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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
Figure 4. Digital photographs of oat plot during the growing 
season. 
Besides, digital photographs and growth height from three 
random places from each test plots were collected using tape 
measure. Similarly, ground moisture measurements were 
collected from five random places from each test plots using 
moisture meter and Kotkaniemi meteorological station 
observations were recorded for each scanning day. 
Plots were threshed on the 16th August 2006 with a combined 
harvester designed for harvesting trial plots. The whole plot was 
threshed and total fresh weight of grains was weighed. A 1 kg 
sample was taken for each plot to determine moisture content of 
grains. Fresh grain weight was converted into grain yield value 
(kg/ha) using grain moisture content and plot area. Measured 
grain yield was reported at 15% moisture content. 
4. RESULTS AND DISCUSSION 
4.1 Initial works 
Laser data was filtered using Faro Scene Software to remove 
noise and points generated from both the ground and vegetation. 
4.2 Growth height estimation of crops 
Growth heights were determined from each test plot using laser 
scanner data. A single test plot was divided into smaller grid 
cells and growth heights were determined from each cell. 
Growth height measures were compared to threshing results and 
we found strong correlation between measured growth heights 
and grain yield from each studied cultivars. 
MEASURED VS SCANNED 
Figure 5. Measured and scanned maximum growth heights of 
Justina, Picolo and Belinda. 
4.3 Ear detection 
Ears of spring wheat cultivar Picolo were determined. An 
algorithm was developed to automatically recognize ears and 
estimate their size from laser scanner data. The algorithm was 
based on the idea that point cloud was converted into voxel 
model and voxels with enough laser points were marked. 
Several marked voxels side by side in vertical direction were 
associated as ear of wheat. 
PICOLO 
160N 
120N 
80N 
40N 
ON 
Grain yield, 
3556 
2268 
205 
224 
887 
kg/ha 
9 
8 
Estimated 
2054 
1133 
123 
115 
709 
ears 
4 
4 
Table 1. Measured grain yield of Picolo at different N levels 
and grain yields estimated using ear detection of scanner data. 
Cultivar 
Measured 
height 
Scanned 
height 
Estimated 
ears 
Picolo 
0.93 
0.93 
0.97 
Justina 
0.90 
0.95 
0.96 
Belinda 
0.99 
0.88 
0.99 
Mean 
0.94 
0.92 
0.98 
Table 2. Correlation coefficient between measured heights, 
scanned heights, grain yields estimated using ear detection of 
scanner data and precision harvesting. Correlations coefficients 
are calculated using five different rates of fertilization. 
Calculated ear size also correlates with the grain yield but the 
problem was to find suitable parameters for the algorithm and 
algorithm provides rather relative than absolute results of grain 
yield. Detailed features were extracted from the voxels of ears 
based on the idea that overlapping ear voxels contained 
different amount of laser points and they provide more 
information about the ear but this study was restricted by lack 
of very detailed reference data. 
5. CONCLUSIONS 
In this study, terrestrial laser scanning was found to be a useful 
tool for growth height and grain yield estimation. Growth 
height of cereal plants was easy to estimate using laser scanner 
data and estimated results correlates with tape measures. 
Besides, ears of wheat were automatically recognized and their 
size was estimated using laser scanner data. 
Thus, our study shows that laser scanner could be used as 
precision farming tool in agriculture. The scanner that we used 
is not suitable for operational use but the similar methods can 
be used for example to estimate data of laser scanner that is 
mounted on a moving platform. Even if operational laser 
scanning applications for agriculture seems not so relevant at 
the time, it is worthwhile to study more this field because 
development of instruments is fast and ongoing process.