Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205616532> ?p ?o ?g. }
Showing items 1 to 99 of
99
with 100 items per page.
- W4205616532 endingPage "1339" @default.
- W4205616532 startingPage "1330" @default.
- W4205616532 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 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." @default.
- W4205616532 created "2022-01-26" @default.
- W4205616532 creator A5019462442 @default.
- W4205616532 creator A5022863238 @default.
- W4205616532 creator A5025092193 @default.
- W4205616532 creator A5036494932 @default.
- W4205616532 creator A5058869522 @default.
- W4205616532 creator A5065191057 @default.
- W4205616532 creator A5072053188 @default.
- W4205616532 date "2022-08-01" @default.
- W4205616532 modified "2023-09-26" @default.
- W4205616532 title "Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves" @default.
- W4205616532 cites W1604705558 @default.
- W4205616532 cites W1996363564 @default.
- W4205616532 cites W1996777760 @default.
- W4205616532 cites W2009795359 @default.
- W4205616532 cites W2044131129 @default.
- W4205616532 cites W2052016905 @default.
- W4205616532 cites W2073011913 @default.
- W4205616532 cites W2119943273 @default.
- W4205616532 cites W2159543062 @default.
- W4205616532 cites W2213087239 @default.
- W4205616532 cites W2249376573 @default.
- W4205616532 cites W2258541558 @default.
- W4205616532 cites W2322413868 @default.
- W4205616532 cites W2538883206 @default.
- W4205616532 cites W2605155918 @default.
- W4205616532 cites W2761176843 @default.
- W4205616532 cites W2765783050 @default.
- W4205616532 cites W2791623150 @default.
- W4205616532 cites W2820988345 @default.
- W4205616532 cites W2885770726 @default.
- W4205616532 cites W2962165968 @default.
- W4205616532 cites W2982033479 @default.
- W4205616532 cites W2987908834 @default.
- W4205616532 cites W2988166940 @default.
- W4205616532 cites W3047211622 @default.
- W4205616532 doi "https://doi.org/10.1016/j.dt.2022.01.003" @default.
- W4205616532 hasPublicationYear "2022" @default.
- W4205616532 type Work @default.
- W4205616532 citedByCount "6" @default.
- W4205616532 countsByYear W42056165322022 @default.
- W4205616532 countsByYear W42056165322023 @default.
- W4205616532 crossrefType "journal-article" @default.
- W4205616532 hasAuthorship W4205616532A5019462442 @default.
- W4205616532 hasAuthorship W4205616532A5022863238 @default.
- W4205616532 hasAuthorship W4205616532A5025092193 @default.
- W4205616532 hasAuthorship W4205616532A5036494932 @default.
- W4205616532 hasAuthorship W4205616532A5058869522 @default.
- W4205616532 hasAuthorship W4205616532A5065191057 @default.
- W4205616532 hasAuthorship W4205616532A5072053188 @default.
- W4205616532 hasBestOaLocation W42056165321 @default.
- W4205616532 hasConcept C116834253 @default.
- W4205616532 hasConcept C118518473 @default.
- W4205616532 hasConcept C119857082 @default.
- W4205616532 hasConcept C12267149 @default.
- W4205616532 hasConcept C127413603 @default.
- W4205616532 hasConcept C154945302 @default.
- W4205616532 hasConcept C169258074 @default.
- W4205616532 hasConcept C18903297 @default.
- W4205616532 hasConcept C41008148 @default.
- W4205616532 hasConcept C54924851 @default.
- W4205616532 hasConcept C86803240 @default.
- W4205616532 hasConcept C88463610 @default.
- W4205616532 hasConceptScore W4205616532C116834253 @default.
- W4205616532 hasConceptScore W4205616532C118518473 @default.
- W4205616532 hasConceptScore W4205616532C119857082 @default.
- W4205616532 hasConceptScore W4205616532C12267149 @default.
- W4205616532 hasConceptScore W4205616532C127413603 @default.
- W4205616532 hasConceptScore W4205616532C154945302 @default.
- W4205616532 hasConceptScore W4205616532C169258074 @default.
- W4205616532 hasConceptScore W4205616532C18903297 @default.
- W4205616532 hasConceptScore W4205616532C41008148 @default.
- W4205616532 hasConceptScore W4205616532C54924851 @default.
- W4205616532 hasConceptScore W4205616532C86803240 @default.
- W4205616532 hasConceptScore W4205616532C88463610 @default.
- W4205616532 hasFunder F4320334627 @default.
- W4205616532 hasIssue "8" @default.
- W4205616532 hasLocation W42056165321 @default.
- W4205616532 hasLocation W42056165322 @default.
- W4205616532 hasLocation W42056165323 @default.
- W4205616532 hasOpenAccess W4205616532 @default.
- W4205616532 hasPrimaryLocation W42056165321 @default.
- W4205616532 hasRelatedWork W1996541855 @default.
- W4205616532 hasRelatedWork W2985924212 @default.
- W4205616532 hasRelatedWork W3195168932 @default.
- W4205616532 hasRelatedWork W3195610867 @default.
- W4205616532 hasRelatedWork W4308191010 @default.
- W4205616532 hasRelatedWork W4321636153 @default.
- W4205616532 hasRelatedWork W4323021782 @default.
- W4205616532 hasRelatedWork W4327511089 @default.
- W4205616532 hasRelatedWork W4377964522 @default.
- W4205616532 hasRelatedWork W4381414210 @default.
- W4205616532 hasVolume "18" @default.
- W4205616532 isParatext "false" @default.
- W4205616532 isRetracted "false" @default.
- W4205616532 workType "article" @default.