O' BEERI, A' PELED, S' KITAIN

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PRECISION AGRICULTURE and REMOTE SENSING
O. BEERI, A. PELED, S. KITAIN University of
Haifa, Department of Geography, Haifa, 31905
Israel
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Table of Contents
1. The Research Goals
2. The Importance of the Research
3. The Experimentation
3.1 Agricultural Treatments
3.2 Data Collection from Air and from Ground
4. Analyses Results
4.1 Monitoring Indices and their correlation to
the yield
4.2 RS-Predicted-Yield-map and Combine-GPS Yield
map
4.3 Development of Monitoring Algorithm for
Real-Time RS-Predicted-Yield-map
5. Summary
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The Research Goals
(1) To test various vegetation parameters and
spectral indices for monitoring wheat under
nitrogen pressure
(2) To compare predicted remote-sensing-based
yield-maps with combine-GPS yield-maps
(3) To develop a formula for predicted yield-maps
for monitoring wheat under nitrogen pressure
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The Importance of the Research
This research is a part of a multi-annual
research project aimed to develop a GIS model
for agricultural monitoring based on remotely
sensed data.
The purpose of agricultural monitoring is to
manage the crop throughout
its growing period and consequently apply the
required treatments
for obtaining optimal yields at the lowest
possible cost
The management of the crop is made by applying
water and nitrogen
This research examines spatial and temporal
variation of nitrogen and water in the wheat,
and demonstrates the ability of Remote
Sensing to control the process
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The Experimentation Agriculture treatments
Initial levels of nitrogen (top-dressing)
12 0 6 0 6 12 12 6 0
Legend Two Irrigations One Irrigation Scale
- North
0 5 0 5 0 0 Nitrogen levels
during fallow fertilization
24m.
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The Experimentation Data collection
Dates of data collection
27 days after emergence, prior to mid season
irrigation and fertilization
63 days after emergence, grain-filling stage of
the wheat
92 days after emergence, wheats maturity before
harvest
The data
Plant mean weight Percentage of dry
matter Percentage of general nitrogen Nitrate -
nitrogen Chlorophyll by SPAD
Vegetation Parameters
Air Photography
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The Spectral Indices used in this research
Color-IR indices
Visible light indices
The yellow colored cells in the table, mark
indices developed in this study.
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The Spectral Indices used in this research
Visible light indices
The yellow colored cells in the table, mark
indices developed in this study.
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Yield harvest was made in two steps
1. A 1.25 meters-wide combine harvested the
field passing through the center of each plot
By correlation between the yield segments and
their spectral vision, yield-map was made for
each 0.1 sq.. Meters pixel
2. The entire field was harvested by a
combine-GPS.
This process produced two yield-maps that can be
compared, and enabled to run a QC evaluation of
the RS yield-map
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Analyses Results Spectral Indices
Table 1 Monitoring Indices and their
correlation to the yield
Shaded cells - the 5 best indices for each date.
Color Infrared indices Vs.Visible indices - No
significant differences, and no advantage of CIR.
RS Indices for wheat monitoring - 3 indices have
high correlation between wheat and image data,
throughout the season
TCHVIgreen from CIR
BLUE from Visible
5BLUE from Visible
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Analyses Results Yield-maps
Predicted-yield map were produced by a regression
analysis
and fertilization
Spatial variance is correlated to irrigation
Legend Scale - North
24m.
High yield low yield
Yield map based on Remote-Sensing data
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Analyses Results comparing Yield-maps
The amounts of the yield aren't fully correlated,
but the tendency is the same
Yield based on Remote-Sensing data
Yield based on GPS-Combine data
Legend Scale - North
24m.
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Analyses Results Developing RS formula for
mapping Wheat-Nitrogen-Stress
The advantage of Remote Sensing is the ability to
map the predicted yield and to determine the
farmer action for better yield per minimal effort
Joining of the 3 higher correlation indices into
one formula that will demonstrate the wheat
need for nitrogen 1300-(3.6Blue)-(0.85Blue)-
(9.2TCHVIgreen)
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Analyses Results Developing RS formula for
mapping Wheat-Nitrogen-Stress
The first map show spatial diversity because
level of nitrogen in top-dressing, and
uncultivated land
The second map show spatial diversity because
heterogeneous of irrigation
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Summary
Conclusions
1. Spectral indices are more correlated to
yield than vegetation indices and can show the
spatial diversity of the variables that influence
this diversity.
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Summary
Conclusions
2. Spectral indices based on Color-Infrared are
equal to Visible indices in their correlation to
the yield. In this research, no advantage was
detected for using CIR in mapping wheat under
nitrogen stress
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Summary
Conclusions
3. Based on the three indices which were found to
have the highest correlation values (Blue, 5Blue
visible light, TCHVIgreen CIR), a general
algorithm for yield-prediction mapping throughout
the growing season was developed.
This formula is the base for mapping the
predicted yield in real-time, and can show were
the farmer need to interfere during fallow
fertilization
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Summary
Conclusions
4. The yield maps define the factors that
affected the final yield. The spatial variance in
nitrogen was a dominant factor in the beginning
of the growing season. Irregularity in irrigation
was a major factor effective towards the end of
the season
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ACKNOWLEDGMENTS
The authors wish to thank the following
institutions for their support

• University of Haifa, Laboratories for G.I.S.
  Remote Sensing
• Chim-Nir flight company
• Deshen-Gat
• Kibbutz Saad farming
• The Negev Growers Committee