Machine learning in agriculture: A comprehensive updated review

L Benos, AC Tagarakis, G Dolias, R Berruto, D Kateris… - Sensors, 2021 - mdpi.com
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

Technological revolutions in smart farming: Current trends, challenges & future directions

V Sharma, AK Tripathi, H Mittal - Computers and Electronics in Agriculture, 2022 - Elsevier
With increasing population, the demand for agricultural productivity is rising to meet the goal
of “Zero Hunger”. Consequently, farmers have optimized the agricultural activities in a …

A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things

H Xu, Z Sun, Y Cao, H Bilal - Soft Computing, 2023 - Springer
Cyber-attacks and network intrusion have surfaced as major concerns for modern days
applications of the Internet of Things (IoT). The existing intrusion detection and prevention …

[HTML][HTML] A review on fog computing: issues, characteristics, challenges, and potential applications

R Das, MM Inuwa - Telematics and Informatics Reports, 2023 - Elsevier
Fog computing is a paradigm that utilizes the advantages of both the cloud and the edge
devices providing quality services, reducing latency, providing mobility support, multi …

[HTML][HTML] Digital livestock farming

S Neethirajan, B Kemp - Sensing and Bio-Sensing Research, 2021 - Elsevier
As the global human population increases, livestock agriculture must adapt to provide more
livestock products and with improved efficiency while also addressing concerns about …

At the confluence of artificial intelligence and edge computing in iot-based applications: A review and new perspectives

A Bourechak, O Zedadra, MN Kouahla, A Guerrieri… - Sensors, 2023 - mdpi.com
Given its advantages in low latency, fast response, context-aware services, mobility, and
privacy preservation, edge computing has emerged as the key support for intelligent …

[HTML][HTML] The role of sensors, big data and machine learning in modern animal farming

S Neethirajan - Sensing and Bio-Sensing Research, 2020 - Elsevier
Ever since man began domesticating animals several thousand years ago, we have always
relied on our intuition, collective knowledge, and sensory signals to make effective animal …

Industry 4.0 and precision livestock farming (PLF): an up to date overview across animal productions

S Morrone, C Dimauro, F Gambella, MG Cappai - Sensors, 2022 - mdpi.com
Precision livestock farming (PLF) has spread to various countries worldwide since its
inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry …

Fog computing: A taxonomy, systematic review, current trends and research challenges

J Singh, P Singh, SS Gill - Journal of Parallel and Distributed Computing, 2021 - Elsevier
There has been rapid development in the number of Internet of Things (IoT) connected
nodes and devices in our daily life in recent times. With this increase in the number of …

A review of edge computing: Features and resource virtualization

Y Mansouri, MA Babar - Journal of Parallel and Distributed Computing, 2021 - Elsevier
With the advent of Internet of Things (IoT) connecting billions of mobile and stationary
devices to serve real-time applications, cloud computing paradigms face some significant …