title: [Research on maize multispectral image accurate segmentation and chlorophyll index estimation].
abstract: In order to rapidly acquire maize growing information in the field, a non-destructive method of maize chlorophyll content index measurement was conducted based on multi-spectral imaging technique and imaging processing technology. The experiment was conducted at Yangling in Shaanxi province of China and the crop was Zheng-dan 958 planted in about 1 000 m X 600 m experiment field. Firstly, a 2-CCD multi-spectral image monitoring system was available to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (Blue (B), Green (G), Red (R): 400-700 nm) and near-infrared (NIR, 760-1 000 nm) band. The multispectral images were output as RGB and NIR images via the system vertically fixed to the ground with vertical distance of 2 m and angular field of 50°. SPAD index of each sample was'measured synchronously to show the chlorophyll content index. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the NIR maize image was selected to segment the maize leaves from background, because there was a big difference showed in gray histogram between plant and soil background. The NIR image segmentation algorithm was conducted following steps of preliminary and accuracy segmentation: (1) The results of OTSU image segmentation method and the variable threshold algorithm were discussed. It was revealed that the latter was better one in corn plant and weed segmentation. As a result, the variable threshold algorithm based on local statistics was selected for the preliminary image segmentation. The expansion and corrosion were used to optimize the segmented image. (2) The region labeling algorithm was used to segment corn plants from soil and weed background with an accuracy of 95. 59 %. And then, the multi-spectral image of maize canopy was accurately segmented in R, G and B band separately. Thirdly, the image parameters were abstracted based on the segmented visible and NIR images. The average gray value of each channel was calculated including red (ARed), green (AGreen), blue (ABlue), and near-infrared (ANIR). Meanwhile, the vegetation indices (NDVI (normalized difference vegetation index), RVI (ratio vegetation index); and NDGI(normalized difference green index)) which are widely used in remote sensing were applied. The chlorophyll index detecting model based on partial least squares regression method (PLSR) was built with detecting R2=0. 5960 and predicting R2= 0. 568 5. It was feasible to diagnose chlorophyll index of maize based on multi-spectral images.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wu
authors.forename: Hong
authors.initials: H
authors.lastname: Sun
authors.forename: Min-zan
authors.initials: MZ
authors.lastname: Li
authors.forename: Yuan-yuan
authors.initials: YY
authors.lastname: Song
authors.forename: Yan-e
authors.initials: YE
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D002734:Chlorophyll / Q000032:analysis*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D018515:Plant Leaves / Q000737:chemistry*; D012044:Regression Analysis; D012987:Soil; D013057:Spectrum Analysis; D003313:Zea mays / Q000737:chemistry*
chemical_list: D012987:Soil; D002734:Chlorophyll