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Dr. Luciano Shiratsuchi
Louisiana State University

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Research Keywords & Expertise

0 Geostatistics
0 Precision Agriculture
0 Agronomy and Agricultural Research
0 Machine learning (deep learning, random forests)
0 Remote sensing (both satellite and drones)

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Preprint
Published: 29 January 2019
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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.

ACS Style

Rodrigo Gonçalves Trevisan; Luciano Shozo Shiratsuchi; David S. Bullock; Nicolas Federico Martin. Improving Yield Mapping Accuracy Using Remote Sensing. 2019, 1 .

AMA Style

Rodrigo Gonçalves Trevisan, Luciano Shozo Shiratsuchi, David S. Bullock, Nicolas Federico Martin. Improving Yield Mapping Accuracy Using Remote Sensing. . 2019; ():1.

Chicago/Turabian Style

Rodrigo Gonçalves Trevisan; Luciano Shozo Shiratsuchi; David S. Bullock; Nicolas Federico Martin. 2019. "Improving Yield Mapping Accuracy Using Remote Sensing." , no. : 1.