Deep learning classification system for coconut maturity levels based on acoustic signals

TL;DR

This study develops a deep learning-based system using acoustic signals to classify coconut maturity levels, employing data augmentation and comparing RNN and LSTM models, achieving over 97% accuracy.

Contribution

It introduces a novel acoustic signal-based classification approach for coconuts, utilizing data augmentation and deep learning models to improve accuracy.

Findings

Deep learning models achieved 97.42% accuracy.

RNN and LSTM models performed similarly.

Data augmentation improved classification performance.

Abstract

The advancement of computer image processing, pattern recognition, signal processing, and other technologies has gradually replaced the manual methods of classifying fruit with computer and mechanical methods. In the field of agriculture, the intelligent classification of post-harvested fruit has enabled the use of smart devices that creates a direct impact on farmers, especially on export products. For coconut classification, it remains to be traditional in process. This study presents a classification of the coconut dataset based on acoustic signals. To address the imbalanced dataset, a data augmentation technique was conducted through audiomentation and procedural audio generation methods. Audio signals under premature, mature, and overmature now have 4,050, 4,050, and 5,850 audio signals, respectively. To address the updation of the classification system and the classification accuracy performance, deep learning models were utilized for classifying the generated audio signals from data generation. Specifically, RNN and LSTM models were trained and tested, and their performances were compared with each other and the machine learning methods used by Caladcad et al. (2020). The two DL models showed impressive performance with both having an accuracy of 97.42% and neither of them outperformed the other since there are no significant differences in their classification performance.