Monitoring health status and quality assessment of leaves using terahertz frequency

Zahid, A., Abbas, H. T., Sheikh, F., Kaiser, T., Zoha, A. , Imran, M. A. and Abbasi, Q. H. (2019) Monitoring health status and quality assessment of leaves using terahertz frequency. FERMAT, 35, 6.

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Publisher's URL: https://www.e-fermat.org/files/communication/Zahid-2019-Vol35-Sep.-Oct.-06.pdf

Abstract

The demand for effective use of water resources has increased due to ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for precise estimation of water content (WC) in plants leaves on different days. For this purpose, multi-domain features are extracted from frequency, time, time-frequency domains using observations data to incorporate three different machine learning algorithms such as support vector machine, (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). The results demonstrate SVM outperformed other classifiers using 10-fold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for coffee, pea-shoot, and spinach leaves respectively. In addition, using SFS technique, coffee showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers. Lastly, in baby-spinach leaf, SVM exhibited an upgrade of 21.28%, 10.01%, and 8.53% was noticed in operating time for SVM, KNN and D-Tree classifiers and which eventually enhanced the classification accuracy. Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for farmers to take proactive actions in relations to plants health monitoring.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed and Abbasi, Dr Qammer and Imran, Professor Muhammad and Sheikh, Fawad and Zahid, Mr Adnan
Authors: Zahid, A., Abbas, H. T., Sheikh, F., Kaiser, T., Zoha, A., Imran, M. A., and Abbasi, Q. H.
College/School:College of Science and Engineering
College of Science and Engineering > School of Engineering
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:FERMAT
Publisher:University of Central Florida
ISSN:2470-4202
ISSN (Online):2470-4202
Copyright Holders:Copyright © 2019 The Author
First Published:First published in FERMAT 35:6
Publisher Policy:Reproduced with the permission of the publisher

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