A Cloud-Based Platform for Soybean Plant Disease Classification Using Archimedes Optimization Based Hybrid Deep Learning Model
Tóm tắt
Bean which is botanically called Phaseolus vulgaris L. belongs to the Fabaceae family. Unnecessary economic losses arise during bean disease identification due to a delay in treatment, inappropriate treatment, and a lack of understanding. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean Data Set. The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short-term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. To help the farmers in real-time, the proposed model is also deployed in a cloud-based collaboration framework. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.
Tài liệu tham khảo
Argüeso, D., Picon, A., Irusta, U., Medela, A., San-Emeterio, M. G., Bereciartua, A., & Alvarez-Gila, A. (2020). Few-Shot Learning approach for plant disease classification using images taken in the field. Computers and Electronics in Agriculture, 175, 105542.
Mitchell, D. C., Lawrence, F. R., Hartman, T. J., & Curran, J. M. (2009). Consumption of dry beans, peas, and lentils could improve diet quality in the US population. Journal of the American dietetic association, 109(5), 909–913.
Tavakoli, H., Alirezazadeh, P., Hedayatipour, A., Banijamali Nasib, A. H., & Landwehr, N. (2021). Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. Computers and Electronics in Agriculture, 181, 105935.
Kantale, P., & Thakare, S. (2020). A review on pomegranate disease classification using machine learning and image segmentation techniques. In 2020 4th International conference on intelligent computing and control systems (ICICCS) (pp. 455–460). IEEE.
Kabagambe, K. E., Baylin, A., Ruiz-Narvarez, E., Siles, X., & Campos, H. (2005). Decreased consumption of dried mature beans is positively associated with urbanization and nonfatal acute myocardial infarction. The Journal of Nutrition, 135(7), 1770–1775.
Sahu, P., Chug, A., Singh, A. P., Singh, D., & Singh, R. P. (2020). Implementation of CNNs for Crop diseases classification: A comparison of pre-trained model and training from scratch. IJCSNS, 20(10), 206.
Grant, G., Dorward, P. M., Buchan, W. C., Armour, J. C., & Pusztai, A. (1995). Consumption of diets containing raw soya beans (Glycine max), kidney beans (Phaseolus vulgaris), cowpeas (Vigna unguiculata) or lupin seeds (Lupinus angustifolius) by rats for up to 700 days: Effect on body composition and organ weights. British Journal of Nutrition, 73(1), 17–29.
Lindgren, B. P., Peet, R. C., & Panopoulos, N. J. (1986). Gene cluster of Pseudomonas syringaepv. phaseolicola controls pathogenicity of bean plants and hypersensitivity of nonhost plants. Journal of Bacteriology, 168(2), 512–522.
Gowthul Alam, M. M., & Baulkani, S. (2017). Reformulated query-based document retrieval using optimised kernel fuzzy clustering algorithm. Int J Bus Intell Data Min, 12(3), 299.
Sundararaj, V. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst, 9(3), 117–126.
Gowthul Alam, M. M., & Baulkani, S. (2019). Geometric structure information based multi-objective function to increase fuzzy clustering performance with artificial and real-life data. Soft Computing, 23(4), 1079–1098.
Sundararaj, V. (2019). Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. International Journal of Biomedical Engineering and Technology, 31(4), 325.
Gowthul Alam, M. M., & Baulkani, S. (2019). Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction. Knowledge and Information Systems, 60(2), 971–1000.
Hassan, B. A., & Rashid, T. A. (2020). Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms. Data in Brief, 28, 105046.
Hassan, B. A. (2020). CSCF: A chaotic sine cosine firefly algorithm for practical application problems. Neural Computing and Applications, 2020, 1–20.
Rejeesh, M. R. (2019). Interest point based face recognition using adaptive neuro fuzzy inference system. Multimedia Tools and Applications, 78(16), 22691–22710.
Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers & Security, 77, 277–288.
Sundararaj, V., Anoop, V., Dixit, P., Arjaria, A., Chourasia, U., Bhambri, P., Rejeesh, M. R., & Regu, S. (2020). CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Progress in Photovoltaics: Research and Applications, 28(11), 1128–1145.
Vinu, S. (2019). Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Communications, 104(1), 173–197.
Sundararaj, V., & Selvi, M. (2021). Opposition grasshopper optimizer based multimedia data distribution using user evaluation strategy. Multimedia Tools and Applications, 80(19), 29875–29891. https://doi.org/10.1007/s11042-021-11123-4.
Brandt, T., Sack, L. M., Arjona, D., Tan, D., Mei, H., Cui, H., Gao, H., et al. (2020). Adapting ACMG/AMP sequence variant classification guidelines for single-gene copy number variants. Genetics in Medicine, 22(2), 336–344.
Tunio, N., Memon, A. L., Khuhawar, F. Y., & Abro, G. M. (2019). Detection of infected leaves and botanical diseases using curvelet transform. International Journal of Advanced Computer Science and Applications (IJACSA), 10(1), 2019.
Mishra, S., Ellappan, V., Satapathy, S., Dengia, G., Mulatu, B. T., & Tadele, F. (2021). Identification and classification of mango leaf disease using wavelet transform based segmentation and wavelet neural network model. Annals of the Romanian Society for Cell Biology, 2021, 1982–1989.
Kiptoo, G. J., Kinyua, M. G., Matasyoh, L. G., & Kiplagat, O. K. (2020). Morphological traits associated with anthracnose (Colletotrichum lindemuthianum) resistance in selected common bean (Phaseolus vulgaris L.) genotypes. African Journal of Plant Science, 14(2), 45–56.
Uğuz, S., & Uysal, N. (2020). Classification of olive leaf diseases using deep convolutional neural networks. Neural Computing and Applications, 2020, 1–17.
Esgario, J. G. M., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169, 105162.
Hernández, S., & López, J. L. (2020). Uncertainty quantification for plant disease detection using Bayesian deep learning. Applied Soft Computing, 96, 106597.
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393.
Li, K., Lin, J., Liu, J., & Zhao, Y. (2020). Using deep learning for Image-Based different degrees of ginkgo leaf disease classification. Information, 11(2), 95.
Coifman, R. R., Meyer, Y., Quake, S., & Wickerhauser, M. V. (1994). Signal processing and compression with wavelet packets. In Wavelets and their applications (pp. 363–379). Dordrecht: Springer.
Zhitong, C., Hongping, C., Guoguang, H., & Ritchie, E. (2001, August). Rotor fault diagnosis of induction motor based on wavelet reconstruction. In Proceedings of the fifth international conference on electrical machines and systems, ICEMS'2001 (IEEE Cat. No. 01EX501) (Vol. 1, pp :374–377). IEEE.
Hochreiter, S. (1998). The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), 107–116.
Zhang, N., & Behera, P. K. (2012). Solar radiation prediction based on recurrent neural networks trained by Levenberg-Marquardt backpropagation learning algorithm. In 2012 IEEE PES innovative smart grid technologies (ISGT) (pp. 1–7). IEEE.
Li, Y., Zhu, H., Wang, D., Wang, K., Kong, W., & Wu, X. (2021). Comprehensive optimization of distributed generation considering network reconstruction based on Archimedes optimization algorithm. In IOP conference series: earth and environmental science (Vol. 647(1), p. 012031). IOP Publishing.
Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2020). Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 2020, 1–21.
Khan, A., Nawaz, U., Ulhaq, A., & Robinson, R. W. (2020). Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens. PLoS ONE, 15(12), e0243243.
Jain, S., Zhang, X., Zhou, Y., Ananthanarayanan, G., Jiang, J., Shu, Y., Bahl, P., Gonzalez, J. (2020, November). Spatula: Efficient cross-camera video analytics on large camera networks. In 2020 IEEE/ACM symposium on edge computing (SEC) (pp. 110–124). IEEE.
Turkoglu, M., Hanbay, D., & Sengur, A. (2019). Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests. Journal of Ambient Intelligence and Humanized Computing, 2019, 1–11.
Dua, D., & Graff, C. (2019). UCI machine learning repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Markell, S. G., Harveson, R. M., & Pasche, J. (2016). Dry edible bean disease diagnostic series.
Jayakumar, D., Elakkiya, A., Rajmohan, R., & Ramkumar, M. O. (2020). Automatic prediction and classification of diseases in melons using stacked RNN based deep learning model. In 2020 international conference on system, computation, automation and networking (ICSCAN) (pp. 1–5). IEEE.