نوع مقاله : مقاله پژوهشی
نویسندگان
گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران
چکیده
بیماریهای درخت بِه یکی از نگرانیهای عمده باغداران میباشد و شناسایی آنها در پایش درختان ضروری است چرا که زیآنهای اقتصادی قابلتوجهی وارد میکند. از این رو، تشخیص بهموقع و موثر بیماریهای برگی درختان بِه، نقش مهمی در جلوگیری از این ضرر اقتصادی دارد. بیشتر علائم بیماری این درخت در برگ ظاهر میشود و تشخیص آنها نیاز به متخصصان خبره داشته و از طرفی زمانبر بوده و هزینه آزمایشگاهی بالایی دارد. اصلیترین بیماریهای این محصول شامل آتشک، زخم برگ و سفیدک پودری است. با پیشرفت الگوریتمهای هوش مصنوعی، شبکههای عصبی مختلفی برای طبقهبندی معرفی شدهاند که از مهمترین آنها میتوان به شبکههای عصبی پیچشی (کانولوشنی) اشاره کرد. هدف اصلی این مطالعه بهینهسازی و تنظیم پارامترهای اصلی این شبکهها بهمنظور افزایش دقت تشخیص بیماریهای برگی درخت بِه میباشد. در این مطالعه در رویکرد اول با استفاده از یادگیری انتقالی، دو الگوریتم مهم Inception-ResNet-v2 و ResNet-101 و در رویکرد دوم یک الگوریتم بهینهشده پیشنهادی برای طبقهبندی بیماریها استفاده شد. نتایج مدلها نشان داد که حذف تصادفی باعث اصلاح دقت بعضی مدلها گردید و بیشترین عملکرد با 64 نورون در لایه مخفی حاصل گردید. مدل پیشنهادی دقت بالاتری نسبت به روش انتقالی داشت. با بررسی نتایج کلی، مدل پبشنهادی با چهار لایه پیچشی در بلوک کانولوشنی، یک لایه مخفی در بلوک شبکه عصبی و ضریب دراپاوت 0.5 بیشترین عملکرد را ارایه داد.
کلیدواژهها
موضوعات
©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)
- Ali, M. M., Bachik, N. A., Muhadi, N., Yusof, T. N. T., & Gomes, C. (2019). Non-destructive techniques of detecting plant diseases: A review. Physiological and Molecular Plant Pathology, 108, 101426. https://doi.org/10.1016/j.pmpp.2019.101426
- Al-Zughbi, I., & Krayem, M. (2022). Quince fruit Cydonia oblonga Mill nutritional composition, antioxidative properties, health benefits and consumers preferences towards some industrial quince products: A review. Food Chemistry, 393, 133362. https://doi.org/10.1016/j.foodchem.2022.133362
- Baldi, P., & Sadowski, P. (2014). The dropout learning algorithm. Artificial Intelligence, 210, 78-122.
- Bradshaw, M., Braun, U., Götz, M., & Jurick, W. (2022). Phylogeny and taxonomy of powdery mildew caused by Erysiphe species on Lupinus hosts. Mycologia, 114(1), 76-88. https://doi.org/10.1080/00275514.2021.1973287
- Chen, J., Liu, Q., & Gao, L. (2019). Visual tea leaf disease recognition using a convolutional neural network model. Symmetry, 11, 343. https://doi.org/10.3390/sym11030343.
- Chen, R. C., Dewi, C., Huang, S. W., & Caraka, R. E. (2020). Selecting critical features for data classification based on machine learning methods. Journal of Big Data 7, 52. https://doi.org/10.1186/s40537-020-00327-4
- Cruz, A., Ampatzidis, Y., Pierro, R., Materazzi, A., Panattoni, A., De Bellis, L., & Luvisi, A. (2019). Detection of grapevine yellows symptoms in Vitis vinifera with artificial intelligence. Computers and Electronics in Agriculture, 157, 63-76. https://doi.org/10.1016/j.compag.2018.12.028
- Dai, G., & Fan, J. (2022). An industrial-grade solution for crop disease image detection tasks. Frontiers in Plant Science., 13, 921057. https://doi.org/10.3389/fpls.2022.921057.
- David, M. (2023). Quince tree for the UK gardener. Retrieved March 28, 2024, from https://gardenfocused.co.uk/fruitarticles/quince.php
- Dawod, R. G., & Dobre, C. (2022). Upper and lower leaf side detection with machine learning methods. Sensors, 22, 2696. https://doi.org/10.3390/s22072696
- FAO. (2021). Crops production data. Retrieved from http://www.fao.org/faostat
- Farokhzad,, Modaress Motlagh, A., Ahmadi Moghaddam, P., Jalali Honarmand, S., & Kheiralipour, K. (2024). A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing. Scientific Reports, 14(1), 1995. https://doi.org/10.1038/s41598-023-50948-x
- Gupta, T. (2017). Plant leaf disease analysis using image-processing technique with modified SVM-CS classifier. International Journal of Engineering & Management Technology, 5, 11-17.
- Gutiérrez, S., Hernández, I., Ceballos, S., Barrio, I., Díez-Navajas, A. M., & Tardaguila, J. (2021). Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions. Computers and Electronics in Agriculture, 182,105991. https://doi.org/10.1016/j.compag.2021.105991
- Harteveld, D. O. C., Akinsanmi, O. A., & Drenth, A. (2013). Multiple Alternaria species groups are associated with leaf blotch and fruit spot diseases of apple in Australia. Plant Pathology, 62(2), 289-297. https://doi.org/10.1111/j.1365-3059.2012.02637.x
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 770-778).
- Hosainpour, A., Kheiralipour, K., Nadimi, M., & Paliwal, J. (2022). Quality assessment of dried white mulberry (Morus alba) using machine vision. Horticulturae, 8(11), 1011. https://doi.org/10.3390/horticulturae8111011
- Hossain, S., Mou, R. M., Hasan, M. M., Chakraborty, S., & Razzak, M. A. (2018). Recognition and detection of tea leaf’s diseases using support vector machine. In Proceedings of the 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), Penang, Malaysia. https://doi.org/1109/CSPA.2018.8368703
- Islam, M., Dinh, A., Wahid, K., & Bhowmik, P. (2017). Detection of potato diseases using image segmentation and multiclass support vector machine. In Proceedings of the 30th IEEE Canadian Conference on Electrical andComputer Engineering, Windsor, ON, Canada, pp. 1-4. https://doi.org/1109/CCECE.2017.7946594
- Jiang, F., Lu, Y., Chen, Y., Cai, D., & Li, G. (2020). Image recognition of four rice leaf diseases based on deep learning and support vector machine. Computers and Electronics in Agriculture, 179. https://doi.org/10.1016/j.compag.2020.105824.
- Joshi, R. C., Kaushik, M., Dutta, M. K., Srivastava, A., & Choudhary, N. (2021). VirLeafNet: automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101197
- Kawasaki,, Uga, H., Kagiwada, S., & Iyatomi, H. (2015). Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In Proceedings of the 12th International Symposium on Visual Computing, Las Vegas, NV, USA, pp. 638-645. https://doi.org/10.1016/j.neucom.2017.06.023
- Kheiralipour, K., Nadimi, M., & Paliwal, J. (2022). Development of an intelligent imaging system for ripeness determination of wild pistachios. Sensors, 22(19), 7134. https://doi.org/10.3390/s22197134
- Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y. (2017). Identification of Rice diseases using deep convolutional neural networks, Neuro Computing, 267, 378-384. https://doi.org/10.1016/j.neucom.2017.06.023
- Mianjy, P., Arora, R., &Vidal, R. (2018), July. On the implicit bias of dropout. In International conference on machine learning(pp. 3540-3548). PMLR.
- Miranda, J. L., Gerardo, B. D., & Tanguilig, B. T. (2014). Pest detection and extraction using image processing techniques. International Journal of Computer and Communication Engineering, 3, 189. https://doi.org/7763/IJCCE.2014.V3.317
- Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection, Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
- Moore, J. (2022). Quince tree disease – Quince leaf blight. Retrieved April 1, 2024, from https://www.pyracantha.co.uk/quince-tree-disease-quince-leaf-blight.
- Qin, F., Liu, D. X., Sun, B. D., Ruan, L., Ma, Z., & Wang, H. (2016). Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE, 11. https://doi.org/10.1371/journalpone.0168274.
- Rothe, P., & Kshirsagar, R. V. (2015). Cotton leaf disease identification using pattern recognition techniques. In Proceedings of the 2015 International Conference on Pervasive Computing, Pune, India, 1-6. https://doi.org/10.1109/PERVASIVE.2015.7086983
- Saygili, H., Aysan, Y., Mirik, M., & Sahin, F. (2004), July. Severe outbreak of fire blight on quince in Turkey. In X International Workshop on Fire Blight, 704, 51-54. https://doi.org/10.17660/ActaHortic.2006.704.4
- Shojaeian, A., Bagherpour, H., Bagherpour, R., Parian, J. A., Fatehi, F., & Taghinezhad, E. (2023). The Potential Application of Innovative Methods in Neural Networks for Surface Crack Recognition of Unshelled Hazelnut. Journal of Food Processing and Preservation, 2023. https://doi.org/10.1155/2023/2177724
- Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks-based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, https://doi.org/10.1155/2016/3289801
- Sujatha, R., Chatterjee, J. M., Jhanjhi, N. Z., & Brohi, S. N. (2021). Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess. Microsyst. https://doi.org/10.1016/j.micpro.2020.103615
- Sun, C., Huang, C., Zhang, H., Chen, B., An, F., Wang, L., & Yun, T. (2022). Individual tree crown segmentation and crown width extraction from a heightmap derived from aerial laser scanning data using a deep learning framework. Frontiers in Plant Science, 13, 914-974. https://doi.org/10.3389/fpls.2022.914974
- Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 2818-2826).
- Taner, A., Öztekin, Y. B., & Duran, H. (2021). Performance analysis of deep learning CNN models for variety classification in hazelnut. Sustainability, 13(12), 6527. https://doi.org/10.3390 /su13126527
- Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019). Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Computers and Electronics in Agriculture., 157, 417-426. https://doi.org/10.1016/j.compag.2019.01.012
- Tiwari, V., Joshi, R. C., & Dutta, M. K. (2021). Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecological Informatics, 63, 101289. https://doi.org/10.1016/j.ecoinf.2021.101289
- Vidyarthi, S. K., Singh, S. K., Xiao, H. W., & Tiwari, R. (2021). Deep learnt grading of almond kernels. Journal of Food Process Engineering, 44(4), p.e13662.
- Yang, L., Luo, J., Wang, Z., Chen, Y., & Wu, C. (2019). Research on recognition for cotton spider mites’ damage level based on deep learning. International Journal of Agricultural and Biological Engineering, 12(6), 129. https://doi.org/134. 10.25165/j.ijabe.20191206.4816
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