Diagnosis of total phosphorus content in young sandalwood based on visible light and near infrared images

ObjectiveSandalwood is a typical precious tree species. During its young stage, more or less fertilization will affect its growth and reduce survival rate. In this paper, a total phosphorus nutrition diagnosis method for young sandalwood based on visible light and near infrared image recognition is proposed. It provides a reference for real-time monitoring of the growth state and nutrient requirements of precious tree species.

MethodS and Ⅰ channels were extracted after converting field acquired sandalwood images to HSI color space. By combing the advantage of S and Ⅰ channels segmentation results using Otsu method and morphological operation, sandalwood was extracted from the complex background. We used different methods to optimize BP neural network. On one hand, ST and MIV methods were used to select the variables in shape, texture, spectrum and vegetation index. On the other hand, genetic algorithm (GA) was used to initialize the weights and thresholds, and the prediction results were finally obtained.

Results(1) In the complex background of sandalwood segmentation, the combination of H channel and S channel successfully separated most of the background (sky, soil and other green plants) from target sandalwood. Median filter sized in 7×7, morphological operation and super G factor were used to remove other burrs. (2) The characteristics under different levels of phosphorus application showed that the appropriate increase fertilizer could promote the compound of chlorophyll, make the texture more uniform and clear, and increase the growth of the leaves. When the application exceeded the best value, the chloroplast could be destroyed, texture changed and leaf color turned into yellow. (3) The variables selected by ST and MIV were different. GA-BPNN training results showed that the variables selected by the MIV method had greater influence on the total phosphorus content. The determinant coefficient of the prediction set was 0.801, the mean residual was 0.032 g/kg, and the root mean square error was 0.666 g/kg.

ConclusionIn this paper, the total phosphorus content of young sandalwood was predicted by processing the visible light and near infrared image. By this method, the utilization rate of phosphate fertilizer will be improved effectively, and the ecological problems such as groundwater pollution caused by excessive fertilization could also be reduced.