Researchers and planter manufacturers have been working closely to develop an automated system for evaluating performance of seeding. In the present study, an innovative use of acoustic signal for laboratory evaluation of seeding-machine application is described. Seed detection technique of the proposed system was based on a rising voltage value that a microphone sensed in each impaction of seeds to a steel plate. Online determining of seed spacing was done with a script which was written in MATLAB software. To evaluate the acoustic system with desired seed spacing, a testing rig was designed. Seeds of wheat, corn and pelleted tomato were used as experimental material. Typical seed patterns were positioned manually on a belt stand with different spacing patterns. When the belt was running, the falling seeds from the end point of the belt impacted to the steel plate, and their acoustic signal was sensed by the microphone. In each impact, data was processed and spacing between the seeds was automatically obtained. Coefficient of determination of gathered data from the belt system and the corresponding seeds spacing measured with the acoustic system in all runs was about 0.98. This strong correlation indicates that the acoustic system worked well in determining the seeds spacing.

In the present study, feasibility of laboratory detection of damaged seeds in precision planters caused by malfunction of seed metering device was investigated. An acoustic-based intelligent system was developed for detection of damaged pelleted tomato seeds. To improve the Artificial Neural Network (ANN) models a total of 2000 seeds sound signals, 1000 samples for damaged seeds and 1000 for undamaged ones were recorded. When seed metering device drove out seeds, the ejected seeds were impacted to steel plate, and their acoustic signals were recorded from the impact. The bounced seeds lied on the running grease belt. In each stage of experiments, damaged seeds were determined manually in grease belt and related damaged seed sound signals were designated. Achieved acoustic signals, were processed and potential features were extracted from the analysis of sound signals in time and frequency domains. The method is based on feature generation by Fast Fourier Transform (FFT), feature selection by statistical methods and classification by Multilayer Feed forward Neural Network. Features such as amplitude, phase and power spectrum of sound signals were computed through a 1024-point FFT. By using statistical factors (maximum, minimum, median, mean and variance) for each vector of data, feature vector was reduced to 15 factors. In developing the ANN models, several ANN architectures, each having different numbers of neurons in hidden layer, were evaluated. The best model was chosen after a number of evaluations based on minimizing the mean square error (MSE), correct detection rate (CDR) and correlation coefficient (r). Selected ANN, 15-17-2 was configured for classification. CDR of the proposed ANN model for undamaged and damaged seeds was 99.49 and 100 respectively. MSE of the system was found to be 0.0109.