Perkembangan Teknologi Cerdas Dalam Deteksi Penyakit Tanaman
DOI:
https://doi.org/10.66031/reset.v2i1.686Keywords:
intelligent technology, plant disease detection, deep learning, object detection, Internet of Things, hyperspectral imagingAbstract
Perkembangan teknologi kecerdasan buatan dan teknologi digital telah mendorong perubahan dalam metode deteksi penyakit tanaman, dari identifikasi visual secara konvensional menuju pendekatan berbasis analisis citra, sensor, dan komputasi cerdas. Penelitian ini bertujuan menganalisis perkembangan teknologi cerdas dalam deteksi penyakit tanaman serta mengidentifikasi pendekatan, karakteristik, tantangan, dan peluang pengembangannya. Penelitian menggunakan metode Systematic Literature Review (SLR) dengan pedoman PRISMA 2020. Literatur diperoleh melalui Scopus, ScienceDirect, IEEE Xplore, dan Google Scholar dengan cakupan publikasi periode 2021–2025. Setelah melalui proses identifikasi, penyaringan, dan penilaian kelayakan berdasarkan kriteria inklusi dan eksklusi, diperoleh 20 artikel utama untuk dianalisis. Hasil kajian menunjukkan bahwa deep learning berbasis Convolutional Neural Network (CNN) menjadi salah satu pendekatan dominan, sementara object detection berbasis YOLO dan segmentasi berbasis U-Net memperluas kemampuan deteksi melalui lokalisasi dan pemetaan area penyakit. Selain itu, IoT dan AIoT mendukung pemanfaatan data lingkungan, sedangkan hyperspectral imaging berpotensi mendukung deteksi penyakit secara lebih dini. Tantangan utama meliputi generalisasi model pada kondisi lapangan, keterbatasan dataset, kebutuhan komputasi, dan integrasi berbagai sumber data. Pengembangan selanjutnya perlu diarahkan pada sistem yang akurat, ringan, adaptif, dan mampu diterapkan pada kondisi pertanian nyata.
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References
Ahmed, I., & Yadav, P. K. (2023). A systematic analysis of machine learning and deep learning based approaches for identifying and diagnosing plant diseases. Sustainable Operations and Computers, 4, 96–104. https://doi.org/10.1016/j.susoc.2023.03.001
Abade, A., Ferreira, P. A., & Vidal, F. de B. (2021). Plant diseases recognition on images using convolutional neural networks: A systematic review. Computers and Electronics in Agriculture, 185, 106125. https://doi.org/10.1016/j.compag.2021.106125
Bhatti, U. A., Bazai, S. U., Hussain, S., Fakhar, S., Ku, C. S., Marjan, S., Yee, P. L., & Jing, L. (2023). Deep learning-based trees disease recognition and classification using hyperspectral data. Computers, Materials & Continua, 77(1), 681–697. https://doi.org/10.32604/cmc.2023.037958
Blanco-Carmona, P., Baeza-Moreno, L., Hidalgo-Fort, E., Martín-Clemente, R., González-Carvajal, R., & Muñoz-Chavero, F. (2023). AIoT in agriculture: Safeguarding crops from pest and disease threats. Sensors, 23(24), 9733. https://doi.org/10.3390/s23249733
Chin, P.-W., Ng, K.-W., & Palanichamy, N. (2024). Plant disease detection and classification using deep learning methods: A comparison study. Journal of Informatics and Web Engineering, 3(1), 155–168. https://doi.org/10.33093/jiwe.2024.3.1.10
Fuentes, A., Yoon, S., Lee, M. H., & Park, D. S. (2021). Improving accuracy of tomato plant disease diagnosis based on deep learning with explicit control of hidden classes. Frontiers in Plant Science, 12, 682230. https://doi.org/10.3389/fpls.2021.682230
Gomez, D., Selvaraj, M. G., Casas, J., Mathiyazhagan, K., Rodriguez, M., Assefa, T., Mlaki, A., Nyakunga, G., Kato, F., Mukankusi, C., Girma, E., Mosquera, G., Arredondo, V., & Espitia, E. (2024). Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI. Scientific Reports, 14, 15596. https://doi.org/10.1038/s41598-024-66281-w
Hnatiuc, M., Ghita, S., Alpetri, D., Ranca, A., Artem, V., Dina, I., Cosma, M., & Abed Mohammed, M. (2023). Intelligent grapevine disease detection using IoT sensor network. Bioengineering, 10(9), 1021. https://doi.org/10.3390/bioengineering10091021
Isinkaye, F. O., Olusanya, M. O., & Singh, P. K. (2024). Deep learning and content-based filtering techniques for improving plant disease identification and treatment recommendations: A comprehensive review. Heliyon, 10(9), e29583. https://doi.org/10.1016/j.heliyon.2024.e29583
Janarthan, S., Thuseethan, S., Rajasegarar, S., & Yearwood, J. (2022). P2OP—Plant Pathology on Palms: A deep learning-based mobile solution for in-field plant disease detection. Computers and Electronics in Agriculture, 202, 107371. https://doi.org/10.1016/j.compag.2022.107371
Jelali, M. (2024). Deep learning networks-based tomato disease and pest detection: A first review of research studies using real field datasets. Frontiers in Plant Science, 15, 1493322. https://doi.org/10.3389/fpls.2024.1493322
Khan, A. T., Jensen, S. M., Khan, A. R., & Li, S. (2023). Plant disease detection model for edge computing devices. Frontiers in Plant Science, 14, 1308528. https://doi.org/10.3389/fpls.2023.1308528
Liu, J., & Wang, X. (2021). Plant diseases and pests detection based on deep learning: A review. Plant Methods, 17, 22. https://doi.org/10.1186/s13007-021-00722-9
Mallick, M. T., Banerjee, S., Thakur, N., Saha, H. N., & Chakrabarti, A. (2025). Evaluation of state-of-the-art deep learning techniques for plant disease and pest detection. Computers, Materials & Continua, 85(1), 121–180. https://doi.org/10.32604/cmc.2025.065250
Nyawose, T., Maswanganyi, R. C., & Khumalo, P. (2025). A review on the detection of plant disease using machine learning and deep learning approaches. Journal of Imaging, 11(10), 326. https://doi.org/10.3390/jimaging11100326
Pacal, I., Kunduracioglu, I., Alma, M. H., Deveci, M., Kadry, S., Nedoma, J., Slany, V., & Martinek, R. (2024). A systematic review of deep learning techniques for plant diseases. Artificial Intelligence Review, 57, 304. https://doi.org/10.1007/s10462-024-10944-7
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., & Koffel, J. B. (2021). PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic Reviews, 10, 39. https://doi.org/10.1186/s13643-020-01542-z
Shoaib, M., Sadeghi-Niaraki, A., Ali, F., Hussain, I., & Khalid, S. (2025). Leveraging deep learning for plant disease and pest detection: A comprehensive review and future directions. Frontiers in Plant Science, 16, 1538163. https://doi.org/10.3389/fpls.2025.1538163
Terentev, A., Dolzhenko, V., Fedotov, A., & Eremenko, D. (2022). Current state of hyperspectral remote sensing for early plant disease detection: A review. Sensors, 22(3), 757. https://doi.org/10.3390/s22030757
Tomaszewski, M., Nalepa, J., Moliszewska, E., Ruszczak, B., Smykała, K., et al. (2023). Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning. Scientific Reports, 13, 7671. https://doi.org/10.1038/s41598-023-34079-x
Verma, S., Chug, A., & Singh, A. P. (2023). Plant disease detection and classification: A systematic literature review. Sensors, 23(10), 4769. https://doi.org/10.3390/s23104769
Wang, J., Jia, J., Zhang, Y., Wang, H., & Zhu, S. (2024). RAAWC-UNet: An apple leaf and disease segmentation method based on residual attention and atrous spatial pyramid pooling improved UNet with weight compression loss. Frontiers in Plant Science, 15, 1305358. https://doi.org/10.3389/fpls.2024.1305358
Wang, X., & Liu, J. (2024). Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment. Scientific Reports, 14, 4261. https://doi.org/10.1038/s41598-024-54540-9
Zhang, S., & Zhang, C. (2023). Modified U-Net for plant diseased leaf image segmentation. Computers and Electronics in Agriculture, 204, 107511. https://doi.org/10.1016/j.compag.2022.107511
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All data generated or analyzed during this study are included in this published article. Additional datasets are available from the corresponding author upon reasonable request.
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