Open AccessResearch on Tomato Nitrogen Content Nondestructive Testing Method Based on Multidimensional Image Processing Technology
Author(s) -
Xue Wei Zhang,
Xiao Dong Zhang,
Hanping Mao,
Hong Yan Gao,
Zi Yu Zuo,
Yu Qiang Ruan
Publication year - 2018
Publication title -
journal of advances in agriculture
Language(s) - English
Resource type - Journals
ISSN - 2349-0837
DOI - 10.24297/jaa.v8i1.7469
Subject(s) - hyperspectral imaging , feature (linguistics) , artificial intelligence , nitrogen , partial least squares regression , mathematics , pattern recognition (psychology) , laser scanning , remote sensing , biological system , laser , computer science , chemistry , statistics , optics , biology , geography , physics , philosophy , linguistics , organic chemistry
This paper is aimed at greenhouse tomato nitrogen detection using hyperspectral imaging combined with three dimensional laser scanning technology. This technology extracts the nitrogen hyperspectral feature image and the plant three dimensional morphological characters, to achieve the rapid quantitative analysis of nitrogen in tomato. The characteristic spectrum of nitrogen was extracted, and the mean intensity characteristic of the image feature was obtained. Then based on the acquisition of the tomato hyperspectral image data cube at different nitrogen levels, the sensitive region stepwise regression combined with correlation analysis was performed. Based on the acquired three dimensional laser scanning data of tomatoes, the stem diameter, the plant height and other biomass characteristics of different nitrogen levels were obtained by establishing the spatial geometric model of tomato three dimensional point cloud. A multi-feature fusion model for tomato nitrogen detection was established by partial least square regression. The results showed that the R2 in the constructed model was 0.94, with the accuracy significantly better than that of the single feature model established by using hyperspectral image and three dimensional laser scanning.