Monitoring of maize chlorophyll content based on multispectral vegetation indices

In order to estimate the nutrient status of maize, the multi-spectral image was used to monitor the chlorophyll content in the field. The experiments were conducted under three different fertilizer treatments (High, Normal and Low). A multispectral CCD camera was used to collect ground-based images of maize canopy in green (G, 520~600nm), red (R, 630~690nm) and near-infrared (NIR, 760~900nm) band. Leaves of maize were randomly sampled to detect the chlorophyll content by UV-Vis spectrophotometer. The images were processed following image preprocessing, canopy segmentation and parameter calculation: Firstly, the median filtering was used to improve the visual contrast of image. Secondly, the leaves of maize canopy were segmented in NIR image. Thirdly, the average gray value (GIA, RIA and NIRIA) and the vegetation indices (DVI, RVI, NDVI, et al.) widely used in remote sensing were calculated. A new vegetation index, combination of normalized difference vegetation index (CNDVI), was developed. After the correlation analysis between image parameter and chlorophyll content, six parameters (GIA, RIA, NIRIA, GRVI, GNDVI and CNDVI) were selected to estimate chlorophyll content at shooting and trumpet stages respectively. The results of MLR predicting models showed that the R2 was 0.88 and the adjust R2 was 0.64 at shooting stage; the R2 was 0.77 and the adjust R2 was 0.31 at trumpet stage. It was indicated that vegetation indices derived from multispectral image could be used to monitor the chlorophyll content. It provided a feasible method for the chlorophyll content detection.

[1]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[2]  Gunter Menz,et al.  Multi-temporal wheat disease detection by multi-spectral remote sensing , 2007, Precision Agriculture.

[3]  Weixing Cao,et al.  Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[4]  J. R. Thomas,et al.  Estimating Nitrogen Content of Sweet Pepper Leaves by Reflectance Measurements1 , 1972 .

[5]  B. Mistele,et al.  Estimating the nitrogen nutrition index using spectral canopy reflectance measurements , 2008 .

[6]  F. D. Whisler,et al.  Analysis of a precision agriculture approach to cotton production , 2001 .

[7]  X. Yao,et al.  Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance , 2011 .

[8]  S. G. Nelson,et al.  Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape , 2008, Sensors.

[9]  Xue Li.  CORRELATION BETWEEN LEAF NITROGEN STATUS AND CANOPY SPECTRAL CHARACTERISTICS IN WHEAT , 2004 .

[10]  Georg Bareth,et al.  Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages , 2010, Precision Agriculture.

[11]  Youngwook Kim,et al.  Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data , 2010 .

[12]  Yongliang Lv,et al.  New Reflectance Spectral Vegetation Indices for Estimating Rice Nitrogen Nutrition III: Development of a New Vegetation Index Based on Canopy Red-Edge Reflectance Spectra to Monitor Rice Canopy Leaf Nitrogen Concentration , 2011 .

[13]  A. Kuusk,et al.  Investigating relationships between Landsat ETM+ sensor data and leaf area index in a boreal conifer forest , 2001 .

[14]  H. Noh,et al.  A Neural Network Model of Maize Crop Nitrogen Stress Assessment for a Multi-spectral Imaging Sensor , 2006 .

[15]  John B. Solie,et al.  Optical sensing of turfgrass chlorophyll content and tissue nitrogen , 2004 .

[16]  Hong Sun,et al.  [Detection of corn chlorophyll content using canopy spectral reflectance]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[17]  Yuxin Miao,et al.  Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn , 2008, Precision Agriculture.