Construction of LM-BP Neural Network Prediction Model for the Softness of Flue-cured Tobacco Leaves Based on Fresh Leaf Appearance Parameters
In order to predict the softness of tobacco leaves by using the appearance parameters of fresh tobacco leaves,a LM-BP neural network prediction model was established by studying the relationship between the field ripening appearance parameters such as lightness and darkness(L),redness value(a),yellowness value(b),color saturation(C),hue angle(H),SPAD value,etc.and the softness of post-roasted leaves of YUNYU 87 with different retention numbers of the upper leaves.The results showed that the appearance characteristic parameters of upper leaves with different numbers of retained leaves were different,and the softness of tobacco leaves after baking was also different,and the value of softness after baking was lower in the number of retained leaves of 19 leaves than that in the number of leaves of 16-18 leaves,which ranged from 5.62 to 13.29 mN;There was a correlation between the parameters of tobacco appearance characteristics and the softness of post-roasted tobacco;stepwise regression analysis screened out the factors with greater influence on the softness of post-roasted tobacco as the number of retained leaves,L,H and SPAD value;The LM algorithm was used to replace the gradient algorithm to create the LM-BP neural network prediction model,and the training results showed that the prediction accuracy R2 was close to 1,the average absolute percentage error MAPE<5%,and the root-mean-square error RMSE<3.Properly retaining more leaves increased the softness of the tobacco after roasting;There was a correlation between the maturation appearance characteristics of tobacco leaves in the field and the softness of the tobacco after roasting;The LM-BP neural network was used to create a prediction model with high accuracy,which could be used for intelligent judgement of tobacco maturity in the field.
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