DETEKSI AWAL PENYAKIT TANAMAN BERBASIS CITRA DAUN: STUDI PERBANDINGAN ALGORITME SVM DAN CNN
Keywords:
Deteksi penyakit tanaman, analisis citra daun, Deep Learning, Machine Learning, SVM, CNNAbstract
Deteksi dini penyakit tanaman sangat penting untuk menjaga hasil panen dan ketahanan pangan.Namun, metode deteksi manual yang masih banyak digunakan saat ini sering memakan waktu dan membutuhkan tenaga ahli, sehingga kurang efisien jika diterapkan di lahan pertanian yang luas. Penelitian ini menghadirkan solusi berbasis kecerdasan buatan dengan menganalisis gambar daun untuk mendeteksi penyakit sejak awal. Dua pendekatan algoritma dibandingkan dalam studi ini: Support Vector Machine (SVM) dan Convolutional Neural Network (CNN). Data yang digunakan mencakup 5.000 gambar daun dari tanaman tomat, kentang, dan jagung, baik yang sehat maupun yang terinfeksi penyakit seperti bercak bakteri, bercak daun, dan virus mosaik. Untuk meningkatkan akurasi, gambar terlebih dahulu iproses melalui tahap resize, normalisasi, dan augmentasi. Pada metode SVM, fitur diekstraksi menggunakan Histogram of Oriented Gradients (HOG) dan Local Binary Patterns (LBP), sedangkan CNN secara otomatis mempelajari fitur penting melalui lapisan-lapisan dalamnya.Hasil pengujian menunjukkan bahwa CNN memiliki performa lebih baik, dengan akurasi 94,2%, presisi 93,8%, dan recall 94,1%. Sebaliknya, SVM menghasilkan akurasi 87,5%, presisi 86,9%, dan recall 87,2%. CNN terbukti lebih andal dalam mengenali pola gambar yang kompleks serta lebih tahan terhadap variasi pencahayaan. Temuan ini menunjukkan potensi besar penerapanteknologi cerdas untuk membantu petani mendeteksi penyakit lebih awal, sehingga kerugian dapat ditekan dan praktik pertanian menjadi lebih berkelanjutan.
References
Pusat data dan sistem informasi pertanian-kementerian pertanian. (2024). Statistik Makro Sektor Pertanian Tahun 2024. https://satudata.pertanian.go.id/details/publikasi/661.
Kementerian Pertanian. (2024). Laporan kinerja kementerian pertanian 2023. https://ppid.pertanian.go.id/doc/1/LAKIN%20Kementan%202023.pdf
Food and Agriculture Organization of the United Nations. (2023). The State of Food Security and Nutrition in the World 2023. https://openknowledge.fao.org/items/445c9d27-b396-4126-96c950b335364d01.
Jackulin, C., & S. J. M. S. Murugavalli. (2022). A comprehensive review on detection of plant disease using machine learning and deep learning approaches." Measurement: Sensors, 24, 100441.https://doi.org/10.1016/j.measen.2022.100441.
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318. https://doi.org/10.1016/j.compag.2018.01.009.
Budiman, F., & Sugiarto, E. (2021). Non-linear multiclass SVM classification optimization using large datasets of geometric motif image. International Journal of Advanced Computer Science and Applications, 12(9). https://doi.org/10.14569/IJACSA.2021.0120932.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 7553, 436-444. [8] Hughes, D., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv:1511.08060. https://doi.org/10.48550/arXiv.1511.08060.
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016.
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419, 2016. https://doi.org/10.3389/fpls.2016.01419.
Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017). Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31(4), 299-315.https://doi.org/10.1080/08839514.2017.1315516.
Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96-107. https://doi.org/10.1016/j.biosystemseng.2019.02.002.
Zhang, S., Huang, W., & Zhang, C. (2015). Plant disease recognition based on plant leaf image. Journal of Animal and Plant Sciences, 25(1), 42-45.
Liu, B., Zhang, Y., He, D., & Li, Y. (2018). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 11. https://doi.org/10.3390/sym10010011.
Tm, P. Pranathi, A., SaiAshritha, K., Chittaragi, N. B., & Koolagudi, S. G. (2018). Tomato leaf disease detection using convolutional neural networks. 2018 Eleventh International Conference on Contemporary Computing (IC3), 1-5. https://doi.org/10.1109/IC3.2018.8530532.
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272279. https://doi.org/10.1016/j.compag.2018.03.032.
Singh, U. P., Chouhan, S. S., & S. Jain. (2019). Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access, 7, 43721-43729. https://doi.org/10.1109/ACCESS.2019.2907383.
Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. (2022). A robust deep-learning-based detector for realtime tomato plant diseases and pests recognition.
Sensors, 17(9). https://doi.org/10.3390/s17092022.
Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 1-11. https://doi.org/10.1155/2016/3289801.
