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, 18(8), pp. 1330-1339. (doi: 10.1016/j.dt.2022.01.003)

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: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Zahid, Mr Adnan and Ren, Dr Aifeng and Abbas, Dr Hasan and Abbasi, Professor 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 18(8): 1330-1339
• Publisher Policy: Reproduced under a Creative Commons licence

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
172865		EPSRC DTP 16/17 and 17/18	Tania Galabova	Engineering and Physical Sciences Research Council (EPSRC)	EP/N509668/1	Research and Innovation Services