| In recent years, in spite of the increasing amount of forest resources, the high quality wood with large diameter grade is decreased. Implementation of natural forest protection project has concentrated the wood resources supply pressure on planted forest. However, the planted forest was puzzled by the low production rate, poor quality and unreasonable forest age distribution. Therefore, forest cultivation and wood science researchers from the whole world have put their eyes on high quality planted forest breeding. Researches on predictive models of wood characteristics of planted forest will contribute to the reasonable time to fell planted forest, the update selection of natural forest, and the economical, efficient and reasonable utilization.The paper had focused on wood properties and physical and mechanical characteristics between growth rings of walnut (Juglans mandshurica Max.) and ash (Fraxinus mandshurica Rupr.) plantation. There are three main aspects:(1) On the basis of radial variation rules on wood properties between growth rings, sequential clustering optimal segmentation, principal component clustering, BP neural network and support vector machine(SVM) methods were used respectively for the demarcation of juvenile and mature wood. A comparison of the results, including characteristic and accuracy, were analyzed and confirmed.(2) After demarcated of the juvenile and mature wood, prediction methods of the regression equation, time series, BP neural network and support vector machine(SVM) were compared on the relative deviation and standard deviation of mature period prediction and the whole period prediction respectively based on the prediction of mature wood properties from the juvenile wood properties. The characteristic and accuracy of each predicted methods were analyzed.(3) After confirm that SVM method has the best regression and fitting capacity and generalization capability, firstly, relational models among wood properties’ characteristic factors were established; secondly, relational models among wood physical and mechanics characteristic factors were established; finally, relational models between wood properties and wood physical and mechanics characteristic factors were established. In core of the characteristic factors which with the correlation coefficient R more than0.83, wood characteristic models were established in the end.The main conclusions of the paper were listed below:(1) Comprehensive indexes of wood properties among the growth rings were acted as the research object. The demarcation of juvenile and mature walnut wood determined by SVM method was the18th-year. The training sets were selected mainly on the group of earlier6to 10years and the later2to6years. The demarcation of juvenile and mature ash wood was the23th-year. The training sets were selected mainly on the group of earlier10to14years and the later2to10years.(2) Walnut and ash juvenile and mature wood were demarcated with the research objuect of comprehensive indexes and single index of wood properties among the growth rings. The classification result of SVM method was primarily the same to the result of the principal component clustering and BP nueral network methods; while it was obviously different to the result of sequential clustering optimal segmentation method based on comprehensive indexes; it was primarily the same to the result of the sequential clustering optimal segmentation method based on single index.(3) In the process of juvenile and mature wood demarcation, the sequential clustering optimal segmentation method had better classification result with single index as research object than that with comprehensive indexes; BP neural network and SVM methods had better classification result with comprehensive indexes as research object than that with single index; with the comprehensive indexes of wood properties among growth rings as the research object, principal component clustering method can get the contribution from the single index of wood properties. Two pingcipal components can be used to summarize the wood properties among growth rings and the classification result can be directly showed by the graphical method.(4) In the process of mature wood properties prediction, the predicted value using the regression equation method had poor fitting effect to part of the discrete point of measured value; and the predicted curve could reflect the variation trend of juvenile wood but mature wood. The predicted value using the time series method could fit the juvenile wood discrete point of measured value, but part of the mature wood discrete point of measured value; and the predicted curve could reflect the variation trend of juvenile wood but mature wood. The deviation between predicted value and measured value of mature wood was small using the BP neural network method; but the fitting of discrete points was bad in part of the measured value. The prediction curve has poor reflection on the overall variation trend of mature wood properties. The predicted value using SVM method could fit the discrete point of measured value, but part of the mature wood discrete point of measured value; and the predicted curve could reflect the overall variation trend, while it has poor reflection on partly ups and downs of mature wood.(5) To predict the mature wood properties from juvenile wood properties, the regression equation method is easy handling, low or middle level prediction accuracy, and low fitting effect; the time series method is multi-steps, complicated handling, middle or high level prediction accuracy, and good fitting effect; the BP neural network method is easy handling, middle or high level prediction accuracy, and capacity to get mature wood predicted trend insteading of the overall trend prediction; SVM method is easy handling, middle or high level prediction accuracy, high predicted generalization capability, high fitting effect, and low predicted relative error and standard deviation when predicting from the indexes with insignificant variation relationship.(6) There was high relevance with correlation coefficient R more than0.9310among wood fiber length, density among growth rings, wood basic density, bending strength, and compressive strength parallel to grain of walnut. There was high relevance with correlation coefficient R more than0.8674among wood fiber length, wood fiber lumen diameter, wood cell walls percentage, growth rate, wood basic density and bending strength of ash wood. The radial variation rule among anatomy characteristics factors of walnut wood was separated into two parts mainly in the7th-year or the14th-year. There was insignificant radial variation rule among physical and mechanics characteristic factors, which was separated into two parts approximately in the4th specimen from the pith. The radial variation rule among anatomy characteristics factors of ash wood was separated into two parts mainly in the11th-year or the20th-year. There was insignificant radial variation rule among physical and mechanics characteristic factors, which was separated into two parts approximately in the4th to5th specimen from the pith. |
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