Cranberry ICM

Spatial Variability in Cranberry, Conference on Precision Agriculture, July 18-22,  1998, St. Paul, MN
 

Spatial Detection and Quantification of Phytophthora Root Rot Effects on Cranberry Yield, Second Int'l Conf. on Geospatial Info. in Ag. and For., Jan. 10-12, 2000, FL
 

Evaluating Commercial Cranberry Beds for  Variability  and Yield using Remote Sensing Techniques, Second Int'l Conf. on Geospatial Info. in Ag. and For., Jan. 10-12, 2000, FL
 

Cranberry Images - Work in Progress
 

Rutgers University Blueberry Cranberry Research and Extension Center Web Page
 
 




















































 



















































































































































































 
EVALUATING COMMERCIAL CRANBERRY BEDS FOR VARIABILITY AND YIELD USING REMOTE SENSING TECHNIQUES 
 Marilyn G. Hughes 
Rutgers Cooperative Extension & Center for Remote Sensing 
Phone (732-932-1582) 
E-mail (mghughes@crssa.rutgers.edu) 

Peter V. Oudemans 
Rutgers Blueberry & Cranberry Research Center,Chatsworth, NJ 
Phone (609)-726-1590 x 20 
E-mail (oudemans@aesop.rutgers.edu) 

Joan R. Davenport, Soil Sciences, Washington State University 
Keri Ayres, Rutgers Blueberry & Cranberry Research Center 
Teuvo M. Airola, Rutgers Center for Remote Sensing 
Abbott Lee, Lee Brothers, Inc., Chatsworth, New Jersey

Lee Brother's Farm, Chatsworth, NJ
Image Source: CIR-aerial photography, Ocean Spray, Inc.
ABSTRACT

This study uses GPS/GIS/RS techniques to analyze cranberry (Vaccinium macrocarpon Ait.) crop health and yield. Extensive field sampling has been used in the past as a means of estimating potential bed yields. The major problem for predicting yield appears to be to high intra-bed spatial variability. For this study, color-IR photography from commercial cranberry beds (May 1996) was rectified to earth coordinates using GPS technology. An unsupervised multi-spectral classification and an NDVI were done to statistically group pixels in the image. Results indicate that a number of features within cranberry beds can be identified, including variations of vegetative cover, irrigation and drainage systems, and areas of beds damaged by insects and fungal disease (Phytophthora cinnamomi). In the future, remotely sensed imagery will be linked to ground based data to gain further insight into the spatial variation of factors affecting crop yield and health. 

Click here to view full paper
 

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