Technological advancements enable the use of intelligent systems in various fields such as operations, management, as well as in agriculture. In the field of agriculture, smart systems create a positive impact on fruit classification, specifically for export needs. Even with its wide application, there have been limited studies on its utilization in coconut exportation. Another challenge identified is that gathering a large amount of dataset has become difficult, expensive, time-consuming, and prone to errors due to human inconsistencies. Due to this majority of the initial datasets in existing studies are unbalanced and sometimes, inadequate to contribute significant analysis and results. In this study, an investigation is conducted using an existing dataset of coconut acoustic signals to generate more data to balance out the samples across the three maturity levels. Data and data augmentation techniques are utilized along with combining techniques for acoustic signals, feature extraction, and data clustering. The data points under three maturity levels were clustered appropriately using the summing method and Mel Frequency Cepstral Coefficient feature. All data synthesizers failed to generate quality synthetic data. However, both audiomentation and procedural audio generation were able to produce quality augmented data after validating using linear layers, 1-dimensional convolution, and long-short term memory model as the audio classification technique. The new dataset was then fed to the same models and the results yielded to significant increase in its classification performance for all models. The study can be further improved by incorporating other coconut features to increase performance and widen its application.