enhanced grain crop yield monitor accuracy through sensor fusion and post-processing algorithms

233 Pages·2016·5.56 MB·English

by  Matthew Wayne Veal

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University of Kentucky Doctoral Dissertations Graduate School
2006
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Matthew Wayne Veal
University of Kentucky, [email protected]
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Veal, Matthew Wayne, "ENHANCED GRAIN CROP YIELD MONITOR ACCURACY THROUGH SENSOR
FUSION AND POST-PROCESSING ALGORITHMS" (2006). University of Kentucky Doctoral Dissertations.
249.
https://uknowledge.uky.edu/gradschool_diss/249
This Dissertation is brought to you for free and open access by the Graduate School at UKnowledge. It has been
accepted for inclusion in University of Kentucky Doctoral Dissertations by an authorized administrator of
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ABSTRACT OF DISSERTATION

Matthew Wayne Veal

The Graduate School
University of Kentucky
2006
ENHANCED GRAIN CROP YIELD MONITOR ACCURACY THROUGH SENSOR
FUSION AND POST-PROCESSING ALGORITHMS

ABSTRACT OF DISSERTATION

A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy in Biosystems and Agricultural Engineering
in the College of Engineering
at the University of Kentucky

By
Matthew Wayne Veal
Lexington, Kentucky

Director: Dr. Scott A. Shearer,
Professor of Biosystems and Agricultural Engineering
Lexington, Kentucky

2006

Copyright © Matthew Wayne Veal 2006
ABSTRACT OF DISSERTATION

ENHANCED GRAIN CROP YIELD MONITOR ACCURACY THROUGH SENSOR FUSION
AND POST-PROCESSING ALGORITHMS

Yield monitors have become an indispensable part of precision agriculture systems
because of their ability to measure the yield variability. Accurate yield monitor data availability
is essential for the assessment of farm practices. The current technology of measuring grain
yields is prone to errors that can be attributed to mass flow variations caused by the mechanisms
within a grain combine. Because of throughput variations, there are doubts regarding the
correlation between the mass flow measurement and the actual grain volume produced at a
specific location. Another inaccuracy observed in yield monitor data can be attributed to inexact
cut-widths values entered by the machine operator.
To effectively address these yield monitor errors, two crop mass flow sensing devices
were developed and used to correct yield monitor data. The two quantities associated with crop
material mass flow that were sensed were tension on the feeder housing drive chain and the
hydraulic pressure on the threshing cylinder’s variable speed drive. Both sensing approaches
were capable of detecting zero mass flow conditions better than the traditional grain mass flow
sensor. The alternative sensors also operate without being adversely affected by material
transport delays. The feeder housing-based sensor was more sensitive to variations in crop
material throughput than the hydraulic pressure sensor. Crop mass flow is not a surrogate for
grain mass flow because of a weak relationship (R2 < 0.60) between the two quantities. The crop
mass flow signal does denote the location and magnitude of material throughput variations into
the combine. This delineation was used to redistribute grain mass flow by aligning grain and
crop mass flow transitions using sensor fusion techniques. Significant improvements (α = 0.05)
in yield distribution profile were found after the correction was applied.
To address the cut-width entry error, a GIS-based post-processing algorithm was
developed to calculate the true harvest area for each yield monitor data point. Based on the
results of this method, a combine operator can introduce yield calculation errors of 15%. When
these two correction methods applied to yield monitor data, the result is yield maps with
dramatically improved yield estimates and enhanced spatial accuracy.

KEYWORDS: Yield Monitor, Precision Agriculture, Combine Harvester, Sensor Fusion,
Geographic Information Systems (GIS)

Matthew W. Veal

4 August 2006
ENHANCED GRAIN CROP YIELD MONITOR ACCURACY THROUGH SENSOR FUSION
AND POST-PROCESSING ALGORITHMS

By

Matthew Wayne Veal

Dr. Scott A. Shearer
Director of Dissertation

Dr. Dwayne R. Edwards
Director of Graduate Studies

4 August 2006
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DISSERTATION

Matthew Wayne Veal

The Graduate School
University of Kentucky
2006
ENHANCED GRAIN CROP YIELD MONITOR ACCURACY THROUGH SENSOR FUSION
AND POST-PROCESSING ALGORITHMS

DISSERTATION

A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy in Biosystems and Agricultural Engineering
in the College of Engineering
at the University of Kentucky

By
Matthew Wayne Veal
Lexington, Kentucky

Director: Dr. Scott A. Shearer,
Professor of Biosystems and Agricultural Engineering
Lexington, Kentucky

2006

Copyright © Matthew Wayne Veal 2006
This work is dedicated to the memory of my brother Andy, who is a reminder to never take
yourself too seriously and that there are more important pursuits in life than dissertations and
scholarly achievement.