Durian Ripeness Classification from the Knocking Sounds Using Convolutional Neural Network

Kharamat, Weangchai; Wongsaisuwan, Manop; Wattanamongkhol, Norrarat

doi:10.1109/ieecon48109.2020.229571

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…Weangchai Kharamat et al checked the ripeness of the durian via acoustic data [5]. The durians were separated into three categories, ripe, mid-ripe, and unripe.…”

Section: Related Workmentioning

confidence: 99%

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Artificial Intelligence-Based Real-Time Pineapple Quality Classification Using Acoustic Spectroscopy

et al. 2022

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The pineapple is an essential fruit in Taiwan. Farmers separate pineapples into two types, according to the percentages of water in the pineapples. One is the “drum sound pineapple” and the other is the “meat sound pineapple”. As there is more water in the meat sound pineapple, the meat sound pineapple more easily rots and is more challenging to store than the drum sound pineapple. Thus, farmers need to filter out the meat sound pineapple, so that they can sell pineapples overseas. The classification, based on striking the pineapple fruit with rigid objects (e.g., plastic rulers) is most commonly used by farmers due to the negligibly low costs and availability. However, it is a time-consuming job, so we propose a method to automatically classify pineapples in this work. Using embedded onboard computing processors, servo, and an ultrasonic sensor, we built a hitting machine and combined it with a conveyor to automatically separate pineapples. To classify pineapples, we proposed a method related to acoustic spectrogram spectroscopy, which uses acoustic data to generate spectrograms. In the acoustic data collection step, we used the hitting machine mentioned before and collected many groups of data with different factors; some groups also included the noise in the farm. With these differences, we tested our deep learning-based convolutional neural network (CNN) performances. The best accuracy of the developed CNN model is 0.97 for data Group V. The proposed hitting machine and the CNN model can assist in the classification of pineapple fruits with high accuracy and time efficiency.

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“…Weangchai Kharamat et al checked the ripeness of the durian via acoustic data [5]. The durians were separated into three categories, ripe, mid-ripe, and unripe.…”

Section: Related Workmentioning

confidence: 99%

“…Signal db = 10 log Signal watt (5) The signal-to-noise ratio was chosen from a uniform distribution from 0 to 20. For each datum, we created two new data, so the size of the data would be enlarged three times.…”

Section: Noise Additionmentioning

confidence: 99%

Artificial Intelligence-Based Real-Time Pineapple Quality Classification Using Acoustic Spectroscopy

et al. 2022

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“…Kharamat et al [5] proposed a durian ripeness classification by knocking sound and used Mel Frequency Cepstral Coefficients (MFCCs) for feature extraction. The dataset consisted in 900 files divided into three classes: (1) 300 "ripe" files, (2) 300 "mid-ripe" files, and (3) 300 "unripe" files.…”

Section: Proliferation Of Deep Learning In Acoustic Sensingmentioning

confidence: 99%

“…Several researchers have studied how to classify the maturity of agricultural products such as cacao [3], pineapple [1], pineapple ripeness [4], durian ripeness [5], and described optimal pineapple harvesting [6]. Furthermore, researchers have adapted the structure of pre-trained convolutional neural networks (CNNs), using transfer learning, to pre-train AlexNet and VGGNet networks for apple mealiness detection [7].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Sensing for Quality Edible Evaluation of Sriracha Pineapple Using Convolutional Neural Network

Pongpinigpinyo

Phawiakkharakun

Taeprasartsit

2021

Curr. Appl. Sci. Technol.

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Sriracha pineapple is a pineapple of the Smooth Cayenne variety and is one of Thailand's favorite tropical fruits. It is not only one of the most favorite agricultural products for the pineapple processing and canning industries but it is also widely consumed as a fresh fruit. Many exporters and consumers prefer to measure juiciness before processing food products and consuming, respectively. The traditional method used by pineapple sellers or farmers for fruit juiciness classification is to tap the pineapple using force impulse techniques with a rubber-tipped stick or a person's middle finger tapping. However, these traditional methods of classification require a lot of expertise and experience. Thus, the inspector's perception of accomplishing the classification process may be inclined to errors and uneven results. This paper proposes a combination of acoustic sensing and convolutional neural network (CNN). The tapping sound of 30 Sriracha pineapple samples using force impulse techniques was recorded on smartphone. The tapping sounds were processed into a juiciness classification system of three classes: (1) Juiciness 1, (2) Juiciness 2, and (3) Juiciness 3. The system involved a combination of acoustic sensing and CNN to compare the results between Mel Frequency Cepstral Coefficient (MFCC) and Mel-Spectrogram features extraction, with replication of the same CNN model, to evaluate the pineapple's edibility from its juiciness level. Experimental results showed that MFCC combined with CNN performed the best, with an accuracy of 96.67% and F1-score of 0.97. It outperformed the Mel-spectrogram combined with CNN.

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“…Aside from these methods, integration of deep neural networks like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) also gained popularity in areas of computer vision, and audio processing wherein captured signals are passed through acoustic spectrogram transformations, and then classified based on extracted spectrogram features. For instance, studies on ripeness estimation of grapes [22], banana [23], pineapple [24], durian [25], coffee beans [26], and other artificially ripened fruits [27] have utilized the potential of CNN in carrying out high accuracy classification. Other useful studies on the manipulation of CNN modeling are shown in other applications like in [28]- [30], while commonly used machine learning like SVM, Decision Tree, and Naïve Bayes can also be used in training the data [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Maturity classification of cacao through spectrogram and convolutional neural network

Bueno

Valenzuela

Arboleda

2020

Jurnal Teknologi dan Sistem Komputer

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Cacao pod's ideal harvesting time is when it is about to be ripe. Immature harvest would result in hard cacao beans not suitable for fermentation, while overripe cacao pods lead to fungal-infected, defective, and poor-quality yields. The demand for high-quality cacao products is expected to rise due to advancing technology in the present. Pre-harvesting needs to provide optimal identification of which amongst the pods are ripened enough and ready for the next stage of the cacao process. This paper recommends a technique to determine the ripeness of cacao. Nine hundred thirty-three cacao samples were used to collect thumping audio data at five different pod's exocarp locations. Each sound file is 1 second long, creating 4665 cacao sound file datasets at 16kHz sample rate and 16-bit audio bit depth. The process of the Mel-Frequency Cepstral Coefficient Spectogram was then applied to extract recognizable features for the training process. The deep learning method integrated was a convolutional neural network (CNN) to classify the cacao sound successfully. The experimental design model's output exhibits an accuracy of 97.50 % for the training data and 97.13 % for the validation data. While the overall accuracy mean of the classification system is 97.46 %, whether the cacao is unripe or ripe.

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Durian Ripeness Classification from the Knocking Sounds Using Convolutional Neural Network

Cited by 12 publications

References 9 publications

Artificial Intelligence-Based Real-Time Pineapple Quality Classification Using Acoustic Spectroscopy

Artificial Intelligence-Based Real-Time Pineapple Quality Classification Using Acoustic Spectroscopy

Acoustic Sensing for Quality Edible Evaluation of Sriracha Pineapple Using Convolutional Neural Network

Maturity classification of cacao through spectrogram and convolutional neural network

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