<p>In recent years, hyperspectral imaging technologies have been broadly applied to evaluate complex plant physiological features such as leaf moisture content, nutrient level and disease stress. A  critical  component  of  this  technique  is  white  referencing  used  to  remove  the  effect  of  non-uniform  lighting  intensity  in  different  wavelengths  on  raw  hyperspectral  images. Based  on  the  literature,  the leaf  geometry (e.g.,  tilt  angles)  and its interaction  with  the  illumination  severely impact  the  plant  reflectance  spectra  and vegetation  indices  such  as  the  normalized  difference  vegetation index (NDVI).  This thesis is  aimed to address the issues caused by the tilt angles across the leaf surface. To achieve this, two methods based on the fusion of the hyperspectral images and 3D  point  clouds  were  proposed.  The  first  method  was  to  build  a  3D  white  reference  library  in  which a point with almost the same tilt angle, height and position with the pixel on the plant leaf can be found, and then the white reference spectrum at that point can be used to calibrate the raw spectrum of the leaf pixel. The second method was to observe and summarize how the plant spectra and NDVI values changed with the leaf angles. Using the changing trends, the original NDVI and spectra  of  leaf  pixels  at  different  angles  can  be  calibrate  to  a  same  standard  as  if  the  leaf  was  imaged  at  a  flat  and  horizontal  surface.  The  approach  was  called  3D  calibration.  The  results  showed  that  the NDVI  values significantly  changed  with  leaf  angles  and  the  changing  trends differed  between  the  corn  and  soybean  species.  To evaluate the  performance  of  3D  calibration, 180 soybean plants with different genotypes, nitrogen (N), phosphorus (P) and water treatments were  grown  in  the  greenhouse. Each  plant  was  imaged  in three systems:  the high-throughput greenhouse hyperspectral imaging system, the indoor desktop imaging system with a visible-near infrared  (VINIR)  hyperspectral camera and  an  Intel  RealSense  depth  camera  and  the handheld device hyperspectral imaging system. In the greenhouse system, the whole canopy was captured. In the indoor desktop system, the partial canopy was captured because of the space limitation and the  top-matured  leaf  (the  middle  leaf  of  the  uppermost  matured  trifoliate)  was  focused.  The proposed  3D  calibration  was  applied  on  the  top-matured  leaf  to  remove  angle  impacts.  In  the  handheld device system, the flat top-matured leaf was captured. After done with imaging work, the plants were harvested to collect the ground truth data such as relative water content (RWC), N content and P content. Combined with the ground truth data, the NDVI values from three systems were  used  to  discriminate  different  genotypes  and  biochemical treatments,  whereas,  the  spectra from three systems were used to build partial least squares regression (PLSR) models for N, P and RWC. The results showed that the averaged tilt angles of top-matured leaves were impacted by different treatments. For instance, the low-nitrogen (LN) plants showed significantly higher leaf angles than high-nitrogen (HN) plants; the leaf angles on water-stressed (WS) plants were higher than those on well-watered (WW) plants. The leaf angles carried some signals that influenced not only the NDVI discrimination but also the PLSR modelling results. The signals were lost after 3D calibration.  For  the  top-matured  leaves,  the  discrimination  and  modelling  results  after  3D  calibration in the indoor desktop system were close to those from the flat leaves in the handheld device  system.  The  proposed  3D  calibration  approach  has  a  potential  to  eliminate  leaf  angle  impacts.</p>