Knowledge and decision support for variable rate application of materials in prescription farming

Ambuel, Jack

doi:10.31274/rtd-180813-10000

Cited by 3 publications

(2 citation statements)

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“…Summing the horizontal compressive force components and multiplying by the simulation timestep facilitated the calculation of the momentum imparted to the impact plate, given by Eqn. (10.1) 1) where F N is the normal force. Figure 11.6 displays the results of simulations as a function of mass flowing in the system, and Figure 11.7 displays the results as a function of impact plate orientation.…”

Section: Application Through Simulationmentioning

confidence: 99%

“…It has been noted that when using a yield monitoring system "the most important factor in achieving good accuracy was good calibration" [13], and that the requirement for calibration of yield monitors each time they are used is an inconvenient procedure and one that is prone to error [1]. These calibration difficulties are further confounded by the need to calibrate over the entire range of flow rates encountered during harvest [3,17] and for each type of grain that is to be harvested [14], as well as the need to recalibrate to account for changes in grain moisture contents or crop conditions [14].…”

Section: Mechanisms For Self-calibrationmentioning

confidence: 99%

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Self-Calibrating Mass Flow Sensor

Reinke

Dankowicz

2012

Volume 6: 1st Biennial International Conference on Dynamics for Design; 14th International Conference on Advanced Vehicle Techn

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A comprehensive analysis was conducted for increased accuracy and self-calibration for a mass flow sensing system on a combine. This was undertaken as part of the John Deere Technology Innovation Center (JDTIC)-sponsored research program "Self-calibrating mass-flow sensor", in turn part of a John Deere Moline Technology Innovation Center (MTIC) effort toward optimization and closer integration of the components of the mass-flow sensing system in Deere harvesting combines. The long-term objective was to achieve a self-calibrating sensor system capable of adapting to varying input conditions due, for example, to changes in grain moisture content and aging of the system's elevator paddles.In analyzing the mass flow sensing system, a physics-based model was developed to describe the relationship between the rate of mass flow through the combine and the measured force imparted to the impact plate in terms of mechanical properties of the grains and the interior geometry of the combine. A computational realization of this model was constructed in Matlab. Accurate mass flow rate estimation was achieved through model-based estimation based on nonlinear regression applied to the physics-based model and data acquired through simulation and experimentation. Model-based estimation was also extended as a means for self-calibration of the sensing system. Through development of the physicsbased model, the dependence of the force imparted to the impact plate on the orientation of the impact plate was identified. By inducing known changes to the impact plate orientation and implementing model-based estimation, a means of self-calibration of the sensing system was achieved. Three methods of model-based estimation were successfully demonstrated using data generated from the physics-based model. Additionally, these were further verified using data collected from discrete element modeling simulations, and experimental data collected in two fashions: using a full-scale replica of the mass flow sensing system, and using a small-scale, benchtop testing apparatus. Furthermore, the ability of the developed algorithm to update theoretical model parameters while simultaneously estimating mass flow rate was shown to enable the system to self-calibrate. This was argued to allow the system to accommodate different operating conditions that may be encountered during combine harvesting, such as changes in crop moisture, grain variety, and aging of combine components.ii

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Section: Application Through Simulationmentioning

confidence: 99%

Section: Mechanisms For Self-calibrationmentioning

confidence: 99%

Self-Calibrating Mass Flow Sensor

Reinke

Dankowicz

2012

Volume 6: 1st Biennial International Conference on Dynamics for Design; 14th International Conference on Advanced Vehicle Techn

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Arslan

Colvin

2002

Precision Agriculture

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Evaluation of yield sensors for site-specific management

Arslan

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The performance of an impact-based yield monitor was investigated via field and laboratory experiments. In another study. X-ray techniques were used to examine the potential use of x-rays for yield measurements of harvested crops. Field and laboratory experiments showed that proper yield sensor calibration is die most important factor to keep the error less than± 4% when individual grain tank loads are considered. Field and laboratory tests provided evidence of decreasing error with increasing harvest area. In field tests, the accuracy of yield monitors increased consistently when yield measurements were averaged over IS, 30, and 60 m long segments, respectively. In laboratory experiments, the fluctuations observed in instantaneous yield data suggested that yield signals should be averaged over 4 to 10 seconds to attain the accuracy found for individual grain tank loads. Therefore, the accuracy determined at the end of harvest strips, using a weigh wagon as a comparison reference, should not be considered as true accuracy for small area applications. Grain yield variations can have an impact on accuracy. The smallest error occurred at constant flow rates whereas yield monitor error increased when step changes and transient flow rates were induced using the laboratory test stand. Another factor affecting the accuracy of the yield monitor was the combine ground speed. Changing the ground speed, depending on yield variations, also increased the yield monitor error. The accuracy of a yield monitor then would change across a field due to spatial yield variability and changing combine ground speed. The yield sensor's response to sudden flow rate changes was quite satisfactory. X-ray techniques provided high accuracy in com flow rate measurements showing repeatable correlation coefHcients of 0.99 between gray scale values and mass flow rate ranging from 2 to 6 kg/s. It is possible to increase the dynamic range of flow rates by making proper arrangements in the test setup and using different algorithms.

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Knowledge and decision support for variable rate application of materials in prescription farming

Abstract: I want to thank my advisor, Thomas S. Colvin, for giving me the fieedom to pursue my own ideas as well as for always being available to talk and give excellent advice based on his considerable e^qperience. I also want to thank my family and friends for their support.

Cited by 3 publications

References 43 publications

Self-Calibrating Mass Flow Sensor

Self-Calibrating Mass Flow Sensor

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Evaluation of yield sensors for site-specific management

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