Identifikasi penyakit tanaman tomat melalui citra daun menggunakan DenseNet201
DOI:
https://doi.org/10.37859/coscitech.v6i2.9965
Abstract
This study focuses on implementing the DenseNet201 algorithm for disease classification in tomato plants using leaf images from PlantVillage dataset. The agricultural sector plays a central role in the Indonesian economy, with tomatoes being one of the important horticultural crops. However, tomato productivity is often hindered by various plant diseases. Accurate disease diagnosis is crucial for improving production stability. Image processing-based approaches, such as Convolutional Neural Network (CNN), have facilitated effective plant disease diagnosis. In this study, the PlantVillage dataset consisting of 18,835 tomato leaf images is utilized. The data is divided into training (10,000 images), validation (7,000 images), and test (500 images) sets. A classification model is constructed using the DenseNet201 architecture with some modifications. The results show that the DenseNet201 model achieves an accuracy of 95.20% on the testing data, with an overall F1-score of 0.95. Compared to previous studies using VGG16 (77.2%), InceptionV3 (63.4%), and MobileNet (63.75%), the DenseNet201 model demonstrates a significant performance improvement. This study concludes that DenseNet201 is highly effective in classifying tomato plant diseases and has the potential to be implemented in widespread plant disease diagnosis applications.Downloads
References
V. K. Vishnoi, K. Kumar, and B. Kumar, Plant disease detection using computational intelligence and image processing, vol. 128, no. 1. Springer Berlin Heidelberg, 2021. doi: 10.1007/s41348-020-00368-0.
M. Faried, G. El Mirzaq, and R. Helilintar, “Sistem Pakar Diagnosa Penyakit Tanaman Tomat Menggunakan Metode Certainty Factor,” Pros. SEMNAS INOTEK (Seminar Nas. Inov. Teknol., vol. 5, no. 2, pp. 230–235, Aug. 2021, doi: 10.29407/INOTEK.V5I2.1134.
T. Ha, “Produktivitas Tomat Menurut Provinsi , 2012-2016,” vol. 2016, p. 2016, 2016.
N. I. Yahya, S. Lestanti, and S. N. Budiman, “SISTEM PAKAR DIAGNOSIS HAMA DAN PENYAKIT TANAMAN AGLAONEMA,” vol. 6, no. 2, pp. 734–741, 2022.
D. Ali and S. Frimpong, “Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector,” Artif. Intell. Rev., vol. 53, no. 8, pp. 6025–6042, 2020, doi: 10.1007/s10462-020-09841-6.
G. Abdurrahman, “Klasifikasi Penyakit Diabetes Melitus Menggunakan Adaboost Classifier,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 7, no. 1, pp. 59–66, 2022, doi: 10.32528/justindo.v7i1.4949.
P. Pradhan, B. Kumar, and S. Mohan, “Comparison of various deep convolutional neural network models to discriminate apple leaf diseases using transfer learning,” J. Plant Dis. Prot., vol. 129, no. 6, pp. 1461–1473, 2022, doi: 10.1007/s41348-022-00660-1.
M. Agarwal, A. Singh, S. Arjaria, A. Sinha, and S. Gupta, “ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 293–301, 2020, doi: 10.1016/j.procs.2020.03.225.
S. D. Meena, M. Susank, T. Guttula, S. H. Chandana, and J. Sheela, “Crop Yield Improvement with Weeds, Pest and Disease Detection,” Procedia Comput. Sci., vol. 218, no. 2022, pp. 2369–2382, 2022, doi: 10.1016/j.procs.2023.01.212.
P. Sitompul, H. Okprana, A. Prasetio, and G. Artikel, “Identifikasi Penyakit Tanaman Padi Melalui Citra Daun Menggunakan DenseNet 201 Identification of Rice Plant Diseases Through Leaf Image Using DenseNet 201 Article Info ABSTRAK,” JOMLAI J. Mach. Learn. Artif. Intell., vol. 1, no. 2, pp. 143–150, 2022, doi: 10.55123/jomlai.v1i2.889.
E. Retnoningsih and R. Pramudita, “Mengenal Machine Learning Dengan Teknik Supervised dan Unsupervised Learning Menggunakan Python,” vol. 7, no. 2, pp. 156–165, 2020.
A. Fatih, M. Najamuddin, and D. Miharja, “Implementasi Sistem Pengenalan Candi Kecil Di Yogyakarta Menggunakan Machine Learning Berbasis Cloud.,” Pros. Sains dan Teknol., vol. 1, no. 1, pp. 96–102, Aug. 2022, Accessed: Dec. 21, 2023. [Online]. Available: https://www.jurnal.pelitabangsa.ac.id/index.php/SAINTEK/article/view/1155
D. P. Utomo and M. Mesran, “Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung,” J. Media Inform. Budidarma, vol. 4, no. 2, p. 437, 2020, doi: 10.30865/mib.v4i2.2080.
R. S. Tantika and A. Kudus, “Penggunaan Metode Support Vector Machine Klasifikasi Multiclass pada Data Pasien Penyakit Tiroid,” Bandung Conf. Ser. Stat., vol. 2, no. 2, pp. 159–166, 2022, doi: 10.29313/bcss.v2i2.3590.
J. Pardede and D. A. L. Putra, “Implementasi DenseNet Untuk Mengidentifikasi Kanker Kulit Melanoma,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 3, pp. 425–433, 2020, doi: 10.28932/jutisi.v6i3.2814.
I. N. Husna et al., “Rancang Bangun Sistem Deteksi Dan Perhitungan Jumlah Orang Menggunakan Metode Convolutional Neural Network ( CNN ),” pp. 3–8.
Luqman Hakim, Z. Sari, and Handhajani, “Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network,” vol. 1, no. 10, pp. 379–385, 2021.
P. A. Nugroho, I. Fenriana, R. Arijanto, and M. Kom, “IMPLEMENTASI DEEP LEARNING MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK ( CNN ) PADA EKSPRESI MANUSIA,” vol. 1, 2020.
N. Khunafa Qudsi, R. A. Asmara, and A. R. Syulistyo, “Identifikasi Citra Tulisan Tangan Digital Menggunakan Convolutional Neural Network (CNN),” Semin. Inform. Apl., pp. 48–53, 2019.
A. Faizin, A. Tri Arsanto, Moch. Lutfi, and A. Rochim Musa, “Deep Pre-Trained Model Menggunakan Arsitektur Densenet Untuk Identifikasi Penyakit Daun Padi,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 615–621, 2022, doi: 10.36040/jati.v6i2.5475.
A. W. Salehi et al., “A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope,” Sustain., vol. 15, no. 7, 2023, doi: 10.3390/su15075930.
D. Sumarlie et al., “Pengenalan Kue Tradisional Indonesia,” vol. 2, pp. 164–171, 2022.
P. R. Sihombing and I. F. Yuliati, “Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir Rendah di Indonesia,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 2, pp. 417–426, 2021, doi: 10.30812/matrik.v20i2.1174.
