Identifikasi Penyakit Tanaman Padi Disebabkan Oleh Bakteri dan Jamur Menggunakan Metode Convolutional Neural Network (CNN)
Abstract
Beras merupakan salah satu makanan utama yang banyak dikonsumsi di Indonesia, akan tetapi bidang produksi beras menghadapi tantangan yaitu serangan penyakit pada tanaman padi. Penyakit seperti Bacterial Leaf Blight dan Brown Spot dapat menyebabkan kehilangan hasil panen yang signifikan. Oleh karena itu, diperlukan sistem untuk mendeteksi dan mengklasifikasikan penyakit pada tanaman padi secara otomatis. Penelitian ini merupakan penelitian kuantitatif yang mengembangkan model klasifikasi penyakit padi menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur MobileNetV3 untuk perangkat mobile. Metode penelitian yang digunakan meliputi pengumpulan data dari dataset publik Kaggle, tahap pra-pemrosesan citra dengan teknik foreground extraction, perancangan dan pelatihan model CNN berbasis MobileNetV3, serta evaluasi model menggunakan k-fold cross-validation (k = 10) untuk memastikan keandalan hasil. Eksperimen dilakukan dengan menerapkan teknik k-fold tersebut, dan hasil terbaik diperoleh pada nilai k = 1 dengan akurasi mencapai 98% dan nilai loss sebesar 0,02, menunjukkan bahwa model yang dikembangkan memiliki performa yang sangat baik dalam mengklasifikasi penyakit pada tanaman padi.
Kata Kunci: Bakteri; Convolutional Neural Network (CNN); Identifikasi; Jamur; Penyakit Tanaman Padi.Full Text:
PDFReferences
Bhattarai, Y., Prasun, K., Sharma, G., & Rai, P. 2024. Enhancing Rice Disease Binary Classification. an Analysis of Deep Neural Network Models. International Journal of Research Publications, 147(1): 111-115.
Castillo-Martínez, M., Gallegos-Funes, F. J., Carvajal-Gámez, B. E., Urriolagoitia-Sosa, G., & Rosales-Silva, A. J. 2020. Color Index Based Thresholding Method For Background and Foreground Segmentation of Plant Images. Computers and Electronics in Agriculture. 11(4): 20-21.
Chen, J., Zhang, D., Zeb, A., & Nanehkaran, Y. A. 2021. Identification of Rice Plant Diseases Using Lightweight Attention Networks. Expert Systems with Applications. 12(2): 10-11.
Chouhan, V., Kumar, A., & Singh, R. 2021. Evaluation of Fungicides for Management of Brown Leaf Spot Disease of Rice. Middle East Journal of Applied Science & Technology. 4(4): 75–79.
Deng, R., Tao, M., Xing, H., Yang, X., Liu, C., Liao, K., & Qi, L. 2021. Automatic Diagnosis of Rice Diseases Using Deep Learning. Frontiers in Plant Science. 12(5): 5-8.
Gavrilov, A. D., Jordache, A., Vasdani, M., & Deng, J. 2019. Preventing Model Overfitting and Underfitting in Convolutional Neural Networks. International Journal of Software Science and Computational Intelligence. 10(4): 19–28.
Ghorbani, F., Shabanpour, J., Beyraghi, S., Soleimani, H., Oraizi, H., & Soleimani, M. 2021. A deep learning approach for inverse design of the metasurface for dual-polarized waves. Applied Physics A: Materials Science and Processing. 127(11): 50 – 58.
Howard, A., Wang, W., Chu, G., Chen, L., Chen, B., & Tan, M. 2019. Searching for MobileNetV3 Accuracy vs MADDs vs model size. International Conference on Computer Vision. 110(11): 1314–1324.
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. 2020. Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(8): 2011–2023.
Indolia, S., Goswami, A. K., Mishra, S. P., & Asopa, P. 2018. Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach. Procedia Computer Science. 132(4): 679–688.
Latif, G., Abdelhamid, S. E., Mallouhy, R. E., Alghazo, J., & Kazimi, Z. A. 2022. Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model. Plants. 11(17): 80-88.
Liang, W. jie, Zhang, H., Zhang, G. feng, & Cao, H. xin. 2019. Rice Blast Disease Recognition Using a Deep Convolutional Neural Network. Scientific Reports. 9(1): 1–11.
Liu, Z., Shen, Z., Li, S., Helwegen, K., Huang, D., & Cheng, K.-T. 2021. How Do Adam and Training Strategies Help BNNs Optimization. International Conference on Machine Learning. 18-24 July 2021, Vienna, Austria. Hal 90-95.
Luu, N. D., Thi, L., Diem, T., Thi, H., & Anh, P. 2024. Application Of Deep Learning For Rice Leaf Disease Detection In The Mekong Delta. CTU Journal of Innovation and Sustainable Development. 16: 1–7.
Naqvi, S. A. H., Umar, U. ud D., Hasnain, A., Rehman, A. ur, & Perveen, R. 2019. Effect of Botanical Extracts: A Potential Biocontrol Agent for Xanthomonas Oryzae Pv. Oryzae, Causing Bacterial Leaf Blight Disease of Rice. Pakistan Journal of Agricultural Research. 32(1): 59–72.
Nawrocka, A., Nawrocki, M., & Kot, A. 2023. Research study of image classification algorithms based on Convolutional Neural Networks. Proceedings of the 2023 24th International Carpathian Control Conference, ICCC. 2023: 299–302.
Nguyen, H. L., Tran, T. H., Thi, H. H. Le, & Nguyen, D. C. 2023. From a Proposed CNN Model to a Real-World Application in Rice Disease Classification. Proceedings of the Seventh International Conference on Research in Intelligent and Computing in Engineering. 33: 177–182.
Nti, I. K., Nyarko-Boateng, O., & Aning, J. 2021. Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation. International Journal of Information Technology and Computer Science. 13(6): 61–71.
Persaud, R., Payman, G., Khan, A., Singh, N., Persaud, D. A., & Subramanian, G. 2022. Effectiveness of new fungicides and neem plant aqueous extract against brown spot disease of rice. International Journal of Agricultural Policy and Research. 11(1): 1–15.
Quach, L. Da, Quoc, K. N., Quynh, A. N., & Ngoc, H. T. 2022. Evaluation of the Efficiency of the Optimization Algorithms for Transfer Learning on the Rice Leaf Disease Dataset. International Journal of Advanced Computer Science and Applications. 13(10): 83–91.
Rahman, C. R., Arko, P. S., Ali, M. E., Iqbal Khan, M. A., Apon, S. H., Nowrin, F., & Wasif, A. 2020. Identification and recognition of rice diseases and pests using convolutional neural networks. Biosystems Engineering. 194: 112–120.
Roseno, M. T., Oktarina, S., Nearti, Y., & Jayanti, N. 2024. Comparing CNN Models for Rice Disease Detection . Journal of Information Systems and Informatics. 6(3): 2099–2109.
Saxe, A. M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B. D., & Cox, D. D. 2018. on the Information Bottleneck Theory of Deep Learning. Journal of Statistical Mechanics: Theory and Experiment (JSTAT). 12(5): 56-80.
Sharma, N., Jain, V., & Mishra, A. 2018. An Analysis of Convolutional Neural Networks for Image Classification. Procedia Computer Science. 132(4): 377–384.
Sharma, R., Singh, A., Kavita, Jhanjhi, N. Z., Masud, M., Jaha, E. S., & Verma, S. 2022. Plant Disease Diagnosis and Image Classification Using Deep Learning. Computers, Materials and Continua. 71(2): 2125–2140.
Shrivastava, V. K., Pradhan, M. K., Minz, S., & Thakur, M. P. 2019. Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 42(3): 631–635.
Sreenivas, A. 2020. Indian Sign Language Communicator Using Convolutional Neural Network. International Journal of Advanced Science and Technology. 29(3): 11015–11031.
Sudhasha, S., Balabaskar, P., Kumar, K. S., & Sivakumar, T. 2020. Prevalence of Rice Brown Spot Disease Incidence in Northern Districts of Tamilnadu. India and Observations on Morpho Pathogenic Variability Among Isolates of Bipolaris Oryzae. 20: 3819–3825.
Sutardi, Apriyana, Y., Rejekiningrum, P., Alifia, A. D., Ramadhani, F., Darwis, V., Setyowati, N., Setyono, D. E. D., Gunawan, Malik, A., Abdullah, S., Muslimin, Wibawa, W., Triastono, J., Yusuf, Arianti, F. D., & Fadwiwati, A. Y. 2023. The Transformation of Rice Crop Technology in Indonesia: Innovation and Sustainable Food Security. 13(1): 1–14.
Taye, M. M. 2023. Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. Computation, 11(3): 180-190.
Walascha, A., Febriana, A., Saputri, D., Sri Nur Haryanti, D., Tsania, R., Sanjaya, Y., & Priyanti. 2021. Review Artikel: Inventarisasi Jenis Penyakit yang Menyerang Daun Tanaman Padi (Oryza sativa L.). Prosiding Seminar Nasional Biologi. 1(2): 471–478.
Wang, Y., Wang, H., & Peng, Z. 2021. Rice diseases detection and classification using attention based neural network and bayesian optimization. Expert Systems with Applications. 178(3): 1147.
Wayahdi, M. R., Syahputra, D., Hafiz, S., & Ginting, N. 2020. Evaluation of the K-Nearest Neighbor Model With K-Fold Cross Validation on Image Classification. Infokum. 9(1): 1–6.
Yani, M., Irawan, B., & Setiningsih, C. 2019. Application of Transfer Learning Using Convolutional Neural Network Method for Early Detection of Terry’s Nail. Journal of Physics: Conference Series. 1201(1): 111-119.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.