CLASSIFICATION OF POTATO LEAF DISEASES USING CONVOLUTIONAL NEURAL NETWORK
Abstract
Potatoes are an agricultural product that has the fourth highest content of wheat flour after corn, wheat, and rice. Although potatoes play a critical role in agriculture, this crop is susceptible to various diseases and pests. There are several potato leaf diseases that are not yet known to farmers. Dry spot potato leaf disease (late blight) and late blight. If not treated, this disease on potato leaves will spread to the stem and reduce crop yields, causing crop failure. By using technology in the form of digital image processing, this problem can be overcome. This research proposes an appropriate method for detecting disease in potato leaves. Classification will be carried out in three classes, namel, Early Blight, Healthy and Late Blight using the Deep Learning method of Convolutional Neural Network (CNN). The data used comes from an online dataset via the kaggle.com page with the file name Potato Disease Leaf Dataset (PLD) totaling 3251 training datasets which are then divided into training, testing, and validation. The processes carried out are image pre-processing, image augmentation, then image processing using a Convolutional Neural Network (CNN). In the classification process using the CNN method with RMSprop optimizer, the accuracy was 97.53% with a loss value of 0.1096.
References
Ahmad, Nazaruddin, and Iskandar. 2020. “Metode Forward Chaining Untuk Deteksi Penyakit Pada Tanaman Kentang.” JINTECH: Journal Of Information Technology 1(2):7–20.
Amatullah, Luthfiyah, Ivtytah Ein, and Mayanda Mega Santoni. 2021. “Identifikasi Penyakit Daun Kentang Berdasarkan Fitur Tekstur Dan Warna Dengan Menggunakan Metode K-Nearest Neighbor.” Seminar Nasional Mahasiswa Ilmu Komputer Dan Aplikasinya (SENAMIKA) (April):783–91.
Asif, Md Khalid Rayhan, Md Asfaqur Rahman, and Most Hasna Hena. 2020. “CNN Based Disease Detection Approach on Potato Leaves.” Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020 428–32.
Darmanto, Heri. 2019. “Introduction To Fish Species Based on Otolith Conture Using the Convolutional Neural Network.” Joined Jurnal 2(1):41–59.
Erlangga, K. A. (2023). Analisis Daya Saing Ekspor Produk Kentang Indonesia Terhadap Pasar ASEAN: Bahasa Indonesia. Jurnal Multidisiplin Indonesia, 2(8), 1840-1855.
Fitriana, Anna, and Lukman Hakim. 2019. “Efektivitas Cendawan Endofit Asal Akar Tanaman Kentang Dalam Media Kompos Kulit Kopi Untuk Menekan Perkembangan Penyakit Haar Daun Kentang (Phytophtora Infestans) Endophytic Fungus Effectiveness Origin Root Crops Potatoes in Compost Media Skin Diseases Co.” Jurnal Agrista 23(1):9–10.
Fuadi, Anwar, and Aries Suharso. 2022. “Perbandingan Arsitektur Mobilenet Dan Nasnetmobile Untuk Klasifikasi Penyakit Pada Citra Daun Kentang.” JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika) 7(3):701–10.
Hidayat, T., Saputri, D. U. E., & Aziz, F. (2022). Meat Image Classification Using Deep Learning With Resnet152v2 Architecture. Jurnal Techno Nusa Mandiri, 19(2), 131-140.
Lesmana, Alang Mulya, Ronna Putri Fadhillah, and Chaerur Rozikin. 2022. “Identifikasi Penyakit Pada Citra Daun Kentang Menggunakan Convolutional Neural Network (CNN).” Jurnal Sains Dan Informatika 8(1):21–30.
Lubis, Baginda Oloan, Dony Oscar, Firstianty Wahyuhening Fibriany, Budi Santoso, Jefi, and Arief Rusman. 2023. “Classification of Tomato Leaf Disease and Combination Extraction Features Using K-NN Algorithm.” AIP Conference Proceedings 2714(November 2021).
Nauval, Kurniawan Irfan, and Sri Lestari. 2022. “Implementasi Deteksi Objek Penyakit Daun Kentang Dengan Metode Convolutional Neutral Network.” Jurnal Aplikasi Teknologi Informasi Dan Manajemen (JATIM) 3(2):136–49.
Omori, Yuki, and Yoshihiro Shima. 2020. “Image Augmentation for Eye Contact Detection Based on Combination of Pre-Trained Alex-Net CNN and SVM.” Journal of Computers 15(3):85–97.
Peryanto, Ari, Anton Yudhana, and Rusydi Umar. 2020. “Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network.” Format : Jurnal Ilmiah Teknik Informatika 8(2):138.
Rashid, Javed, Imran Khan, Ghulam Ali, Sultan H. Almotiri, Mohammed A. Alghamdi, and Khalid Masood. 2021. “Multi-Level Deep Learning Model for Potato Leaf Disease Recognition.” Electronics (Switzerland) 10(17):1–27.
Rozaqi, Abdul Jalil, Andi Sunyoto, and M. rudyanto Arief. 2021. “Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra Dengan Metode Convolutional Neural Network.” Creative Information Technology Journal 8(1):22.
Sholihati, Rizqi Amaliatus, Indra Adji Sulistijono, Anhar Risnumawan, and Eny Kusumawati. 2020. “Potato Leaf Disease Classification Using Deep Learning Approach.” IES 2020 - International Electronics Symposium: The Role of Autonomous and Intelligent Systems for Human Life and Comfort 392–97.
Teresia Ompusunggu, Putri. 2022. “Klasifikasi Penyakit Tanaman Pada Daun Kentang Dengan Metode Convolutional Neural Network Arsitektur Mobilenet.” Jurnal Syntax Fusion 2(09):740–51.
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