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Agricultural Harvester Sound Classification using Convolutional Neural Networks and Spectrograms

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Applied Engineering in Agriculture. 38(2): 455-459. (doi: 10.13031/aea.14668) @2022
Authors:   Nioosha E Khorasani, Gabriel Thomas, Simone Balocco, Danny Mann
Keywords:   Convolutional neural networks, Deep learning, Spectrograms, Stacking, Voting.


Automatic classification of harvester sounds.

Final classification obtained using three convolutional neural networks.

The results of the networks were combined via stacking and voting to achieve 100% accuracy.

Abstract. The use of deep learning in agricultural tasks has recently become popular. Deep learning networks have been used for analyzing images of crops, identifying paddy areas, distinguishing sick plants from healthy ones, to name a few applications. Besides visual systems, sound analysis of agricultural machinery is a time-sensitive task that can also be incorporated in decision making and can be done with the help of deep learning models. We propose a method to generate spectrogram images from the sound of a harvester and classify them into three working modes in real-time. We used three convolutional neural networks and use the outputs of these networks as inputs to a stacking ensemble method to improve the accuracy of the system. To achieve 100% classification accuracy, a final decision is made by voting based on several consecutive classifications made by the stacking step. We were able to perform classifications in less than 1 s which was the standard to be considered as a safe time for the harvester.

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