Visualizations of Uncertainties in Precision Agriculture:
Lessons Learned from Farm Machinery

J 2020

Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

ŘEZNÍK, Tomáš, Petr KUBÍČEK, Lukáš HERMAN, Tomáš PAVELKA, Šimon LEITGEB et. al.

Základní údaje

Originální název

Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

Autoři

ŘEZNÍK, Tomáš (203 Česká republika, garant, domácí), Petr KUBÍČEK (203 Česká republika, domácí), Lukáš HERMAN (203 Česká republika, domácí), Tomáš PAVELKA (203 Česká republika, domácí), Šimon LEITGEB (203 Česká republika, domácí), Martina KLOCOVÁ (203 Česká republika, domácí) a Filip LEITNER (703 Slovensko, domácí)

Vydání

Applied Sciences, Basel, MDPI, 2020, 2076-3417

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10508 Physical geography

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

on-line verze článku

Impakt faktor

Impact factor: 2.679

Kód RIV

RIV/00216224:14310/20:00116335

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.3390/app10176132

UT WoS

000569616100001

Klíčová slova anglicky

yield measurements; point Big Data; uncertainty expression; interactive 3D visualization; ISO 19157

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 18. 3. 2021 19:17, Mgr. Marie Šípková, DiS.

Anotace

V originále

Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations.

Návaznosti

818346, interní kód MU

Název: Si-EU-Soil (Akronym: SIEUSIOL)

Investor: Evropská unie, Si-EU-Soil, Food security, sustainable agriculture and forestry, marine and maritime and inland water research (Societal Challenges)

Citovat

ŘEZNÍK, Tomáš, Petr KUBÍČEK, Lukáš HERMAN, Tomáš PAVELKA, Šimon LEITGEB, Martina KLOCOVÁ a Filip LEITNER. Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery. Applied Sciences. Basel: MDPI, 2020, roč. 10, č. 17, s. 1-21. ISSN 2076-3417. Dostupné z: https://dx.doi.org/10.3390/app10176132.

@article{1677137,
   author = {Řezník, Tomáš and Kubíček, Petr and Herman, Lukáš and Pavelka, Tomáš and Leitgeb, Šimon and Klocová, Martina and Leitner, Filip},
   article_location = {Basel},
   article_number = {17},
   doi = {http://dx.doi.org/10.3390/app10176132},
   keywords = {yield measurements; point Big Data; uncertainty expression; interactive 3D visualization; ISO 19157},
   language = {eng},
   issn = {2076-3417},
   journal = {Applied Sciences},
   title = {Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery},
   url = {https://www.mdpi.com/2076-3417/10/17/6132/htm},
   volume = {10},
   year = {2020}
}

TY  - JOUR
ID  - 1677137
AU  - Řezník, Tomáš - Kubíček, Petr - Herman, Lukáš - Pavelka, Tomáš - Leitgeb, Šimon - Klocová, Martina - Leitner, Filip
PY  - 2020
TI  - Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery
JF  - Applied Sciences
VL  - 10
IS  - 17
SP  - 1-21
EP  - 1-21
PB  - MDPI
SN  - 20763417
KW  - yield measurements
KW  - point Big Data
KW  - uncertainty expression
KW  - interactive 3D visualization
KW  - ISO 19157
UR  - https://www.mdpi.com/2076-3417/10/17/6132/htm
L2  - https://www.mdpi.com/2076-3417/10/17/6132/htm
N2  - Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations.
ER  -

ŘEZNÍK, Tomáš, Petr KUBÍČEK, Lukáš HERMAN, Tomáš PAVELKA, Šimon LEITGEB, Martina KLOCOVÁ a Filip LEITNER. Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery. \textit{Applied Sciences}. Basel: MDPI, 2020, roč.~10, č.~17, s.~1-21. ISSN~2076-3417. Dostupné z: https://dx.doi.org/10.3390/app10176132.

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