TY - JOUR T1 - USING CONVOLUTIONAL NEURAL NETWORK FOR GRAPE PLANT DISEASE CLASSIFICATION TT - Evrişimli Sinir Ağının Üzüm Bitkisi Hastalık Sınıflandırması için Kullanılması AU - Bırant, Derya AU - Sofuoğlu, Cemal İhsan PY - 2023 DA - December Y2 - 2023 DO - 10.17482/uumfd.1277418 JF - Uludağ Üniversitesi Mühendislik Fakültesi Dergisi JO - UUJFE PB - Bursa Uludağ University WT - DergiPark SN - 2148-4155 SP - 809 EP - 820 VL - 28 IS - 3 LA - en AB - Plant disease classification is the use of machine learning techniques for determining the type of disease from the input leaf images of the plants based on certain features. It is an important research area since early identification and treatment of plant disease is critical for saving crops, preventing agricultural disasters, and improving productivity in agriculture. This study proposes a new convolutional neural network model that accurately classifies the diseases on the plant leaves for the agriculture sectors. It especially works on the classification of plant diseases for grape leaves from images by designing a deeplearning architecture. A web application was also implemented to help the agricultural workers. The experiments carried out on real-world images showed that a significant improvement (8.7%) on average was achieved by the proposed model (98.53%) against the state-of-the-art models (89.84%) in terms of accuracy. KW - Deep Learning KW - Convolutional Neural Network KW - Image Classification KW - Agriculture KW - Grape KW - Plant Disease N2 - Bitki hastalık sınıflandırması, belirli özelliklere dayalı olarak bitkilerin yaprak görüntülerinden hastalık türünün belirlenmesi için makine öğrenmesi tekniklerinin kullanılmasıdır. Bitki hastalıklarının erken teşhisi ve tedavisi, ekinleri kurtarmak, tarımsal felaketleri önlemek ve tarımda verimliliği artırmak için kritik olduğundan, önemli bir araştırma alanıdır. Bu çalışma, tarım sektörü için bitki yapraklarındaki hastalıkları doğru bir şekilde sınıflandıran yeni bir evrişimli sinir ağı modeli önermektedir. Bir derin öğrenme mimarisi tasarlayarak özellikle üzüm yapraklarındaki hastalıkların sınıflandırılması üzerine çalışmaktadır. Tarım işçilerine yardımcı olması için bir web uygulaması da geliştirilmiştir. Gerçek dünya görüntüleri üzerinde yapılan denemeler, önerilen modelin (%98,53) doğruluk açısından son teknoloji modellere (%89,84) göre ortalamada önemli bir iyileştirme (%8,7) sağladığını göstermiştir. CR - 1. Adeel, A., Khan, M. A., Sharif, M., Azam, F., Shah, J. H., Umer, T. and Wan, S. (2019) Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion, Sustainable Computing: Informatics and Systems, 24, 1-11. doi: 10.1016/j.suscom.2019.08.002 CR - 2. Ahil, M. N., Vanitha, V. and Rajathi, N. (2021) Apple and grape leaf disease classification using MLP and CNN, International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), IEEE, India, 1-4. doi: 10.1109/icaeca52838.2021.9675567 CR - 3. Ahmed, I. and Yadav, P. K. (2023) Plant disease detection using machine learning approaches, Expert Systems, 40(5), 1-16. doi:10.1111/exsy.1313616 CR - 4. Chadha, S., Sharma, M. and Sayyed, A. (2021) Advances in sensing plant diseases by imaging and machine learning methods for precision crop protection, Microbial Management of Plant Stresses: Current Trends, Application and Challenges, 2021, 157–183. doi:10.1016/b978-0-323-85193-0.00012-7 CR - 5. Ghosh, A. and Roy, P. (2021) AI based automated model for plant disease detection, a deep learning approach, Communications in Computer and Information Science, 1406, 199-213. doi:10.1007/978-3-030-75529-4_16 CR - 6. He, Y., Gao, Q. and Ma, Z. (2022) A crop leaf disease image recognition method based on bilinear residual networks, Mathematical Problems in Engineering, 2022, 1-15. doi:10.1155/2022/2948506 CR - 7. Hughes, D.P. and Salathe, M. (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics, ArXiv, arXiv:1511.08060. doi:10.48550/arXiv.1511.08060 CR - 8. Jaisakthi, S., Mirunalini, P., Thenmozhi, D. and Vatsala. (2019) Grape leaf disease identification using machine learning techniques, International Conference on Computational Intelligence in Data Science (ICCIDS), IEEE, India, 1-6. doi:10.1109/iccids.2019.8862084 CR - 9. Jeyalakshmi, S. and Radha, R. (2020) An effective approach to feature extraction for classification of plant diseases using machine learning, Indian Journal of Science and Technology, 13(32), 3295-3314. doi:10.17485/ijst/v13i32.827 CR - 10. Joshi, K., Awale, R., Ahmad, S., Patil, S. and Pisal, V. (2022) Plant leaf disease detection using computer vision techniques and machine learning, ITM Web of Conferences, 44, 1-6. doi:10.1051/itmconf/20224403002 CR - 11. Kaur, P., Harnal, S., Tiwari, R., Upadhyay, S., Bhatia, S., Mashat, A. and Alabdali, A. M. (2022) Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction, Sensors, 22(2), 1-16. doi:10.3390/s22020575 CR - 12. Kaur, P., Pannu, H. S. and Malhi, A. K. (2019) Plant disease recognition using fractional-order Zernike moments and SVM classifier, Neural Computing and Applications, 31(12), 8749-8768. doi:10.1007/s00521-018-3939-6 CR - 13. Kaur, S. and Sharma, S. (2022) Plant disease detection using deep transfer learning, Journal of Positive School Psychology, 6(5), 193-201. CR - 14. Kurmi, Y. and Gangwar, S. (2021) A leaf image localization based algorithm for different crops disease classification, Information Processing in Agriculture, 9(3), 456-474. doi: 10.1016/j.inpa.2021.03.001 CR - 15. McBeath, J. H. and McBeath, J. (2010) Plant diseases, pests and food security, Environmental Change and Food Security in China, 35, 117-156. doi:10.1007/978-1-4020-9180-3_5 CR - 16. Monowar, M. M., Hamid, A., Kateb, F., Ohi, A. Q. and Mridha, M. F. (2022) Self-Supervised clustering for leaf disease identification, Agriculture, 12(6), 1-14. doi:10.3390/agriculture12060814 CR - 17. Nagi, R. and Tripathy, S. S. (2023) Plant disease identification using fuzzy feature extraction and PNN, Signal, Image and Video Processing, in press. doi:10.1007/s11760-023-02499-x CR - 18. Prajna U. (2021) Detection and classification of grain crops and legumes disease: a survey, Sparklinglight Transactions on Artificial Intelligence and Quantum Computing, 1(1), 41-55. doi:10.55011/staiqc.2021.1105 CR - 19. Shrestha, A. and Mahmood, A. (2019) Review of deep learning algorithms and architectures, IEEE Access, 7, 53040-53065. doi:10.1109/ACCESS.2019.2912200 CR - 20. Singh, V. and Misra, A. (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques, Information Processing in Agriculture, 4(1), 41–49. doi:10.1016/j.inpa.2016.10.005 CR - 21. Singh, V., Sharma, N. and Singh, S. (2020) A review of imaging techniques for plant disease detection, Artificial Intelligence in Agriculture, 4, 229-242. doi:10.1016/j.aiia.2020.10.002 CR - 22. Suo, J., Zhan, J., Zhou, G., Chen, A., Hu, Y., Huang, W., Cai, W., Hu, Y. and Li, L. (2022) CASM-AMFMNet: A network based on coordinate attention shuffle mechanism and asymmetric multi-scale fusion module for classification of grape leaf diseases, Frontiers in Plant Science, 13, 1-22. doi:10.3389/fpls.2022.846767 CR - 23. Swetha, V. and Jayaram, R. (2019) A novel method for plant leaf malady recognition using machine learning classifiers, 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, India, 1360-1365. doi:10.1109/iceca.2019.8822094 CR - 24. Tarannum, Z., Sankha, B. S., Nayak, N., Smitha, N. and Rao, A. (2017) Classification of diseases in grape plants using multiclass support vector machine, International Journal of Emerging Research in Management & Technology, 6(5), 250-254. CR - 25. Thenmozhi, S., Jothi Lakshmi, R., Kumudavalli, M. V., Irshadh, I. and Mohan, R. (2021) A novel plant leaf ailment recognition method using image processing algorithms, Journal of Scientific & Industrial Research, 80, 979-984. CR - 26. Wagle, S. A. and Harikrishnan, R. (2021) Comparison of Plant Leaf Classification Using Modified AlexNet and Support Vector Machine, Traitement Du Signal, 38(1), 79-87. doi:10.18280/ts.380108 UR - https://doi.org/10.17482/uumfd.1277418 L1 - https://dergipark.org.tr/en/download/article-file/3060845 ER -