Smart Vibration Sensing and Machine Learning for Non-Destructive Estimation of Tender Coconut Water Volume

Authors: Abhinav Dubey; Ruchika Zalpouri; Chandni; Shrikrishna Nishani; Sandeep Mann

Book: Transforming Food Analysis and Control: Non-Destructive Technologies and Innovations

Editors: K. Prasad, S. Shekhar, M. K. Paswan, and R. Ahuja

Publisher: SLIET Longowal

Publication year: 2026

Publication date: 2026-07-20

ISBN: 978-81-990304-1-1 (e-book); 978-81-990304-0-4 (hardback)

Chapter pages: 105–116

DOI: To be added after DOI registration.

Abstract

Tender coconut quality is primarily determined by internal attributes such as water volume and physiological maturity, which are difficult to assess using conventional destructive or subjective methods. Acoustic non-destructive techniques have emerged as effective tools for evaluating these attributes by exploiting the relationship between mechanical vibration response and internal mass stiffness characteristics. This chapter reviews the principles, instrumentation, and signal processing approaches of acoustic resonance, mechanical vibration, ultrasonic, and acoustic emission methods specifically applied to tender coconut. Recent advances integrating machine learning models for water volume prediction are discussed, highlighting their relevance for large-scale grading and quality assurance in the coconut industry.

Keywords: Non-destructive evaluation; vibration; sensing; tender coconut

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