Sugarcane yield mapping based on vehicle tracking

Md Abdul Momin, Tony E Grift, Domingos S. Valente, Alan Christopher Hansen

Research output: Contribution to journalArticle

Abstract

The agricultural industry is increasingly reliant upon the development of technologies that employ real-time monitoring of machine performance to generate pertinent information for machine operators, owners, and managers. Yield mapping in particular is an important component of implementing precision agricultural practices and assessing spatial variability. In an attempt to generate yield maps in sugarcane, this research estimated yield in the field based on GPS data from harvesters, tractors and semi-trucks. The method was based on identifying “fill events”, which represent a distance through which the tractor/wagon combination traveled in parallel with the harvester, indicating that the wagon was being filled. Each wagon was filled to approximately 10 Mg of sugarcane, which was divided by the fill event distance and row width to determine the yield in Mg ha−1. A total of 76 fill events were observed from a 7.1 ha harvested area. Based on the estimated yield per fill event, a rudimentary yield map was developed, which was expanded into a generalized yield map for the 7.1 ha harvested area.

Original languageEnglish (US)
Pages (from-to)896-910
Number of pages15
JournalPrecision Agriculture
Volume20
Issue number5
DOIs
StatePublished - Oct 15 2019

Fingerprint

yield mapping
Saccharum
sugarcane
wagons
Motor Vehicles
Industry
harvesters
tractors
Technology
equipment operators
Research
trucks
agricultural industry
managers
monitoring

Keywords

  • Machine productivity
  • Precision agriculture
  • Sugarcane harvester

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)

Cite this

Sugarcane yield mapping based on vehicle tracking. / Momin, Md Abdul; Grift, Tony E; Valente, Domingos S.; Hansen, Alan Christopher.

In: Precision Agriculture, Vol. 20, No. 5, 15.10.2019, p. 896-910.

Research output: Contribution to journalArticle

Momin, Md Abdul ; Grift, Tony E ; Valente, Domingos S. ; Hansen, Alan Christopher. / Sugarcane yield mapping based on vehicle tracking. In: Precision Agriculture. 2019 ; Vol. 20, No. 5. pp. 896-910.
@article{b4046e54480d4246ba3fa341d2ea8b09,
title = "Sugarcane yield mapping based on vehicle tracking",
abstract = "The agricultural industry is increasingly reliant upon the development of technologies that employ real-time monitoring of machine performance to generate pertinent information for machine operators, owners, and managers. Yield mapping in particular is an important component of implementing precision agricultural practices and assessing spatial variability. In an attempt to generate yield maps in sugarcane, this research estimated yield in the field based on GPS data from harvesters, tractors and semi-trucks. The method was based on identifying “fill events”, which represent a distance through which the tractor/wagon combination traveled in parallel with the harvester, indicating that the wagon was being filled. Each wagon was filled to approximately 10 Mg of sugarcane, which was divided by the fill event distance and row width to determine the yield in Mg ha−1. A total of 76 fill events were observed from a 7.1 ha harvested area. Based on the estimated yield per fill event, a rudimentary yield map was developed, which was expanded into a generalized yield map for the 7.1 ha harvested area.",
keywords = "Machine productivity, Precision agriculture, Sugarcane harvester",
author = "Momin, {Md Abdul} and Grift, {Tony E} and Valente, {Domingos S.} and Hansen, {Alan Christopher}",
year = "2019",
month = "10",
day = "15",
doi = "10.1007/s11119-018-9621-2",
language = "English (US)",
volume = "20",
pages = "896--910",
journal = "Precision Agriculture",
issn = "1385-2256",
publisher = "Springer Netherlands",
number = "5",

}

TY - JOUR

T1 - Sugarcane yield mapping based on vehicle tracking

AU - Momin, Md Abdul

AU - Grift, Tony E

AU - Valente, Domingos S.

AU - Hansen, Alan Christopher

PY - 2019/10/15

Y1 - 2019/10/15

N2 - The agricultural industry is increasingly reliant upon the development of technologies that employ real-time monitoring of machine performance to generate pertinent information for machine operators, owners, and managers. Yield mapping in particular is an important component of implementing precision agricultural practices and assessing spatial variability. In an attempt to generate yield maps in sugarcane, this research estimated yield in the field based on GPS data from harvesters, tractors and semi-trucks. The method was based on identifying “fill events”, which represent a distance through which the tractor/wagon combination traveled in parallel with the harvester, indicating that the wagon was being filled. Each wagon was filled to approximately 10 Mg of sugarcane, which was divided by the fill event distance and row width to determine the yield in Mg ha−1. A total of 76 fill events were observed from a 7.1 ha harvested area. Based on the estimated yield per fill event, a rudimentary yield map was developed, which was expanded into a generalized yield map for the 7.1 ha harvested area.

AB - The agricultural industry is increasingly reliant upon the development of technologies that employ real-time monitoring of machine performance to generate pertinent information for machine operators, owners, and managers. Yield mapping in particular is an important component of implementing precision agricultural practices and assessing spatial variability. In an attempt to generate yield maps in sugarcane, this research estimated yield in the field based on GPS data from harvesters, tractors and semi-trucks. The method was based on identifying “fill events”, which represent a distance through which the tractor/wagon combination traveled in parallel with the harvester, indicating that the wagon was being filled. Each wagon was filled to approximately 10 Mg of sugarcane, which was divided by the fill event distance and row width to determine the yield in Mg ha−1. A total of 76 fill events were observed from a 7.1 ha harvested area. Based on the estimated yield per fill event, a rudimentary yield map was developed, which was expanded into a generalized yield map for the 7.1 ha harvested area.

KW - Machine productivity

KW - Precision agriculture

KW - Sugarcane harvester

UR - http://www.scopus.com/inward/record.url?scp=85057751112&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85057751112&partnerID=8YFLogxK

U2 - 10.1007/s11119-018-9621-2

DO - 10.1007/s11119-018-9621-2

M3 - Article

AN - SCOPUS:85057751112

VL - 20

SP - 896

EP - 910

JO - Precision Agriculture

JF - Precision Agriculture

SN - 1385-2256

IS - 5

ER -