High speed detection of potato and clod using an acoustic based intelligent system

Discriminating between potato tubers and clods is the first step in developing an automatic separation system on potato harvesters. In this study, an acoustic-based intelligent system was developed for high speed discriminating between potato tubers and soil clods. About 500 kg mixture of potato tubers and clods were loaded on a belt conveyer and were impacted against a steel plate at four different velocities. The resulting acoustic signals were recorded, processed and potential features were extracted from the analysis of sound signals in both time and frequency domains. A multilayer perceptron neural network with a back propagation algorithm was used for pattern recognition. Altogether, 17 potential discriminating features were selected and fed as input vectors to the artificial neural network models. Optimal network was selected based on mean square error, correct detection rate and correlation coefficient. At the belt velocity of 1 m s−1, detection accuracy of the presented system was about 97.3% and 97.6% for potatoes and clods, respectively. Increasing the belt velocity resulted in the reduction of detection accuracy and increase in the number of miss classified samples. By using this system, it is expected that a potato harvester may operate at a capacity of 20 ton hr−1 with the accuracy of about 97%.