پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شمارهها | 6,243 |
| تعداد مقالات | 67,878 |
| تعداد مشاهده مقاله | 115,518,714 |
| تعداد دریافت فایل اصل مقاله | 90,288,609 |
Cucumber Leaf Disease Detection and Classification Using a Deep Convolutional Neural Network | ||
| Journal of Information Technology Management | ||
| دوره 15، Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-2022)، 2023، صفحه 94-110 اصل مقاله (1.72 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2023.95248 | ||
| نویسندگان | ||
| Manish K. Singh* ؛ Avadhesh Kumar | ||
| School of Computer and Engineering, Galgotia’s University, India. | ||
| چکیده | ||
| Due to obstruction in photosynthesis, the leaves of the plants get affected by the disease. Powdery mildew is the main disease in cucumber plants which generally occurs in the middle and late stages. Cucumber plant leaves are affected by various diseases, such as powdery mildew, downy mildew and Alternaria leaf spot, which ultimately affect the photosynthesis process; that’s why it is necessary to detect diseases at the right time to prevent the loss of plants. This paper aims to identify and classify diseases of cucumber leaves at the right time using a deep convolutional neural network (DCNN). In this work, the Deep-CNN model based on disease classification is used to enhance the performance of the ResNet50 model. The proposed model generates the most accurate results for cucumber disease detection using data enhancement based on a different data set. The data augmentation method plays an important role in enhancing the characteristics of cucumber leaves. Due to the requirements of the large number of parameters and the expensive computations required to modify standard CNNs, the pytorch library was used in this work which provides a wide range of deep learning algorithms. To assess the model accuracy large quantity of four types of healthy and diseased leaves and specific parameters such as batch size and epochs were compared with various machine learning algorithms such as support vector machine method, self-organizing map, convolutional neural network and proposed method in which the proposed DCNN model gave better results. | ||
| کلیدواژهها | ||
| DCNNs (Deep Convolution Neural Network)؛ CNNs (Convolution Neural Network)؛ Classification | ||
| مراجع | ||
|
Alshammari, H., Gasmi, K., Ben Ltaifa, I., Krichen, M., Ben Ammar, L., & Mahmood, M. A. (2022). Olive disease classification based on vision transformer and CNN models. Computational Intelligence and Neuroscience, 2022.
Atila, Ü. Uçar, M., Akyol, K., & Uçar, E. (2021). Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 61, 101182.
Bodhwani, V., Acharjya, D. P., & Bodhwani, U. (2019). Deep residual networks for plant identification. Procedia Computer Science, 152, 186-194.
Borhani, Y., Khoramdel, J., & Najafi, E. (2022). A deep learning based approach for automated plant disease classification using vision transformer. Scientific Reports, 12(1), 11554.
Ertam, F., & Aydın, G. (2017, October). Data classification with deep learning using Tensorflow. In 201
Hari, S. S., Sivakumar, M., Renuga, P., & Suriya, S. (2019, March). Detection of plant disease by leaf image using convolutional neural network. In 2019 International conference on vision towards emerging trends in communication and networking (ViTECoN) (pp. 1-5). IEEE.
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).
International conference on computer science and engineering (UBMK) (pp. 755-758). IEEE. De Luna, R. G., Dadios, E. P., & Bandala, A. A. (2018, October). Automated image capturing system for deep learning-based tomato plant leaf disease detection and recognition. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 1414-1419). IEEE.
Ismael, S. A. A., Mohammed, A., & Hefny, H. (2020). An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artificial intelligence in medicine, 102, 101779.
Jain, A., Awan, A. A., Anthony, Q., Subramoni, H., & Panda, D. K. D. (2019, September). Performance characterization of dnn training using tensorflow and pytorch on modern clusters. In 2019 IEEE International Conference on Cluster Computing (CLUSTER) (pp. 1-11). IEEE.
Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019). Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access, 7, 59069-59080.
Kaushik, M., Prakash, P., Ajay, R., & Veni, S. (2020, June). Tomato leaf disease detection using convolutional neural network with data augmentation. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 1125-1132). IEEE.
Khan, M. A., Akram, T., Sharif, M., Javed, K., Raza, M., & Saba, T. (2020). An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimedia Tools and Applications, 79, 18627-18656.
Liu, J., Yang, S., Cheng, Y., & Song, Z. (2018, November). Plant leaf classification based on deep learning. In 2018 Chinese Automation Congress (CAC) (pp. 3165-3169). IEEE.
Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., & Sun, Z. (2018). A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and electronics in agriculture, 154, 18-24.
Manavalan, R. (2021). Automatic Detection of Fungal and Bacterial Plant Leaves Diseases: A Review. Asian Basic and Applied Research Journal, 133-143.
Manavalan, R. (2021). Cucumber Leaves Diseases Detection through Computational Approaches: A Review. Asian Journal of Research in Biosciences, 100-110.
Nashrullah, F. H., Suryani, E., Salamah, U., Prakisya, N. P. T., & Setyawan, S. (2021). Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves. Revue d'Intelligence Artificielle, 35(4).
Phadikar, S., & Sil, J. (2008, December). Rice disease identification using pattern recognition techniques. In 2008 11th International Conference on Computer and Information Technology (pp. 420-423). IEEE.
Pooja, V., Das, R., & Kanchana, V. (2017, April). Identification of plant leaf diseases using image processing techniques. In 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 130-133). IEEE.
Prakash, R. M., Saraswathy, G. P., Ramalakshmi, G., Mangaleswari, K. H., & Kaviya, T. (2017, March). Detection of leaf diseases and classification using digital image processing. In 2017 international conference on innovations in information, embedded and communication systems (ICIIECS) (pp. 1-4). IEEE.
Rezende, E., Ruppert, G., Carvalho, T., Ramos, F., & De Geus, P. (2017, December). Malicious software classification using transfer learning of resnet-50 deep neural network. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1011 1014). IEEE.
Sankaran, S., Mishra, A., Ehsani, R., & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and electronics in agriculture, 72(1), 1-13.
Sannakki, S. S., Rajpurohit, V. S., Nargund, V. B., & Kulkarni, P. (2013, July). Diagnosis and classification of grape leaf diseases using neural networks. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
Şengür, A., Guo, Y., Budak, Ü. & Vespa, L. J. (2017, September). A retinal vessel detection approach using convolution neural network. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1-4). Ieee.
Shoaib, M., Hussain, T., Shah, B., Ullah, I., Shah, S. M., Ali, F., & Park, S. H. (2022). Deep learning based segmentation and classification of leaf images for detection of tomato plant disease. Frontiers in Plant Science, 13, 1031748.
Zhou, B., Xu, J., Zhao, J., Li, A., & Xia, Q. (2015, August). Research on cucumber downy mildew detection system based on SVM classification algorithm. In 3rd international conference on material, mechanical and manufacturing engineering (IC3ME 2015) (pp. 1681-1684). Atlantis Press. | ||
|
آمار تعداد مشاهده مقاله: 179 تعداد دریافت فایل اصل مقاله: 172 |
||