5G-Enabled Drone System

2026-04-21T06:50:08+00:00

This study provides an evidence-based decision framework and a quantitative feasibility assessment of a 5G-based drone system in precision agriculture. Uplink transmission times under 4G and several 5G scenarios were simulated using a data-driven model based on actual drone image transmissions (0.574 GB per mission). The simulation showed a more than 90% increase in performance using 5G, which successfully addresses the 4G transmission constraint by reducing the average transmission delay (τ1) from 1,205 seconds (20.1 minutes) under 4G to between 61 and 96 seconds (1-1.6 minutes) under 5G. However, a rural edge coverage stress test (100 simulations) showed a high standard deviation in τ1 (approximately 43 seconds), which resulted in a long tail in the latency distribution, quantifying the unreliability of the network as a primary business risk. A technical artefact, the MNDVI Heatmap algorithm, validated the system’s ability to extract valuable information from low-cost RGB sensors. To address the high capital costs, a conditional adoption approach using a Hybrid Network Architecture (5G as a service for bandwidth, a mesh network for C2 reliability) and a Drone as a Service (DaaS) business model is proposed.

Image Classification Using Convolutional Neural Networks

2026-04-21T07:13:20+00:00

The given paper explores the issue of Convolutional Neural Network (CNN)-based image classification on the Fashion-MNIST dataset and elaborates on the findings in relation to the impacts on smart retail artificial intelligence and the process of its implementation into practice through transfer learning and edge computing. It is a custom three-block sequential CNN, which consists of progressive convolutional layers consisting of 32, 64, and 128 filters, max-pooling, fully connected dense layer of 128 neurons, and a 10-class softmax output, trained, and evaluated on 10 epochs with the Adam optimizer and sparse categorical cross-entropy loss in TensorFlow/Keras. This model attains an approximate test accuracy of 88.8 per-class F1-scores are high in categories that are morphologically distinct, like Trouser, Bag, and Sandal (F1[?] 0.97) and lowly with those that are closely similar in appearance like Shirt and T-shirt/top by the greyscale as they may be inter-classes. These empirical results are conceptually mapped into real-world implementation scenarios: automated retailing, e-commerce product tagging with AI, virtual try-on, and fashion recognition on the edge. The article also discusses transfer learning models, specifically MobileNetV2 and EfficientNetB3, as computationally efficient frameworks to be used in computing resource-limited deployment settings, comparing their parameter efficiency and inference rate with the specialized architecture. The business environment of AI-in-fashion is the global market, which is currently USD 2.23 billion with a compound annual growth rate of 39 percent (SmartDev, 2025) and is expected to increase to USD 60 billion in 2034.

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence

5G-Enabled Drone System

Image Classification Using Convolutional Neural Networks