Carregant...
Fitxers
Tipus de document
ArticleVersió
Versió publicadaData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/212281
Agricultural Harvester Sound Classification Using Convolutional Neural Networks And Spectrograms
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
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.
Matèries (anglès)
Citació
Citació
KHORASANI, Nioosha e., THOMAS, Gabriel, BALOCCO, Simone, MANN, Danny. Agricultural Harvester Sound Classification Using Convolutional Neural Networks And Spectrograms. _2022_. vol. 38. Vol. 2, núm. 455-459. [consulta: 23 de gener de 2026]. ISSN: 0883-8542. [Disponible a: https://hdl.handle.net/2445/212281]