| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 2,944 |
| تعداد مقالات | 33,229 |
| تعداد مشاهده مقاله | 43,866,139 |
| تعداد دریافت فایل اصل مقاله | 20,372,402 |
تشخیص سه نوع بیماری برگ انگور بر پایۀ پردازش تصویر با استفاده از الگوریتم بهینهساز پروانه و ماشین بردار پشتیبان | ||
| تحقیقات سامانه‌ها و مکانیزاسیون کشاورزی | ||
| دوره 24، شماره 87، تیر 1402، صفحه 39-54 اصل مقاله (1.68 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/amsr.2024.365272.1482 | ||
| نویسندگان | ||
| محسن نجف آبادی ها1؛ داود محمدزمانی* 2؛ محمد غلامی پرشکوهی3 | ||
| 1دانشجوی دکتری، گروه مهندسی بیوسیستم، واحد تاکستان، دانشگاه آزاد اسلامی، تاکستان، ایران | ||
| 2دانشیار گروه مهندسی بیوسیستم، واحد تاکستان،دانشگاه آزاد اسلامی، تاکستان، ایران. | ||
| 3دانشیار گروه مهندسی مکانیک، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران | ||
| چکیده | ||
| امروزه تکنیک‌های هوش مصنوعی و فناوری‌های یادگیری ماشین توانسته است شناسایی و طبقهبندی بیماریهای گیاهی را آسان کند. در این پژوهش، به منظور تشخیص و طبقهبندی برخی بیماریهای برگ گیاه انگور با نامهای پوسیدگی سیاه، اسکای (زوال) و لکه برگی، پس از حذف پس زمینه از تصویر برگها و استخراج ویژگیهای بافت و رنگ و شکل از تصاویر، از ترکیبی از طبقهبند ماشین بردار پشتیبان و الگوریتم بهینهساز پروانه برای انتخاب مهمترین ویژگیها در تشخیص بیماری برگ گیاه انگور استفاده شد. نتایج صحت طبقهبندی برای بیماریهای پوسیدگی سیاه، اسکای (زوال) و لکه برگی و برگ سالم به ترتیب 100، 100، 100 و 95 درصد و دقت طبقهبندی برای تشخیص کل گروههای بیمار و سالم 75/98 درصد به دست آمد. نتایج طبقهبندی نشان داد که توان پردازش تصویر و یادگیری ماشین در تشخیص و طبقهبندی برخی بیماریهای گیاهی برگ انگور عالی است. در این پژوهش همچنین 15 ویژگی بافت، رنگ و شکل به کمک الگوریتم انتخاب ویژگی بهینهساز پروانه به پژوهشگران بیماری شناسی گیاهی و علوم داده معرفی شده است. | ||
| کلیدواژه‌ها | ||
| استخراج ویژگی؛ انتخاب ویژگی؛ بیماری های انگور؛ تشخیص و طبقهبندی؛ هوش مصنوعی | ||
| عنوان مقاله [English] | ||
| Diagnosis of Three Types of Grape Leaf Diseases Based on Image Processing using Butterfly Optimization Algorithm and Support Vector Machine | ||
| نویسندگان [English] | ||
| mohsen najafabadiha1؛ Davood Mohammad Zamani2؛ mohammad Gholami Parashkoohi3 | ||
| 1PhD student, Department of Biosystems Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran | ||
| 2Associate Professor, Department of Biosystems Engineering,Takestan Branch, Islamic Azad University, Takestan, Iran | ||
| 3Associate Professor, Department of Mechanical Engineering, Shahr-e-Qods Baranch, Islamic Azad University, Tehran, Iran. | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژه‌ها [English] | ||
| Artificial Intelligence, Diagnosis and Classification, Feature Extraction, Feature Selection, Grape Diseases | ||
| مراجع | ||
|
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 8th 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. | ||
|
آمار تعداد مشاهده مقاله: 314 تعداد دریافت فایل اصل مقاله: 493 |
||