From Food Industry 4.0 to Food Industry 5.0: Identifying technological enablers and potential future applications in the food sector

A Hassoun, S Jagtap, H Trollman… - … reviews in food …, 2024‏ - Wiley Online Library
Although several food‐related fields have yet to fully grasp the speed and breadth of the
fourth industrial revolution (also known as Industry 4.0), growing literature from other sectors …

[HTML][HTML] Non-terrestrial uav clients for beyond 5g networks: A comprehensive survey

MMH Qazzaz, SAR Zaidi, DC McLernon, AM Hayajneh… - Ad Hoc Networks, 2024‏ - Elsevier
The rapid proliferation of consumer UAVs, or drones, is resha** the wireless
communication landscape. These agile, autonomous devices find new life as UE in cellular …

TinyML algorithms for Big Data Management in large-scale IoT systems

A Karras, A Giannaros, C Karras, L Theodorakopoulos… - Future Internet, 2024‏ - mdpi.com
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data,
enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive …

Training machine learning models at the edge: A survey

AR Khouas, MR Bouadjenek, H Hacid… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Edge computing has gained significant traction in recent years, promising enhanced
efficiency by integrating artificial intelligence capabilities at the edge. While the focus has …

[HTML][HTML] Tinyml olive fruit variety classification by means of convolutional neural networks on iot edge devices

AM Hayajneh, S Batayneh, E Alzoubi, M Alwedyan - AgriEngineering, 2023‏ - mdpi.com
Machine learning (ML) within the edge internet of things (IoT) is instrumental in making
significant shifts in various industrial domains, including smart farming. To increase the …

Efficient unmanned aerial vehicle-based data collection for IoT smart farming

SA Haider, KM Ahmad, AA Khan - Internet of Things, 2024‏ - Elsevier
The extensive range of unmanned aerial vehicles (UAVs) is essential for efficiently gathering
data in inaccessible areas. The data received is processed near the end-user to reduce …

Wheat Leaf Disease Detection: A Lightweight Approach with Shallow CNN Based Feature Refinement

O Jouini, MOE Aoueileyine, K Sethom, A Yazidi - AgriEngineering, 2024‏ - mdpi.com
Improving agricultural productivity is essential due to rapid population growth, making early
detection of crop diseases crucial. Although deep learning shows promise in smart …

[HTML][HTML] A Review on Resource-Constrained Embedded Vision Systems-Based Tiny Machine Learning for Robotic Applications

M Beltrán-Escobar, TE Alarcón, JY Rumbo-Morales… - Algorithms, 2024‏ - mdpi.com
The evolution of low-cost embedded systems is growing exponentially; likewise, their use in
robotics applications aims to achieve critical task execution by implementing sophisticated …

[HTML][HTML] Digital technologies for water use and management in agriculture: Recent applications and future outlook

C Parra-López, SB Abdallah, G Garcia-Garcia… - Agricultural Water …, 2025‏ - Elsevier
This article provides a comprehensive overview of digital technologies for water use and
management in agriculture, examining recent applications and future prospects. It examines …

Probabilistic Caching Strategy and TinyML-Based Trajectory Planning in UAV-Assisted Cellular IoT System

X Gao, X Wang, Z Qian - IEEE Internet of Things Journal, 2024‏ - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) deployed as an aerial assisted base station has the
characteristics of flexibility and mobility. As an effective way to reduce the communication …