Augmented Reality: Survey

Bamboos, also known as non-timber forest products (NTFPs) and belonging to the family Poaceae and subfamily Bambusoideae, have a wide range of flowering cycles from 3 to 120 years; hence, it is difficult to identify species. Here, the focus is on supervised machine learning (ML) and deep learning (DL) as a potential automated approach for the identification and classification of commercial bamboo species, with the help of the majority multiclass voting (MajMulVot) algorithm. We created an image dataset of 2000 bamboo instances, followed by a texture dataset prepared using local binary patterns (LBP) and gray-level cooccurrence matrix (GLCM)-based methods. First, we deployed five ML models for the texture datasets, where support vector machine (SVM) shows an accuracy rate of 82.27%. We next deployed five DL-based convolutional neural network (CNN) models for bamboo classification, namely AlexNet, VGG16, ResNet18, VGG19, and GoogleNet, using the transfer learning (TL) approach, where VGG16 prevails, with an accuracy rate of 88.75%. Further, a MajMulVot-based ensemble approach was introduced to improve the classification accuracy of all ML- and DL-based models. The ML-MajMulVot enhanced the accuracy for the texture dataset to 86.96%. In the same way, DL-MajMulVot increased the accuracy to 92.8%. We performed a comparative analysis of all classification models with and without K-fold cross-validation and MajMulVot methods. The proposed findings indicate that even difficult-to-identify species may be identified accurately with adequate image datasets. The suggested technology can also be incorporated into a mobile app to offer farmers effective agricultural methods.

Section: Normalization and Standardizationmentioning

confidence: 99%

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Sawarkar,

Shrimankar,

Ali

et al. 2024

“…The usage of AR devices is increasingly prevalent in both professional and everyday contexts. These devices enable the augmentation of realworld environments with visualized entities, either directly or indirectly [6,7]. Moreover, AR devices not only present 2D or 3D virtual elements in the real world but also allow realtime interactions between humans and the rendered objects [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Assessments of Order-Picking Tasks Using a Paper List and Augmented Reality Glasses with Different Order Information Displays

Li,

Khaday,

Peng

2023

This study investigated the effects of wearing AR devices on users’ performance and comfort ratings while performing order-picking (OP) tasks. In addition to a picking-by-paper list, two AR devices combined with three order information display designs were examined. Thirty adult participants joined. They searched for and found the boxes in the order list, picked them up, and returned to the origin. The time to complete the task and the number of incorrect boxes picked up were analyzed to assess the performance of the tasks. The subjective ratings of the participants on the comfort rating scale (CRS) and the simulator sickness questionnaire (SSQ) were collected to assess the designs of both the AR devices and the order information displays. It was found that the participants could complete the OP tasks faster when adopting the order map (4.97 ± 1.57 min) or the 3D graph display (4.87 ± 1.50 min) using either one of the AR devices than when using a paper list (6.03 ± 1.28 min). However, they needed more time to complete the OP tasks when wearing both types of AR glasses when the Quick Response (QR) code option was adopted (10.16 ± 4.30 min) than when using a paper list. The QR code scanning and display design using either one of the AR devices guaranteed 100% accuracy but sacrificed efficiency in task completion. The AR device with a binocular display and hand gesture recognition functions had a significantly lower CRS score in the dimensions of attachment and movement (5.6 and 6.3, respectively) than the corresponding dimensions (8.0 and 8.3, respectively) of the other device with a monocular and hand touch input design. There were complaints of eye strain after using both AR devices examined in this study. This implies that these AR devices may not be suitable to wear for extended periods of time. Users should take off the AR device whenever they do not need to view the virtual image to avoid eye strain and other discomfort symptoms.

“…AR systems are now extensively used as an effective guiding tool for extremely challenging maintenance tasks [2][3][4][5][6][7][8][9][10][11][12]. Compared to traditional assistance techniques for delivering instructions and job-relevant information, AR tools have been found to be more engaging, leading to increased productivity and fulfilling a quality control function [13][14][15][16][17][18][19]. Furthermore, for more challenging tasks requiring strategic planning and intensive problem solving, AR methods have shown considerable benefits over conventional methods by enhancing the cognitive comprehension of tasks [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Physical Stress across Task Difficulty Levels in Augmented Reality-Assisted Industrial Maintenance

Alessa,

Alhaag,

Al-harkan

et al. 2023

Many studies have revealed the benefits of augmented reality (AR) to enhance productivity and fulfill a quality function in industry. Yet, the potential adverse effects of AR, such as physical stress, have not been extensively studied. This study investigates the effect of task difficulty (simple versus complex maintenance tasks) on the overall performance and physical stress of individuals while interacting with multimedia instructions (i.e., AR via HoloLens versus. paper-based guidance). Twenty-eight healthy male participants with a mean age of 32.12 years (standard deviation of 2.45 years) were randomly assigned to two groups: AR-based group and paper-based group. The metrics of total maintenance time, NASA-TLX scores, and heart rate variability (HRV) were used to evaluate overall performance, perceived workload, and physical stress, respectively. The findings suggest that AR-based instruction for a complex task significantly reduced both total maintenance time (by 14.94%) and physical stress in comparison with paper-based instruction. Paper-based instruction was found to destabilize heart rhythm, increase heart rate, and cause a shift in autonomic balance due to heightened sympathetic nerve activation. This study concludes that AR-based instruction is more efficient and less stressful than paper-based instruction, indicating that the AR system may offer independent benefits in reducing physical workloads.