AI-enhanced IoT sensors for real-time crop monitoring: an era towards self-monitored agriculture

Syed M. Zaigham Abbas Naqvi (), M. Naveed Tahir (), Vijaya Raghavan (), M. Awais (), Jiandong Hu (), Yahia Said (), Nashwan Adnan Othman (), Mirjalol Ashurov () and M. Ijaz Khan ()
Additional contact information
Syed M. Zaigham Abbas Naqvi: Henan Agricultural University
M. Naveed Tahir: PMAS-Arid Agriculture University
Vijaya Raghavan: McGill University
M. Awais: Henan Agricultural University
Jiandong Hu: Henan Agricultural University
Yahia Said: Northern Border University
Nashwan Adnan Othman: Knowledge University
Mirjalol Ashurov: Tashkent State Pedagogical University
M. Ijaz Khan: College of Engineering, Prince Mohammad Bin

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 3, No 20, 15 pages

Abstract: Abstract Smart farming is a rapidly growing field that uses sensors and internet of things (IoT) devices to collect and analyze data from farmlands and optimize crop production. This mini-ecosystem is potentially viable for gathering highly accurate information in real time. Still, continuously varying circumstances in the field pose the issues of self-degradation and noises during data collection. The current review covers different aspects of smart agriculture using artificially intelligent IoT platforms. Firstly, plant sensors for in vivo detections of plant signaling networks are briefly explained. Then there is a discussion that the data from various sensory devices for quantifying biomarker activities contain some noise which is refined using various algorithmic models. Proceedings further state that artificial intelligence (AI) helps to automate data acquisition with decreased human interference. Furthermore, different sensors are classified by their use in agriculture, with pros and cons for continuous monitoring and intelligent decisions. Plant wearable sensors have been discussed for their long-term data collection abilities from the field. Conclusively, plant wearable sensors are promising tools for smart agriculture, but they face some challenges such as biocompatibility, durability and scalability. From a future perspective, the current study comprehends the research gap to eliminate or reduce the issues of biodegradability and noise removal from the obtained data to perform more precise, continuous, and accurate data acquisitions remotely and smartly. Future research should focus on developing biodegradable microneedles, reducing the size of microneedles, creating biocompatible electrodes, and improving the performance of sensors in different climatic conditions.

Keywords: IoT platform; Agricultural sensors; Plant wearable sensors; Algorithmic models; Artificial Intelligence (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11235-025-01326-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:88:y:2025:i:3:d:10.1007_s11235-025-01326-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-025-01326-7

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().