Quantum-Inspired Moth Flame Optimizer Enhanced Deep Learning for Automated Rice Variety Classification

• Published in: IEEE access Vol. 11; pp. 125593 - 125600
• Main Authors: Alshahrani, Haya Mesfer, Saeed, Muhammad Kashif, Alotaibi, Saud S., Mohamed, Abdullah, Assiri, Mohammed, Ibrahim, Sara Saadeldeen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agricultural management; Agricultural products; Algorithms; Artificial neural networks; Automation; Classification; Classification algorithms; Computer vision; Convolutional neural networks; Crops; Deep learning; Feature extraction; Fires; Image processing; Leaching; Machine learning; Manuals; moth flame optimizer; Neural networks; Plant diseases; Quality assessment; Quantum computing; Rice variety; Wiener filtering
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3330918

Automated rice variety detection and classification is a task that includes automatically categorizing and identifying varieties or different types of rice based on different characteristics namely grain texture, shape, color, and size. This process is essential for quality assessment, agricultural management, and research purposes. Deep learning (DL) is a subfield of machine leaching (ML) that focuses on training an artificial neural network (ANN) with multiple layers to learn hierarchical representations of data. Convolutional Neural Network (CNN) was widely applied in image-based tasks such as rice variety detection, as they could efficiently capture visual features and patterns. In this study, we propose an Automated Rice Variety Detection and Classification using Quantum Inspired Moth Flame Optimizer with Deep Learning (ARVDC-QIMFODL) technique. The presented ARVDC-QIMFODL technique focuses on the automated identification and classification of distinct kinds of rice varieties. To accomplish this, the ARVDC-QIMFODL technique uses the Median modified wiener filter (MMWF) technique for the noise removal process. Followed by, the feature extraction process takes place by an improved ShuffleNet model. For rice variety detection and classification, the long short-term memory (LSTM) approach was applied. At last, the QIMFO algorithm-based hyperparameter selection process is performed to optimize the detection results of the LSTM system. The simulation outcome of the ARVDC-QIMFODL method is tested on a rice image dataset. An extensive set of experiments showed the remarkable efficiency of the ARVDC-QIMFODL system over other models.