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Machine learning strategy for soil nutrients prediction using spectroscopic method
ID
Trontelj, Janez
(
Avtor
),
ID
Chambers, Olga
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(1,01 MB)
MD5: DDAE752DB89FBD26B29237259C4161EC
URL - Izvorni URL, za dostop obiščite
https://www.mdpi.com/1424-8220/21/12/4208
Galerija slik
Izvleček
The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy for soil properties prediction using optical spectroscopy sensors. Several research results have been stated and investigated. A comparison is made between six commonly used techniques: Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine, Least-Square Support Vector Machine and Artificial Neural Network, showing that the best prediction accuracy cannot always be achieved by the most common and complicated method. The influence of the chosen category for nutrient characterization was investigated, indicating better prediction when a multi-component strategy was used. In contrast, the prediction of single-component soil properties was less accurate. In addition, the influence of category levels was not as significant as expected when choosing between 3-level, 5-level or 13-level nutrient characterization for some nutrients, which can be used for a more precise nutrient characterization strategy. A comparative analysis was performed between soil from a local farm with similar texture and soils collected from different locations in Slovenia, which gave a better prediction for a local farm. Finally, the influence of principal component analysis was validated using 5, 10, 20 and 50 first principal components, indicating the better performance of machine learning when using the 50 principal components.
Jezik:
Angleški jezik
Ključne besede:
machine learning
,
nutrients prediction
,
soil spectra
,
soil analysis
,
soil category
,
precision farming
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FE - Fakulteta za elektrotehniko
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2021
Št. strani:
13 str.
Številčenje:
Vol. 21, iss. 12, art. 4208
PID:
20.500.12556/RUL-135434
UDK:
631.4:004.8
ISSN pri članku:
1424-8220
DOI:
10.3390/s21124208
COBISS.SI-ID:
67722755
Datum objave v RUL:
15.03.2022
Število ogledov:
565
Število prenosov:
104
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Sensors
Skrajšan naslov:
Sensors
Založnik:
MDPI
ISSN:
1424-8220
COBISS.SI-ID:
10176278
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:
19.06.2021
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
strojno učenje
,
ugotavljanje rodovitnosti prsti
,
spekter prsti
,
analiza prsti
,
kategorija prsti
,
natančno kmetovanje
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