Authors: Adnan Zahid, Hasan Tahir Abbas, Fawad Sheikh, Thomas Kaiser, Ahmed Zoha, Muhammad Imran, Qammer H. Abbasi
Source: FERMAT, Volume 35, Communication 6, Sep.-Oct., 2019
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.
Index Terms: THz sensing, machine learning, terahertz, plant health
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Monitoring Health Status and Quality Assessment of Leaves Using Terahertz Frequency