Development of two smart acoustic yam quality detection devices using a machine learning approach.

Quality detection has been a major problem in the agriculture and food industries. This operation is mostly done by a subjective sensory method which is prone to high error and food destruction. Therefore, there is a need to apply artificial intelligence using a machine learning approach. This study developed two intelligent acoustic yam quality detection and classification devices using two sound-generating techniques. The software (multi-wave frequency generator) sound-generating technique generated sound from a laptop to a speaker inside a detecting chamber. This sound passes through the yam and was received on the opposite side by a microphone, into another laptop for analysis using visual analyzer software. The impact sound-generating technique used sound generated from a gentle impact of the yam on a flat surface placed inside the detection chamber. The sound produced was picked up by a microphone into a laptop for analysis. Acoustic properties considered were amplitude, frequency, sound velocity, wavelength, period and sound intensity. Discriminant analysis algorithm only was used in this first stage of the study to prove the applicability of machine learning. Three qualities (good, diseased damaged and insect-damaged) of two yam varieties (white and yellow yam) were tested. The device's performance of white yam was 79% and 68.7%, yellow yam was 82.3% and 68.7% for the software sound generation-technique and surface impact sound-generating technique, respectively. The study shows that the software sound-generating technique performed better in terms of overall yam quality detection and also proves the applicability of machine learning.