Design optimisation and development of thresher machine using artificial intelligence and machine learning

The design validation of thresher mechanism is done with artificial neural network (ANN). The supervised and unsupervised learning models are developed through design test data and experimental test results. The ANN model is developed and trained using back propagation algorithm with seven epoches and data set of 700 test trail results. The trained model gives minimum RSME 0.0057. The model obtained is compared through correlation analysis and average correlation coefficient 0.9623. The parametric design model obtained from ANN is implied through Arduino sketch developed for real-time controlling of thresher parameters in machine. The designed sketch with an interfacing of IR speed sensor is used to address the crop configuration as a function of crop strength. The real-time monitoring of crop configuration is noted and processed for controlling thresher encoder motor speed. The designed ANN model prevents application of single failure model and real-time controlling of threshing parameter commit highest efficiency.