Model For Predicting Apple Pre-Storage Heat Treatment And Storage Period Respiration Rate And Color By Using BP Neural Network And Genetic Algorithm

In this paper, we used the material of Red Fuji (Malus domestica Borkh) apple andmeasured the data of respiration rate and color on the process of apple pre-storage heattreatment and storage period (38℃, 96h; 38℃,72h; 20℃,1440h;0℃,720h). The model ofpredicting respiration rate and color(background color a~*, H°) was determined respectivelyby using the BP neural network and can realize the simulation and prediction of therespiration rate and color on the process of apple pre-storage heat treatment and storage.Meanwhile, the application of genetic algorithms global search ability to optimization ofthe weight and threshold of matrix of BP neural network can improve the predictionprecision and decrease the time of constitution model to increase the model applicationvalue.1, A neural network topological structure was determined and an artificial neuralnetwork model for predicting respiration rate of Red Fuji apple was established by using BPneural network during the apple per-storage heat treatment and storage period and thecorrelation between multi-dimension input data (temperature, time) and respiration rate. Byverification, the model can fit the change of respiration rate on the process of applepre-storage heat treatment and storage period. At last, we use the model to predict thenon-experimental respiration rate and by contrast the prediction precision of the model is89.95% and conclude the model can be used to predict the respiration rate.2, A neural network topological structure was determined and an artificial neuralnetwork model for simulation and predicting color(a~*, H°) of Red Fuji apple was established by using BP neural network during the apple per-storage heat treatment andstorage period and the correlation between multi-dimension input data(temperature, time)and color(a~*, H°) as output data. By verification, the model can fit thechange of color on the process of apple pre-storage heat treatment and storage period. Atlast, we use the model to predict the non-experimental respiration rate and by contrast theprediction precision of the background color a~* model is 89.95% and the predictionprecision of the background color H°model is 93.49%.3, We use the genetic algorithm global searching ability and hold the program of BPneural network as the genetic algorithm fitness function and the mode of Genetic Algorithmand Back Propagation Neural Network (GA-BPNN) was determined. And we use theneeded optimization of the weight and threshold of matrix of BP neural network as thegenetic algorithm population and find the optimization of the weight and threshold ofmatrix of BP neural network by the process of selection, crossover, mutation to improve theBP neural network prediction precision and decrease the model established time. Byverification, the respiration rate model's average prediction precision is about 95.01%and thebackground color a~* model's average prediction precision is about 93.3% and thebackground color H°model's average prediction precision is about 95.79%.