Abstract: Nitrogen is one of the indispensable nutritional components for plant growth. Proper fertilization is not only conducive to the healthy growth of plants, but also can reduce the pollution of soil and groundwater. In this study, we proposed a nondestructive diagnosis method of total nitrogen content in canopy leaves of Dalbergia odorifera based on multi-feature and improved BPNN. We divided the canopy image by digital image processing technology, and obtained 27 image features (color, texture, and shape). By calculating the Pearson coefficient, we screened out the factors significantly related to total nitrogen content by principal components analysis. We extracted the first four principal components as the input factors of improved BPNN which was optimized by PSO and Adaboost algorithm (PSO-BPNN-Adaboost). The results showed that the use of multiple features can more comprehensively and accurately reflect the nitrogen content of canopy leaves in Dalbergia odorifera. By comparing BPNN, PSO-BPNN, BPNN-Adaboost and PSO-BPNN-Adaboost, we found that the PSO-BPNN-Adaboost was more reliable and the PSO algorithm had a more optimization effect. Therefore, for the BP neural network, it is necessary to find a suitable initial value and threshold before enhancing them. Our method fully considered the influence of nitrogen stress on plants and outcompeted the traditional method which only considered color factors. Our method provided reference for the precision fertilization in the management of precious trees, which could effectively reduce environmental pollution.

Key words: erosion dynamic mechanism, sediment characte-ristics, Lou soil engineering accumulation, hydrodynamic characteristics