A new acoustic sensing approach for predicting the percentage of filled rice grains based on the acoustic absorption spectrum using the Deep Spectra | ||
| مهندسی بیوسیستم ایران | ||
| Volume 54, Issue 4, January 2024, Pages 87-102 PDF (2.22 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22059/ijbse.2024.376335.665548 | ||
| Authors | ||
| Majid Fathi Ghalemiri1; Ali Maleki* 2; Majid Lashgari3; Ali Loghmani4 | ||
| 1Ph.D. Student, Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran | ||
| 2Associate professor of Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran | ||
| 3Associate Professor, Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Arak University, Arak, Iran | ||
| 4Associate Professor, Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran | ||
| Abstract | ||
| Rice is recognized as one of the main cereals in the world, feeding two-thirds of the global population, especially in Asian countries. Accurate assessment of the percentage of filled grains (PFG) is crucial for the efficiency and quality of rice harvesting. Traditional methods of measuring PFG are practical and based on personal judgment. This study introduces an innovative and non-destructive approach based on an acoustic sensor alongside deep learning models to predict PFG based on the acoustic spectrum of rice grains. Using an advanced deep learning architecture, the Deep Spectrum, which works directly on raw spectral data, eliminates the need for preprocessing and enhances prediction accuracy. A modified impedance tube was used to measure the acoustic spectrum, which was then analyzed using the Deep Spectrum model to predict PFG. Results indicated that this approach significantly improves the quantitative analysis of spectral data and provides a reliable prediction of rice grain filling. The prediction accuracy of the Deep Spectrum model was significantly higher compared to traditional methods, with a low root mean square error of prediction (RMSEP) of 0.24 ± 0.05 and a coefficient of determination (R²) of 0.95 ± 0.02. This prediction is vital for assessing rice quality, breeding, and genetic research. This study introduces new perspectives and methods in the field of grain quality prediction and classification using acoustic spectrum analysis and deep learning, which could be beneficial for future research in this area. | ||
| Keywords | ||
| Acoustic Absorption Coefficient; Deep Spectra; Non-destructive Measurement; Rice Acoustic Sensor; Spectral Analysis | ||
| References | ||
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