Durian ripeness prediction using audio processing and deep learning | |
| Author | Sarach Rujiranurak |
| Call Number | AIT Thesis no.DSAI-22-10 |
| Subject(s) | Computer sound processing Neural networks (Computer science) Deep learning (Machine learning) Durian--Thailand |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence |
| Publisher | Asian Institute of Technology |
| Abstract | Due to its taste and odor, durian is a high-value fruit product in Thailand. With their custard-like pulps, durians can be eaten at various ripeness stages, giving a variety of sweetness appreciated by consumers. However, determining the level of ripeness of a durian is complicated. The fruit is covered with hard spikes and a thick peel, making it challenging to observe its condition from the outside. In practice, experts knock a durian with a rubber stick and listen to the sound that is typical to distinguish ripe and unripe fruit. This technique requires a great deal of experience on the part of the listener to make accurate predictions. This study indicated the audio processing and deep learning techniques applied for durian ripeness detection from the knocking sound. The audio samples were recorded from 611 Mon-thong durians harvested from the south region of Thailand, Chumphon, Surat Thani, and Yala provinces. There were four classes of ripeness levels, unripe, mid-ripe, ripe, and overripe, for the target of the deep learning models. The sound from each hit was extracted and transformed into three forms of spectrograms, Short Time Fourier Transform (STFT), mel spectrogram, and Mel Frequency Cepstral Coefficients (MFCCs). Two deep learning models, ResNet50 and Audio Spectrogram Transformer (AST), were evaluated for their performance when trained with different data structures and learning procedures. This study found that ResNet-50 provided the best prediction at 74.5% accuracy when predicting in four levels and 89.4% accuracy when predicting two classes when grouping the targets into unripe and ripe conditions. |
| Year | 2022 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Dailey, Matthew N. |
| Examination Committee(s) | Chaklam Silpasuwanchai;Loc, Thai Nguyen |
| Scholarship Donor(s) | The Royal Thai Government |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2022 |