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

Abstract

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.

Classification and identification of agricultural products based on improved MobileNetV2

“…In Ref. 27 , an automatic pineapple classification method was introduced. This method utilized an embedded onboard computing processor, servos, and ultrasonic sensors to create a knocking machine integrated with a conveyor belt for automatic separation of pineapples.…”

Enhanced non-destructive of degree of pineapple juiciness using ensemble learning model based on tapping sound sensing

“…Acoustic signals have been the subject of numerous researches that have attempted to classify the quality and maturity of various fruits such as durian (Kharamat et al, 2020), coconut (Rahmawati et al, 2019;Caladcad et al, 2020;Fadchar and Cruz, 2020), cacao (Bueno et al, 2020), watermelon (Chawgien and Kiattisin, 2021), pistachio nut (Hosseinpour et al, 2022), and pineapple (Huang et al, 2022;Phawiakkharakun et al, 2022).…”

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“…Gao et al [31] used long short-term memory to predict soil moisture. Huang et al [32] built their own convolutional neural network to achieve the classification of pineapple quality. Liu et al [33,34] used an optimized neural network to identify spectral peaks and initially determine target locations.…”