Fine hyperspectral classification of rice varieties based on self-attention mechanism

Meng, Ying; Wang, Yuan; Aktilek, Erkinbek Uulu; Zhong, Zhuozhi; Wang, Yue; Gao, Rui; Su, Zhongbin

doi:10.1016/j.ecoinf.2023.102035

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…Popularity has been gained by transformers because they can read the entire sequence of words at once and create a relation between each word. The success of self-attention mechanisms in NLP have inspired their use in computer vision tasks [24] , [25] , [26] .…”

Section: Related Workmentioning

confidence: 99%

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

Boulila,

Ghandorh,

Masood

et al. 2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

Boulila,

Ghandorh,

Masood

et al. 2024

Heliyon

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“…The outcomes of such methods are associated with the ensemble method of ML. In [15], a CNN classification method related to (self-attention-1D-CNN) for enhancing precision in differentiating crop species utilizing canopy spectral data. Five pre-processing approaches and 3 extraction approaches can be exploited for processing data.…”

Section: Related Workmentioning

confidence: 99%

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

Alshahrani,

Saeed,

Alotaibi

et al. 2023

IEEE Access

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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, colour, 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.

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“…Although CycleGAN has made significant progress, its images are too similar, lack diversity and its unsupervised nature makes it challenging to provide fine control over the generated images. The self-attention mechanism is used to process sequential data and is widely used in deep-learning models, such as crop leaf disease identification and detection 14 – 16 , rice pest detection 17 and crop classification and identification 18 , 19 . Its introduction helps improve the performance of the model, allows the model to capture global and local image information better and helps generate higher quality and a greater number of images.…”

Section: Introductionmentioning

confidence: 99%

SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

Liu,

Cao,

Yang

et al. 2024

Sci Rep

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Crop disease detection and crop baking stage judgement require large image data to improve accuracy. However, the existing crop disease image datasets have high asymmetry, and the poor baking environment leads to image acquisition difficulties and colour distortion. Therefore, we explore the potential of the self-attention mechanism on crop image datasets and propose an innovative crop image data-enhancement method for recurrent generative adversarial networks (GANs) fused with the self-attention mechanism to significantly enhance the perception and information capture capabilities of recurrent GANs. By introducing the self-attention mechanism module, the cycle-consistent GAN (CycleGAN) is more adept at capturing the internal correlations and dependencies of image data, thus more effectively capturing the critical information among image data. Furthermore, we propose a new enhanced loss function for crop image data to optimise the model performance and meet specific task requirements. We further investigate crop image data enhancement in different contexts to validate the performance and stability of the model. The experimental results show that, the peak signal-to-noise ratio of the SM-CycleGAN for tobacco images and tea leaf disease images are improved by 2.13% and 3.55%, and the structural similarity index measure is improved by 1.16% and 2.48% compared to CycleGAN, respectively.

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Fine hyperspectral classification of rice varieties based on self-attention mechanism

Cited by 15 publications

References 9 publications

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

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

SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

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