Open Access Open Access  Restricted Access Subscription Access

A Comprehensive Review of Internet of Things and Cutting-edge Technologies Empowering Smart Farming


Affiliations
1 Department of Computer Engineering, Vishwakarma Government Engineering College, Gujarat Technological University, Ahmedabad 382 424, India
2 Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382 481, India

The agricultural sector plays an important role in contributing significantly to the gross domestic product (GDP) growth in developing countries. On the other hand, agriculture is widely affected by major factors such as environmental changes, natural disasters, pesticide control, and soil and irrigation-related issues, which reduce crop yield. The convergence of Industry 4.0 and agriculture offers an opportunity to move into the next generation of Agriculture 4.0. The internet of things (IoT), remote sensing, machine learning, deep learning, big data, cloud computing, thermal imaging, end-user apps and unmanned aerial vehicles offer a full-stack solution. IoT provides the ubiquitous connectivity of smart devices to the internet to collect, process and analyse a large amount of agriculture field data more quickly and synthesize them to make smart decisions using various machine learning and deep learning algorithms. This study reviews the challenges and major issues in the IoT agriculture domain and explores its emergence with new technologies. It covers the existing literature and illustrates how IoT application-based precision agriculture solutions have contributed. A case study on weed detection for smart agriculture using the YOLOv5 model is presented, achieving high accuracy. Finally, various IoT agriculture use cases are discussed, along with current research issues and possible solutions for future IoT-based agriculture advancement

Keywords

Cutting-edge technologies, internet of things, precision farming, smart agriculture, weed detection
User
Notifications
Font Size

Abstract Views: 147




  • A Comprehensive Review of Internet of Things and Cutting-edge Technologies Empowering Smart Farming

Abstract Views: 147  | 

Authors

Bhavin Patel
Department of Computer Engineering, Vishwakarma Government Engineering College, Gujarat Technological University, Ahmedabad 382 424, India
Jitendra Bhatia
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382 481, India

Abstract


The agricultural sector plays an important role in contributing significantly to the gross domestic product (GDP) growth in developing countries. On the other hand, agriculture is widely affected by major factors such as environmental changes, natural disasters, pesticide control, and soil and irrigation-related issues, which reduce crop yield. The convergence of Industry 4.0 and agriculture offers an opportunity to move into the next generation of Agriculture 4.0. The internet of things (IoT), remote sensing, machine learning, deep learning, big data, cloud computing, thermal imaging, end-user apps and unmanned aerial vehicles offer a full-stack solution. IoT provides the ubiquitous connectivity of smart devices to the internet to collect, process and analyse a large amount of agriculture field data more quickly and synthesize them to make smart decisions using various machine learning and deep learning algorithms. This study reviews the challenges and major issues in the IoT agriculture domain and explores its emergence with new technologies. It covers the existing literature and illustrates how IoT application-based precision agriculture solutions have contributed. A case study on weed detection for smart agriculture using the YOLOv5 model is presented, achieving high accuracy. Finally, various IoT agriculture use cases are discussed, along with current research issues and possible solutions for future IoT-based agriculture advancement

Keywords


Cutting-edge technologies, internet of things, precision farming, smart agriculture, weed detection



DOI: https://doi.org/10.18520/cs%2Fv126%2Fi2%2F137-152