Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves

Zahid, A., Dashtipour, K., Abbas, H. T. , Mabrouk, I. B., Al-Hasan, M., Ren, A. , Imran, M. A. , Alomainy, A. and Abbasi, Q. H. (2022) Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves. Defence Technology, (doi: 10.1016/j.dt.2022.01.003) (Early Online Publication)

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Abstract

Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 THz to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.

Item Type:Articles
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Zahid, Mr Adnan and Ren, Dr Aifeng and Abbas, Dr Hasan and Abbasi, Dr Qammer and Dashtipour, Dr Kia
Authors: Zahid, A., Dashtipour, K., Abbas, H. T., Mabrouk, I. B., Al-Hasan, M., Ren, A., Imran, M. A., Alomainy, A., and Abbasi, Q. H.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Defence Technology
Publisher:Elsevier
ISSN:2214-9147
ISSN (Online):2214-9147
Published Online:07 January 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Defence Technology 2022
Publisher Policy:Reproduced under a Creative Commons licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172865EPSRC DTP 16/17 and 17/18Tania GalabovaEngineering and Physical Sciences Research Council (EPSRC)EP/N509668/1Research and Innovation Services