@inproceedings{2e3c6498713d419a9babe6a86c29199c,
title = "Improving yield mapping accuracy using remote sensing",
abstract = "The objective of this work was to investigate the use of remotely sensed vegetation indices to improve the quality of yield maps. The method was applied to the yield data of twelve cornfields from the Data Intensive Farm Management project. The results revealed the need to time shift the yield values up to three seconds to better match the sensor readings with the geographic coordinates. The residuals of the yield prediction model were used to identify points with unlikely yield values for that location, as an alternative to traditional approaches using local spatial statistics, without any assumption of spatial dependence or stationarity. The temporal and spatial distribution of the standardized coefficients for each experimental unit highlighted the presence of trends in the data. At least five out of the twelve fields presented trends that could have been induced by data collection.",
keywords = "Data filtering, Error correction, Normalized difference vegetation index, On-farm precision experimentation",
author = "{Gon{\c c}alves Trevisan}, R. and Shiratsuchi, {L. S.} and Bullock, {D. S.} and Martin, {N. F.}",
note = "Publisher Copyright: {\textcopyright} Wageningen Academic Publishers 2019; 12th European Conference on Precision Agriculture, ECPA 2019 ; Conference date: 08-07-2019 Through 11-07-2019",
year = "2019",
doi = "10.3920/978-90-8686-888-9_111",
language = "English (US)",
series = "Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019",
publisher = "Wageningen Academic Publishers",
pages = "901--908",
editor = "Stafford, {John V.}",
booktitle = "Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019",
}