USING CONVOLUTIONAL NEURAL NETWORK FOR GRAPE PLANT DISEASE CLASSIFICATION

Yıl 2023, , 809 - 820, 27.12.2023

Cemal İhsan Sofuoğlu , Derya Bırant

https://doi.org/10.17482/uumfd.1277418

Öz

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.

Anahtar Kelimeler

Deep Learning, Convolutional Neural Network, Image Classification, Agriculture, Grape, Plant Disease

Evrişimli Sinir Ağının Üzüm Bitkisi Hastalık Sınıflandırması için Kullanılması

Öz

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.

Anahtar Kelimeler

Derin Öğrenme, Evrişimli Sinir Ağı, Görüntü Sınıflandırma, Tarım, Üzüm, Bitki Hastalığı

Kaynakça

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 13. Kaur, S. and Sharma, S. (2022) Plant disease detection using deep transfer learning, Journal of Positive School Psychology, 6(5), 193-201.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.
• 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.
• 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