You can determine the ripeness and quality of a watermelon by tapping on it

F. Khoshnam, M. Namjoo, H. Golbakhshi. Acoustic Testing for Melon Fruit Ripeness Evaluation during Different Stages of Ripening. 2016

Shows that resonance frequencies obtained from impulse response testing can successfully distinguish maturity stages in melons.

Yinghao Zhang, Xiaoyan Deng, Zhou Xu, Peipei Yuan. Watermelon Ripeness Detection via Extreme Learning Machine with Kernel Principal Component Analysis Based on Acoustic Signals. 2019. https://doi.org/10.1142/S0218001419510029

Demonstrates that machine learning models analyzing acoustic signals from tapped watermelons can accurately classify ripeness.

W. Pamungkas, N. Bintoro. Evaluation of watermelon ripeness using self-developed ripening detector. 2021. https://doi.org/10.1088/1755-1315/653/1/012020

Confirms that a ripening detector based on acoustic impulse response correlates reliably with fruit weight and soluble solids.

Kilari Veera Swamy, Sunkari Rajaneesh, Sagarla Mahalaxmi, Pavushetti Revanth. Watermelon Classification using Machine Learning with Enhanced Features. 2025. https://doi.org/10.1109/AMATHE65477.2025.11081207

Analyzes acoustic resonance patterns from tapping watermelons to successfully predict fruit sweetness and quality.

Yash N T, Pramod Mathew Jacob. Watermelon Ripeness Prediction using Acoustic Signal Processing and Machine Vision. 2026. https://doi.org/10.1109/ICMSCI67830.2026.11469211

Employs microphone sensors to capture acoustic vibrations from thumping watermelons to predict ripeness via frequency analysis.

Wenyu Li, Qihan Wang, Xi Lin, Shuaiqi Guo, Meng Ma. Non-Destructive Assessment of Watermelon Comprehensive Quality Based on Acoustic and Vibration Signals. 2026. https://doi.org/10.3390/s26134000

Validates non-destructive assessment of watermelon quality and ripeness using impact and acoustic vibration signals.