During the fruit sample preparation process, coconut fruits classified under the tall coconut variety in their post-harvest period are considered the subject of this article. All samples are pre-classified by local farmers and experts into three maturity levels; premature, mature, and overmature. Each coconut underwent the synchronized tapping and recording process using developed hardware and software. The analog recordings are then converted into digital signals. Sampled frequency and amplitude in discrete-time signals of each sample went through a quantization process. The data presented in this article provides the general differentiation of the coconuts according to their maturity levels through their acoustic properties. This dataset can also be useful in creating an advanced and intelligent classification system of fruits through machine learning and deep learning techniques.
Keywords: Acoustic spectrum; Coconut; Cocos nucifera; Machine learning; Sound; Tapping.
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