Improvement of chlorophyll content estimation on maize leaf by vein removal in hyperspectral image

Abstract

Leaf chlorophyll content (LCC) is one of nutritional parameters which could be estimated by Hyperspectral image (HSI) technology by combining spatial and spectral information. The objective of this study was to propose a novel segmentation algorithm to remove the influences of the veins in maize leaves so as to improve the accuracy of LCC modeling. Firstly, an enhanced green peak feature (EGPF) was built by local extremum points at 451, 552 and 648 nm, and then the image of EGPF was automatically segmented by OTSU method to get region of interest (ROI) of all leaf regions (ROI-ALR) without background and main stem. Secondly, an enhanced red valley feature (ERVF) was proposed to enlarge the edge difference between veins and mesophylls. ROI of only mesophyll regions (ROI-OMR) were extracted by removing of leaf veins resorted to edge segmentation. For the two spectral reflectance datasets from ROI-ALR and ROI-OMR, multiplicative scattering correction (MSC) was implemented. Correlation analysis (CA) and Random-Frog (RF) coupled with partial least square regression (PLSR) were applied to select characteristic wavelengths and establish LCC estimating models. Compared ROI-ALR- CA-PLSR with ROI-OMR-CA-PLSR, Rv2 increased from 0.43 to 0.52 and RMSE decreased from 4.11 to 3.61; Compared ROI-ALR- RF -PLSR with ROI-OMR- RF –PLSR, Rv2 increased from 0.83 to 0.86 and RMSE decreased from 2.14 to 1.86. The proposed HSI segmentation method for veins removal provides an effective strategy to improve LCC diagnosis of maize leaves.