A Cloud-Based Platform for Soybean Plant Disease Classification Using Archimedes Optimization Based Hybrid Deep Learning Model

Autor: D. Godwin Immanuel, N. Herald Anantha Rufus, J. Annrose, C. R. Edwin Selva Rex
Rok vydání: 2021
Předmět:
Zdroj: Wireless Personal Communications. 122:2995-3017
ISSN: 1572-834X
0929-6212
Popis: 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.
Databáze: OpenAIRE
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