GIS and Remote Sensing for Detecting Yield Loss in Cranberry Culture

  • Peter V. Oudemans
  • Larisa Pozdnyakova
  • Marilyn G. Hughes
  • Faiz Rahman
Keywords: database analysis, gis, perennial crop management, remote sensing, unsupervised classification, yield loss, yield mapping


The primary goal of our research is to develop key elements of a precision agriculture program applicable to high-value woody perennial crops, such as cranberries. These crop systems exhibit tremendous variability in crop yields and quality as imposed by variations in soil properties (water availability and nutrient deficiency) that lead to crop stress (disease development and weed competition). Some of the variability present in the growing environment results in persistent yield losses as well as crop-quality reductions. We are using state-of-the-art methodologies (GIS, GPS, remote sensing) to identify and map spatial variations of the crop. Through image-processing methods (NDVI and unsupervised classification), approximately 65% of the variation in yield was described using 4-m multispectral satellite data as a base image.