Enhancing Real-time Herbal Plant Detection in Agricultural Environments with YOLOv8

  • Ranty Deviana Siahaan Institut Teknologi Del, Indonesia
  • Herimanto Pardede Institut Teknologi Del, Indonesia
  • Iustisia Natalia Simbolon Institut Teknologi Del, Indonesia
  • Ivanston Simbolon Institut Teknologi Del, Indonesia
  • Dian Jorgy Gultom Institut Teknologi Del, Indonesia
Keywords: Herbal Plants, YOLOv8, Object Detection, Androd Application

Abstract

The detection of herbal plants plays a crucial role in the utilization of traditional medicine, particularly in the Toba region of Indonesia. This study aims to develop an Android application capable of real-time detection of herbal plants using the YOLOv8 algorithm. The five types of herbal plants targeted in this study are tempuyung, rimbang, papaya leaves, turmeric leaves, and aloe vera. The research methodology includes the collection of a dataset of herbal plant images, which were then labeled using the Roboflow platform. The YOLOv8 model was trained with this dataset to detect herbal plant objects. After training, the model was exported to TensorFlow Lite and integrated into an Android application. Testing was conducted to evaluate the accuracy and real-time detection performance of the application. The results show that the YOLOv8 model achieved a mean Average Precision (mAP) of 92.4%, with optimal real-time detection capabilities on Android devices. The developed application can quickly and accurately detect and identify herbal plants, providing a practical solution for users to recognize herbal plants. This study indicates that the YOLOv8 algorithm is effective for herbal plant recognition applications in a mobile context, opening up opportunities for further development in the integration of AI technology into everyday applications.

Downloads

Download data is not yet available.

References

D. B. Valdez, C. J. G. Aliac, and L. S. Feliscuzo, “Detecting Medicinal Plants Using YOLOv5: A Mobile Vision Approach,” 6th Int. Conf. Inven. Comput. Technol. ICICT 2023 - Proc., no. Icict, pp. 533–538, 2023, doi: 10.1109/ICICT57646.2023.10134246.

Haryono, Khairul Anam, and Azmi Saleh, “Autentikasi Daun Herbal Menggunakan Convolutional Neural Network dan Raspberry Pi,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 3, pp. 278–286, 2020, doi: 10.22146/.v9i3.302.

H. Xinru, C. Limin, X. Qing, L. Chongyuan, W. Yinchai, and F. Peijun, “Plant disease detection based on improved YOLOv5,” 2023 2nd Int. Conf. Robot. Artif. Intell. Intell. Control. RAIIC 2023, pp. 162–165, 2023, doi: 10.1109/RAIIC59453.2023.10280962.

A. Kumar and D. B. Kumar, “Automatic Recognition of Medicinal Plants using Machine Learning Techniques,” Gedrag Organ. Rev., vol. 33, no. 01, pp. 166–175, 2020, doi: 10.37896/gor33.01/012.

F. Liantoni and H. Nugroho, “Klasifikasi Daun Herbal Menggunakan Metode Naïve Bayes Classifier Dan Knearest Neighbor,” J. Simantec, vol. 5, no. 1, pp. 9–16, 2015.

A. E. Sukmayadi, S. A. Sumiwi, M. I. Barliana, and A. D. Aryanti, “The Immunomodulatory Activity of Ethanol Extract of Tempuyung Leaves (Sonchus arvensis Linn.),” Indones. J. Pharm. Sci. Technol., vol. 1, no. 2, pp. 65–72, 2014, doi: 10.15416/ijpst.v1i2.7515.

Z. Chen et al., “Plant Disease Recognition Model Based on Improved YOLOv5,” Agronomy, vol. 12, no. 2, 2022, doi: 10.3390/agronomy12020365.

C. Y. Pelokang, R. Koneri, and D. Katili, “Pemanfaatan Tumbuhan Obat Tradisional oleh Etnis Sangihe di Kepulauan Sangihe Bagian Selatan, Sulawesi Utara (The Usage of Traditional Medicinal Plants by Sangihe Ethnic in the Southern Sangihe Islands, North Sulawesi),” J. Bios Logos, vol. 8, no. 2, p. 45, 2018, doi: 10.35799/jbl.8.2.2018.21446.

H. Herimanto, “Perbandingan Matriks Loss Pada Model Deep Learning Resnet50 dan Xception dalam Deteksi Objek,” J. Media Inform. Budidarma, vol. 7, no. 4, pp. 1994–2002, 2023, doi: 10.30865/mib.v7i4.6849.

B. Dwyer, “When Should I Auto-Orient My Images.”

M. Geiß, R. Wagner, M. Baresch, J. Steiner, and M. Zwick, “Automatic Bounding Box Annotation with Small Training Datasets for Industrial Manufacturing,” Micromachines, vol. 14, no. 2, pp. 1–20, 2023, doi: 10.3390/mi14020442.

L. Marifatul Azizah, S. Fadillah Umayah, and F. Fajar, “Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer,” Semesta Tek., vol. 21, no. 2, pp. 230–236, 2018, doi: 10.18196/st.212229.

C. Y. Pelokang, R. Koneri, and D. Katili, “Pemanfaatan tumbuhan obat tradisional oleh Etnis Sangihe di Kepulauan Sangihe bagian selatan, Sulawesi Utara,” J. Bioslogos, vol. 8, no. 2, pp. 45–51, 2018.

K. Khairunnas, E. M. Yuniarno, and A. Zaini, “Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot,” J. Tek. ITS, vol. 10, no. 1, 2021, doi: 10.12962/j23373539.v10i1.61622.

C. Geraldy and C. Lubis, “Pendeteksian Dan Pengenalan Jenis Mobil Menggunakan Algoritma You Only Look Once Dan Convolutional Neural Network,” J. Ilmu Komput. dan Sist. Inf., vol. 8, no. 2, p. 197, 2020, doi: 10.24912/jiksi.v8i2.11495.

A. K. et al., “Malayalam Sign Language Identification using Finetuned YOLOv8 and Computer Vision Techniques,” 2024, [Online]. Available: http://arxiv.org/abs/2405.06702

Herimanto, A. S. Dharma, H. Manurung, and A. B. Nababan, “Comparison of Support Vector Regression and Artificial Neural Network Algorithm in Predicting the Number of Prospective Student Registrans,” Proc. 2023 IEEE Int. Conf. Data Softw. Eng. ICoDSE 2023, pp. 43–48, 2023, doi: 10.1109/ICoDSE59534.2023.10291384.

B. Wang, Y. Yan, Y. Lan, M. Wang, and Z. Bian, “Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model,” IEEE Access, vol. 11, no. January, pp. 29868–29882, 2023, doi: 10.1109/ACCESS.2023.3258439.

U. Islam and N. Sumatera, “ALACRITY : Journal Of Education,” vol. 1, no. 2, pp. 1–12, 2021.

M. I. H. -, “Software Development Life Cycle (SDLC) Methodologies for Information Systems Project Management,” Int. J. Multidiscip. Res., vol. 5, no. 5, 2023, doi: 10.36948/ijfmr.2023.v05i05.6223.

Published
2024-12-15
Abstract views: 238 times
Download PDF: 160 times
How to Cite
Siahaan, R., Pardede, H., Simbolon, I., Simbolon, I., & Gultom, D. (2024). Enhancing Real-time Herbal Plant Detection in Agricultural Environments with YOLOv8. Journal of Information Systems and Informatics, 6(4), 2491-2507. https://doi.org/10.51519/journalisi.v6i4.889
Section
Articles