Integrating remote sensing and GIS for prediction of rice protein contents

Integrating remote sensing and GIS for prediction of rice protein contents In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer. There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI model (r 2 > 0.424, RMSE < 0.256%) was better than for the Field GreenNDVI model (r 2 > 0.250, RMSE < 0.298%). A general-purpose model (r 2 = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management, it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to select the key management factor by the difference in the Field GreenNDVI. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Integrating remote sensing and GIS for prediction of rice protein contents

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Publisher
Springer US
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-010-9179-0
Publisher site
See Article on Publisher Site

Abstract

In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer. There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI model (r 2 > 0.424, RMSE < 0.256%) was better than for the Field GreenNDVI model (r 2 > 0.250, RMSE < 0.298%). A general-purpose model (r 2 = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management, it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to select the key management factor by the difference in the Field GreenNDVI.

Journal

Precision AgricultureSpringer Journals

Published: Jun 8, 2010

References

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