trunojoyo University (Indonesia)
  • Irham Hamed Ayani Department of Informatics, Faculty of Technology, University of Trunojoyo Madura, Bangkalan, Indonesia (Indonesia)
  • Achmad Bauravindah Department of Informatics, Faculty of Industrial Technology, Universitas Islam Indonesia, Yogyakarta, Indonesia (Indonesia)
  • Indah Agustien Siradjuddin Department of Informatics, Faculty of Technology, University of Trunojoyo Madura, Bangkalan, Indonesia (Indonesia) https://orcid.org/0000-0002-2854-6975
  • Irmalia Suryani Faradisa Department of Electrical Engineering, National Institute of Technology, Malang, Indonesia (Indonesia)

Keywords:

betel leaf diseases, image classification, deep learning, InceptionResNetV2

Abstract

Traditional medicine is the practice of utilizing medicinal plants to treat various illnesses, passed down from generation to generation. In Indonesia, there are various traditional medicines, one of which is using green betel leaves. One part of the green betel plant that is commonly attacked by pests is the leaf. The Convolutional Neural Network (CNN) method is a very common method used for image classification because this method produces the highest accuracy in classification and pattern recognition. This research uses data totaling 4000 images which are divided into four classes: healthy green betel leaves, anthracnose green betel leaves, bacterial spot betel leaves, and healthy red betel leaves. Detecting the disease type facilitates farmers in acknowledging the necessary measures required to provide treatment. Therefore, this study utilizes the benefits of the CNN approach, specifically its capability to conduct precise object detection and classification in image data, to minimize the widespread of disease. The CNN architectures implemented are DenseNet201, EfficientNetB3V2, InceptionResNetV2, MobileNetV2 and XceptionResnet50V2. Based on our research, the InceptionResNetV2 model achieved the highest performance with an accuracy of 86.0%, loss of 0.3880, and ROC of 98.0%. In the other hand, the MobileNetV2 and EfficientNetV2B3 models suffered from overfitting and underfitting and the models failed to classify betel leaf diseases.

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References

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B. Graz, A. Y. Kitua, and H. M. Malebo, To what extent can traditional medicine contribute a complementary or alternative solution to malaria control programmes?, Malaria Journal, vol. 10, no. 1, p. S6, Mar. 2011, doi: 10.1186/1475-2875-10-S1-S6.

S. Hussain, F. Malik, N. Khalid, M. Abdul, and H. Riaz, Alternative and Traditional Medicines Systems in Pakistan: History, Regulation, Trends, Usefulness, Challenges, Prospects and Limitations, in A Compendium of Essays on Alternative Therapy, A. Bhattacharya, Ed., InTech, 2012. doi: 10.5772/28224.

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Published

2025-05-17

How to Cite

[1]
D. R. . Tri Wahyuningrum, I. Hamed Ayani, A. Bauravindah, I. A. Siradjuddin, and I. S. Faradisa, “Classification of Betel Leaf Diseases Based on Convolutional Neural Network to Increase Production Herbal Spice Materials”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 11, no. 1, pp. 13–28, May 2025.

Issue

Vol. 11 No. 1 (2025): January

Section

Article

License

Copyright (c) 2025 Dr. Rima Tri Wahyuningrum, Irham Hamed Ayani, Achmad Bauravindah, Indah Agustien Siradjuddin, Irmalia Suryani Faradisa

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