Improved method for modeling in capacitive grain moisture sensor

Abstract

Food is the material basis of human survival, food quality directly related to people's survival and life. Grains as the main food supply, its quality is also essential. Grain moisture of grain stored is an extremely important influence on the main character. However, the ambient temperature and the degree of compaction of grains have a strong nonlinear relationship between the analytic expression and difficult. Therefore the improved BP neural network was used to solve this problem. With improved orthogonal-optimizing method, the study showed that while the RBF nerve network’s weighing factors were obtained, the numbers of hidden units could be acquired. This method could avoid too few nerve elements that would result in low accuracy, or too many nerve elements that will result in “over learnt”. This method had been approved for its advantages over the ordinary methods with laboratory tests on grains of wheat, rice, corn, etc. Experimental results showed that application of the improved BP model, the measurement accuracy of wheat increased 62.0%, corn increased measurement accuracy of 66.2%, rice increased 66.7% accuracy