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Description: As agricultural machinery incorporates new technologies, significant developments in the available technology make autonomous farm vehicles more feasible, affordable, and desirable. One of the challenges of effective autonomous vehicle control specific to agriculture is the ability of the vehicle to interpret and adapt to constantly changing conditions. There are many types of sensors able identify specific changes in conditions, but a single indicator to signal a variety of changes in operating conditions would be beneficial in triggering an automatic shutdown to prevent machinery damage. Auditory information is a primary indicator of changing conditions to an in-cab operator, particularly in detecting mechanical overload in a combine. This paper explores the potential for auditory information, which has proven valuable to an in-cab operator, to be used in autonomous vehicle control. Sound was recorded at a sampling rate of 48 kHz near the combine chopper for three different operating modes during the same harvest day for canola. Samples from each clip were grouped and analyzed using the Fast Fourier Transform (FFT) in Matlab. An attempt was made to identify characterize features of each transform and these were then classified using a neural network. A neural network using scaled conjugate gradient backpropagation training achieved errors of less than 10%. The network was designed as a two layer network, with the hidden layer consisting of 5 neurons.

Keywords: autonomous, neural networks, control system, agricultural, auditory
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Date: 2019-07-15
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Conference name: CSBE/SCGAB 2019 Annual Conference, Vancouver, BC, 14-17 July 2019.
Session name: Bioproducts

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Type: Presentation
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Publication type: Text.Abstract
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Coverage: Canada
Language 1: en
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Rights: Canadian Society for Bioengineering
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