Internet of Things and Machine Learning for Smart-Agriculture: Technologies, Practices, and Future Direction

Kusuma  Kumari B. M.; Evelina  Sahay; Mohd.  Shahid; Priyanka Shirish  Shinde; Ezudheen  Puliyanjalil

Authors

Kusuma Kumari B. M. Assistant Professor, Department of Studies and Research in Computer Applications, Tumkur University, Tumakuru, Karnataka, India https://orcid.org/0000-0003-2961-7752
Evelina Sahay Assistant Professor, Department of Management Studies (Off Campus), Bharati Vidyapeeth (Deemed to be University), Navi Mumbai https://orcid.org/0000-0003-4348-3762
Mohd. Shahid Assistant Professor, Department of Aeronautical Engineering, Rajasthan Technical University, Kota https://orcid.org/0009-0006-6203-0389
Priyanka Shirish Shinde Assistant Professor, Indira College of Engineering and Management Pune, Maharashtra, Affiliate to Savitribai Phule Pune University https://orcid.org/0009-0001-3544-0773
Ezudheen Puliyanjalil Assistant Professor, Department of Computer Science and Engineering, Government Engineering College Thrissur, Kerala, India https://orcid.org/0009-0001-7197-3606

Keywords:

Agricultural sensors, Smart Agriculture, Wireless technologies, Internet of things, Machine learning

Abstract

Smart agriculture has quickly gained popularity due to the tremendous introduction, growth, and integration of modern techniques with conventional agriculture, including the internet of things (IoT), computer vision (CV), machine learning (ML), big data, edge computing, and cloud computing. With the use of inexpensive sensors, smart agriculture aims to improve the effectiveness and sustainability of agriculture. These comprise airflow, location, optical, and mechanical sensors. Along with the real-time monitoring, identification, and categorization of objects, these sensors can be used to gather information about the position of crops and assess the condition of the soil. Furthermore, because IoT and open wireless communication networks are used in smart-agriculture ecosystems, these channels are vulnerable to a variety of cyber threats and security concerns. It has been stated that these actions could have a significant negative impact on the economy of a wide range of nations due to the rise in harmful assaults on the agricultural industry. Without the necessity for a centralized authority, AI and blockchain can be utilized to address a number of difficulties related to the deployment and management of smart agriculture. The benefits of cloud computing include its capacity to manage enormous amounts of data while offering a range of storage alternatives. Edge computing, on the contrary hand, provides a faster response time and less latency. The technique smart agriculture systems are created is anticipated to change as a result of this interaction. As a consequence, the way these techniques are applied is changing paradigmatically.

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Internet of Things and Machine Learning for Smart-Agriculture: Technologies, Practices, and Future Direction

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