Computerized Unripe and Ripe Durian Striking Sound Recognition Using Syllable-Based HMMs

Abstract

Buying expensive agricultural produce and fruit such as durians that are unripe can result in a bad experience for a consumer and a loss in profit for a retailer. Therefore, the study of durian striking sounds to create an automatic method of recognizing the ripeness of durians without cutting or damaging them is interesting because it could benefit shoppers and the fruit industry. To solve the problem, the following method of recognizing unripe and ripe striking signals is proposed. First, in the recognition process, the spectral features of the signals are extracted. Then, acoustic models of durian striking sounds are created using syllable-based Hidden Markov Models (HMM). Finally, sequences of syllable-based unripe and ripe durians and defined possible durian ripeness results are applied to recognize the ripeness. Average ripeness recognition rates of more than 90.0% were achieved when using any number of strikes from one to five. When the number of strikes was limited to four and five, higher recognition rates of 95.0% and 92.0% were achieved for the untrained and unknown test sets, respectively. An average total recognition time of less than 80 milliseconds was taken to recognize the unripe and ripe durian striking sounds. The experimental results indicate that the proposed method is a time-efficient, accurate and effective way of recognizing durian ripeness.

Recognition of Fruit Types from Striking and Flicking Sounds

“…It can be seen from how some merchants recognize short flicking and striking sounds, which have some frequency differences to predict the internal fruit flesh. Based on the results, using HMMs with frequency-based features could efficiently handle the different impacts of watermelon flicking and durian tapping (Phoophuangpairoj, 2014a;Phoophuangpairoj, 2014b). For guavas, repeatedly flicking the same area can affect the recognition results.…”

Durian Recognition Based on Multiple Features and Linear Discriminant Analysis

“…Fruit recognition uses fruit image's content for analysis, identification, and classification in the agriculture domain. Some existing works such as recognition of various fruits [16][17][18][19][20][21] or even focusing on specific fruits such as olive [22], persimmon [23], almond [24], apple [25], papaya [26], and durian [27][28].…”

“…The focus of authors in [28] is however rather different. Although it is a work on content-based recognition for durian, their aim is on differentiating between unripe and ripe durians based on the spectral features of striking sound instead of classifying durian according to its species.…”

“…The feature descriptor will also need to be invariant to rotation and translation or illumination to increase the robustness of the proposed work. From the literature review and to our knowledge, there is very little exploration being done for content-based durian species recognition [27][28]. For durian species images, suitable feature representations will also need to be determined.…”

A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)

“…ArrÁZola et al [15] evaluated five maturity levels of Tainong papaya fruits through the examination of mechanical resistance and the application of finite element analysis (FEA); an in-depth analysis was conducted. Phoophuangpairoj [16] created an acoustic model of a knocking sound based on the Hidden Markov model of syllables and proposed a new approach for recognizing durian ripe and raw impact signals, with an average ripening recognition rate of more than 90.0%. González-Araiza et al [17] designed a non-destructive device based on electrical bioimpedance measurements to obtain the impedance spectrum of the whole fruit and, thus, analyze the ripeness of strawberry fruits.…”