Diagnosis of Three Types of Grape Leaf Diseases Based on Image Processing using Butterfly Optimization Algorithm and Support Vector Machine

Najafabadiha, Mohsen; Mohammad Zamani, Davood; Gholami Parashkoohi, Mohammad

doi:10.22092/amsr.2024.365272.1482

Document Type : Original Article

Authors

¹ PhD student, Department of Biosystems Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran

² Associate Professor, Department of Biosystems Engineering,Takestan Branch, Islamic Azad University, Takestan, Iran

³ Associate Professor, Department of Mechanical Engineering, Shahr-e-Qods Baranch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.22092/amsr.2024.365272.1482

Abstract

Today, artificial intelligence techniques and machine learning technologies have made it easy to identify and classify plant diseases. In this research, in order to diagnose and classify some diseases of grapevine leaves with the names of black measles, black rot, and leaf blight, after removing the background from the image of the leaves and extracting the characteristics of texture and color and from the images, a combination of support vector machine classification and butterfly optimization algorithm was used to select the most important features in the diagnosis of grape plant leaf disease. The results of classification accuracy for black measles, black rot, leaf blight, and healthy leaf diseases are 100, 100, 100 and 95% respectively, and the classification accuracy for the diagnosis of all diseased and healthy groups is 98.75%. It was achieved. The classification results showed that image processing and machine learning are excellent in diagnosing and classifying some plant diseases of grape leaves. In this research, 15 features of texture, color and shape have been introduced to the researchers of plant pathology and data science with the help of the butterfly optimization feature selection algorithm.

Keywords

References

Abdulridha, J., Ampatzidis, Y., Kakarla, S. C., & Roberts, P. (2020). Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precision Agriculture, 21(7), 955-978. https://doi.org/ 10.1007/s11119-019-09703-4.

Arun Pandian, J., & Gopal, G. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers and Electrical Engineering, 76, 323-338. https://doi.org/10.1016/j.compeleceng.2019.04.011.

Asefpour Vakilian, K., & Massah, J. (2017). A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops. Computers and Electronics in Agriculture, 139(9), 153-163. https://doi.org/10.1016/j.compag.2017.05.012.

Barbedo, J. G. A. (2018). Factors influencing the use of deep learning for plant disease recognition. Biosystems Engineering, 172(6), 84-91. https://doi.org/10.1016/j.biosystemseng.2018.05.013.

Chen, J., Zheng, H., Lin, X., Wu, Y., & Su, M. (2018). A novel image segmentation method based on fast density clustering algorithm. In Engineering Applications of Artificial Intelligence, 73(10), 92-110. https://doi.org/10.1016/j.engappai.2018.04.023.

Cristin, R., Kumar, B. S., Priya, C., & Karthick, K. (2020). Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection. Artificial intelligence review, 53(8), 4993-5018. https://link.springer.com/article/10.1007/s10462-020-09813-w.

Irmak, B., Karakoyun, M., & Gülcü, Ş. (2022). An improved butterfly optimization algorithm for training the feed-forward artificial neural networks. Soft Computing, 27(7), 3887-3905. https://link.springer.com/article/10.1007/s00500-022-07592-w.

Jaisakthi, S. M., Mirunalini P., & Thenmozhi, D. (2019). Grape leaf disease identification using machine learning techniques. In Proceedings of the 2019 International Conference on Computational Intelligence in Data Science (ICCIDS). Feb. 23-25, Chennai, India.

Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. (2022). A feature selection method using slime mould optimization algorithm in order to diagnose plant leaf diseases. In Proceedings of the 8^th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). Dec. 28-29. Kashan, Iran. (in Persian)

Javidan, S. M., Banakar, A., Vakilian, K. A., & Ampatzidis, Y. (2023). Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. Smart Agricultural Technology, 3(3), 100081-100110. https://doi.org/10.1016/j.atech.2022.100081.

Javidan, S. M., Banakar, A., Vakilian, K. A., Ampatzidis, Y., & Rahnama, K. (2024). Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning. Multimedia Tools and Applications, 83(5), 20-43. https://doi.org/10.1007/s11042-024-18214-y.

Kumar, S., Sharma, B., Sharma, V. K., Sharma, H., & Bansal, J. C. (2020). Plant leaf disease identification using exponential spider monkey optimization. Sustainable Computing: Informatics and Systems, 28(3), 100283-100294. https://doi.org/10.1016/j.suscom.2018.10.004.

Liu B., Tan C., Li S., He J., & Wang H. (2020). A data augmentation method based on generative adversarial networks for grape leaf disease identification. IEEE, https://doi.org/10.1109/ACCESS.2020.2998839.

Makhadmeh, S. N., Al-Betar, M. A., Abasi, A. K., Awadallah, M. A., Doush, I. A., Alyasseri, Z. A. A., & Alomari, O. A. (2022). Recent advances in butterfly optimization algorithm, its versions and applications. Archives of Computational Methods in Engineering, 30(2), 1399-1420. https://link.springer.com/article/10.1007/s11831-022-09843-3.

Mirjalili, S., & Lewis, A. (2013). S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization. Swarm and Evolutionary Computation, 9(3), 1-14. https://doi.org/10.1016/j.swevo.2012.09.002.

Mohammadzamani, D., Sajadian, S., & Javidan, S. M. (2020). Detection of Callosobruchus maculatus F. with image processing and artificial neural network. Applied Entomology and Phytopathology, 88(1), 103-112. https://doi.org/10.22092/jaep.2020.341684.1324. (in Persian)

Mohammadzamani, D., Javidan, S. M., Zand, M., & Rasouli, M. (2023). Detection of Cucumber Fruit on Plant Image Using Artificial Neural Network. Journal of Agricultural Machinery, 13(1). 18-29. https://doi.org/10.22067/jam.2022.73827.1077.

Padol, P. B., & Yadav, A. A. (2016). SVM classifier based grape leaf disease detection. In Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), April 12-15. Lisbon, Portugal.

Roostaei, P., Rasouli, M., & Babaei, A. (2015). Study of compatibility and the effect of pollen of some grape cultivars on fruit set and quantitative and qualitative characters of fruit, cv. Rish Baba Sefid. Plant Production Technology, 7(1), 193-210. (in Persian)

Sadeghian, Z., Akbari, E., & Nematzadeh, H. (2021). A hybrid feature selection method based on information theory and binary butterfly optimization algorithm. Engineering Applications of Artificial Intelligence, 97(5), 104079-104084. https://doi.org/10.1016/j.engappai.2020.104079.

Thaiyalnayaki, K., & Joseph, C. (2021). Classification of plant disease using SVM and deep learning. Materials Today: Proceedings, 47(6), 468-470. https://doi.org/10.1016/j.matpr.2021.05.029.

Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of Biomedical Informatics, 85(3), 189–203. https://arxiv.org/abs/1711.08421.

Xie, X., Ma, Y., Liu, B., He, J., Li, S., & Wang, H. (2020). A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Frontiers in Plant Science, 11(2), 751-766. https://doi.org/10.3389/fpls.2020.00751.

Agricultural Mechanization and Systems Research

Diagnosis of Three Types of Grape Leaf Diseases Based on Image Processing using Butterfly Optimization Algorithm and Support Vector Machine

References

References

Volume 24, Issue 87 - Serial Number 87
June 2024
Pages 39-54

Diagnosis of Three Types of Grape Leaf Diseases Based on Image Processing using Butterfly Optimization Algorithm and Support Vector Machine

References

References

Volume 24, Issue 87 - Serial Number 87June 2024Pages 39-54

Volume 24, Issue 87 - Serial Number 87
June 2024
Pages 39-54