Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

1 Downloads (Pure)
OriginalsprogEngelsk
Artikelnummer3506
TidsskriftSensors
Vol/bind19
Udgave nummer16
ISSN1424-8220
DOI
StatusUdgivet - 10 aug. 2019

Fingerprint

Silage
Object recognition
learning
Zea mays
fragments
Learning
Processing
boxes
Process Assessment (Health Care)
annotations
unions
corn
crops
predictions
intersections
Crops
Pixels
pixels
Recognition (Psychology)
Deep learning

Citer dette

@article{c41424a9ddfa4862a09ec413b9c59fd6,
title = "Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images",
keywords = "Deep learning, Forage This work was funded by Innovation Fund Denmark under Grant 7038-00170B, Kernel processing, Object recognition, Precision agriculture, Silage",
author = "Rasmussen, {Christoffer B{\o}gelund} and Moeslund, {Thomas B.}",
year = "2019",
month = "8",
day = "10",
doi = "10.3390/s19163506",
language = "English",
volume = "19",
journal = "Sensors",
issn = "1424-8220",
publisher = "M D P I AG",
number = "16",

}

Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images. / Rasmussen, Christoffer Bøgelund; Moeslund, Thomas B.

I: Sensors, Bind 19, Nr. 16, 3506, 10.08.2019.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images

AU - Rasmussen, Christoffer Bøgelund

AU - Moeslund, Thomas B.

PY - 2019/8/10

Y1 - 2019/8/10

KW - Deep learning

KW - Forage This work was funded by Innovation Fund Denmark under Grant 7038-00170B

KW - Kernel processing

KW - Object recognition

KW - Precision agriculture

KW - Silage

UR - http://www.scopus.com/inward/record.url?scp=85071280862&partnerID=8YFLogxK

U2 - 10.3390/s19163506

DO - 10.3390/s19163506

M3 - Journal article

C2 - 31405164

VL - 19

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 16

M1 - 3506

ER -