Implementasi Pengolahan Citra Digital untuk Deteksi Dini Penyakit Daun Tanaman Menggunakan Arsitektur Komputasi AI
Kata Kunci:
Deteksi Penyakit Tanaman, Pengolahan Citra Digital, Jaringan Saraf Konvolusional, Efisiensi Komputasi, Komputasi TepiAbstrak
Penerapan Convolutional Neural Network (CNN) telah mampu menghasilkan akurasi yang tinggi dalam mendeteksi penyakit tanaman. Namun, sebagian besar penelitian sebelumnya masih menitikberatkan pada peningkatan performa prediktif dan belum banyak memperhatikan efisiensi komputasi yang diperlukan untuk mendukung implementasi real-time di lingkungan pertanian. Berdasarkan kondisi tersebut, penelitian ini bertujuan merancang, mengimplementasikan, serta mengevaluasi metode pengolahan citra digital untuk deteksi dini penyakit daun tanaman dengan mempertimbangkan keseimbangan antara akurasi prediktif dan efisiensi perangkat keras. Penelitian menggunakan desain eksperimental komputasional kuantitatif dengan melatih model CNN menggunakan dataset citra daun yang telah terstandardisasi. Pengujian dilakukan dengan membandingkan kinerja komputasi antara Central Processing Unit (CPU) multi-core dan akselerator Graphics Processing Unit (GPU), serta menganalisis pengaruh format kompresi citra (BMP, PNG, dan JPEG) terhadap latensi Input/Output (I/O) melalui pengukuran metrik prediktif dan komputasi.
Unduhan
Referensi
REFERENCES
[1] A. T. Paraskevopoulou, N. Ntoulas, D. Bourtsoukli, and K. Bertsouklis, “Effect of Seawater Irrigation on Arthrocnemum macrostachyum Growing in Extensive Green Roof Systems under Semi-Arid Mediterranean Climatic Conditions,” Agronomy, vol. 13, no. 5, p. 1198, Apr. 2023, doi: 10.3390/agronomy13051198.
[2] R. D. Peters and S. D. Noble, “Characterization of leaf surface phenotypes based on light interaction,” Plant Methods, vol. 19, no. 1, p. 26, Mar. 2023, doi: 10.1186/s13007-023-01004-2.
[3] M. E. Hossain, M. A. Kabir, L. Zheng, D. L. Swain, S. McGrath, and J. Medway, “A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions,” Artificial Intelligence in Agriculture, vol. 6, pp. 138–155, 2022, doi: 10.1016/j.aiia.2022.09.002.
[4] W. Yang et al., “Effects of Population Regulation on the Source–Sink System of Hybrid Wheat Jingmai 6,” Agronomy, vol. 12, no. 10, p. 2530, Oct. 2022, doi: 10.3390/agronomy12102530.
[5] F. Prasetyo Eka Putra, S. R. Sutarsih, S. Sofiyulloh, P. Permana, and M. Umar Mansyur, “OPTIMALISASI PERANCANGAN APLIKASI MANAJEMEN DATA KOLOMAN, DI DESA PULAU MANDANGIN SAMPANG – MADURA BERBASIS WEBSITE,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 9, no. 2, pp. 285–294, Jul. 2024, doi: 10.36341/rabit.v9i2.4840.
[6] V. S. Dhaka et al., “A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases,” Sensors, vol. 21, no. 14, p. 4749, Jul. 2021, doi: 10.3390/s21144749.
[7] S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, “Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach,” Electronics (Basel)., vol. 10, no. 12, p. 1388, Jun. 2021, doi: 10.3390/electronics10121388.
[8] M. Shoaib et al., “An advanced deep learning models-based plant disease detection: A review of recent research,” Front. Plant Sci., vol. 14, Mar. 2023, doi: 10.3389/fpls.2023.1158933.
[9] T. Zhang, Z. Huang, Y. Zou, J. Zhao, and Y. Ke, “A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features,” Sensors, vol. 22, no. 18, p. 7084, Sep. 2022, doi: 10.3390/s22187084.
[10] K. Luo, Y. Jin, S. Wen, Y. Li, J. Rong, and M. Ding, “Detection and quantification of cotton trichomes by deep learning algorithm,” Comput. Electron. Agric., vol. 210, p. 107936, Jul. 2023, doi: 10.1016/j.compag.2023.107936.
[11] S. Chen, K. Jiang, W. Zheng, D. Jia, and C. Zhao, “Adaptive precision cutting method for rootstock grafting of melons: modeling, analysis, and validation,” Comput. Electron. Agric., vol. 218, p. 108655, Mar. 2024, doi: 10.1016/j.compag.2024.108655.
[12] M. Zali, S. Nurlaila, F. P. Eka Putra, and A. Y. Heryadi, “Preventing dan Manajemen Karapan Sapi Tangghe’ Di Desa Langsar Kecamatan Saronggi Sumenep Madura,” Jurnal Pengabdian Masyarakat Indonesia, vol. 5, no. 4, pp. 1108–1117, Aug. 2025, doi: 10.52436/1.jpmi.3768.
[13] B. Kaur et al., “Internet of Things (IoT) security dataset evolution: Challenges and future directions,” Internet of Things, vol. 22, p. 100780, Jul. 2023, doi: 10.1016/j.iot.2023.100780.
[14] M. K. Noman, S. M. S. Islam, J. Abu-Khalaf, S. M. J. Jalali, and P. Lavery, “Improving accuracy and efficiency in seagrass detection using state-of-the-art AI techniques,” Ecol. Inform., vol. 76, p. 102047, Sep. 2023, doi: 10.1016/j.ecoinf.2023.102047.
[15] M. N. Arifin, M. U. Mansyur, A. Rahman, N. P. Dewi, and F. P. E. Putra, “Enhanced OCR Recognition for Madurese Text Documents: A Genetic Algorithm Approach with Tesseract 5.5,” JUITA: Jurnal Informatika, pp. 109–118, Aug. 2025, doi: 10.30595/juita.v13i2.25794.
[16] D. Rahadiyan, S. Hartati, Wahyono, and A. P. Nugroho, “Feature aggregation for nutrient deficiency identification in chili based on machine learning,” Artificial Intelligence in Agriculture, vol. 8, pp. 77–90, Jun. 2023, doi: 10.1016/j.aiia.2023.04.001.
[17] J. G. Arnal Barbedo, “Plant disease identification from individual lesions and spots using deep learning,” Biosyst. Eng., vol. 180, pp. 96–107, Apr. 2019, doi: 10.1016/j.biosystemseng.2019.02.002.
[18] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, pp. 70–90, Apr. 2018, doi: 10.1016/j.compag.2018.02.016.
[19] S. Savary, L. Willocquet, S. J. Pethybridge, P. Esker, N. McRoberts, and A. Nelson, “The global burden of pathogens and pests on major food crops,” Nat. Ecol. Evol., vol. 3, no. 3, pp. 430–439, Feb. 2019, doi: 10.1038/s41559-018-0793-y.
[20] R. D. Peters and S. D. Noble, “Characterization of leaf surface phenotypes based on light interaction,” Plant Methods, vol. 19, no. 1, p. 26, Mar. 2023, doi: 10.1186/s13007-023-01004-2.
[21] A. T. Paraskevopoulou, N. Ntoulas, D. Bourtsoukli, and K. Bertsouklis, “Effect of Seawater Irrigation on Arthrocnemum macrostachyum Growing in Extensive Green Roof Systems under Semi-Arid Mediterranean Climatic Conditions,” Agronomy, vol. 13, no. 5, p. 1198, Apr. 2023, doi: 10.3390/agronomy13051198.
[22] M. Shoaib et al., “An advanced deep learning models-based plant disease detection: A review of recent research,” Front. Plant Sci., vol. 14, Mar. 2023, doi: 10.3389/fpls.2023.1158933.
[23] S. Thomas and J. Thomas, “Non-destructive silkworm pupa gender classification with X-ray images using ensemble learning,” Artificial Intelligence in Agriculture, vol. 6, pp. 100–110, 2022, doi: 10.1016/j.aiia.2022.08.001.
[24] A. F. Rachman, F. P. E. Putra, S. Syirofi, and D. Wahid, “Case Study of Computer Network Development for the Internet Of Things (IoT) Industry in an Urban Environment,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 399–407, Aug. 2024, doi: 10.47709/brilliance.v4i1.4302.
[25] V. S. Dhaka et al., “A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases,” Sensors, vol. 21, no. 14, p. 4749, Jul. 2021, doi: 10.3390/s21144749.
[26] H. El Rifai, L. Al Qadi, and A. Elnagar, “Arabic text classification: the need for multi-labeling systems,” Neural Comput. Appl., vol. 34, no. 2, pp. 1135–1159, Jan. 2022, doi: 10.1007/s00521-021-06390-z.
[27] A. Abade, P. A. Ferreira, and F. de Barros Vidal, “Plant diseases recognition on images using convolutional neural networks: A systematic review,” Comput. Electron. Agric., vol. 185, p. 106125, Jun. 2021, doi: 10.1016/j.compag.2021.106125.
[28] L. Li, S. Zhang, and B. Wang, “Plant Disease Detection and Classification by Deep Learning—A Review,” IEEE Access, vol. 9, pp. 56683–56698, 2021, doi: 10.1109/ACCESS.2021.3069646.
[29] M. E. Hossain, M. A. Kabir, L. Zheng, D. L. Swain, S. McGrath, and J. Medway, “A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions,” Artificial Intelligence in Agriculture, vol. 6, pp. 138–155, 2022, doi: 10.1016/j.aiia.2022.09.002.
[30] L. Rodriguez-Bonilla et al., “The Genetic Diversity of Cranberry Crop Wild Relatives, Vaccinium macrocarpon Aiton and V. oxycoccos L., in the US, with Special Emphasis on National Forests,” Plants, vol. 9, no. 11, p. 1446, Oct. 2020, doi: 10.3390/plants9111446.
[31] S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Front. Plant Sci., vol. 7, Sep. 2016, doi: 10.3389/fpls.2016.01419.
[32] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput. Electron. Agric., vol. 145, pp. 311–318, Feb. 2018, doi: 10.1016/j.compag.2018.01.009.
[33] F. P. E. Putra, F. Fauzan, S. Syirofi, M. Mursidi, D. Wahid, and A. Nuraini, “Sistem Pengendali Lingkungan Pertanian Dengan Wireless Sensor Network Untuk Mengoptimalkan Budidaya Hidroponik,” Digital Transformation Technology, vol. 3, no. 2, pp. 931–937, Jan. 2024, doi: 10.47709/digitech.v3i2.3461.
[34] S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Comput. Intell. Neurosci., vol. 2016, pp. 1–11, 2016, doi: 10.1155/2016/3289801.
[35] E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput. Electron. Agric., vol. 161, pp. 272–279, Jun. 2019, doi: 10.1016/j.compag.2018.03.032.
[36] M. Brahimi, K. Boukhalfa, and A. Moussaoui, “Deep Learning for Tomato Diseases: Classification and Symptoms Visualization,” Applied Artificial Intelligence, vol. 31, no. 4, pp. 299–315, Apr. 2017, doi: 10.1080/08839514.2017.1315516.
[37] F. P. E. Putra, M. A. Mahmud, and I. S. Maqom, “Pengembangan Sistem Pemantauan Lingkungan Berbasis Internet of Things (IoT) di Kampus,” Digital Transformation Technology, vol. 3, no. 2, pp. 996–1001, Feb. 2024, doi: 10.47709/digitech.v3i2.3457.
[38] G. G. and A. P. J., “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Computers & Electrical Engineering, vol. 76, pp. 323–338, Jun. 2019, doi: 10.1016/j.compeleceng.2019.04.011.
[39] B. S. M. Sawiris and M. A., “A Novel Solution for Distributed Database Problems,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 10, 2020, doi: 10.14569/IJACSA.2020.0111059.
[40] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, p. 105393, Jun. 2020, doi: 10.1016/j.compag.2020.105393.
[41] F. P. E. Putra, U. Ubaidi, R. N. Saputra, F. M. Haris, and S. N. R. Barokah, “Application of Internet of Things Technology in Monitoring Water Quality in Fishponds,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 356–361, Jul. 2024, doi: 10.47709/brilliance.v4i1.4231.
[42] A. Fuentes, S. Yoon, S. Kim, and D. Park, “A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition,” Sensors, vol. 17, no. 9, p. 2022, Sep. 2017, doi: 10.3390/s17092022.
[43] Y. Toda and F. Okura, “How Convolutional Neural Networks Diagnose Plant Disease,” Plant Phenomics, vol. 2019, p. 9237136, 2019, doi: 10.34133/2019/9237136.
[44] T. Zhang, Z. Huang, Y. Zou, J. Zhao, and Y. Ke, “A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features,” Sensors, vol. 22, no. 18, p. 7084, Sep. 2022, doi: 10.3390/s22187084.
[45] F. P. E. Putra, U. Ubaidi, A. Hamzah, W. A. Pramadi, and A. Nuraini, “Systematic Literature Review: Security Gap Detection On Websites Using Owasp Zap,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 348–355, Jul. 2024, doi: 10.47709/brilliance.v4i1.4227.
[46] S. Ghosal, D. Blystone, A. K. Singh, B. Ganapathysubramanian, A. Singh, and S. Sarkar, “An explainable deep machine vision framework for plant stress phenotyping,” Proceedings of the National Academy of Sciences, vol. 115, no. 18, pp. 4613–4618, May 2018, doi: 10.1073/pnas.1716999115.
[47] N. Haidar, F. P. Eka Putra, M. Arifin, M. Yasir Zain, and I. Darmawan, “Desain dan Perancangan Smart Campus berbasis ZigBee Wireless Sensor Network,” Jurnal Inovasi Teknologi dan Edukasi Teknik, vol. 1, no. 11, pp. 842–850, Nov. 2021, doi: 10.17977/um068v1i112021p842-850.
[48] S. Adama, B. Konan, G. Bi, and P. Kimou, “Efficient Reduction of Overgeneration Errors for Automatic Controlled Indexing with an Application to the Biomedical Domain,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 12, 2018, doi: 10.14569/IJACSA.2018.091225.
[49] S. Redondo-Gómez et al., “Consortia of Plant-Growth-Promoting Rhizobacteria Isolated from Halophytes Improve Response of Eight Crops to Soil Salinization and Climate Change Conditions,” Agronomy, vol. 11, no. 8, p. 1609, Aug. 2021, doi: 10.3390/agronomy11081609.
[50] F. P. E. Putra, U. Ubaidi, M. A. Huda, H. Hasbullah, and A. Rohman, “Computer Network Management Optimization Through Big Data Analysis Using Time Series Analysis Method,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 434–442, Aug. 2024, doi: 10.47709/brilliance.v4i1.4373.
[51] J. G. A. Barbedo, “Factors influencing the use of deep learning for plant disease recognition,” Biosyst. Eng., vol. 172, pp. 84–91, Aug. 2018, doi: 10.1016/j.biosystemseng.2018.05.013.
[52] K. R., H. M., S. Anand, P. Mathikshara, A. Johnson, and M. R., “Attention embedded residual CNN for disease detection in tomato leaves,” Appl. Soft Comput., vol. 86, p. 105933, Jan. 2020, doi: 10.1016/j.asoc.2019.105933.
[53] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, “Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks,” IEEE Access, vol. 7, pp. 59069–59080, 2019, doi: 10.1109/ACCESS.2019.2914929.
[54] Y. Zhong and M. Zhao, “Research on deep learning in apple leaf disease recognition,” Comput. Electron. Agric., vol. 168, p. 105146, Jan. 2020, doi: 10.1016/j.compag.2019.105146.
[55] M. Arsenovic, M. Karanovic, S. Sladojevic, A. Anderla, and D. Stefanovic, “Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection,” Symmetry (Basel)., vol. 11, no. 7, p. 939, Jul. 2019, doi: 10.3390/sym11070939.
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Agus Salim, Abdul Halim (Penulis)
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
