Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves
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 le...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-08-01
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| Series: | Defence Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914722000034 |
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| author | Adnan Zahid Kia Dashtipour Hasan T. Abbas Ismail Ben Mabrouk Muath Al-Hasan Aifeng Ren Muhammad A. Imran Akram Alomainy Qammer H. Abbasi |
| author_facet | Adnan Zahid Kia Dashtipour Hasan T. Abbas Ismail Ben Mabrouk Muath Al-Hasan Aifeng Ren Muhammad A. Imran Akram Alomainy Qammer H. Abbasi |
| author_sort | Adnan Zahid |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-04-13T19:06:13Z |
| format | Article |
| id | doaj.art-aad87c1a98aa4763a255a02e44982abd |
| institution | Directory Open Access Journal |
| issn | 2214-9147 |
| language | English |
| last_indexed | 2024-04-13T19:06:13Z |
| publishDate | 2022-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj.art-aad87c1a98aa4763a255a02e44982abd2022-12-22T02:33:58ZengKeAi Communications Co., Ltd.Defence Technology2214-91472022-08-0118813301339Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz wavesAdnan Zahid0Kia Dashtipour1Hasan T. Abbas2Ismail Ben Mabrouk3Muath Al-Hasan4Aifeng Ren5Muhammad A. Imran6Akram Alomainy7Qammer H. Abbasi8School of Engineering and Physical Science, Heriot-Watt University, Edinburgh, EH144AS, UK; James Watt School of Engineering, University of Glasgow, Glasgow, G128QQ, UKJames Watt School of Engineering, University of Glasgow, Glasgow, G128QQ, UKJames Watt School of Engineering, University of Glasgow, Glasgow, G128QQ, UKCollege of Engineering, Al-Ain University, Abu Dhabi, United Arab EmiratesCollege of Engineering, Al-Ain University, Abu Dhabi, United Arab EmiratesSchool of Electronic Engineering, Xidian University, Xi'an, Shaanxi, ChinaJames Watt School of Engineering, University of Glasgow, Glasgow, G128QQ, UKSchool of Electronic Engineering and Computer Science, Queen Mary University of London, London, UKJames Watt School of Engineering, University of Glasgow, Glasgow, G128QQ, UK; Corresponding author.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 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.http://www.sciencedirect.com/science/article/pii/S2214914722000034Terahertz sensingPlants healthMachine learning |
| spellingShingle | Adnan Zahid Kia Dashtipour Hasan T. Abbas Ismail Ben Mabrouk Muath Al-Hasan Aifeng Ren Muhammad A. Imran Akram Alomainy Qammer H. Abbasi Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves Defence Technology Terahertz sensing Plants health Machine learning |
| title | Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves |
| title_full | Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves |
| title_fullStr | Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves |
| title_full_unstemmed | Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves |
| title_short | Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves |
| title_sort | machine learning enabled identification and real time prediction of living plants stress using terahertz waves |
| topic | Terahertz sensing Plants health Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214914722000034 |
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