Version 1
: Received: 3 May 2024 / Approved: 4 May 2024 / Online: 6 May 2024 (08:47:24 CEST)
How to cite:
Abadies, J. I.; Romulo, H. M. P.; Arboleda, E. R.; Sarmiento, J. L. P. Coconut Maturity Detector Using Spectrogram and Convolutional Neural Network. Preprints2024, 2024050229. https://doi.org/10.20944/preprints202405.0229.v1
Abadies, J. I.; Romulo, H. M. P.; Arboleda, E. R.; Sarmiento, J. L. P. Coconut Maturity Detector Using Spectrogram and Convolutional Neural Network. Preprints 2024, 2024050229. https://doi.org/10.20944/preprints202405.0229.v1
Abadies, J. I.; Romulo, H. M. P.; Arboleda, E. R.; Sarmiento, J. L. P. Coconut Maturity Detector Using Spectrogram and Convolutional Neural Network. Preprints2024, 2024050229. https://doi.org/10.20944/preprints202405.0229.v1
APA Style
Abadies, J. I., Romulo, H. M. P., Arboleda, E. R., & Sarmiento, J. L. P. (2024). Coconut Maturity Detector Using Spectrogram and Convolutional Neural Network. Preprints. https://doi.org/10.20944/preprints202405.0229.v1
Chicago/Turabian Style
Abadies, J. I., Edwin R. Arboleda and Judy Lhyn P. Sarmiento. 2024 "Coconut Maturity Detector Using Spectrogram and Convolutional Neural Network" Preprints. https://doi.org/10.20944/preprints202405.0229.v1
Abstract
Coconut is an important multipurpose crop wherein different stages of its maturity are used for different products. Traditional approach of its classification use sound formed by tapping the fruit, and the tapper will base its judgment through the sound he heard. The procedure was highly subjective and attempts had been made by other researchers to automate and classify the adulthood of the coconut objectively. Nowadays, deep learning techniques are being utilized to solve classification problems. One such method is Convolutional Neural Network (CNN) that takes raw pixel data, learns to extract features, and ultimately classifies the input. In this study, a portable device was developed that takes audio signals generated from tapping the fruit mechanically. This audio clip was converted to spectrogram and was used as input to a LeNet CNN architecture. We argue that CNN classifier has improved accuracy compared to the non-deep learning system. Our evaluation confirmed that the use of CNN had improved the accuracy of classifying coconut by about 15%.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
