Identification of diseases for soybean seeds by computer vision applying BP neural network

The use of computer vision for estimating quality in agriculture products has become wide spread in recent years and the composition, variety, or ripeness can be estimated. On the other hand, the appearance is one of the most worrying issues for producers due to its influence on quality. In this research, computer vision technology combined with BP artificial neural network (ANN) was developed to identify soybean frogeye, mildewed soybean, worm-eaten soybean and damaged soybean. Thirty-nine characteristic parameters from color, texture and shape characteristics were computed after preprocessing the acquired soybean images. The dimensionality of the characteristic parameters was reduced from 39 dimensionalities to 12 dimensionalities using the method of principal component analysis (PCA). MALAB software was used to build a prediction model according to 12 characteristic parameters. The identification accuracies of soybean frogeye, mildewed soybean, damaged soybean and worm-eaten soybean are 96%, 95%, 92%, and 92%, respectively. And the accuracy for heterogeneous soybean seeds with several diseases is 90%. The results show that the prediction model constructed by BP neural network can identify the diseases of soybean seeds. And it is useful to estimate appearance quality of soybean by computer vision applying BP neural network.