Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust

Wang, Shiyong; Khan, Asad; Lin, Ying; Jiang, Zibin; Tang, Hao; Alomar, Suliman Yousef; Sanaullah, Muhammad; Bhatti, Uzair Aslam

doi:10.3389/fpls.2023.1142957

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…Reinforcement learning (RL), such as deep Q-learning (DQL), offers a different approach to HS image classification [27][28][29][30][31]. The classification of HS images through DQL represents a significant leap in the analytical capabilities of remote sensing (RS).…”

Section: Related Workmentioning

confidence: 99%

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Shoeibi,

Nevisi,

Salehi

et al. 2024

CMC

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Hyperspectral (HS) image classification plays a crucial role in numerous areas including remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification. This process involves selecting the most informative spectral bands, which leads to a reduction in data volume. Focusing on these key bands also enhances the accuracy of classification algorithms, as redundant or irrelevant bands, which can introduce noise and lower model performance, are excluded. In this paper, we propose an approach for HS image classification using deep Q learning (DQL) and a novel multi-objective binary grey wolf optimizer (MOBGWO). We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification. In the suggested MOBGWO, a new sigmoid function is introduced as a transfer function to modify the wolves' position. The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy. To evaluate the effectiveness of our approach, we conducted experiments on publicly available HS image datasets, including Pavia University, Washington Mall, and Indian Pines datasets. We compared the performance of our proposed method with several state-of-the-art deep learning (DL) and machine learning (ML) algorithms, including long short-term memory (LSTM), deep neural network (DNN), recurrent neural network (RNN), support vector machine (SVM), and random forest (RF). Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques. MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used. For the Indian Pine dataset, the MOBGWO-DQL architecture achieved a kappa coefficient (KC) of 97.68% and an overall accuracy (OA) of 94.32%. This was accompanied by the lowest root mean square error (RMSE) of 0.94, indicating very precise predictions with minimal error. In the case of the Pavia University dataset, the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72% and an impressive OA of 96.01%. It also recorded the lowest RMSE at 0.63, reinforcing its accuracy in predictions. The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.

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Section: Related Workmentioning

confidence: 99%

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Shoeibi,

Nevisi,

Salehi

et al. 2024

CMC

View full text Add to dashboard Cite

show abstract

“…Deep learning, particularly convolutional neural networks (CNNs), has demonstrated exceptional capabilities in image analysis. Implementing the latest CNN architectures allows for hierarchical feature learning, enabling the model to discern intricate patterns in facial expressions that may be challenging for traditional methods [40][41][42][43][44].…”

Section: • Deep Learning Architecturesmentioning

confidence: 99%

Exploring cross-cultural and gender differences in facial expressions: a skin tone analysis using RGB Values

Ali,

Khan,

Khan

et al. 2023

J Cloud Comp

Self Cite

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Facial expressions serve as crucial indicators of an individual's psychological state, playing a pivotal role in face-to-face communication. This research focuses on advancing collaboration between machines and humans by undertaking a thorough investigation into facial expressions. Specifically, we delve into the analysis of emotional variations related to changes in skin tone across different genders and cultural backgrounds (Black and white). The research methodology is structured across three phases. In Phase I, image data is acquired and meticulously processed from the Chicago face dataset, resulting in 12,402 augmented images across five classes (Normal case, Benign case, Adenocarcinoma, Squamous-cell-carcinoma, Large-cell-carcinoma). Phase II involves the identification of Regions of Interest (ROI) and the extraction of RGB values as features from these ROIs. Various methods, including those proposed by Kovac, Swift, and Saleh, are employed for precise skin identification. The final phase, Phase III, centers on the in-depth analysis of emotions and presents the research findings. Statistical techniques, such as Descriptive statistics, independent sample T-tests for gender and cross-cultural comparisons, and two-way ANOVA, are applied to RED, BLUE, and GREEN pixel values as response variables, with gender and emotions as explanatory variables. The rejection of null hypotheses prompts a Post Hoc test to discern significant pairs of means. The results indicate that both cross-cultural backgrounds and gender significantly influence pixel colors, underscoring the impact of different localities on pixel coloration. Across various expressions, our results exhibit a minimal 0.05% error rate in all classifications. Notably, the study reveals that green pixel color does not exhibit a significant difference between Anger and Neutral emotions, suggesting a near-identical appearance for green pixels in these emotional states. These findings contribute to a nuanced understanding of the intricate relationship between facial expressions, gender, and cultural backgrounds, providing valuable insights for future research in human–machine interaction and emotion recognition.

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Stabilizing Electric Vehicle Systems Using Proximal Policy-Based Self-structuring Control

Zhuang,

Wang,

Cheng

et al. 2024

Int.J Automot. Technol.

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An active disturbance rejection control (ADRC) has been developed for stabilizing electric vehicle (EV) systems without the need for model identification. The proximal policy optimization (PPO) algorithm, along with actor and critic neural networks, has been used to fine-tune the adjustable parameters of the ADRC controller to achieve optimal performance in a specific case study. The architecture of PPO implements separate neural networks and ameliorates the PPO adaptability to handle continuous action spaces. By maximizing a reward function based on system output, the PPO agent optimally tunes the gains to reduce undesired speed fluctuations of EVs and improve system stability. Performance evaluation under the new European driving cycle and federal test procedure has been conducted to examine the feasibility of the suggested controller. The disturbance rejection capability of the ADRC controller designed by the PPO algorithm has been tested and compared with prevalent control methodologies. Moreover, real-time examinations of the dynamic behavior of EV systems have been made to identify the capability of the suggested controller in real-world hardware. The results show that the suggested controller outperforms other designed controllers in terms of transient behavior and numerical performance metrics.

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Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust

Cited by 8 publications

References 47 publications

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Exploring cross-cultural and gender differences in facial expressions: a skin tone analysis using RGB Values

Stabilizing Electric Vehicle Systems Using Proximal Policy-Based Self-structuring Control

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