Development and evaluation of an online grading system for pinto beans using machine vision and artificial neural network

Abstract

The design of an intelligent system for qualitative evaluation of beans product impurities is the most important step necessary to make a bean sorting machine. In this research, a real-time system of pinto beans sorting (from red, white, and damaged beans, and stones) was designed and developed by combining image processing and artificial neural networks (ANNs). In total, six parameters were selected from the statistical characteristics of beans for classification of pinto beans from other beans and stones. Several ANN classifiers each with different number of neurons in the hidden layer were trained to determine the optimal structure. Optimal topology of ANN classifier was 6-12-8-2. In the first step the offline system was evaluated. The correct classification rate for pinto, white, red, and damaged beans and stones were 86.27, 100, 100, 54.9 and 65.3%, respectively. The average accuracy of offline method was 81.2%. The corresponding MSE were calculated as 0.05, 0.059, 0.013, 0.099 and 0.096, respectively. The accuracy of the online sorting system for pinto bean from others was 97.87%. The results showed that the designed system combined with ANN technique had acceptable efficiency in pinto been grading.