Computer Vision Technology Innovation Education to Support Early Warning Systems for Rice Diseases

Authors

  • Wahyu Saptha Negoro Universitas Potensi Utama
  • Ratih Adinda Destari Universitas Potensi Utama
  • Asbon Hendra Azhar Universitas Potensi Utama
  • Achmad Syahrian Universitas Potensi Utama

DOI:

https://doi.org/10.35134/jmi.v32i2.199

Keywords:

Computer Vision, Object Identification, Segmentation, Deep Learning, Rice Diseases

Abstract

Rice plant diseases are one of the main factors causing decreased productivity and threatening national food security. Farmers' limited knowledge in recognizing early symptoms of disease often leads to delays in treatment. The results of this research are educated in community service with the aim of developing and implementing Computer Vision-based technological innovation education to support an early warning system for rice diseases. The methods used include collecting rice leaf images in the field, digital image processing, and applying Computer Vision models to recognize visual patterns of disease symptoms. Educational activities with students are carried out through training and mentoring for farmers and agricultural extension workers regarding the use of this technology as an early detection tool. The expected results of this service are increased understanding and ability of users or partners in identifying rice diseases more quickly and accurately, so that they can support appropriate decision-making in disease control and increase rice agricultural productivity in a sustainable manner.

References

D. Yuliani, Santoso, A.W. Anggara, “Monitoring Penyakit Blas Padi Di Agroekosistem Rawa Kalimantan Tengah”, Jurnal AGROSWAGATI 10(2), Vol 10 No 2 (2022), p-ISSN2339-0085serta e-ISSN 2580-5185.

R. Kusuma, & Rajkumar, R. “Plant leaf disease detection and classification using artificial intelligence techniques: a review”. Indonesian Journal of Electrical Engineering and Computer Science, 2025, 38(2), 1308–1323. DOI:10.11591/ijeecs.v38.i2.pp1308-1323

R. Jayashree & B. M. Kusuma Kumari, “A review on plant leaf disease detection”. International Journal of Human Computations and Intelligence, 2023. 2(6), 275–279.

E. Aditya, A. K. Wardhana, “Exploration of Machine Learning Algorithms and Class Imbalance Handling on Plant Disease Detection” Journal of Applied Informatics and Computing (JAIC), Vol. 9 No. 5 (2025): October 2025, e-ISSN: 2548-6861

S. U. Khan, A. Alsuhaibani, A. Alabduljabbar, F. Almarshad, Y. N. Altherwy, and T. Akram, A review on automated plant disease detection: motivation, limitations, challenges, and recent advancements for future research, vol. 37, no. 3. Springer International Publishing, 2025. doi: 10.1007/s44443-025-00040-3.

W. Ding, M. Abdel-Basset, I. Alrashdi, and H. Hawash, “Next generation of computer vision for plant disease monitoring in precision agriculture: A contemporary survey, taxonomy, experiments, and future direction,” Information Sciences, vol. 665, p. 120338, 2024, doi: 10.1016/j.ins.2024.120338

H. N. Ngugi, A. E. Ezugwu, A. A. Akinyelu, and L. Abualigah, “Revolutionizing crop disease detection with computational deep learning: a comprehensive review,” 2024. doi: 10.1007/s10661-024-12454-z.

S. S. Harakannanavar, J. M. Rudagi, V. I. Puranikmath, A. Siddiqua, and R. Pramodhini, “Plant leaf disease detection using computer vision and machine learning algorithms,” Glob. Transitions Proc., vol. 3, no. 1, pp. 305–310, 2022, doi: 10.1016/j.gltp.2022.03.016.

S. Ali, M. Hassan, J. Y. Kim, M. I. Farid, M. Sanaullah, and H. Mufti, “FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases,” Appl. Sci., vol. 12, no. 7, 2022, doi: 10.3390/app12073514.

H. Ghazouani, W. Barhoumi, E. Chakroun, and A. Chehri, “Dealing with Unbalanced Data in Leaf Disease Detection: A Comparative Study of Hierarchical Classification, Clustering-based Undersampling and Reweighting-based Approaches,” in Procedia Computer Science, 2023. doi: 10.1016/j.procs.2023.10.489.

A. S. A. Hidayat, Diagnosa hama dan penyakit padi berbasis graf pengetahuan dan Semantic Web Rule Language (SWRL), Univ. Jend. Soedirman, 2024.

S. Wijaya Samdoria and N. Nisar, “Implementasi Deep Learning Menggunakan Arsitektur Vgg-19 Untuk Deteksi Penyakit Pada Tebu Berdasarkan Citra Daun Berbasis Website,” J. Comprehensive Sci., vol. 4, no. 5, pp. 1650–1667, 2025, doi: 10.59188/jcs.v4i5.3155.

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Published

2025-12-28

How to Cite

Saptha Negoro, W., Adinda Destari, R. ., Hendra Azhar, A. ., & Syahrian, A. (2025). Computer Vision Technology Innovation Education to Support Early Warning Systems for Rice Diseases. Majalah Ilmiah UPI YPTK, 32(2), 137–142. https://doi.org/10.35134/jmi.v32i2.199

Issue

Vol. 32 (2025) No. 2

Section

Articles

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Copyright (c) 2025 Majalah Ilmiah UPI YPTK

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