Smart Farm-Care using a Deep Learning Model on Mobile Phones

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident. Doi: 10.28991/ESJ-2023-07-05-011 Full Text: PD

Effect of surgical experience and spine subspecialty on the reliability of the {AO} Spine Upper Cervical Injury Classification System

OBJECTIVE The objective of this paper was to determine the interobserver reliability and intraobserver reproducibility of the AO Spine Upper Cervical Injury Classification System based on surgeon experience (< 5 years, 5–10 years, 10–20 years, and > 20 years) and surgical subspecialty (orthopedic spine surgery, neurosurgery, and "other" surgery). METHODS A total of 11,601 assessments of upper cervical spine injuries were evaluated based on the AO Spine Upper Cervical Injury Classification System. Reliability and reproducibility scores were obtained twice, with a 3-week time interval. Descriptive statistics were utilized to examine the percentage of accurately classified injuries, and Pearson’s chi-square or Fisher’s exact test was used to screen for potentially relevant differences between study participants. Kappa coefficients (κ) determined the interobserver reliability and intraobserver reproducibility. RESULTS The intraobserver reproducibility was substantial for surgeon experience level (< 5 years: 0.74 vs 5–10 years: 0.69 vs 10–20 years: 0.69 vs > 20 years: 0.70) and surgical subspecialty (orthopedic spine: 0.71 vs neurosurgery: 0.69 vs other: 0.68). Furthermore, the interobserver reliability was substantial for all surgical experience groups on assessment 1 (< 5 years: 0.67 vs 5–10 years: 0.62 vs 10–20 years: 0.61 vs > 20 years: 0.62), and only surgeons with > 20 years of experience did not have substantial reliability on assessment 2 (< 5 years: 0.62 vs 5–10 years: 0.61 vs 10–20 years: 0.61 vs > 20 years: 0.59). Orthopedic spine surgeons and neurosurgeons had substantial intraobserver reproducibility on both assessment 1 (0.64 vs 0.63) and assessment 2 (0.62 vs 0.63), while other surgeons had moderate reliability on assessment 1 (0.43) and fair reliability on assessment 2 (0.36). CONCLUSIONS The international reliability and reproducibility scores for the AO Spine Upper Cervical Injury Classification System demonstrated substantial intraobserver reproducibility and interobserver reliability regardless of surgical experience and spine subspecialty. These results support the global application of this classification system

Variations in management of A3 and A4 cervical spine fractures as designated by the AO Spine Subaxial Injury Classification System

© 2022 The authors.OBJECTIVE Optimal management of A3 and A4 cervical spine fractures, as defined by the AO Spine Subaxial Injury Classification System, remains controversial. The objectives of this study were to determine whether significant management variations exist with respect to 1) fracture location across the upper, middle, and lower subaxial cervical spine and 2) geographic region, experience, or specialty. METHODS A survey was internationally distributed to 272 AO Spine members across six geographic regions (North America, South America, Europe, Africa, Asia, and the Middle East). Participants’ management of A3 and A4 subaxial cervical fractures across cervical regions was assessed in four clinical scenarios. Key characteristics considered in the vignettes included degree of neurological deficit, pain severity, cervical spine stability, presence of comorbidities, and fitness for surgery. Respondents were also directly asked about their preferences for operative management and misalignment acceptance across the subaxial cervical spine. RESULTS In total, 155 (57.0%) participants completed the survey. Pooled analysis demonstrated that surgeons were more likely to offer operative intervention for both A3 (p < 0.001) and A4 (p < 0.001) fractures located at the cervicothoracic junction compared with fractures at the upper or middle subaxial cervical regions. There were no significant variations in management for junctional incomplete (p = 0.116) or complete (p = 0.342) burst fractures between geographic regions. Surgeons with more than 10 years of experience were more likely to operatively manage A3 (p < 0.001) and A4 (p < 0.001) fractures than their younger counterparts. Neurosurgeons were more likely to offer surgical stabilization of A3 (p < 0.001) and A4 (p < 0.001) fractures than their orthopedic colleagues. Clinicians from both specialties agreed regarding their preference for fixation of lower junctional A3 (p = 0.866) and A4 (p = 0.368) fractures. Overall, surgical fixation was recommended more often for A4 than A3 fractures in all four scenarios (p < 0.001). CONCLUSIONS The subaxial cervical spine should not be considered a single unified entity. Both A3 and A4 fracture subtypes were more likely to be surgically managed at the cervicothoracic junction than the upper or middle subaxial cervical regions. The authors also determined that treatment strategies for A3 and A4 subaxial cervical spine fractures varied significantly, with the latter demonstrating a greater likelihood of operative management. These findings should be reflected in future subaxial cervical spine trauma algorithms.N