NDLI: Predicting Cotton Lint Yield Maps from Aerial Photographs

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Predicting Cotton Lint Yield Maps from Aerial Photographs

Content Provider	Springer Nature Link
Author	Vellidis, G. Tucker, M. A. Perry, C. D. Thomas, D. L. Wells, N. Kvien, C. K.
Copyright Year	2004
Abstract	It is generally accepted that aerial images of growing crops provide spatial and temporal information about crop growth conditions and may even be indicative of crop yield. The focus of this study was to develop a straightforward technique for creating predictive cotton yield maps from aerial images. A total of ten fields in southern Georgia, USA, were studied during three growing seasons. Conventional (true color) aerial photographs of the fields were acquired during the growing season in two to four week intervals. The aerial photos were then digitized and analyzed using an unsupervised classification function of image analysis software. During harvest, conventional yield maps were created for each of the fields using a cotton picker mounted yield monitor. Classified images and yield maps were compared quantitatively and qualitatively. A pixel by pixel comparison of the classified images and yield maps showed that spatial agreement between the two gradually increased in the weeks after planting, maintained spatial agreement of between 40% and 60% during weeks eight to fourteen, and then gradually declined again. The highest spatial agreement between a classified image and a yield map was 78%. The highest average agreement was 52% and occurred 9.9 weeks after planting. The visual similarity between the classified images and the yield maps were striking. In all cases, the dates with the best visual agreement occurred between eight and ten weeks after planting, and generally, during July for southern Georgia. This method offers great potential for offering cotton farmers early-season maps that predict the spatial distribution of yield. Although these maps can not provide magnitudes, they clearly show the resulting yield patterns. With inherent knowledge of past performance, farmers can use this information to allocate resources, address crop growth problems, and, perhaps, improve the profitability of their farm operation. These maps are well suited to be offered to farmers as a service by a crop consultant or a cooperative.
Starting Page	547
Ending Page	564
Page Count	18
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	5
Issue Number	6
e-ISSN	15731618
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2004-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	cotton yield maps predictive aerial photographs image analysis remote sensing Meteorology/Climatology Remote Sensing/Photogrammetry Agriculture Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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